From 238dab7538c2014299cbac1f45ee2e3eb6aa5ee3 Mon Sep 17 00:00:00 2001
From: Ana Ordonez <anaordonez@comcast.net>
Date: Wed, 13 Jan 2021 15:06:53 -0800
Subject: [PATCH 001/133] add version

---
 pcmdi_metrics/version.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/pcmdi_metrics/version.py b/pcmdi_metrics/version.py
index 3e6afddb6..89bdb0cbb 100644
--- a/pcmdi_metrics/version.py
+++ b/pcmdi_metrics/version.py
@@ -1,3 +1,3 @@
 __version__ = 'v1.2.1'
-__git_tag_describe__ = 'v1.2.1-402-ge0d97dd'
-__git_sha1__ = 'e0d97dd9d5e596adea0c09b158172592d5cf71b6'
+__git_tag_describe__ = 'v1.2.1-416-geb53517'
+__git_sha1__ = 'eb53517001256403966e816bf74a1e15939dec77'

From 01348bcc20dd445d9cf729e2bc562a0fe8ed3579 Mon Sep 17 00:00:00 2001
From: Jiwoo Lee <lee1043@llnl.gov>
Date: Tue, 23 May 2023 13:00:12 -0700
Subject: [PATCH 002/133] initial commit

---
 .../mean_climate/lib/mean_climate_metrics_to_json.py     | 9 ++++++++-
 pcmdi_metrics/mean_climate/mean_climate_driver.py        | 5 +++++
 share/DefArgsCIA.json                                    | 2 +-
 3 files changed, 14 insertions(+), 2 deletions(-)

diff --git a/pcmdi_metrics/mean_climate/lib/mean_climate_metrics_to_json.py b/pcmdi_metrics/mean_climate/lib/mean_climate_metrics_to_json.py
index b614f3559..a723d1f9a 100644
--- a/pcmdi_metrics/mean_climate/lib/mean_climate_metrics_to_json.py
+++ b/pcmdi_metrics/mean_climate/lib/mean_climate_metrics_to_json.py
@@ -20,7 +20,14 @@ def mean_climate_metrics_to_json(
         models_in_dict = list(json_dict["RESULTS"].keys())
         for m in models_in_dict:
             if m == model:
-                for ref in list(json_dict["RESULTS"][m].keys()):
+                ref_list = list(json_dict["RESULTS"][m].keys())
+                if debug:
+                    print('debug:: mean_climate_metrics_to_json:: m, ref_list: ', m, ref_list)
+                if 'units' in ref_list:
+                    ref_list.remove('units')
+                for ref in ref_list:
+                    if debug:
+                        print('debug:: mean_climate_metrics_to_json:: m, ref, json_dict["RESULTS"][m][ref]: ', m, ref, json_dict["RESULTS"][m][ref])
                     runs_in_model_dict = list(json_dict["RESULTS"][m][ref].keys())
                     for r in runs_in_model_dict:
                         if (r != run) and (run is not None):
diff --git a/pcmdi_metrics/mean_climate/mean_climate_driver.py b/pcmdi_metrics/mean_climate/mean_climate_driver.py
index 3eac17b48..1ccb94da2 100755
--- a/pcmdi_metrics/mean_climate/mean_climate_driver.py
+++ b/pcmdi_metrics/mean_climate/mean_climate_driver.py
@@ -278,6 +278,8 @@
 
                         # write individual JSON
                         # --- single simulation, obs (need to accumulate later) / single variable
+                        if debug:
+                            print('write individual JSON start: model, run:', model, run)
                         json_filename_tmp = "_".join([model, var, target_grid, regrid_tool, "metrics", ref])
                         mean_climate_metrics_to_json(
                             os.path.join(metrics_output_path, var),
@@ -296,11 +298,14 @@
                         print(e)
 
     # write collective JSON --- all models / all obs / single variable
+    if debug:
+        print('write collective JSON start')
     json_filename = "_".join([var, target_grid, regrid_tool, "metrics"])
     mean_climate_metrics_to_json(
         metrics_output_path,
         json_filename,
         result_dict,
         cmec_flag=cmec,
+        debug=debug
     )
     print('pmp mean clim driver completed')
diff --git a/share/DefArgsCIA.json b/share/DefArgsCIA.json
index 8507f33ba..cd33a055d 100644
--- a/share/DefArgsCIA.json
+++ b/share/DefArgsCIA.json
@@ -163,4 +163,4 @@
         ],
         "help":"A list of variables to be processed"
     }
-}
+}
\ No newline at end of file

From 03f6fa1fea78aaff4c09e9b13cc5d988037d7f50 Mon Sep 17 00:00:00 2001
From: Jiwoo Lee <lee1043@llnl.gov>
Date: Tue, 23 May 2023 13:37:09 -0700
Subject: [PATCH 003/133] update

---
 .../mean_climate/lib/compute_metrics.py       | 73 ++++++++++---------
 .../mean_climate/lib/load_and_regrid.py       |  1 +
 2 files changed, 38 insertions(+), 36 deletions(-)

diff --git a/pcmdi_metrics/mean_climate/lib/compute_metrics.py b/pcmdi_metrics/mean_climate/lib/compute_metrics.py
index 7d1e842f3..11c98a583 100644
--- a/pcmdi_metrics/mean_climate/lib/compute_metrics.py
+++ b/pcmdi_metrics/mean_climate/lib/compute_metrics.py
@@ -54,9 +54,10 @@ def compute_metrics(Var, dm, do, debug=False):
 
     # SET CONDITIONAL ON INPUT VARIABLE
     if var == "pr":
-        conv = 86400.0
-    else:
-        conv = 1.0
+        if do[var].units == "kg m-2 s-1":
+            do[var] = do[var] * 86400
+        if dm[var].units == "kg m-2 s-1":
+            dm[var] = dm[var] * 86400
 
     if var in ["hus"]:
         sig_digits = ".5f"
@@ -145,25 +146,25 @@ def compute_metrics(Var, dm, do, debug=False):
     ]):
         metrics_dictionary[stat] = OrderedDict()
 
-    metrics_dictionary["mean-obs_xy"]["ann"] = format(meanObs_xy * conv, sig_digits)
-    metrics_dictionary["mean_xy"]["ann"] = format(mean_xy * conv, sig_digits)
-    metrics_dictionary["std-obs_xy"]["ann"] = format(stdObs_xy * conv, sig_digits)
-    metrics_dictionary["std_xy"]["ann"] = format(std_xy * conv, sig_digits)
-    metrics_dictionary["std-obs_xyt"]["ann"] = format(stdObs_xyt * conv, sig_digits)
-    metrics_dictionary["std_xyt"]["ann"] = format(std_xyt * conv, sig_digits)
+    metrics_dictionary["mean-obs_xy"]["ann"] = format(meanObs_xy, sig_digits)
+    metrics_dictionary["mean_xy"]["ann"] = format(mean_xy, sig_digits)
+    metrics_dictionary["std-obs_xy"]["ann"] = format(stdObs_xy, sig_digits)
+    metrics_dictionary["std_xy"]["ann"] = format(std_xy, sig_digits)
+    metrics_dictionary["std-obs_xyt"]["ann"] = format(stdObs_xyt, sig_digits)
+    metrics_dictionary["std_xyt"]["ann"] = format(std_xyt, sig_digits)
     metrics_dictionary["std-obs_xy_devzm"]["ann"] = format(
-        stdObs_xy_devzm * conv, sig_digits
+        stdObs_xy_devzm, sig_digits
     )
-    metrics_dictionary["std_xy_devzm"]["ann"] = format(std_xy_devzm * conv, sig_digits)
-    metrics_dictionary["rms_xyt"]["ann"] = format(rms_xyt * conv, sig_digits)
-    metrics_dictionary["rms_xy"]["ann"] = format(rms_xy * conv, sig_digits)
-    metrics_dictionary["rmsc_xy"]["ann"] = format(rmsc_xy * conv, sig_digits)
+    metrics_dictionary["std_xy_devzm"]["ann"] = format(std_xy_devzm, sig_digits)
+    metrics_dictionary["rms_xyt"]["ann"] = format(rms_xyt, sig_digits)
+    metrics_dictionary["rms_xy"]["ann"] = format(rms_xy, sig_digits)
+    metrics_dictionary["rmsc_xy"]["ann"] = format(rmsc_xy, sig_digits)
     metrics_dictionary["cor_xy"]["ann"] = format(cor_xy, sig_digits)
-    metrics_dictionary["bias_xy"]["ann"] = format(bias_xy * conv, sig_digits)
-    metrics_dictionary["mae_xy"]["ann"] = format(mae_xy * conv, sig_digits)
+    metrics_dictionary["bias_xy"]["ann"] = format(bias_xy, sig_digits)
+    metrics_dictionary["mae_xy"]["ann"] = format(mae_xy, sig_digits)
     # ZONAL MEAN CONTRIBUTIONS
-    metrics_dictionary["rms_y"]["ann"] = format(rms_y * conv, sig_digits)
-    metrics_dictionary["rms_devzm"]["ann"] = format(rms_xy_devzm * conv, sig_digits)
+    metrics_dictionary["rms_y"]["ann"] = format(rms_y, sig_digits)
+    metrics_dictionary["rms_devzm"]["ann"] = format(rms_xy_devzm, sig_digits)
 
     # CALCULATE SEASONAL MEANS
     for sea in ["djf", "mam", "jja", "son"]:
@@ -186,15 +187,15 @@ def compute_metrics(Var, dm, do, debug=False):
         meanObs_xy_sea = pcmdi_metrics.mean_climate.lib.mean_xy(do_sea, var)
         mean_xy_sea = pcmdi_metrics.mean_climate.lib.mean_xy(dm_sea, var)
 
-        metrics_dictionary["bias_xy"][sea] = format(bias_sea * conv, sig_digits)
-        metrics_dictionary["rms_xy"][sea] = format(rms_sea * conv, sig_digits)
-        metrics_dictionary["rmsc_xy"][sea] = format(rmsc_sea * conv, sig_digits)
+        metrics_dictionary["bias_xy"][sea] = format(bias_sea, sig_digits)
+        metrics_dictionary["rms_xy"][sea] = format(rms_sea, sig_digits)
+        metrics_dictionary["rmsc_xy"][sea] = format(rmsc_sea, sig_digits)
         metrics_dictionary["cor_xy"][sea] = format(cor_sea, ".2f")
-        metrics_dictionary["mae_xy"][sea] = format(mae_sea * conv, sig_digits)
-        metrics_dictionary["std-obs_xy"][sea] = format(stdObs_xy_sea * conv, sig_digits)
-        metrics_dictionary["std_xy"][sea] = format(std_xy_sea * conv, sig_digits)
-        metrics_dictionary["mean-obs_xy"][sea] = format(meanObs_xy_sea * conv, sig_digits)
-        metrics_dictionary["mean_xy"][sea] = format(mean_xy_sea * conv, sig_digits)
+        metrics_dictionary["mae_xy"][sea] = format(mae_sea, sig_digits)
+        metrics_dictionary["std-obs_xy"][sea] = format(stdObs_xy_sea, sig_digits)
+        metrics_dictionary["std_xy"][sea] = format(std_xy_sea, sig_digits)
+        metrics_dictionary["mean-obs_xy"][sea] = format(meanObs_xy_sea, sig_digits)
+        metrics_dictionary["mean_xy"][sea] = format(mean_xy_sea, sig_digits)
 
     rms_mo_l = []
     rmsc_mo_l = []
@@ -240,15 +241,15 @@ def compute_metrics(Var, dm, do, debug=False):
         meanObs_xy_mo = pcmdi_metrics.mean_climate.lib.mean_xy(do_mo, var)
         mean_xy_mo = pcmdi_metrics.mean_climate.lib.mean_xy(dm_mo, var)
 
-        rms_mo_l.append(format(rms_mo * conv, sig_digits))
-        rmsc_mo_l.append(format(rmsc_mo * conv, sig_digits))
+        rms_mo_l.append(format(rms_mo, sig_digits))
+        rmsc_mo_l.append(format(rmsc_mo, sig_digits))
         cor_mo_l.append(format(cor_mo, ".2f"))
-        mae_mo_l.append(format(mae_mo * conv, sig_digits))
-        bias_mo_l.append(format(bias_mo * conv, sig_digits))
-        stdObs_xy_mo_l.append(format(stdObs_xy_mo * conv, sig_digits))
-        std_xy_mo_l.append(format(std_xy_mo * conv, sig_digits))
-        meanObs_xy_mo_l.append(format(meanObs_xy_mo * conv, sig_digits))
-        mean_xy_mo_l.append(format(mean_xy_mo * conv, sig_digits))
+        mae_mo_l.append(format(mae_mo, sig_digits))
+        bias_mo_l.append(format(bias_mo, sig_digits))
+        stdObs_xy_mo_l.append(format(stdObs_xy_mo, sig_digits))
+        std_xy_mo_l.append(format(std_xy_mo, sig_digits))
+        meanObs_xy_mo_l.append(format(meanObs_xy_mo, sig_digits))
+        mean_xy_mo_l.append(format(mean_xy_mo, sig_digits))
 
     metrics_dictionary["bias_xy"]["CalendarMonths"] = bias_mo_l
     metrics_dictionary["rms_xy"]["CalendarMonths"] = rms_mo_l
@@ -265,8 +266,8 @@ def compute_metrics(Var, dm, do, debug=False):
 
 # ZONAL AND SEASONAL MEAN CONTRIBUTIONS
 #           metrics_dictionary['rms_y'][sea] = format(
-#               rms_y * conv,
+#               rms_y,
 #               sig_digits)
 #           metrics_dictionary['rms_devzm'][sea] = format(
-#               rms_xy_devzm * conv,
+#               rms_xy_devzm,
 #               sig_digits)
diff --git a/pcmdi_metrics/mean_climate/lib/load_and_regrid.py b/pcmdi_metrics/mean_climate/lib/load_and_regrid.py
index 64041ec11..9a96c6f95 100644
--- a/pcmdi_metrics/mean_climate/lib/load_and_regrid.py
+++ b/pcmdi_metrics/mean_climate/lib/load_and_regrid.py
@@ -62,6 +62,7 @@ def load_and_regrid(data_path, varname, varname_in_file=None, level=None, t_grid
 
     if varname != varname_in_file:
         ds_regridded[varname] = ds_regridded[varname_in_file]
+        ds_regridded = ds_regridded.drop_vars([varname_in_file])
 
     # preserve units
     try:

From 8127cc252551bafdc79e02e0d40c3e176ce1e17c Mon Sep 17 00:00:00 2001
From: Jiwoo Lee <lee1043@llnl.gov>
Date: Wed, 24 May 2023 14:38:59 -0700
Subject: [PATCH 004/133] update

---
 pcmdi_metrics/mean_climate/lib/compute_metrics.py | 7 +++++--
 pcmdi_metrics/mean_climate/mean_climate_driver.py | 3 ++-
 2 files changed, 7 insertions(+), 3 deletions(-)

diff --git a/pcmdi_metrics/mean_climate/lib/compute_metrics.py b/pcmdi_metrics/mean_climate/lib/compute_metrics.py
index 11c98a583..8b0393159 100644
--- a/pcmdi_metrics/mean_climate/lib/compute_metrics.py
+++ b/pcmdi_metrics/mean_climate/lib/compute_metrics.py
@@ -54,10 +54,13 @@ def compute_metrics(Var, dm, do, debug=False):
 
     # SET CONDITIONAL ON INPUT VARIABLE
     if var == "pr":
-        if do[var].units == "kg m-2 s-1":
-            do[var] = do[var] * 86400
+        print('Adjust units for pr')
+        #if do[var].units == "kg m-2 s-1":
+        do[var] = do[var] * 86400
+        print('REF DATA pr units adjusted to [mm d-1] from [kg m-2 s-1] by 86400 multiplied')
         if dm[var].units == "kg m-2 s-1":
             dm[var] = dm[var] * 86400
+            print('TEST DATA pr units adjusted to [mm d-1] from [kg m-2 s-1] by 86400 multiplied')
 
     if var in ["hus"]:
         sig_digits = ".5f"
diff --git a/pcmdi_metrics/mean_climate/mean_climate_driver.py b/pcmdi_metrics/mean_climate/mean_climate_driver.py
index 1ccb94da2..3c9f6b3cb 100755
--- a/pcmdi_metrics/mean_climate/mean_climate_driver.py
+++ b/pcmdi_metrics/mean_climate/mean_climate_driver.py
@@ -211,10 +211,11 @@
                 test_data_full_path = os.path.join(
                     test_data_path,
                     filename_template).replace('%(variable)', varname).replace('%(model)', model).replace('%(model_version)', model).replace('%(realization)', run)
+                print('test_data (model in this case) full_path:', test_data_full_path)
                 if os.path.exists(test_data_full_path):
                     print('-----------------------')
                     print('model, run:', model, run)
-                    print('test_data (model in this case) full_path:', test_data_full_path)
+                    # print('test_data (model in this case) full_path:', test_data_full_path)
                     try:
                         ds_test_dict = OrderedDict()
 

From 91e6a9a577973f61563796b87718aae345c7d829 Mon Sep 17 00:00:00 2001
From: Jiwoo Lee <lee1043@llnl.gov>
Date: Mon, 29 May 2023 03:00:44 -0700
Subject: [PATCH 005/133] update

---
 doc/obs_info_dictionary.json                  |  6 +--
 .../mean_climate/lib/compute_metrics.py       | 42 ++++++++-----------
 .../mean_climate/lib/load_and_regrid.py       | 42 ++++++++++++++++---
 .../mean_climate/mean_climate_driver.py       | 17 +++++++-
 4 files changed, 72 insertions(+), 35 deletions(-)

diff --git a/doc/obs_info_dictionary.json b/doc/obs_info_dictionary.json
index 61ec68f64..d8d6acf99 100644
--- a/doc/obs_info_dictionary.json
+++ b/doc/obs_info_dictionary.json
@@ -100,12 +100,12 @@
         },
         "GPCP-2-3": {
             "CMIP_CMOR_TABLE": "Amon",
-            "MD5sum": "3792901034585d3d495722f10a0dfecb",
+            "MD5sum": "036e14d0124f4d00921d58e3c1c3e9e1",
             "RefTrackingDate": "Wed Aug  4 12:22:10 2021",
-            "filename": "pr_mon_GPCP-2-3_PCMDI_gn.200301-201812.AC.v20210804.nc",
+            "filename": "pr_monC_GPCP-2-3_PCMDI_gn_197901-201907.AC.v20230516.nc",
             "period": "200301-201812",
             "shape": "(12, 72, 144)",
-            "template": "pr/GPCP-2-3/v20210804/pr_mon_GPCP-2-3_PCMDI_gn.200301-201812.AC.v20210804.nc"
+            "template": "NOAA-NCEI/GPCP-2-3/monC/pr/gn/v20230516/pr_monC_GPCP-2-3_PCMDI_gn_197901-201907.AC.v20230516.nc"           
         },
         "TRMM-3B43v-7": {
             "CMIP_CMOR_TABLE": "Amon",
diff --git a/pcmdi_metrics/mean_climate/lib/compute_metrics.py b/pcmdi_metrics/mean_climate/lib/compute_metrics.py
index 8b0393159..cbdf5eebb 100644
--- a/pcmdi_metrics/mean_climate/lib/compute_metrics.py
+++ b/pcmdi_metrics/mean_climate/lib/compute_metrics.py
@@ -36,32 +36,23 @@ def compute_metrics(Var, dm, do, debug=False):
         print('before time and time bounds unifying')
         print('dm.time: ', dm['time'])
         print('do.time: ', do['time'])
-        
+
+    """
     # Below is temporary...
     dm['time'] = do['time']
-    dm[dm.time.attrs['bounds']] = do[do.time.attrs['bounds']]
+    dm[dm.time.encoding['bounds']] = do[do.time.attrs['bounds']]
+    """
     
     if debug:
         print('after time and time bounds unifying')
         print('dm.time: ', dm['time'])
         print('do.time: ', do['time'])
-
-    #if debug:
-    #    dm.to_netcdf('dm.nc')
-    #    do.to_netcdf('do.nc')
+        
+        dm.to_netcdf('dm.nc')
+        do.to_netcdf('do.nc')
 
     metrics_dictionary = OrderedDict()
 
-    # SET CONDITIONAL ON INPUT VARIABLE
-    if var == "pr":
-        print('Adjust units for pr')
-        #if do[var].units == "kg m-2 s-1":
-        do[var] = do[var] * 86400
-        print('REF DATA pr units adjusted to [mm d-1] from [kg m-2 s-1] by 86400 multiplied')
-        if dm[var].units == "kg m-2 s-1":
-            dm[var] = dm[var] * 86400
-            print('TEST DATA pr units adjusted to [mm d-1] from [kg m-2 s-1] by 86400 multiplied')
-
     if var in ["hus"]:
         sig_digits = ".5f"
     else:
@@ -69,33 +60,36 @@ def compute_metrics(Var, dm, do, debug=False):
 
     # CALCULATE ANNUAL CYCLE SPACE-TIME RMS, CORRELATIONS and STD
     print('compute_metrics-CALCULATE ANNUAL CYCLE SPACE-TIME RMS, CORRELATIONS and STD')
-    print('compute_metrics, rms_xyt')
+    
     rms_xyt = pcmdi_metrics.mean_climate.lib.rms_xyt(dm, do, var)
-    print('compute_metrics, stdObs_xyt')
+    print('compute_metrics, rms_xyt:', rms_xyt)
+
     stdObs_xyt = pcmdi_metrics.mean_climate.lib.std_xyt(do, var)
-    print('compute_metrics, std_xyt')
+    print('compute_metrics, stdObs_xyt:', stdObs_xyt)
+    
     std_xyt = pcmdi_metrics.mean_climate.lib.std_xyt(dm, var)
+    print('compute_metrics, std_xyt:', std_xyt)
 
     # CALCULATE ANNUAL MEANS
-    print('compute_metrics-CALCULATE ANNUAL MEANS')
     dm_am, do_am = pcmdi_metrics.mean_climate.lib.annual_mean(dm, do, var)
+    print('compute_metrics-CALCULATE ANNUAL MEANS')
 
     # CALCULATE ANNUAL MEAN BIAS
-    print('compute_metrics-CALCULATE ANNUAL MEAN BIAS')
     bias_xy = pcmdi_metrics.mean_climate.lib.bias_xy(dm_am, do_am, var)
+    print('compute_metrics-CALCULATE ANNUAL MEAN BIAS, bias_xy:', bias_xy)
 
     # CALCULATE MEAN ABSOLUTE ERROR
-    print('compute_metrics-CALCULATE MSE')
     mae_xy = pcmdi_metrics.mean_climate.lib.meanabs_xy(dm_am, do_am, var)
+    print('compute_metrics-CALCULATE MSE, mae_xy:', mae_xy)
 
     # CALCULATE ANNUAL MEAN RMS (centered and uncentered)
-    print('compute_metrics-CALCULATE MEAN RMS')
     rms_xy = pcmdi_metrics.mean_climate.lib.rms_xy(dm_am, do_am, var)
     rmsc_xy = pcmdi_metrics.mean_climate.lib.rmsc_xy(dm_am, do_am, var)
+    print('compute_metrics-CALCULATE MEAN RMS: rms_xy, rmsc_xy: ', rms_xy, rmsc_xy)
 
     # CALCULATE ANNUAL MEAN CORRELATION
-    print('compute_metrics-CALCULATE MEAN CORR')
     cor_xy = pcmdi_metrics.mean_climate.lib.cor_xy(dm_am, do_am, var)
+    print('compute_metrics-CALCULATE MEAN CORR: cor_xy:', cor_xy)
 
     # CALCULATE ANNUAL OBS and MOD STD
     print('compute_metrics-CALCULATE ANNUAL OBS AND MOD STD')
diff --git a/pcmdi_metrics/mean_climate/lib/load_and_regrid.py b/pcmdi_metrics/mean_climate/lib/load_and_regrid.py
index 9a96c6f95..f47779eb0 100644
--- a/pcmdi_metrics/mean_climate/lib/load_and_regrid.py
+++ b/pcmdi_metrics/mean_climate/lib/load_and_regrid.py
@@ -22,8 +22,19 @@ def load_and_regrid(data_path, varname, varname_in_file=None, level=None, t_grid
         varname_in_file = varname
 
     # load data
-    ds = xc.open_mfdataset(data_path, data_var=varname_in_file, decode_times=decode_times)  # NOTE: decode_times=False will be removed once obs4MIP written using xcdat
+    ds = xc.open_mfdataset(
+        data_path, 
+        data_var=varname_in_file, 
+        decode_times=decode_times)  # NOTE: decode_times=False will be removed once obs4MIP written using xcdat
+    
+    # SET CONDITIONAL ON INPUT VARIABLE
+    if varname == "pr":
+        print('Adjust units for pr')
+        if ds[varname_in_file].units == "kg m-2 s-1":
+            ds[varname_in_file] = ds[varname_in_file] * 86400
+            print('pr units adjusted to [mm d-1] from [kg m-2 s-1] by 86400 multiplied')
 
+    """
     # calendar quality check
     if "calendar" in list(ds.time.attrs.keys()):
         if debug:
@@ -32,18 +43,30 @@ def load_and_regrid(data_path, varname, varname_in_file=None, level=None, t_grid
             if debug:
                 print('ds.calendar:', ds.calendar)
             if ds.calendar != ds.time.attrs["calendar"]:
-                print('[WARNING]: calendar info mismatch. ds.time.attrs["calendar"] is adjusted to ds.calendar')
-                ds.time.attrs["calendar"] = ds.calendar
+                ds.time.encoding["calendar"] = ds.calendar
+                print('[WARNING]: calendar info mismatch. ds.time.attrs["calendar"] is adjusted to ds.calendar, ', ds.calendar)
     else:
         if 'calendar' in ds.attrs.keys():
             ds.time.attrs["calendar"] = ds.calendar
-                
-    # time bound check -- add proper time bound info if cdms-generated annual cycle is loaded
+            print('[WARNING]: calendar info not found for time axis. ds.time.attrs["calendar"] is adjusted to ds.calendar, ', ds.calendar)
+        else:
+            ds.time.attrs["calendar"] = 'standard'
+            print('[WARNING]: calendar info not found for time axis. ds.time.attrs["calendar"] is adjusted to standard')
+    """
+    
+    # time bound check #1 -- add proper time bound info if cdms-generated annual cycle is loaded
     if isinstance(ds.time.values[0], np.float64):  # and "units" not in list(ds.time.attrs.keys()):
         ds.time.attrs['units'] = "days since 0001-01-01"
         ds = xc.decode_time(ds)
         if debug:
             print('decode_time done')
+            
+    # time bound check #2 -- add time bounds itself it it is missing
+    if 'bounds' in list(ds.time.attrs.keys()):
+        time_bnds_key = ds.time.attrs['bounds']
+        if time_bnds_key not in list(ds.keys()):
+            ds = ds.bounds.add_missing_bounds(['T'])
+            print('[WARNING]: bounds.add_missing_bounds conducted for T axis')
     
     # level - extract a specific level if needed
     if level is not None:
@@ -64,7 +87,7 @@ def load_and_regrid(data_path, varname, varname_in_file=None, level=None, t_grid
         ds_regridded[varname] = ds_regridded[varname_in_file]
         ds_regridded = ds_regridded.drop_vars([varname_in_file])
 
-    # preserve units
+    # preserve units in regridded dataset
     try:
         units = ds[varname].units
     except Exception as e:
@@ -73,6 +96,13 @@ def load_and_regrid(data_path, varname, varname_in_file=None, level=None, t_grid
     print('units:', units)
 
     ds_regridded[varname] = ds_regridded[varname].assign_attrs({'units': units})
+    
+    # time bound check #3 -- preserve time bnds in regridded dataset
+    if 'bounds' in list(ds_regridded.time.attrs.keys()):
+        time_bnds_key = ds_regridded.time.attrs['bounds']
+        if time_bnds_key not in list(ds_regridded.keys()):
+            ds_regridded = ds_regridded.bounds.add_missing_bounds(['T'])
+            print('[WARNING]: bounds.add_missing_bounds conducted for T axis')
         
     if debug:
         print('ds_regridded:', ds_regridded)
diff --git a/pcmdi_metrics/mean_climate/mean_climate_driver.py b/pcmdi_metrics/mean_climate/mean_climate_driver.py
index 3c9f6b3cb..80284434a 100755
--- a/pcmdi_metrics/mean_climate/mean_climate_driver.py
+++ b/pcmdi_metrics/mean_climate/mean_climate_driver.py
@@ -168,20 +168,33 @@
     # observation loop
     # ----------------
     if "all" in reference_data_set:
-        reference_data_set = [x for x in list(obs_dict[varname].keys()) if (x == "default" or "alternate" in x)]
+        reference_data_set = [
+            x for x in list(obs_dict[varname].keys()) if (x == "default" or "alternate" in x)]
         print("reference_data_set (all): ", reference_data_set)
 
     for ref in reference_data_set:
         print('ref:', ref)
+        
         # identify data to load (annual cycle (AC) data is loading in)
         ref_dataset_name = obs_dict[varname][ref]
         ref_data_full_path = os.path.join(
             reference_data_path,
             obs_dict[varname][ref_dataset_name]["template"])
         print('ref_data_full_path:', ref_data_full_path)
+        
         # load data and regrid
-        ds_ref = load_and_regrid(data_path=ref_data_full_path, varname=varname, level=level, t_grid=t_grid, decode_times=False, regrid_tool=regrid_tool, debug=debug)
+        ds_ref = load_and_regrid(
+            data_path=ref_data_full_path, 
+            varname=varname, 
+            level=level, 
+            t_grid=t_grid, 
+            # decode_times=False, 
+            decode_times=True, 
+            regrid_tool=regrid_tool, 
+            debug=debug)
+        
         ds_ref_dict = OrderedDict()
+        
         # for record in output json
         result_dict['References'][ref] = obs_dict[varname][ref_dataset_name]
 

From 50b5a08fe07009172fd68ed8665ee012fda3bd5f Mon Sep 17 00:00:00 2001
From: Jiwoo Lee <lee1043@llnl.gov>
Date: Mon, 29 May 2023 03:05:29 -0700
Subject: [PATCH 006/133] pre-commit check

---
 doc/obs_info_dictionary.json | 2 +-
 share/DefArgsCIA.json        | 2 +-
 2 files changed, 2 insertions(+), 2 deletions(-)

diff --git a/doc/obs_info_dictionary.json b/doc/obs_info_dictionary.json
index d8d6acf99..adc0515b0 100644
--- a/doc/obs_info_dictionary.json
+++ b/doc/obs_info_dictionary.json
@@ -105,7 +105,7 @@
             "filename": "pr_monC_GPCP-2-3_PCMDI_gn_197901-201907.AC.v20230516.nc",
             "period": "200301-201812",
             "shape": "(12, 72, 144)",
-            "template": "NOAA-NCEI/GPCP-2-3/monC/pr/gn/v20230516/pr_monC_GPCP-2-3_PCMDI_gn_197901-201907.AC.v20230516.nc"           
+            "template": "NOAA-NCEI/GPCP-2-3/monC/pr/gn/v20230516/pr_monC_GPCP-2-3_PCMDI_gn_197901-201907.AC.v20230516.nc"
         },
         "TRMM-3B43v-7": {
             "CMIP_CMOR_TABLE": "Amon",
diff --git a/share/DefArgsCIA.json b/share/DefArgsCIA.json
index cd33a055d..8507f33ba 100644
--- a/share/DefArgsCIA.json
+++ b/share/DefArgsCIA.json
@@ -163,4 +163,4 @@
         ],
         "help":"A list of variables to be processed"
     }
-}
\ No newline at end of file
+}

From de248849595ef08b5a5b7b4394a271f45fbef319 Mon Sep 17 00:00:00 2001
From: Jiwoo Lee <lee1043@users.noreply.github.com>
Date: Fri, 30 Jun 2023 21:27:57 -0700
Subject: [PATCH 007/133] Update version release history for v3.1.1

Update README.md
---
 README.md | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/README.md b/README.md
index ca36bd97b..70884fcfa 100755
--- a/README.md
+++ b/README.md
@@ -98,6 +98,7 @@ Release Notes and History
 
 | <div style="width:300%">[Versions]</div> | Update summary   |
 | ------------- | ------------------------------------- |
+| [v3.1.1]      | Technical and documentation update
 | [v3.1]        | New metric added: **Precipitation Benchmarking -- distribution bimodality**
 | [v3.0.2]      | Minor patch and more documentation added
 | [v3.0.1]      | Minor technical patch                 
@@ -122,6 +123,7 @@ Release Notes and History
 
 
 [Versions]: https://github.com/PCMDI/pcmdi_metrics/releases
+[v3.1.1]: https://github.com/PCMDI/pcmdi_metrics/releases/tag/v3.1.1
 [v3.1]: https://github.com/PCMDI/pcmdi_metrics/releases/tag/v3.1
 [v3.0.2]: https://github.com/PCMDI/pcmdi_metrics/releases/tag/v3.0.2
 [v3.0.1]: https://github.com/PCMDI/pcmdi_metrics/releases/tag/v3.0.1

From 312b2d35de4f088ac0a814205480cde8ab74c0c2 Mon Sep 17 00:00:00 2001
From: Jiwoo Lee <lee1043@llnl.gov>
Date: Mon, 3 Jul 2023 13:11:02 -0700
Subject: [PATCH 008/133] clean up

---
 .../Demo/Demo_4_modes_of_variability.ipynb    | 44 +------------------
 1 file changed, 1 insertion(+), 43 deletions(-)

diff --git a/doc/jupyter/Demo/Demo_4_modes_of_variability.ipynb b/doc/jupyter/Demo/Demo_4_modes_of_variability.ipynb
index bd8ec100c..62396cc5d 100644
--- a/doc/jupyter/Demo/Demo_4_modes_of_variability.ipynb
+++ b/doc/jupyter/Demo/Demo_4_modes_of_variability.ipynb
@@ -1831,48 +1831,6 @@
     "fig.tight_layout()"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": 31,
-   "id": "8d5e9f34-dc16-4ee3-8223-fe6f62da38d4",
-   "metadata": {},
-   "outputs": [
-    {
-     "ename": "type",
-     "evalue": "name 'cdms2' is not defined",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
-      "Cell \u001b[0;32mIn[31], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m fo3 \u001b[38;5;241m=\u001b[39m \u001b[43mcdms2\u001b[49m\u001b[38;5;241m.\u001b[39mopen(demo_output_directory \u001b[38;5;241m+\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m/PDO/PDO_ts_EOF1_monthly_cmip5_ACCESS1-0_historical_r1i1p1_mo_atm_1900-2005_cbf.nc\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n",
-      "\u001b[0;31mNameError\u001b[0m: name 'cdms2' is not defined"
-     ]
-    }
-   ],
-   "source": [
-    "fo3 = cdms2.open(demo_output_directory + \"/PDO/PDO_ts_EOF1_monthly_cmip5_ACCESS1-0_historical_r1i1p1_mo_atm_1900-2005_cbf.nc\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "9356bf5f-f86b-472e-9b46-d7f7413d601d",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "d = fo3('eof')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "43c5517d-4750-4e9e-8426-87c4a42b6fda",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "quickplot(d)"
-   ]
-  },
   {
    "cell_type": "markdown",
    "id": "7927e481-4367-4260-86fa-3855a357974c",
@@ -2291,7 +2249,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.12"
+   "version": "3.9.7"
   }
  },
  "nbformat": 4,

From 01474bef5ce306488aa3469632190748a7a456fe Mon Sep 17 00:00:00 2001
From: Jiwoo Lee <lee1043@llnl.gov>
Date: Mon, 3 Jul 2023 16:24:13 -0700
Subject: [PATCH 009/133] move old notebooks to deprecated directory

---
 doc/jupyter/{ => Jsons}/JsonClass.ipynb                           | 0
 .../{ => deprecated}/Embarassingly_parallelize_drivers.ipynb      | 0
 doc/jupyter/{ => deprecated}/PortraitPlots.ipynb                  | 0
 doc/jupyter/{ => deprecated}/ReusablePortraitPlot.ipynb           | 0
 doc/jupyter/{ => deprecated}/sample_parameter_file.py             | 0
 doc/jupyter/{ => deprecated}/sample_parameter_file_updated.py     | 0
 6 files changed, 0 insertions(+), 0 deletions(-)
 rename doc/jupyter/{ => Jsons}/JsonClass.ipynb (100%)
 rename doc/jupyter/{ => deprecated}/Embarassingly_parallelize_drivers.ipynb (100%)
 rename doc/jupyter/{ => deprecated}/PortraitPlots.ipynb (100%)
 rename doc/jupyter/{ => deprecated}/ReusablePortraitPlot.ipynb (100%)
 rename doc/jupyter/{ => deprecated}/sample_parameter_file.py (100%)
 rename doc/jupyter/{ => deprecated}/sample_parameter_file_updated.py (100%)

diff --git a/doc/jupyter/JsonClass.ipynb b/doc/jupyter/Jsons/JsonClass.ipynb
similarity index 100%
rename from doc/jupyter/JsonClass.ipynb
rename to doc/jupyter/Jsons/JsonClass.ipynb
diff --git a/doc/jupyter/Embarassingly_parallelize_drivers.ipynb b/doc/jupyter/deprecated/Embarassingly_parallelize_drivers.ipynb
similarity index 100%
rename from doc/jupyter/Embarassingly_parallelize_drivers.ipynb
rename to doc/jupyter/deprecated/Embarassingly_parallelize_drivers.ipynb
diff --git a/doc/jupyter/PortraitPlots.ipynb b/doc/jupyter/deprecated/PortraitPlots.ipynb
similarity index 100%
rename from doc/jupyter/PortraitPlots.ipynb
rename to doc/jupyter/deprecated/PortraitPlots.ipynb
diff --git a/doc/jupyter/ReusablePortraitPlot.ipynb b/doc/jupyter/deprecated/ReusablePortraitPlot.ipynb
similarity index 100%
rename from doc/jupyter/ReusablePortraitPlot.ipynb
rename to doc/jupyter/deprecated/ReusablePortraitPlot.ipynb
diff --git a/doc/jupyter/sample_parameter_file.py b/doc/jupyter/deprecated/sample_parameter_file.py
similarity index 100%
rename from doc/jupyter/sample_parameter_file.py
rename to doc/jupyter/deprecated/sample_parameter_file.py
diff --git a/doc/jupyter/sample_parameter_file_updated.py b/doc/jupyter/deprecated/sample_parameter_file_updated.py
similarity index 100%
rename from doc/jupyter/sample_parameter_file_updated.py
rename to doc/jupyter/deprecated/sample_parameter_file_updated.py

From ccf80952cab698fd26272b46af8f6cdca3cf6a25 Mon Sep 17 00:00:00 2001
From: Ana Ordonez <anaordonez@comcast.net>
Date: Fri, 28 Jul 2023 15:59:26 -0700
Subject: [PATCH 010/133] add page

---
 docs/metrics_precip-variability.rst | 60 +++++++++++++++++++++++++++++
 1 file changed, 60 insertions(+)
 create mode 100644 docs/metrics_precip-variability.rst

diff --git a/docs/metrics_precip-variability.rst b/docs/metrics_precip-variability.rst
new file mode 100644
index 000000000..9119795bd
--- /dev/null
+++ b/docs/metrics_precip-variability.rst
@@ -0,0 +1,60 @@
+.. _metrics_precip-variability:
+
+*****************
+Precip-variability
+*****************
+
+Overview
+========
+This set of metrics is designed to measure precipitation variabilty across multiple timescales, including subdaily.
+
+Demo
+====
+* `PMP demo Jupyter notebook`_
+
+
+Required Data sets 
+==================
+
+Input files must use the following name convention:  
+variable_frequency_model_experiment_ensemble_startdate-enddate.nc  
+
+Because underscores are used to separate these elements, they may not be used anywhere else in the file name.
+Start and end dates must use the YYYYMMDD or YYYYMMDDHHHH format.  
+
+For example, these are valid input file names:  
+pr_day_bcc-csm1-1_historical_r1i1p1_19800101-19841231.nc  
+pr_3hr_IMERG-v06B-Final_PCMDI_2x2_201004010000-201004302100.nc  
+If the time series for a single data set is spread across multiple files, those files must be located in a single directory.
+
+Usage
+=====
+Users will set up a parameter file and run the precipitation variability driver on the command line.
+To run the driver, use:
+`variability_across_timescales_PS_driver.py -p parameter_file`  
+
+Results are reported on a 2x2 degree latitude/longitude grid.
+
+Options available to set in the parameter file include:
+* **mip**: Name of MIP
+* **exp**: Name of experiment
+* **var**: Name of data set variable, e.g. "pr"
+* **frq**: Frequency of data set, either "day" or "3hr"
+* **modpath**: Path to directory containing input data files
+* **mod**: Name of model file or wildcard "*" to use all files in directory. Symlinks may be used.
+* **results_dir**: Results directory path
+* **case_id**: Case id
+* **prd**: Start and end years for analysis as list, e.g. [start_year, end_year]
+* **fac**: Factor to convert from data set units to mm/day. Set to 1 for no conversion.
+* **nperseg**: Length of segment in power spectra
+* **noverlap**: Length of overlap between segments in power spectra
+* **ref**: Reference data path
+* **cmec**: Set to True to output CMEC formatted JSON
+
+
+Reference
+==========
+Ahn, M.-S., P. J. Gleckler, J. Lee, A. G. Pendergrass, and C. Jakob, 2022: Benchmarking Simulated Precipitation Variability Amplitude across Timescales. Journal of Climate. https://doi.org/10.1175/JCLI-D-21-0542.1
+
+
+.. _PMP demo Jupyter notebook: https://github.com/PCMDI/pcmdi_metrics/blob/main/doc/jupyter/Demo/Demo_7_precip_variability.ipynb
\ No newline at end of file

From 54248bffc76143f5b395f2ef4630ec2436751663 Mon Sep 17 00:00:00 2001
From: Ana Ordonez <anaordonez@comcast.net>
Date: Fri, 28 Jul 2023 16:10:33 -0700
Subject: [PATCH 011/133] fix formatting

---
 docs/metrics_precip-variability.rst | 37 +++++++++++++++--------------
 1 file changed, 19 insertions(+), 18 deletions(-)

diff --git a/docs/metrics_precip-variability.rst b/docs/metrics_precip-variability.rst
index 9119795bd..86cf0ad17 100644
--- a/docs/metrics_precip-variability.rst
+++ b/docs/metrics_precip-variability.rst
@@ -16,13 +16,14 @@ Demo
 Required Data sets 
 ==================
 
-Input files must use the following name convention:  
+Input files must use the following name convention: ::
 variable_frequency_model_experiment_ensemble_startdate-enddate.nc  
 
 Because underscores are used to separate these elements, they may not be used anywhere else in the file name.
+
 Start and end dates must use the YYYYMMDD or YYYYMMDDHHHH format.  
 
-For example, these are valid input file names:  
+For example, these are valid input file names: ::
 pr_day_bcc-csm1-1_historical_r1i1p1_19800101-19841231.nc  
 pr_3hr_IMERG-v06B-Final_PCMDI_2x2_201004010000-201004302100.nc  
 If the time series for a single data set is spread across multiple files, those files must be located in a single directory.
@@ -33,23 +34,23 @@ Users will set up a parameter file and run the precipitation variability driver
 To run the driver, use:
 `variability_across_timescales_PS_driver.py -p parameter_file`  
 
-Results are reported on a 2x2 degree latitude/longitude grid.
-
-Options available to set in the parameter file include:
-* **mip**: Name of MIP
-* **exp**: Name of experiment
-* **var**: Name of data set variable, e.g. "pr"
-* **frq**: Frequency of data set, either "day" or "3hr"
-* **modpath**: Path to directory containing input data files
-* **mod**: Name of model file or wildcard "*" to use all files in directory. Symlinks may be used.
-* **results_dir**: Results directory path
-* **case_id**: Case id
-* **prd**: Start and end years for analysis as list, e.g. [start_year, end_year]
+Results are reported on a 2x2 degree latitude/longitude world grid.
+
+Options available to set in the parameter file include:  ::
+* **mip**: Name of MIP.
+* **exp**: Name of experiment. 
+* **var**: Name of data set variable, e.g. "pr". 
+* **frq**: Frequency of data set, either "day" or "3hr". 
+* **modpath**: Path to directory containing input data files. 
+* **mod**: Name of model file or wildcard "*" to use all files in directory. Symlinks may be used. 
+* **results_dir**: Results directory path.
+* **case_id**: Case id.
+* **prd**: Start and end years for analysis as list, e.g. [start_year, end_year].
 * **fac**: Factor to convert from data set units to mm/day. Set to 1 for no conversion.
-* **nperseg**: Length of segment in power spectra
-* **noverlap**: Length of overlap between segments in power spectra
-* **ref**: Reference data path
-* **cmec**: Set to True to output CMEC formatted JSON
+* **nperseg**: Length of segment in power spectra.
+* **noverlap**: Length of overlap between segments in power spectra.
+* **ref**: Reference data path.
+* **cmec**: Set to True to output CMEC formatted JSON.
 
 
 Reference

From 4198e46e4eacc856c233398dbdf1b4127368b565 Mon Sep 17 00:00:00 2001
From: Ana Ordonez <anaordonez@comcast.net>
Date: Fri, 28 Jul 2023 16:11:32 -0700
Subject: [PATCH 012/133] fix formatting

---
 docs/metrics_precip-variability.rst | 9 +++++----
 1 file changed, 5 insertions(+), 4 deletions(-)

diff --git a/docs/metrics_precip-variability.rst b/docs/metrics_precip-variability.rst
index 86cf0ad17..0efeaa358 100644
--- a/docs/metrics_precip-variability.rst
+++ b/docs/metrics_precip-variability.rst
@@ -17,15 +17,15 @@ Required Data sets
 ==================
 
 Input files must use the following name convention: ::
-variable_frequency_model_experiment_ensemble_startdate-enddate.nc  
+   variable_frequency_model_experiment_ensemble_startdate-enddate.nc  
 
 Because underscores are used to separate these elements, they may not be used anywhere else in the file name.
 
 Start and end dates must use the YYYYMMDD or YYYYMMDDHHHH format.  
 
 For example, these are valid input file names: ::
-pr_day_bcc-csm1-1_historical_r1i1p1_19800101-19841231.nc  
-pr_3hr_IMERG-v06B-Final_PCMDI_2x2_201004010000-201004302100.nc  
+   pr_day_bcc-csm1-1_historical_r1i1p1_19800101-19841231.nc  
+   pr_3hr_IMERG-v06B-Final_PCMDI_2x2_201004010000-201004302100.nc  
 If the time series for a single data set is spread across multiple files, those files must be located in a single directory.
 
 Usage
@@ -36,7 +36,8 @@ To run the driver, use:
 
 Results are reported on a 2x2 degree latitude/longitude world grid.
 
-Options available to set in the parameter file include:  ::
+Options available to set in the parameter file include:
+
 * **mip**: Name of MIP.
 * **exp**: Name of experiment. 
 * **var**: Name of data set variable, e.g. "pr". 

From e14aa158711cff9a41899639d2cf2070b8ead8fb Mon Sep 17 00:00:00 2001
From: Ana Ordonez <anaordonez@comcast.net>
Date: Fri, 28 Jul 2023 16:12:46 -0700
Subject: [PATCH 013/133] fix formatting

---
 docs/metrics_precip-variability.rst | 8 ++++++--
 1 file changed, 6 insertions(+), 2 deletions(-)

diff --git a/docs/metrics_precip-variability.rst b/docs/metrics_precip-variability.rst
index 0efeaa358..e73ac3ac6 100644
--- a/docs/metrics_precip-variability.rst
+++ b/docs/metrics_precip-variability.rst
@@ -17,6 +17,7 @@ Required Data sets
 ==================
 
 Input files must use the following name convention: ::
+
    variable_frequency_model_experiment_ensemble_startdate-enddate.nc  
 
 Because underscores are used to separate these elements, they may not be used anywhere else in the file name.
@@ -24,15 +25,18 @@ Because underscores are used to separate these elements, they may not be used an
 Start and end dates must use the YYYYMMDD or YYYYMMDDHHHH format.  
 
 For example, these are valid input file names: ::
+
    pr_day_bcc-csm1-1_historical_r1i1p1_19800101-19841231.nc  
    pr_3hr_IMERG-v06B-Final_PCMDI_2x2_201004010000-201004302100.nc  
+
 If the time series for a single data set is spread across multiple files, those files must be located in a single directory.
 
 Usage
 =====
 Users will set up a parameter file and run the precipitation variability driver on the command line.
-To run the driver, use:
-`variability_across_timescales_PS_driver.py -p parameter_file`  
+To run the driver, use: ::
+
+   variability_across_timescales_PS_driver.py -p parameter_file  
 
 Results are reported on a 2x2 degree latitude/longitude world grid.
 

From dddc7c2629553c0884d7b48dd80b3e01ea53d482 Mon Sep 17 00:00:00 2001
From: Ana Ordonez <anaordonez@comcast.net>
Date: Fri, 28 Jul 2023 16:25:02 -0700
Subject: [PATCH 014/133] add link

---
 docs/metrics_precip-variability.rst | 8 ++++++--
 1 file changed, 6 insertions(+), 2 deletions(-)

diff --git a/docs/metrics_precip-variability.rst b/docs/metrics_precip-variability.rst
index e73ac3ac6..7c79dbb50 100644
--- a/docs/metrics_precip-variability.rst
+++ b/docs/metrics_precip-variability.rst
@@ -12,8 +12,11 @@ Demo
 ====
 * `PMP demo Jupyter notebook`_
 
+Exampe parameter files
+======================
+A set of example parameter files for models and observations can be viewed at `this link`_.
 
-Required Data sets 
+Required data sets 
 ==================
 
 Input files must use the following name convention: ::
@@ -63,4 +66,5 @@ Reference
 Ahn, M.-S., P. J. Gleckler, J. Lee, A. G. Pendergrass, and C. Jakob, 2022: Benchmarking Simulated Precipitation Variability Amplitude across Timescales. Journal of Climate. https://doi.org/10.1175/JCLI-D-21-0542.1
 
 
-.. _PMP demo Jupyter notebook: https://github.com/PCMDI/pcmdi_metrics/blob/main/doc/jupyter/Demo/Demo_7_precip_variability.ipynb
\ No newline at end of file
+.. _PMP demo Jupyter notebook: https://github.com/PCMDI/pcmdi_metrics/blob/main/doc/jupyter/Demo/Demo_7_precip_variability.ipynb
+.. _this link: https://github.com/PCMDI/pcmdi_metrics/tree/main/pcmdi_metrics/precip_variability/param

From ec83ae9659b917e48aa5ec642ba9946c6583b84e Mon Sep 17 00:00:00 2001
From: Ana Ordonez <anaordonez@comcast.net>
Date: Fri, 28 Jul 2023 16:26:20 -0700
Subject: [PATCH 015/133] fix link

---
 docs/metrics.rst | 1 +
 1 file changed, 1 insertion(+)

diff --git a/docs/metrics.rst b/docs/metrics.rst
index a399be6f6..69e841d3d 100644
--- a/docs/metrics.rst
+++ b/docs/metrics.rst
@@ -17,5 +17,6 @@ A suite of demo scripts and interactive Jupyter notebooks are provided with `thi
    metrics_enso
    metrics_mjo
    metrics_monsoon
+   metrics_precip-variability
 
 

From 8e682478fd01f53e8b8552b6d29be6cd466203b1 Mon Sep 17 00:00:00 2001
From: Ana Ordonez <anaordonez@comcast.net>
Date: Fri, 28 Jul 2023 16:27:23 -0700
Subject: [PATCH 016/133] add title

---
 docs/metrics_precip-variability.rst | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/docs/metrics_precip-variability.rst b/docs/metrics_precip-variability.rst
index 7c79dbb50..1b0248b53 100644
--- a/docs/metrics_precip-variability.rst
+++ b/docs/metrics_precip-variability.rst
@@ -1,8 +1,8 @@
 .. _metrics_precip-variability:
 
-*****************
-Precip-variability
-*****************
+*******************************************
+Precipitation Variability Across Timescales
+*******************************************
 
 Overview
 ========

From aa71f3cbef6afc5f44dbf24c7f91535ece0616d1 Mon Sep 17 00:00:00 2001
From: Ana Ordonez <anaordonez@comcast.net>
Date: Fri, 28 Jul 2023 17:13:44 -0700
Subject: [PATCH 017/133] add precip distribution page

---
 docs/metrics.rst                     |  1 +
 docs/metrics_precip-distribution.rst | 57 ++++++++++++++++++++++++++++
 2 files changed, 58 insertions(+)
 create mode 100644 docs/metrics_precip-distribution.rst

diff --git a/docs/metrics.rst b/docs/metrics.rst
index 69e841d3d..abb022601 100644
--- a/docs/metrics.rst
+++ b/docs/metrics.rst
@@ -18,5 +18,6 @@ A suite of demo scripts and interactive Jupyter notebooks are provided with `thi
    metrics_mjo
    metrics_monsoon
    metrics_precip-variability
+   metrics_precip-distribution
 
 
diff --git a/docs/metrics_precip-distribution.rst b/docs/metrics_precip-distribution.rst
new file mode 100644
index 000000000..7e15072b6
--- /dev/null
+++ b/docs/metrics_precip-distribution.rst
@@ -0,0 +1,57 @@
+.. _metrics_precip-distribution:
+
+**************************
+Precipitation Distribution
+**************************
+
+Overview
+========
+
+
+Exampe parameter files
+======================
+A set of example parameter files for models and observations can be viewed at `this link`_.
+
+Required data sets 
+==================
+
+Input files must use the following name convention: ::
+
+   variable_frequency_model_experiment_ensemble_startdate-enddate.nc  
+
+Because underscores are used to separate these elements, they may not be used anywhere else in the file name.
+
+Start and end dates must use the YYYYMMDD or YYYYMMDDHHHH format.  
+
+For example, these are valid input file names: ::
+
+   pr_day_bcc-csm1-1_historical_r1i1p1_19800101-19841231.nc  
+   pr_3hr_IMERG-v06B-Final_PCMDI_2x2_201004010000-201004302100.nc  
+
+If the time series for a single data set is spread across multiple files, those files must be located in a single directory.
+
+Usage
+=====
+Users will set up a parameter file and run the precipitation variability driver on the command line.
+To run the driver, use: ::
+
+   precip_distribution_driver.py -p parameter_file  
+
+Options available to set in the parameter file include:
+
+* **mip**: Name of MIP.
+* **var**: Name of data set variable, e.g. "pr". 
+* **frq**: Frequency of data set, either "day" or "3hr". 
+* **modpath**: Path to directory containing input data files. 
+* **mod**: Name of model file or wildcard "*" to use all files in directory. Symlinks may be used. 
+* **results_dir**: Results directory path.
+* **case_id**: Case id.
+* **prd**: Start and end years for analysis as list, e.g. [start_year, end_year].
+* **fac**: Factor to convert from data set units to mm/day. Set to 1 for no conversion.
+* **res**: List of target horizontal resolutions in degrees for interporation.
+* **ref**: Reference data path.
+* **ref_dir**: Reference directory path.
+* **cmec**: Set to True to output CMEC formatted JSON.
+
+
+.. _this link: https://github.com/PCMDI/pcmdi_metrics/tree/main/pcmdi_metrics/precip_distribution/param

From b6cfe1fbc9de60a5adf01aee2b1325f6083579d1 Mon Sep 17 00:00:00 2001
From: Ana Ordonez <ordonez4@llnl.gov>
Date: Mon, 31 Jul 2023 13:14:30 -0700
Subject: [PATCH 018/133] Add details from readme

---
 docs/metrics_precip-distribution.rst | 12 ++++++++++--
 1 file changed, 10 insertions(+), 2 deletions(-)

diff --git a/docs/metrics_precip-distribution.rst b/docs/metrics_precip-distribution.rst
index 7e15072b6..5a9e87e11 100644
--- a/docs/metrics_precip-distribution.rst
+++ b/docs/metrics_precip-distribution.rst
@@ -15,18 +15,20 @@ A set of example parameter files for models and observations can be viewed at `t
 Required data sets 
 ==================
 
+This driver expects daily averaged precipitation.
+
 Input files must use the following name convention: ::
 
    variable_frequency_model_experiment_ensemble_startdate-enddate.nc  
 
 Because underscores are used to separate these elements, they may not be used anywhere else in the file name.
 
-Start and end dates must use the YYYYMMDD or YYYYMMDDHHHH format.  
+Start and end dates must use the YYYYMMDD format.  
 
 For example, these are valid input file names: ::
 
    pr_day_bcc-csm1-1_historical_r1i1p1_19800101-19841231.nc  
-   pr_3hr_IMERG-v06B-Final_PCMDI_2x2_201004010000-201004302100.nc  
+   pr_3hr_IMERG-v06B-Final_PCMDI_2x2_20100401-20100430.nc  
 
 If the time series for a single data set is spread across multiple files, those files must be located in a single directory.
 
@@ -37,6 +39,12 @@ To run the driver, use: ::
 
    precip_distribution_driver.py -p parameter_file  
 
+This code should be run for a reference observation initially as some metrics (e.g., Perkins score) need a reference.
+
+After completing calculation for a reference observation, this code can work for multiple datasets at once.
+
+This benchmarking framework provides three tiers of area averaged outputs for i) large scale domain (Tropics and Extratropics with separated land and ocean) commonly used in the PMP , ii) large scale domain with clustered precipitation characteristics (Tropics and Extratropics with separated land and ocean, and separated heavy, moderate, and light precipitation regions), and iii) modified IPCC AR6 regions shown in the reference paper.
+
 Options available to set in the parameter file include:
 
 * **mip**: Name of MIP.

From 9aa13715fb0bef366d00308e69201db1d40586df Mon Sep 17 00:00:00 2001
From: Ana Ordonez <ordonez4@llnl.gov>
Date: Mon, 31 Jul 2023 14:01:38 -0700
Subject: [PATCH 019/133] Add calc_ratio

---
 docs/metrics_precip-variability.rst | 22 ++++++++++++++++++++++
 1 file changed, 22 insertions(+)

diff --git a/docs/metrics_precip-variability.rst b/docs/metrics_precip-variability.rst
index 1b0248b53..cc67101ff 100644
--- a/docs/metrics_precip-variability.rst
+++ b/docs/metrics_precip-variability.rst
@@ -36,6 +36,10 @@ If the time series for a single data set is spread across multiple files, those
 
 Usage
 =====
+
+Spectral averages
+*****************
+
 Users will set up a parameter file and run the precipitation variability driver on the command line.
 To run the driver, use: ::
 
@@ -60,6 +64,23 @@ Options available to set in the parameter file include:
 * **ref**: Reference data path.
 * **cmec**: Set to True to output CMEC formatted JSON.
 
+Metric 
+******
+
+The precipitation variability metric can be generated after model and observational spectral averages are made.
+
+A script called `calc_ratio.py`_ is provided in the precip_variability codebase. This script can be called with three arguments to generate the ratio.
+
+* **ref**: path to obs results JSON
+* **modpath**: directory containing model results JSONS (not CMEC formatted JSONs)
+* **results_dir**: directory for calc_ratio.py results
+
+The calc_ratio.py script must be called with python directly. For example, to run this script using files from a directory called "results": ::
+
+   python pcmdi_metrics/pcmdi_metrics/precip_variability/scripts_pcmdi/calc_ratio.py \
+   --ref results/precip_variability/GPCP-1-3/PS_pr.day_regrid.180x90_area.freq.mean_GPCP-1-3.json \
+   --modpath results/precip_variability/GISS-E2-H/ \
+   --results_dir results/precip_variability/ratio/
 
 Reference
 ==========
@@ -68,3 +89,4 @@ Ahn, M.-S., P. J. Gleckler, J. Lee, A. G. Pendergrass, and C. Jakob, 2022: Bench
 
 .. _PMP demo Jupyter notebook: https://github.com/PCMDI/pcmdi_metrics/blob/main/doc/jupyter/Demo/Demo_7_precip_variability.ipynb
 .. _this link: https://github.com/PCMDI/pcmdi_metrics/tree/main/pcmdi_metrics/precip_variability/param
+.. _calc_ratio.py: https://github.com/PCMDI/pcmdi_metrics/blob/main/pcmdi_metrics/precip_variability/scripts_pcmdi/calc_ratio.py

From eddccbdd7a16843b80f7087c1d2fc7db5292cada Mon Sep 17 00:00:00 2001
From: Jiwoo Lee <lee1043@llnl.gov>
Date: Thu, 10 Aug 2023 16:03:45 -0700
Subject: [PATCH 020/133] minor bug fix

---
 pcmdi_metrics/graphics/share/read_json_mean_clim.py | 5 ++---
 1 file changed, 2 insertions(+), 3 deletions(-)

diff --git a/pcmdi_metrics/graphics/share/read_json_mean_clim.py b/pcmdi_metrics/graphics/share/read_json_mean_clim.py
index 457c78d51..5dc6aac75 100644
--- a/pcmdi_metrics/graphics/share/read_json_mean_clim.py
+++ b/pcmdi_metrics/graphics/share/read_json_mean_clim.py
@@ -146,12 +146,11 @@ def extract_data(results_dict, var_list, region, stat, season, mip, debug=False)
     Return a pandas dataframe for metric numbers at given region/stat/season.
     Rows: models, Columns: variables (i.e., 2d array)
     """
+    model_list = sorted(list(results_dict[var_list[0]]["RESULTS"].keys()))
     if "rlut" in list(results_dict.keys()):
         if "rlut" in list(results_dict["rlut"]["RESULTS"].keys()):
             model_list = sorted(list(results_dict["rlut"]["RESULTS"].keys()))
-    else:
-        model_list = sorted(list(results_dict[var_list[0]]["RESULTS"].keys()))
-
+    
     data_list = []
     for model in model_list:
         if "rlut" in list(results_dict.keys()):

From 11f013704378a513b31f05a4df871ec3f97c8241 Mon Sep 17 00:00:00 2001
From: Jiwoo Lee <lee1043@llnl.gov>
Date: Thu, 10 Aug 2023 16:20:10 -0700
Subject: [PATCH 021/133] minor bug fix

---
 pcmdi_metrics/graphics/share/read_json_mean_clim.py | 3 +++
 1 file changed, 3 insertions(+)

diff --git a/pcmdi_metrics/graphics/share/read_json_mean_clim.py b/pcmdi_metrics/graphics/share/read_json_mean_clim.py
index 5dc6aac75..d011f8e1a 100644
--- a/pcmdi_metrics/graphics/share/read_json_mean_clim.py
+++ b/pcmdi_metrics/graphics/share/read_json_mean_clim.py
@@ -147,9 +147,12 @@ def extract_data(results_dict, var_list, region, stat, season, mip, debug=False)
     Rows: models, Columns: variables (i.e., 2d array)
     """
     model_list = sorted(list(results_dict[var_list[0]]["RESULTS"].keys()))
+    # update model_list
     if "rlut" in list(results_dict.keys()):
         if "rlut" in list(results_dict["rlut"]["RESULTS"].keys()):
             model_list = sorted(list(results_dict["rlut"]["RESULTS"].keys()))
+            
+    print('extract_data:: model_list: ', model_list)
     
     data_list = []
     for model in model_list:

From 925d1ddb5ee0bc3e55bfbb809aa87fa487da882d Mon Sep 17 00:00:00 2001
From: Jiwoo Lee <lee1043@llnl.gov>
Date: Thu, 10 Aug 2023 16:23:48 -0700
Subject: [PATCH 022/133] bug fix

---
 pcmdi_metrics/graphics/share/read_json_mean_clim.py | 7 +++----
 1 file changed, 3 insertions(+), 4 deletions(-)

diff --git a/pcmdi_metrics/graphics/share/read_json_mean_clim.py b/pcmdi_metrics/graphics/share/read_json_mean_clim.py
index d011f8e1a..823f78e72 100644
--- a/pcmdi_metrics/graphics/share/read_json_mean_clim.py
+++ b/pcmdi_metrics/graphics/share/read_json_mean_clim.py
@@ -156,13 +156,12 @@ def extract_data(results_dict, var_list, region, stat, season, mip, debug=False)
     
     data_list = []
     for model in model_list:
+        run_list = sort_human(list(
+            results_dict[var_list[0]]["RESULTS"][model]["default"].keys())
+        )
         if "rlut" in list(results_dict.keys()):
             if "rlut" in list(results_dict["rlut"]["RESULTS"].keys()):
                 run_list = sort_human(list(results_dict["rlut"]["RESULTS"][model]["default"].keys()))
-        else:
-            run_list = sort_human(list(
-                results_dict[var_list[0]]["RESULTS"][model]["default"].keys())
-            )
 
         if debug:
             print("model, run_list:", model, run_list)

From 456ab0e5ee090d44b463ee6721db2d1bf11470c2 Mon Sep 17 00:00:00 2001
From: Jiwoo Lee <lee1043@llnl.gov>
Date: Fri, 11 Aug 2023 17:40:31 -0700
Subject: [PATCH 023/133] bug fix

---
 pcmdi_metrics/graphics/share/plot_utils.py | 15 ++++++++++++++-
 1 file changed, 14 insertions(+), 1 deletion(-)

diff --git a/pcmdi_metrics/graphics/share/plot_utils.py b/pcmdi_metrics/graphics/share/plot_utils.py
index 6560ae09e..7527c4b3f 100644
--- a/pcmdi_metrics/graphics/share/plot_utils.py
+++ b/pcmdi_metrics/graphics/share/plot_utils.py
@@ -98,11 +98,14 @@ def combine_ref_dicts(d1, d2):
     # Merge dicts
     dd = defaultdict(list)
     for d in (d1, d2):  # you can list as many input dicts as you want here
+        print('d:', d)
         for key, value in d.items():
+            print('key, value:', key, value)
             dd[key].append(value)
     # Check consistency in content
+    print('dd:', dd)
     for key in dd:
-        if len(list(set(dd[key]))) == 1:
+        if len(list(set(flatten(dd[key])))) == 1:
             dd[key] = dd[key][0]
         else:
             print(
@@ -112,3 +115,13 @@ def combine_ref_dicts(d1, d2):
     # Convert outcome to normal dict and return
     dd = dict(dd)
     return dd
+
+
+def flatten(list_):
+    list_final = list()
+    for item in list_:
+        if isinstance(item, list):
+            list_final.extend(item)
+        else:
+            list_final.append(item)
+    return list_final

From 63c1a8709f85194ab26ccd8a175141b95fe0e6ba Mon Sep 17 00:00:00 2001
From: Jiwoo Lee <lee1043@llnl.gov>
Date: Fri, 11 Aug 2023 17:43:06 -0700
Subject: [PATCH 024/133] clean up -- remove print used for debug

---
 pcmdi_metrics/graphics/share/plot_utils.py | 2 --
 1 file changed, 2 deletions(-)

diff --git a/pcmdi_metrics/graphics/share/plot_utils.py b/pcmdi_metrics/graphics/share/plot_utils.py
index 7527c4b3f..9912a24b7 100644
--- a/pcmdi_metrics/graphics/share/plot_utils.py
+++ b/pcmdi_metrics/graphics/share/plot_utils.py
@@ -98,9 +98,7 @@ def combine_ref_dicts(d1, d2):
     # Merge dicts
     dd = defaultdict(list)
     for d in (d1, d2):  # you can list as many input dicts as you want here
-        print('d:', d)
         for key, value in d.items():
-            print('key, value:', key, value)
             dd[key].append(value)
     # Check consistency in content
     print('dd:', dd)

From 253f93279af14b538f1dd257265fac1b81b43b97 Mon Sep 17 00:00:00 2001
From: Jiwoo Lee <lee1043@llnl.gov>
Date: Fri, 11 Aug 2023 17:45:39 -0700
Subject: [PATCH 025/133] clean up -- remove print used for debug

---
 pcmdi_metrics/graphics/share/plot_utils.py | 1 -
 1 file changed, 1 deletion(-)

diff --git a/pcmdi_metrics/graphics/share/plot_utils.py b/pcmdi_metrics/graphics/share/plot_utils.py
index 9912a24b7..70fc41aad 100644
--- a/pcmdi_metrics/graphics/share/plot_utils.py
+++ b/pcmdi_metrics/graphics/share/plot_utils.py
@@ -101,7 +101,6 @@ def combine_ref_dicts(d1, d2):
         for key, value in d.items():
             dd[key].append(value)
     # Check consistency in content
-    print('dd:', dd)
     for key in dd:
         if len(list(set(flatten(dd[key])))) == 1:
             dd[key] = dd[key][0]

From 9ea91e82bd7db6d5ae8be0063ecd6dd73951e939 Mon Sep 17 00:00:00 2001
From: Jiwoo Lee <lee1043@llnl.gov>
Date: Fri, 11 Aug 2023 18:10:04 -0700
Subject: [PATCH 026/133] bug fix

---
 pcmdi_metrics/graphics/share/read_json_mean_clim.py | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/pcmdi_metrics/graphics/share/read_json_mean_clim.py b/pcmdi_metrics/graphics/share/read_json_mean_clim.py
index 823f78e72..0db0be805 100644
--- a/pcmdi_metrics/graphics/share/read_json_mean_clim.py
+++ b/pcmdi_metrics/graphics/share/read_json_mean_clim.py
@@ -131,6 +131,8 @@ def extract_region_stat(var, results_dict_var):
     region_list = sorted(
         list(results_dict_var["RESULTS"][model_list[0]]["default"][run_list[0]].keys())
     )
+    if "InputClimatologyFileName" in region_list:
+        region_list.remove("InputClimatologyFileName")
     stat_list = sorted(
         list(
             results_dict_var["RESULTS"][model_list[0]]["default"][run_list[0]][

From e2995f27dd9113c71aa19b98f31f887bb228811c Mon Sep 17 00:00:00 2001
From: Ana Ordonez <ordonez4@llnl.gov>
Date: Tue, 15 Aug 2023 13:44:58 -0700
Subject: [PATCH 027/133] add custom grid

---
 .../mean_climate/mean_climate_driver.py       | 43 +++++++++++--------
 1 file changed, 25 insertions(+), 18 deletions(-)

diff --git a/pcmdi_metrics/mean_climate/mean_climate_driver.py b/pcmdi_metrics/mean_climate/mean_climate_driver.py
index 3eac17b48..7c77bb894 100755
--- a/pcmdi_metrics/mean_climate/mean_climate_driver.py
+++ b/pcmdi_metrics/mean_climate/mean_climate_driver.py
@@ -101,24 +101,31 @@
 print('--- prepare mean climate metrics calculation ---')
 
 # generate target grid
-if target_grid == "2.5x2.5":
-    # target grid for regridding
-    t_grid = xc.create_uniform_grid(-88.875, 88.625, 2.5, 0, 357.5, 2.5)
-    if debug:
-        print('type(t_grid):', type(t_grid))  # Expected type is 'xarray.core.dataset.Dataset'
-        print('t_grid:', t_grid)
-    # identical target grid in cdms2 to use generateLandSeaMask function that is yet to exist in xcdat
-    t_grid_cdms2 = cdms2.createUniformGrid(-88.875, 72, 2.5, 0, 144, 2.5)
-    # generate land sea mask for the target grid
-    sft = cdutil.generateLandSeaMask(t_grid_cdms2)
-    if debug:
-        print('sft:', sft)
-        print('sft.getAxisList():', sft.getAxisList())
-    # add sft to target grid dataset
-    t_grid['sftlf'] = (['lat', 'lon'], np.array(sft))
-    if debug:
-        print('t_grid (after sftlf added):', t_grid)
-        t_grid.to_netcdf('target_grid.nc')
+res=target_grid.split("x")
+lat_res=float(res[0])
+lon_res=float(res[1])
+start_lat=-90+lat_res/2
+start_lon=0
+end_lat = 90-lat_res/2
+end_lon = 360-lon_res
+nlat = ((end_lat - start_lat) * 1/lat_res) + 1
+nlon = ((end_lon - start_lon) * 1/lon_res) + 1
+t_grid=xc.create_uniform_grid(start_lat,end_lat,lat_res,start_lon,end_lon,lon_res)
+if debug:
+    print('type(t_grid):', type(t_grid))  # Expected type is 'xarray.core.dataset.Dataset'
+    print('t_grid:', t_grid)
+# identical target grid in cdms2 to use generateLandSeaMask function that is yet to exist in xcdat
+t_grid_cdms2 = cdms2.createUniformGrid(start_lat,nlat,lat_res,start_lon,nlon,lon_res)
+# generate land sea mask for the target grid
+sft = cdutil.generateLandSeaMask(t_grid_cdms2)
+if debug:
+    print('sft:', sft)
+    print('sft.getAxisList():', sft.getAxisList())
+# add sft to target grid dataset
+t_grid['sftlf'] = (['lat', 'lon'], np.array(sft))
+if debug:
+    print('t_grid (after sftlf added):', t_grid)
+    t_grid.to_netcdf('target_grid.nc')
 
 # load obs catalogue json
 egg_pth = resources.resource_path()

From f30981c20ce35db51458903b597d190595115cd3 Mon Sep 17 00:00:00 2001
From: Ana Ordonez <ordonez4@llnl.gov>
Date: Tue, 15 Aug 2023 13:50:39 -0700
Subject: [PATCH 028/133] add custom obs json

---
 .../mean_climate/lib/create_mean_climate_parser.py        | 1 +
 pcmdi_metrics/mean_climate/mean_climate_driver.py         | 8 ++++++--
 2 files changed, 7 insertions(+), 2 deletions(-)

diff --git a/pcmdi_metrics/mean_climate/lib/create_mean_climate_parser.py b/pcmdi_metrics/mean_climate/lib/create_mean_climate_parser.py
index ab5861051..02719bd73 100644
--- a/pcmdi_metrics/mean_climate/lib/create_mean_climate_parser.py
+++ b/pcmdi_metrics/mean_climate/lib/create_mean_climate_parser.py
@@ -186,6 +186,7 @@ def create_mean_climate_parser():
         dest="custom_observations",
         help="Path to an alternative, custom observation file",
         required=False,
+        default=""
     )
 
     parser.add_argument(
diff --git a/pcmdi_metrics/mean_climate/mean_climate_driver.py b/pcmdi_metrics/mean_climate/mean_climate_driver.py
index 3eac17b48..a489be9dc 100755
--- a/pcmdi_metrics/mean_climate/mean_climate_driver.py
+++ b/pcmdi_metrics/mean_climate/mean_climate_driver.py
@@ -46,6 +46,7 @@
 reference_data_path = parameter.reference_data_path
 metrics_output_path = parameter.metrics_output_path
 diagnostics_output_path = parameter.diagnostics_output_path
+custom_obs = parameter.custom_observations
 debug = parameter.debug
 cmec = parameter.cmec
 
@@ -122,8 +123,11 @@
 
 # load obs catalogue json
 egg_pth = resources.resource_path()
-obs_file_name = "obs_info_dictionary.json"
-obs_file_path = os.path.join(egg_pth, obs_file_name)
+if len(custom_obs) > 0:
+    obs_file_path = custom_obs
+else:
+    obs_file_name = "obs_info_dictionary.json"
+    obs_file_path = os.path.join(egg_pth, obs_file_name)
 with open(obs_file_path) as fo:
     obs_dict = json.loads(fo.read())
 # if debug:

From 371c3f1dc0e8c3460db7b897a3224dc346857ed2 Mon Sep 17 00:00:00 2001
From: Ana Ordonez <ordonez4@llnl.gov>
Date: Thu, 17 Aug 2023 11:24:33 -0700
Subject: [PATCH 029/133] explicit float

---
 pcmdi_metrics/mean_climate/mean_climate_driver.py | 12 ++++++------
 1 file changed, 6 insertions(+), 6 deletions(-)

diff --git a/pcmdi_metrics/mean_climate/mean_climate_driver.py b/pcmdi_metrics/mean_climate/mean_climate_driver.py
index a14d0ed37..5d41bca63 100755
--- a/pcmdi_metrics/mean_climate/mean_climate_driver.py
+++ b/pcmdi_metrics/mean_climate/mean_climate_driver.py
@@ -105,12 +105,12 @@
 res=target_grid.split("x")
 lat_res=float(res[0])
 lon_res=float(res[1])
-start_lat=-90+lat_res/2
-start_lon=0
-end_lat = 90-lat_res/2
-end_lon = 360-lon_res
-nlat = ((end_lat - start_lat) * 1/lat_res) + 1
-nlon = ((end_lon - start_lon) * 1/lon_res) + 1
+start_lat=-90.+lat_res/2
+start_lon=0.
+end_lat = 90.-lat_res/2
+end_lon = 360.-lon_res
+nlat = ((end_lat - start_lat) * 1./lat_res) + 1
+nlon = ((end_lon - start_lon) * 1./lon_res) + 1
 t_grid=xc.create_uniform_grid(start_lat,end_lat,lat_res,start_lon,end_lon,lon_res)
 if debug:
     print('type(t_grid):', type(t_grid))  # Expected type is 'xarray.core.dataset.Dataset'

From ca6d38a53d18f2219331631f068a3073215af70e Mon Sep 17 00:00:00 2001
From: Ana Ordonez <ordonez4@llnl.gov>
Date: Thu, 17 Aug 2023 11:25:15 -0700
Subject: [PATCH 030/133] add default target grid

---
 pcmdi_metrics/mean_climate/lib/create_mean_climate_parser.py | 1 +
 1 file changed, 1 insertion(+)

diff --git a/pcmdi_metrics/mean_climate/lib/create_mean_climate_parser.py b/pcmdi_metrics/mean_climate/lib/create_mean_climate_parser.py
index 02719bd73..06633b1e0 100644
--- a/pcmdi_metrics/mean_climate/lib/create_mean_climate_parser.py
+++ b/pcmdi_metrics/mean_climate/lib/create_mean_climate_parser.py
@@ -98,6 +98,7 @@ def create_mean_climate_parser():
         dest="target_grid",
         help='Options are "2.5x2.5" or an actual cdms2 grid object',
         required=False,
+        default="2.5x2.5"
     )
 
     parser.add_argument(

From 42bb3267dce6c17f49fa7cb1bcae1f91630bd5c3 Mon Sep 17 00:00:00 2001
From: Ana Ordonez <ordonez4@llnl.gov>
Date: Tue, 22 Aug 2023 09:53:14 -0700
Subject: [PATCH 031/133] Update target grid help

---
 pcmdi_metrics/mean_climate/lib/create_mean_climate_parser.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/pcmdi_metrics/mean_climate/lib/create_mean_climate_parser.py b/pcmdi_metrics/mean_climate/lib/create_mean_climate_parser.py
index 06633b1e0..dfbe8f2c4 100644
--- a/pcmdi_metrics/mean_climate/lib/create_mean_climate_parser.py
+++ b/pcmdi_metrics/mean_climate/lib/create_mean_climate_parser.py
@@ -96,7 +96,7 @@ def create_mean_climate_parser():
     parser.add_argument(
         "--target_grid",
         dest="target_grid",
-        help='Options are "2.5x2.5" or an actual cdms2 grid object',
+        help='Set to "LATxLON" resolution in degrees. Default is "2.5x2.5"',
         required=False,
         default="2.5x2.5"
     )

From 496cfe5cce35e055ec908a7f3256384a3566505a Mon Sep 17 00:00:00 2001
From: Ana Ordonez <ordonez4@llnl.gov>
Date: Tue, 22 Aug 2023 13:19:06 -0700
Subject: [PATCH 032/133] Update metrics_mean-clim.rst

---
 docs/metrics_mean-clim.rst | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/docs/metrics_mean-clim.rst b/docs/metrics_mean-clim.rst
index cae2a88f4..a42de8e5b 100644
--- a/docs/metrics_mean-clim.rst
+++ b/docs/metrics_mean-clim.rst
@@ -80,7 +80,7 @@ where the list of variables (vars) to run the analysis on includes 'rlut' (outgo
 * **test_data_path**: the path/template where the test_data resides, e.g.: 
 * **reference_data_set**: a python list that specifies 'default', 'alternate1', 'alternate2' or 'all', e.g., ['default']
 * **reference_data_path**: the root path to the observational climatology database, e.g., '~/demo_data/PCMDIobs2_clims/'
-* **target_grid**: '2.5x2.5' or an actual cdms2 grid object
+* **target_grid**: a string giving the desired horizontal resolution in degrees following the form 'LATxLON', e.g. '2.5x2.5'
 * **regrid_tool**: options include 'esmf' and 'regrid2'  
 * **metric_output_path**: the full path to the metrics output in JSON files, e.g., '~/demo_data/PMP_metrics/' 
 

From 700def67e0fa4e06e7a84b64bb54d0a3c288f316 Mon Sep 17 00:00:00 2001
From: lee1043 <lee1043@llnl.gov>
Date: Wed, 23 Aug 2023 11:17:00 -0700
Subject: [PATCH 033/133] improve level extraction based on plev

---
 .../mean_climate/lib/load_and_regrid.py       | 20 +++++++++++++++----
 1 file changed, 16 insertions(+), 4 deletions(-)

diff --git a/pcmdi_metrics/mean_climate/lib/load_and_regrid.py b/pcmdi_metrics/mean_climate/lib/load_and_regrid.py
index 9428f569a..1333804ca 100644
--- a/pcmdi_metrics/mean_climate/lib/load_and_regrid.py
+++ b/pcmdi_metrics/mean_climate/lib/load_and_regrid.py
@@ -49,10 +49,22 @@ def load_and_regrid(data_path, varname, varname_in_file=None, level=None, t_grid
     
     # level - extract a specific level if needed
     if level is not None:
-        level = level * 100  # hPa to Pa
-        ds = ds.sel(plev=level)
-    if debug:
-        print('ds:', ds)
+        if isinstance(level, int) or isinstance(level, float):
+            pass
+        else:
+            level = float(level)
+
+        # check vertical coordinate first
+        if 'plev' in list(ds.coords.keys()):
+            if ds.plev.units == 'Pa':
+                level = level * 100  # hPa to Pa
+            ds = ds.sel(plev=level)
+            if debug:
+                print('ds:', ds)
+        else:
+            print('ERROR: plev is not in the nc file. Check vertical coordinate.')
+            print('Coordinates keys in the nc file:', list(ds.coords.keys()))
+            return
     
     # regrid
     if regrid_tool == 'regrid2':

From 51d301f9f90d466633b09a9a04f1a30a173e8657 Mon Sep 17 00:00:00 2001
From: lee1043 <lee1043@llnl.gov>
Date: Wed, 23 Aug 2023 11:17:45 -0700
Subject: [PATCH 034/133] add option for realization selection: first only
 realization

---
 pcmdi_metrics/mean_climate/mean_climate_driver.py | 9 ++++++++-
 1 file changed, 8 insertions(+), 1 deletion(-)

diff --git a/pcmdi_metrics/mean_climate/mean_climate_driver.py b/pcmdi_metrics/mean_climate/mean_climate_driver.py
index 5d41bca63..e784e9676 100755
--- a/pcmdi_metrics/mean_climate/mean_climate_driver.py
+++ b/pcmdi_metrics/mean_climate/mean_climate_driver.py
@@ -20,6 +20,7 @@
     mean_climate_metrics_to_json,
 )
 from pcmdi_metrics.variability_mode.lib import tree
+from pcmdi_metrics.variability_mode.lib import sort_human
 
 
 parser = create_mean_climate_parser()
@@ -59,6 +60,7 @@
 diagnostics_output_path = diagnostics_output_path.replace('%(case_id)', case_id)
 
 find_all_realizations = False
+first_realization_only = False
 if realization is None:
     realization = ""
     realizations = [realization]
@@ -66,6 +68,8 @@
     if realization.lower() in ["all", "*"]:
         find_all_realizations = True
         realizations = "Search for all realizations!!"
+    elif realization.lower() in ["first", "first_only"]:
+        first_realization_only = True
     else:
         realizations = [realization]
 
@@ -206,7 +210,7 @@
 
             result_dict["RESULTS"][model][ref]["source"] = ref_dataset_name 
 
-            if find_all_realizations:
+            if find_all_realizations or first_realization_only:
                 test_data_full_path = os.path.join(
                     test_data_path,
                     filename_template).replace('%(variable)', varname).replace('%(model)', model).replace('%(model_version)', model).replace('%(realization)', '*')
@@ -215,6 +219,9 @@
                 realizations = []
                 for ncfile in ncfiles:
                     realizations.append(ncfile.split('/')[-1].split('.')[3])
+                realizations = sort_human(realizations)
+                if first_realization_only:
+                    realizations == realizations[0:1]
                 print('realizations (after search): ', realizations)
 
             for run in realizations:

From 3381fe29d4b1d40f2710aee6dc26cc004488bd7c Mon Sep 17 00:00:00 2001
From: lee1043 <lee1043@llnl.gov>
Date: Wed, 23 Aug 2023 11:33:16 -0700
Subject: [PATCH 035/133] test

---
 pcmdi_metrics/mean_climate/mean_climate_driver.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/pcmdi_metrics/mean_climate/mean_climate_driver.py b/pcmdi_metrics/mean_climate/mean_climate_driver.py
index e784e9676..e2002c3e6 100755
--- a/pcmdi_metrics/mean_climate/mean_climate_driver.py
+++ b/pcmdi_metrics/mean_climate/mean_climate_driver.py
@@ -60,7 +60,7 @@
 diagnostics_output_path = diagnostics_output_path.replace('%(case_id)', case_id)
 
 find_all_realizations = False
-first_realization_only = False
+first_realization_only = False   
 if realization is None:
     realization = ""
     realizations = [realization]

From e15269dfc6359b7c52e4fa968d45b9f3df8a179e Mon Sep 17 00:00:00 2001
From: lee1043 <lee1043@llnl.gov>
Date: Wed, 23 Aug 2023 11:33:45 -0700
Subject: [PATCH 036/133] clean up

---
 pcmdi_metrics/mean_climate/mean_climate_driver.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/pcmdi_metrics/mean_climate/mean_climate_driver.py b/pcmdi_metrics/mean_climate/mean_climate_driver.py
index e2002c3e6..e784e9676 100755
--- a/pcmdi_metrics/mean_climate/mean_climate_driver.py
+++ b/pcmdi_metrics/mean_climate/mean_climate_driver.py
@@ -60,7 +60,7 @@
 diagnostics_output_path = diagnostics_output_path.replace('%(case_id)', case_id)
 
 find_all_realizations = False
-first_realization_only = False   
+first_realization_only = False
 if realization is None:
     realization = ""
     realizations = [realization]

From 7f4b42d3945a75deefc72aa9c2fc127ea88b35a3 Mon Sep 17 00:00:00 2001
From: lee1043 <lee1043@llnl.gov>
Date: Wed, 23 Aug 2023 11:46:35 -0700
Subject: [PATCH 037/133] simplify logic

---
 pcmdi_metrics/mean_climate/mean_climate_driver.py | 5 +----
 1 file changed, 1 insertion(+), 4 deletions(-)

diff --git a/pcmdi_metrics/mean_climate/mean_climate_driver.py b/pcmdi_metrics/mean_climate/mean_climate_driver.py
index e784e9676..6dbfb74b0 100755
--- a/pcmdi_metrics/mean_climate/mean_climate_driver.py
+++ b/pcmdi_metrics/mean_climate/mean_climate_driver.py
@@ -63,15 +63,12 @@
 first_realization_only = False
 if realization is None:
     realization = ""
-    realizations = [realization]
 elif isinstance(realization, str):
     if realization.lower() in ["all", "*"]:
         find_all_realizations = True
-        realizations = "Search for all realizations!!"
     elif realization.lower() in ["first", "first_only"]:
         first_realization_only = True
-    else:
-        realizations = [realization]
+realizations = [realization]
 
 if debug:
     print('regions_specs (before loading internally defined):', regions_specs)

From cb5cb43b2cb30a67497995ace11a3c4da6fcaa42 Mon Sep 17 00:00:00 2001
From: lee1043 <lee1043@llnl.gov>
Date: Wed, 23 Aug 2023 22:07:35 -0700
Subject: [PATCH 038/133] fix typo

---
 pcmdi_metrics/mean_climate/mean_climate_driver.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/pcmdi_metrics/mean_climate/mean_climate_driver.py b/pcmdi_metrics/mean_climate/mean_climate_driver.py
index 6dbfb74b0..58d34cea3 100755
--- a/pcmdi_metrics/mean_climate/mean_climate_driver.py
+++ b/pcmdi_metrics/mean_climate/mean_climate_driver.py
@@ -218,7 +218,7 @@
                     realizations.append(ncfile.split('/')[-1].split('.')[3])
                 realizations = sort_human(realizations)
                 if first_realization_only:
-                    realizations == realizations[0:1]
+                    realizations = realizations[0:1]
                 print('realizations (after search): ', realizations)
 
             for run in realizations:

From 2337f5cee516c4a78d7e35756fb0d1e9f045ceaa Mon Sep 17 00:00:00 2001
From: lee1043 <lee1043@llnl.gov>
Date: Wed, 23 Aug 2023 22:08:24 -0700
Subject: [PATCH 039/133] consider difference in plev key value

---
 .../mean_climate/lib/load_and_regrid.py       | 27 ++++++++++++++++++-
 1 file changed, 26 insertions(+), 1 deletion(-)

diff --git a/pcmdi_metrics/mean_climate/lib/load_and_regrid.py b/pcmdi_metrics/mean_climate/lib/load_and_regrid.py
index 1333804ca..594473e07 100644
--- a/pcmdi_metrics/mean_climate/lib/load_and_regrid.py
+++ b/pcmdi_metrics/mean_climate/lib/load_and_regrid.py
@@ -58,9 +58,27 @@ def load_and_regrid(data_path, varname, varname_in_file=None, level=None, t_grid
         if 'plev' in list(ds.coords.keys()):
             if ds.plev.units == 'Pa':
                 level = level * 100  # hPa to Pa
-            ds = ds.sel(plev=level)
+            try:
+                ds = ds.sel(plev=level)
+            except Exception as ex:
+                print('WARNING: ', ex)
+
+                nearest_level = find_nearest(ds.plev.values, level)
+
+                print('  Given level', level)
+                print('  Selected nearest level from dataset:', nearest_level)
+
+                diff_percentage = abs(nearest_level - level) / level * 100
+                if diff_percentage < 0.1:  # acceptable if differance is less than 0.1%
+                    ds = ds.sel(plev=level, method='nearest')
+                    print('  Difference is in acceptable range.')
+                    pass
+                else:
+                    print('ERROR: Difference between two levels are too big!')
+                    return
             if debug:
                 print('ds:', ds)
+                print('ds.plev.units:', ds.plev.units)
         else:
             print('ERROR: plev is not in the nc file. Check vertical coordinate.')
             print('Coordinates keys in the nc file:', list(ds.coords.keys()))
@@ -90,3 +108,10 @@ def load_and_regrid(data_path, varname, varname_in_file=None, level=None, t_grid
     if debug:
         print('ds_regridded:', ds_regridded)
     return ds_regridded
+
+
+def find_nearest(array, value):
+    array = np.asarray(array)
+    idx = (np.abs(array - value)).argmin()
+    return array[idx]
+

From cde0d135716556499d728013b34f955e5cc0dbbe Mon Sep 17 00:00:00 2001
From: lee1043 <lee1043@llnl.gov>
Date: Wed, 23 Aug 2023 22:10:39 -0700
Subject: [PATCH 040/133] improve error message

---
 pcmdi_metrics/mean_climate/lib/load_and_regrid.py | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/pcmdi_metrics/mean_climate/lib/load_and_regrid.py b/pcmdi_metrics/mean_climate/lib/load_and_regrid.py
index 594473e07..b09c963c2 100644
--- a/pcmdi_metrics/mean_climate/lib/load_and_regrid.py
+++ b/pcmdi_metrics/mean_climate/lib/load_and_regrid.py
@@ -81,7 +81,8 @@ def load_and_regrid(data_path, varname, varname_in_file=None, level=None, t_grid
                 print('ds.plev.units:', ds.plev.units)
         else:
             print('ERROR: plev is not in the nc file. Check vertical coordinate.')
-            print('Coordinates keys in the nc file:', list(ds.coords.keys()))
+            print('  Coordinates keys in the nc file:', list(ds.coords.keys()))
+            print('ERROR: load and regrid can not complete')
             return
     
     # regrid

From 95e0a4292ccf886c7342eac4ed1c1a49c23471e6 Mon Sep 17 00:00:00 2001
From: Ana Ordonez <ordonez4@llnl.gov>
Date: Thu, 24 Aug 2023 10:45:36 -0700
Subject: [PATCH 041/133] Add obs note

---
 docs/metrics_precip-variability.rst | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/docs/metrics_precip-variability.rst b/docs/metrics_precip-variability.rst
index cc67101ff..019b61ddc 100644
--- a/docs/metrics_precip-variability.rst
+++ b/docs/metrics_precip-variability.rst
@@ -49,7 +49,7 @@ Results are reported on a 2x2 degree latitude/longitude world grid.
 
 Options available to set in the parameter file include:
 
-* **mip**: Name of MIP.
+* **mip**: Name of MIP. Use "obs" for reference datasets.
 * **exp**: Name of experiment. 
 * **var**: Name of data set variable, e.g. "pr". 
 * **frq**: Frequency of data set, either "day" or "3hr". 

From 2fe0fda72b88648c9dbe24a7dd0b069e64bd280c Mon Sep 17 00:00:00 2001
From: lee1043 <lee1043@llnl.gov>
Date: Thu, 24 Aug 2023 10:55:18 -0700
Subject: [PATCH 042/133] add more regions for mean climate

---
 pcmdi_metrics/io/default_regions_define.py | 14 +++++++++-----
 1 file changed, 9 insertions(+), 5 deletions(-)

diff --git a/pcmdi_metrics/io/default_regions_define.py b/pcmdi_metrics/io/default_regions_define.py
index 4511e2c43..a3654e6c2 100755
--- a/pcmdi_metrics/io/default_regions_define.py
+++ b/pcmdi_metrics/io/default_regions_define.py
@@ -5,25 +5,29 @@ def load_regions_specs():
 
     regions_specs = {
         # Mean Climate
+        "global": {},
         "NHEX": {"domain": {"latitude": (30.0, 90)}},
         "SHEX": {"domain": {"latitude": (-90.0, -30)}},
         "TROPICS": {"domain": {"latitude": (-30.0, 30)}},
-        "global": {},
         "90S50S": {"domain": {"latitude": (-90.0, -50)}},
         "50S20S": {"domain": {"latitude": (-50.0, -20)}},
         "20S20N": {"domain": {"latitude": (-20.0, 20)}},
         "20N50N": {"domain": {"latitude": (20.0, 50)}},
         "50N90N": {"domain": {"latitude": (50.0, 90)}},
+        "CONUS": {"domain": {"latitude": (24.7, 49.4), "longitude": (-124.78, -66.92)}},
+        "land": {"value": 100},
         "land_NHEX": {"value": 100, "domain": {"latitude": (30.0, 90)}},
         "land_SHEX": {"value": 100, "domain": {"latitude": (-90.0, -30)}},
         "land_TROPICS": {"value": 100, "domain": {"latitude": (-30.0, 30)}},
-        "land": {"value": 100},
+        "land_CONUS": {"value": 100, "domain": {"latitude": (24.7, 49.4), "longitude": (-124.78, -66.92)}},
+        "ocean": {"value": 0},
         "ocean_NHEX": {"value": 0, "domain": {"latitude": (30.0, 90)}},
         "ocean_SHEX": {"value": 0, "domain": {"latitude": (-90.0, -30)}},
         "ocean_TROPICS": {"value": 0, "domain": {"latitude": (30.0, 30)}},
-        "ocean": {"value": 0},
-        "CONUS": {"domain": {"latitude": (24.7, 49.4), "longitude": (-124.78, -66.92)}},
-        "land_CONUS": {"value": 100, "domain": {"latitude": (24.7, 49.4), "longitude": (-124.78, -66.92)}},
+        "ocean_50S50N" : {"value":0.,'domain': {latitude=(-50.,50)}},
+        "ocean_50S20S" : {"value":0.,'domain': {latitude=(-50.,-20)}},
+        "ocean_20S20N": {"value":0.,'domain': {latitude=(-20.,20)}},
+        "ocean_20N50N" : {"value":0.,'domain': {latitude=(20.,50)}},
         # Modes of variability
         "NAM": {"domain": {"latitude": (20.0, 90), "longitude": (-180, 180)}},
         "NAO": {"domain": {"latitude": (20.0, 80), "longitude": (-90, 40)}},

From e7490026c1d619535d21ee54a074f74e8e657f01 Mon Sep 17 00:00:00 2001
From: Jiwoo Lee <lee1043@llnl.gov>
Date: Thu, 24 Aug 2023 20:51:06 -0700
Subject: [PATCH 043/133] add capability to overwrite labels of highlighted
 models

---
 .../parallel_coordinate_plot_lib.py                    | 10 +++++++++-
 1 file changed, 9 insertions(+), 1 deletion(-)

diff --git a/pcmdi_metrics/graphics/parallel_coordinate_plot/parallel_coordinate_plot_lib.py b/pcmdi_metrics/graphics/parallel_coordinate_plot/parallel_coordinate_plot_lib.py
index a76496a39..a629c602a 100644
--- a/pcmdi_metrics/graphics/parallel_coordinate_plot/parallel_coordinate_plot_lib.py
+++ b/pcmdi_metrics/graphics/parallel_coordinate_plot/parallel_coordinate_plot_lib.py
@@ -16,6 +16,7 @@ def parallel_coordinate_plot(
     model_names,
     models_to_highlight=list(),
     models_to_highlight_colors=None,
+    models_to_highlight_labels=None,
     fig=None,
     ax=None,
     figsize=(15, 5),
@@ -51,6 +52,7 @@ def parallel_coordinate_plot(
     - `model_names`: list, name of models for markers/lines (axis=0)
     - `models_to_highlight`: list, default=None, List of models to highlight as lines
     - `models_to_highlight_colors`: list, default=None, List of colors for models to highlight as lines
+    - `models_to_highlight_labels`: list, default=None, List of string labels for models to highlight as lines
     - `fig`: `matplotlib.figure` instance to which the parallel coordinate plot is plotted.
              If not provided, use current axes or create a new one.  Optional.
     - `ax`: `matplotlib.axes.Axes` instance to which the parallel coordinate plot is plotted.
@@ -222,7 +224,13 @@ def parallel_coordinate_plot(
                 color = models_to_highlight_colors[mh_index]
             else:
                 color = colors[j]
-            ax.plot(range(N), zs[j, :], "-", c=color, label=model, lw=3)
+                
+            if models_to_highlight_labels is not None:
+                label = models_to_highlight_labels[mh_index]
+            else:
+                label = model
+                
+            ax.plot(range(N), zs[j, :], "-", c=color, label=label, lw=3)
             mh_index += 1
         else:
             if identify_all_models:

From 5b737fd661bd74365026f4f7475f02e7e6b40d0c Mon Sep 17 00:00:00 2001
From: omahs <73983677+omahs@users.noreply.github.com>
Date: Mon, 28 Aug 2023 09:16:39 +0200
Subject: [PATCH 044/133] fix typos

---
 docs/README.md | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/docs/README.md b/docs/README.md
index 528f930b5..d8634ee26 100755
--- a/docs/README.md
+++ b/docs/README.md
@@ -58,9 +58,9 @@ Then go to your forked repo on github.com and create a Pull Request to the `main
 
 Build webpages
 --------------
-Once the changes are merged to the `main` branch of pcmdi_metrics, Github Action will automatically build and deploy web pages using Github Pages. This process will follow what is defined in `.github/workflows/documentation.yaml`. The page will be accssible at http://pcmdi.github.io/pcmdi_metrics/.
+Once the changes are merged to the `main` branch of pcmdi_metrics, Github Action will automatically build and deploy web pages using Github Pages. This process will follow what is defined in `.github/workflows/documentation.yaml`. The page will be accessible at http://pcmdi.github.io/pcmdi_metrics/.
 
-To deploy the web pages via readthedocs, you will need to go to readthedocs project page (https://readthedocs.org/projects/pcmdi-metrics/), go to "Builds" menu, and click "Build Version" button. The page will be accessbile at https://pcmdi-metrics.readthedocs.io/en/latest/.
+To deploy the web pages via readthedocs, you will need to go to readthedocs project page (https://readthedocs.org/projects/pcmdi-metrics/), go to "Builds" menu, and click "Build Version" button. The page will be accessible at https://pcmdi-metrics.readthedocs.io/en/latest/.
 
 Resources
 ---------

From 6866cf95716ff9fd49df15bf310b73a328dedcbb Mon Sep 17 00:00:00 2001
From: omahs <73983677+omahs@users.noreply.github.com>
Date: Mon, 28 Aug 2023 09:17:22 +0200
Subject: [PATCH 045/133] fix typo

---
 docs/index.rst | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/docs/index.rst b/docs/index.rst
index fd9bf03a1..62ebe58cd 100644
--- a/docs/index.rst
+++ b/docs/index.rst
@@ -9,7 +9,7 @@ PCMDI Metrics Package (PMP)
 
 The Program for Climate Model Diagnosis & Intercomparison (`PCMDI`_) Metrics Package (PMP) is used to provide "quick-look" objective comparisons of Earth System Models (ESMs) with one another and available observations.  
 Results are produced in the context of all model simulations contributed to CMIP6 and earlier CMIP phases.  
-Currently, the comparisons emphasize metrics of large- to global-scale annual cycle and both tropcial 
+Currently, the comparisons emphasize metrics of large- to global-scale annual cycle and both tropical 
 and extra-tropical modes of variability. 
 Recent release (v3) include established statistics for mean climate, ENSO, MJO, extratropical modes of variability, 
 regional monsoons, and high frequency characteristics of simulated precipitation as a part of U.S. DOE's Benchmarking of simulated precipitation. 
@@ -87,4 +87,4 @@ BSD 3-Clause License. See `LICENSE <https://github.com/PCMDI/pcmdi_metrics/blob/
    :hidden:
    :caption: Community
 
-   GitHub discussions <https://github.com/PCMDI/pcmdi_metrics/discussions>
\ No newline at end of file
+   GitHub discussions <https://github.com/PCMDI/pcmdi_metrics/discussions>

From cb073011b40c88ec6e3c831c66e444c32dedd2a6 Mon Sep 17 00:00:00 2001
From: omahs <73983677+omahs@users.noreply.github.com>
Date: Mon, 28 Aug 2023 09:18:22 +0200
Subject: [PATCH 046/133] fix typos

---
 docs/metrics.rst | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/docs/metrics.rst b/docs/metrics.rst
index a399be6f6..bfb643c81 100644
--- a/docs/metrics.rst
+++ b/docs/metrics.rst
@@ -4,7 +4,7 @@
 PMP Metrics
 *****************
 
-We provide documentation along with demos to assist users of the PMP.  Most demos are simple examples on how to apply the PMP to one or several datasets.  Example parameter files used for more complex appication of the PMP (e.g., applying the PMP across all CMIP models) via the sample setups used by PCMDI for semi-operational application to the CMIP database. 
+We provide documentation along with demos to assist users of the PMP.  Most demos are simple examples of how to apply the PMP to one or several datasets.  Example parameter files used for more complex application of the PMP (e.g., applying the PMP across all CMIP models) via the sample setups used by PCMDI for semi-operational application to the CMIP database. 
 
 A suite of demo scripts and interactive Jupyter notebooks are provided with `this documentation <https://github.com/PCMDI/pcmdi_metrics/blob/master/doc/jupyter/Demo/README.md>`_. 
 

From 25b984731ba623ac0a93b513c0cc751ce19fc6f4 Mon Sep 17 00:00:00 2001
From: omahs <73983677+omahs@users.noreply.github.com>
Date: Mon, 28 Aug 2023 09:22:08 +0200
Subject: [PATCH 047/133] fix typos

---
 docs/metrics_mean-clim.rst | 20 ++++++++++----------
 1 file changed, 10 insertions(+), 10 deletions(-)

diff --git a/docs/metrics_mean-clim.rst b/docs/metrics_mean-clim.rst
index cae2a88f4..0ae43c0ba 100644
--- a/docs/metrics_mean-clim.rst
+++ b/docs/metrics_mean-clim.rst
@@ -7,8 +7,8 @@ Overview
 
 The mean climate summary statistics are the most routine analysis available from the PMP.
 Because they are quasi-operationally applied to large numbers of simulations and under 
-different conditions, the current mode of opertation is fairly general.  
-Before it can be applied some prepration is needed including:    
+different conditions, the current mode of operation is fairly general.  
+Before it can be applied some preparation is needed including:    
 
 * Setting-up observational climatologies
 
@@ -17,7 +17,7 @@ Before it can be applied some prepration is needed including:
 * Construction of an input parameter file to run the desired operations  
 
 
-Each of these steps are included in the 
+Each of these steps is included in the 
 `mean climate notebook <https://github.com/PCMDI/pcmdi_metrics/blob/master/doc/jupyter/Demo/Demo_1_mean_climate.ipynb>`_ 
 along with a series of examples that demonstrate the options. 
 These steps are also summarized below.
@@ -36,17 +36,17 @@ and you will be promptly provided with the database.
 
 The PMP's mean climate summary statistics can be applied to many fields and 
 in most cases there is more than one reference data set available.  
-To accomodate this, the observational climatologies used by the PMP a
-re managed via `a simple catalogue in the form of a JSON file <https://github.com/PCMDI/pcmdi_metrics/blob/master/doc/pcmdiobs2_clims_byVar_catalogue_v20201210.json>`_.  
+To accommodate this, the observational climatologies used by the PMP are 
+managed via `a simple catalogue in the form of a JSON file <https://github.com/PCMDI/pcmdi_metrics/blob/master/doc/pcmdiobs2_clims_byVar_catalogue_v20201210.json>`_.  
 For many of the variables there are 'default' and 'alternate1' 
 datasets and for some there is also an 'alternate2'.  
 To simplify the use of the different options in the mean climate, 
 the mean_climate_driver.py (see below) expects to be pointed to observational catalogue.  
 Currently, if a user wants to add additional observational data this can be done by 
-including it in the JSON cataloge. However, this most be done carefully to ensure 
+including it in the JSON catalogue. However, this must be done carefully to ensure 
 the file retains JSON compliant structure.       
 
-A recent observational climatology catalogue is included as part of the PMP as a default, so it does not need to be explicitly idenified when using the mean_climate_driver.py (unless the catalogue has been modified to include new observations). However, as described below, the user must provide the base path to the observational database. As indicated in the catalogue, the actual database does incorporate futher directory structure and defined filenames which should not be modified.  If changes are made to the catalogue, this can be done with input parameter settings (below) using the "custom_observations" option.     
+A recent observational climatology catalogue is included as part of the PMP as a default, so it does not need to be explicitly identified when using the mean_climate_driver.py (unless the catalogue has been modified to include new observations). However, as described below, the user must provide the base path to the observational database. As indicated in the catalogue, the actual database does incorporate further directory structure and defined filenames which should not be modified.  If changes are made to the catalogue, this can be done with input parameter settings (below) using the "custom_observations" option.     
 
  
 Preparation of model climatologies
@@ -60,7 +60,7 @@ via the `mean climate metrics notebook <https://github.com/PCMDI/pcmdi_metrics/b
 or the `PMP github repository <https://github.com/PCMDI/pcmdi_metrics/tree/master/sample_setups/pcmdi_parameter_files/mean_climate/make_clims>`_.   
 
 
-Construction of an input paramater file
+Construction of an input parameter file
 #######################################
 
 The PMP mean climate metrics can be controlled via an input parameter file, the command line, or both.  With the command line only it is executed via: ::
@@ -84,12 +84,12 @@ where the list of variables (vars) to run the analysis on includes 'rlut' (outgo
 * **regrid_tool**: options include 'esmf' and 'regrid2'  
 * **metric_output_path**: the full path to the metrics output in JSON files, e.g., '~/demo_data/PMP_metrics/' 
 
-In addition to the above required input parameters, if the default cataolgue of observational climatologies is not being used its replacement needs to be specified, e.g.: ::
+In addition to the above required input parameters, if the default catalogue of observational climatologies is not being used its replacement needs to be specified, e.g.: ::
 
     custom_observations = './pcmdiobs2_clims_byVar_catalogue_v20200615.json'
 
 
-The output of the mean climate summary statistics are saved in a JSON file.  `An example result <https://github.com/PCMDI/pcmdi_metrics/blob/master/sample_setups/jsons/mean_climate/CMIP5/historical/v20190724/tas/ACCESS1-0.tas.CMIP5.historical.regrid2.2p5x2p5.v20190724.json>`_ demonstrates that multiple statistics are computed for different conditions including regions and seasons. The resulting JSON files include the data, software and hardware information on how the summary statistics.  
+The output of the mean climate summary statistics are saved in a JSON file.  `An example result <https://github.com/PCMDI/pcmdi_metrics/blob/master/sample_setups/jsons/mean_climate/CMIP5/historical/v20190724/tas/ACCESS1-0.tas.CMIP5.historical.regrid2.2p5x2p5.v20190724.json>`_ demonstrates that multiple statistics are computed for different conditions including regions and seasons. The resulting JSON files include the data, software and hardware information on the summary statistics.  
 
 
 In addition to the minimum set of parameters noted above, the following **additional options can be controlled** for the mean climate:

From 66cee4d8d251691b63ef264ad64fb9f165421e85 Mon Sep 17 00:00:00 2001
From: omahs <73983677+omahs@users.noreply.github.com>
Date: Mon, 28 Aug 2023 09:24:01 +0200
Subject: [PATCH 048/133] fix typos

---
 docs/overview.rst | 10 +++++-----
 1 file changed, 5 insertions(+), 5 deletions(-)

diff --git a/docs/overview.rst b/docs/overview.rst
index 312742c56..834dcf874 100644
--- a/docs/overview.rst
+++ b/docs/overview.rst
@@ -9,11 +9,11 @@ The primary application of the PMP is to evaluate simulations from the `Coupled
 It can also be used to provide objective performance summaries during the model development process as well as selected research purposes.  
 The notes below provide a brief summary of some of the key aspects of the PMP design.  
 
-Software framework and dependancies
+Software framework and dependencies
 -----------------------------------
 
 Most of the PMP is based on `Python 3 <https://www.python.org/>`_ and built upon the Climate Data Analysis Tools (`CDAT <https://cdat.llnl.gov>`_).  
-The key component of CDAT used by the PMP is the Community Data Management System (`CDMS <https://cdms.readthedocs.io/en/latest/manual/cdms_1.html>`_) which provides access to a powerful collection of climate specific utilites, inclduing cdutil, genutil and cdtime.
+The key component of CDAT used by the PMP is the Community Data Management System (`CDMS <https://cdms.readthedocs.io/en/latest/manual/cdms_1.html>`_) which provides access to a powerful collection of climate specific utilites, including cdutil, genutil and cdtime.
 To modernize, PMP is in transition to implement Xarray Climate Data Analysis Tools (`xCDAT`_) as its primary building block.
 
 
@@ -22,7 +22,7 @@ Input/Output format
 
 The PMP is designed to most readily handle model output that adheres to the `Climate-Forecast (CF) data conventions <https://cfconventions.org/>`_.  
 More specifically, because the PMP is used to routinely analyze simulations contributed to CMIP it leverages `the data conventions developed in support of CMIP <https://pcmdi.llnl.gov/CMIP6/Guide/dataUsers.html>`_.  
-Many modeling groups have a workflow that conforms to CMIP or is very similiar to it, making it possible to adapt the PMP to assist in the model development process. 
+Many modeling groups have a workflow that conforms to CMIP or is very similar to it, making it possible to adapt the PMP to assist in the model development process. 
 
 The PMP statistics are output in `JSON format <https://www.json.org/json-en.html>`_, and the underlying diagnostics from which they were derived are typically saved in `netCDF format <https://www.unidata.ucar.edu/software/netcdf>`_.
 
@@ -47,6 +47,6 @@ which includes both a string variable (period) and a python list (test_data_set)
 Here, the "---variable" option is used to specify "pr" (precipitation) with other options included in the file after the "-p" flag.  
 
 
-The python standard `argparse libary <https://docs.python.org/3/library/argparse.html>`_  is implicitly used in all cases, enabling the inclusion of user-friendly interface options.  As in the above example, this allows users to set input parameters on the command line **or** in an input file.  However, there are circumstances where users of the PMP may want to use a combination of both (an input parameter file and command line setting) for the same execution. This useful combination is possible with the standard argeparse library however with limited functionality.  We make use of the Community Diagnostics Package (`CDP <https://github.com/CDAT/cdp>`_) to enable prioritization between the two input possibilities.  The CDP enables us to use command line options in combination with input parameter files, with the command line inputs overrridding options set in the parameter files.  This provides convenience in setting up and maintaining large jobs. Examples of the combined use of parameter files and command line inputs are included in the PMP demos.
+The python standard `argparse libary <https://docs.python.org/3/library/argparse.html>`_  is implicitly used in all cases, enabling the inclusion of user-friendly interface options.  As in the above example, this allows users to set input parameters on the command line **or** in an input file.  However, there are circumstances where users of the PMP may want to use a combination of both (an input parameter file and command line setting) for the same execution. This useful combination is possible with the standard argeparse library however with limited functionality.  We make use of the Community Diagnostics Package (`CDP <https://github.com/CDAT/cdp>`_) to enable prioritization between the two input possibilities.  The CDP enables us to use command line options in combination with input parameter files, with the command line inputs overriding options set in the parameter files.  This provides convenience in setting up and maintaining large jobs. Examples of the combined use of parameter files and command line inputs are included in the PMP demos.
 
-.. _xCDAT: https://xcdat.readthedocs.io/en/stable/
\ No newline at end of file
+.. _xCDAT: https://xcdat.readthedocs.io/en/stable/

From 25cca8aa83ea937cb3b2bc25b2ed5627a1133d73 Mon Sep 17 00:00:00 2001
From: omahs <73983677+omahs@users.noreply.github.com>
Date: Mon, 28 Aug 2023 09:25:30 +0200
Subject: [PATCH 049/133] fix typo

---
 docs/subdaily-precipitation.rst | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/docs/subdaily-precipitation.rst b/docs/subdaily-precipitation.rst
index ba3eed3d4..2b22fc1b7 100644
--- a/docs/subdaily-precipitation.rst
+++ b/docs/subdaily-precipitation.rst
@@ -7,7 +7,7 @@ Sub-daily precipitation
 Overview
 ========
 
-The PMP can be used to compare observed and simulated sub-daily precipition, including forced (the diurnal and semi-diurnal cycle) and unforced variability (often referred to as "intermittency").  Well established Fourier analysis (e.g., Dai, 2006) with well-established large scale objective performance metrics (Covey et al., 2016) to estimate the phase and amplitude of the diurnal and semi-diurnal cycle of precipitation.  The unforced sub-daily variability stems from methods developed by Trenberth et al. (2017) and Covey et al. (2017).  Both analysis require data at a 3hr time resolution.   
+The PMP can be used to compare observed and simulated sub-daily precipitation, including forced (the diurnal and semi-diurnal cycle) and unforced variability (often referred to as "intermittency").  Well established Fourier analysis (e.g., Dai, 2006) with well-established large scale objective performance metrics (Covey et al., 2016) to estimate the phase and amplitude of the diurnal and semi-diurnal cycle of precipitation.  The unforced sub-daily variability stems from methods developed by Trenberth et al. (2017) and Covey et al. (2017).  Both analysis require data at a 3hr time resolution.   
 
 Analysis of higher frequency data often includes multiple stages of processing.  `The flow diagram of the PMP's sub-daily precipitation <https://github.com/PCMDI/pcmdi_metrics/blob/master/doc/Diurnal%20Cycle%20Diagram.pdf>`_ shows that is the case here.  Each of the steps highlighted in the flow diagram are included in `the diurnal cycle and intermittency Jupyter notebook demo <https://github.com/PCMDI/pcmdi_metrics/blob/master/doc/jupyter/Demo/Demo_3_diurnal_cycle.ipynb>`_. 
 

From 03a3fd4d7c28736435b237c81b87cc7a4b0497de Mon Sep 17 00:00:00 2001
From: omahs <73983677+omahs@users.noreply.github.com>
Date: Mon, 28 Aug 2023 09:25:55 +0200
Subject: [PATCH 050/133] fix typo

---
 docs/supporting-data.rst | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/docs/supporting-data.rst b/docs/supporting-data.rst
index 00379b216..8d16178f3 100644
--- a/docs/supporting-data.rst
+++ b/docs/supporting-data.rst
@@ -17,7 +17,7 @@ A location where you want to store the demo data locally can be set: ::
     demo_data_directory = 'MyDemoPath' 
 
 
-After you have set the location for the demo_output you can downloaded it by entering the following: ::
+After you have set the location for the demo_output you can download it by entering the following: ::
 
     import cdat_info
     cdat_info.download_sample_data_files("data_files.txt", demo_data_directory)

From da7059a49b53fafc41914e784f7314aa8addaac3 Mon Sep 17 00:00:00 2001
From: Ana Ordonez <ordonez4@llnl.gov>
Date: Fri, 8 Sep 2023 15:32:43 -0700
Subject: [PATCH 051/133] Create regionmask.py

---
 pcmdi_metrics/io/regionmask.py | 53 ++++++++++++++++++++++++++++++++++
 1 file changed, 53 insertions(+)
 create mode 100644 pcmdi_metrics/io/regionmask.py

diff --git a/pcmdi_metrics/io/regionmask.py b/pcmdi_metrics/io/regionmask.py
new file mode 100644
index 000000000..3d5a3e200
--- /dev/null
+++ b/pcmdi_metrics/io/regionmask.py
@@ -0,0 +1,53 @@
+import geopandas as gpd
+import numpy as np
+import os
+import pandas as pd
+import regionmask
+import sys
+import xarray as xr
+import xcdat
+
+def mask_region(data,name,coords=None,shp_path=None,column=None):
+    # Return data masked from coordinate list or shapefile.
+    # Masks a single region
+
+    lon = data["lon"].data
+    lat = data["lat"].data
+
+    # Option 1: Region is defined by coord pairs
+    if coords is not None:
+        try:
+            names=[name]
+            regions = regionmask.Regions([np.array(coords)],names=names)
+            mask = regions.mask(lon, lat)
+            val=0
+        except Exception as e:
+            print("Error in creating mask from provided coordinates:")
+            raise e
+
+    # Option 2: region is defined by shapefile
+    elif shp_path is not None:
+        try:
+            regions_file = gpd.read_file(shp_path)
+            if column is not None:
+                regions = regionmask.from_geopandas(regions_file,names=column)
+            else:
+                print("Column name not provided.")
+                regions = regionmask.from_geopandas(regions_file)
+            mask = regions.mask(lon, lat)
+            # Can't match mask by name, rather index of name
+            val = list(regions_file[column]).index(name)
+        except Exception as e:
+            print("Error in creating mask from shapefile:")
+            raise e
+
+    else:
+        raise RuntimeError("Error in masking: Region coordinates or shapefile must be provided.")
+    
+    try:
+        masked_data = data.where(mask == val)
+    except Exception as e:
+        print("Error: Masking failed.")
+        raise e
+
+    return  masked_data

From b613aa5a1f3c4c976ad35add742a94a4012fb72c Mon Sep 17 00:00:00 2001
From: Ana Ordonez <ordonez4@llnl.gov>
Date: Tue, 12 Sep 2023 13:47:30 -0700
Subject: [PATCH 052/133] Change name

---
 pcmdi_metrics/io/{regionmask.py => extract_by_shp.py} | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)
 rename pcmdi_metrics/io/{regionmask.py => extract_by_shp.py} (95%)

diff --git a/pcmdi_metrics/io/regionmask.py b/pcmdi_metrics/io/extract_by_shp.py
similarity index 95%
rename from pcmdi_metrics/io/regionmask.py
rename to pcmdi_metrics/io/extract_by_shp.py
index 3d5a3e200..adb71ccd2 100644
--- a/pcmdi_metrics/io/regionmask.py
+++ b/pcmdi_metrics/io/extract_by_shp.py
@@ -7,7 +7,7 @@
 import xarray as xr
 import xcdat
 
-def mask_region(data,name,coords=None,shp_path=None,column=None):
+def extract_by_shp(data,name,coords=None,shp_path=None,column=None):
     # Return data masked from coordinate list or shapefile.
     # Masks a single region
 

From c843207415a2100ef9f7dde8087ba0de170d11a5 Mon Sep 17 00:00:00 2001
From: Ana Ordonez <ordonez4@llnl.gov>
Date: Tue, 12 Sep 2023 14:07:49 -0700
Subject: [PATCH 053/133] Change var names

---
 pcmdi_metrics/io/extract_by_shp.py | 34 ++++++++++++++++++------------
 1 file changed, 21 insertions(+), 13 deletions(-)

diff --git a/pcmdi_metrics/io/extract_by_shp.py b/pcmdi_metrics/io/extract_by_shp.py
index adb71ccd2..38b8a016f 100644
--- a/pcmdi_metrics/io/extract_by_shp.py
+++ b/pcmdi_metrics/io/extract_by_shp.py
@@ -7,47 +7,55 @@
 import xarray as xr
 import xcdat
 
-def extract_by_shp(data,name,coords=None,shp_path=None,column=None):
+def extract_by_shp(data,feature,coords=None,rgn_path=None,attr=None):
     # Return data masked from coordinate list or shapefile.
     # Masks a single region
+    # Arguments:
+    #    data: xcdat dataset
+    #    feature: str, name of region
+    #    coords: list, coordinates
+    #    rgn_path: str, path to file
+    #    attr: str, attribute name
 
     lon = data["lon"].data
     lat = data["lat"].data
 
     # Option 1: Region is defined by coord pairs
     if coords is not None:
+        print("Using coordinates to select region.")
         try:
-            names=[name]
+            names=[feature]
             regions = regionmask.Regions([np.array(coords)],names=names)
             mask = regions.mask(lon, lat)
             val=0
         except Exception as e:
-            print("Error in creating mask from provided coordinates:")
+            print("Error in extracting region by provided coordinates:")
             raise e
 
     # Option 2: region is defined by shapefile
-    elif shp_path is not None:
+    elif rgn_path is not None:
+        print("Reading region from file.")
         try:
-            regions_file = gpd.read_file(shp_path)
-            if column is not None:
-                regions = regionmask.from_geopandas(regions_file,names=column)
+            regions_df = gpd.read_file(rgn_path)
+            if attr is not None:
+                regions = regionmask.from_geopandas(regions_df,names=attr)
             else:
-                print("Column name not provided.")
-                regions = regionmask.from_geopandas(regions_file)
+                print("Attribute name not provided.")
+                regions = regionmask.from_geopandas(regions_df)
             mask = regions.mask(lon, lat)
             # Can't match mask by name, rather index of name
-            val = list(regions_file[column]).index(name)
+            val = list(regions_file[attr]).index(feature)
         except Exception as e:
-            print("Error in creating mask from shapefile:")
+            print("Error in creating region subset from file:")
             raise e
 
     else:
-        raise RuntimeError("Error in masking: Region coordinates or shapefile must be provided.")
+        raise RuntimeError("Error in region selection: Region coordinates or region file must be provided.")
     
     try:
         masked_data = data.where(mask == val)
     except Exception as e:
-        print("Error: Masking failed.")
+        print("Error: Region selection failed.")
         raise e
 
     return  masked_data

From 8c2a666859991234cc73b07c4527b8728a05f06a Mon Sep 17 00:00:00 2001
From: Ana Ordonez <ordonez4@llnl.gov>
Date: Tue, 12 Sep 2023 14:08:28 -0700
Subject: [PATCH 054/133] Change func name

---
 pcmdi_metrics/io/{extract_by_shp.py => region_from_file.py} | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)
 rename pcmdi_metrics/io/{extract_by_shp.py => region_from_file.py} (96%)

diff --git a/pcmdi_metrics/io/extract_by_shp.py b/pcmdi_metrics/io/region_from_file.py
similarity index 96%
rename from pcmdi_metrics/io/extract_by_shp.py
rename to pcmdi_metrics/io/region_from_file.py
index 38b8a016f..485e4f808 100644
--- a/pcmdi_metrics/io/extract_by_shp.py
+++ b/pcmdi_metrics/io/region_from_file.py
@@ -7,7 +7,7 @@
 import xarray as xr
 import xcdat
 
-def extract_by_shp(data,feature,coords=None,rgn_path=None,attr=None):
+def region_from_file(data,feature,coords=None,rgn_path=None,attr=None):
     # Return data masked from coordinate list or shapefile.
     # Masks a single region
     # Arguments:

From d7d1c4cfae05e95feb75cead2fbabbc99676dba1 Mon Sep 17 00:00:00 2001
From: Ana Ordonez <ordonez4@llnl.gov>
Date: Tue, 12 Sep 2023 14:12:23 -0700
Subject: [PATCH 055/133] remove coordinate opt

---
 pcmdi_metrics/io/region_from_file.py | 51 +++++++++-------------------
 1 file changed, 16 insertions(+), 35 deletions(-)

diff --git a/pcmdi_metrics/io/region_from_file.py b/pcmdi_metrics/io/region_from_file.py
index 485e4f808..9c01347ce 100644
--- a/pcmdi_metrics/io/region_from_file.py
+++ b/pcmdi_metrics/io/region_from_file.py
@@ -7,50 +7,31 @@
 import xarray as xr
 import xcdat
 
-def region_from_file(data,feature,coords=None,rgn_path=None,attr=None):
-    # Return data masked from coordinate list or shapefile.
-    # Masks a single region
+def region_from_file(data,rgn_path,attr,feature):
+    # Return data masked from a feature in input file.
     # Arguments:
     #    data: xcdat dataset
     #    feature: str, name of region
-    #    coords: list, coordinates
     #    rgn_path: str, path to file
     #    attr: str, attribute name
 
     lon = data["lon"].data
     lat = data["lat"].data
 
-    # Option 1: Region is defined by coord pairs
-    if coords is not None:
-        print("Using coordinates to select region.")
-        try:
-            names=[feature]
-            regions = regionmask.Regions([np.array(coords)],names=names)
-            mask = regions.mask(lon, lat)
-            val=0
-        except Exception as e:
-            print("Error in extracting region by provided coordinates:")
-            raise e
-
-    # Option 2: region is defined by shapefile
-    elif rgn_path is not None:
-        print("Reading region from file.")
-        try:
-            regions_df = gpd.read_file(rgn_path)
-            if attr is not None:
-                regions = regionmask.from_geopandas(regions_df,names=attr)
-            else:
-                print("Attribute name not provided.")
-                regions = regionmask.from_geopandas(regions_df)
-            mask = regions.mask(lon, lat)
-            # Can't match mask by name, rather index of name
-            val = list(regions_file[attr]).index(feature)
-        except Exception as e:
-            print("Error in creating region subset from file:")
-            raise e
-
-    else:
-        raise RuntimeError("Error in region selection: Region coordinates or region file must be provided.")
+    print("Reading region from file.")
+    try:
+        regions_df = gpd.read_file(rgn_path)
+        if attr is not None:
+            regions = regionmask.from_geopandas(regions_df,names=attr)
+        else:
+            print("Attribute name not provided.")
+            regions = regionmask.from_geopandas(regions_df)
+        mask = regions.mask(lon, lat)
+        # Can't match mask by name, rather index of name
+        val = list(regions_file[attr]).index(feature)
+    except Exception as e:
+        print("Error in creating region subset from file:")
+        raise e
     
     try:
         masked_data = data.where(mask == val)

From 075703cc7da520992328e00f0605fe90771db8e2 Mon Sep 17 00:00:00 2001
From: Ana Ordonez <ordonez4@llnl.gov>
Date: Tue, 12 Sep 2023 14:40:04 -0700
Subject: [PATCH 056/133] Clean up dependencies

---
 pcmdi_metrics/io/region_from_file.py | 4 ----
 1 file changed, 4 deletions(-)

diff --git a/pcmdi_metrics/io/region_from_file.py b/pcmdi_metrics/io/region_from_file.py
index 9c01347ce..92ae68ec4 100644
--- a/pcmdi_metrics/io/region_from_file.py
+++ b/pcmdi_metrics/io/region_from_file.py
@@ -1,9 +1,5 @@
 import geopandas as gpd
-import numpy as np
-import os
-import pandas as pd
 import regionmask
-import sys
 import xarray as xr
 import xcdat
 

From 71d8f8de53cd1778c118880029f05d50fb86f361 Mon Sep 17 00:00:00 2001
From: Ana Ordonez <ordonez4@llnl.gov>
Date: Tue, 12 Sep 2023 15:06:38 -0700
Subject: [PATCH 057/133] Fix var name

---
 pcmdi_metrics/io/region_from_file.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/pcmdi_metrics/io/region_from_file.py b/pcmdi_metrics/io/region_from_file.py
index 92ae68ec4..998e8cbc0 100644
--- a/pcmdi_metrics/io/region_from_file.py
+++ b/pcmdi_metrics/io/region_from_file.py
@@ -3,7 +3,7 @@
 import xarray as xr
 import xcdat
 
-def region_from_file(data,rgn_path,attr,feature):
+def region_from_file(data,rgn_path,feature,attr=None):
     # Return data masked from a feature in input file.
     # Arguments:
     #    data: xcdat dataset
@@ -24,7 +24,7 @@ def region_from_file(data,rgn_path,attr,feature):
             regions = regionmask.from_geopandas(regions_df)
         mask = regions.mask(lon, lat)
         # Can't match mask by name, rather index of name
-        val = list(regions_file[attr]).index(feature)
+        val = list(regions_df[attr]).index(feature)
     except Exception as e:
         print("Error in creating region subset from file:")
         raise e

From cad4b7e29c1731afe17ed7f9b5f3e89c772d4f48 Mon Sep 17 00:00:00 2001
From: Ana Ordonez <ordonez4@llnl.gov>
Date: Tue, 12 Sep 2023 15:33:37 -0700
Subject: [PATCH 058/133] attr is required

---
 pcmdi_metrics/io/region_from_file.py | 8 ++------
 1 file changed, 2 insertions(+), 6 deletions(-)

diff --git a/pcmdi_metrics/io/region_from_file.py b/pcmdi_metrics/io/region_from_file.py
index 998e8cbc0..ce43aebd9 100644
--- a/pcmdi_metrics/io/region_from_file.py
+++ b/pcmdi_metrics/io/region_from_file.py
@@ -3,7 +3,7 @@
 import xarray as xr
 import xcdat
 
-def region_from_file(data,rgn_path,feature,attr=None):
+def region_from_file(data,rgn_path,attr,feature):
     # Return data masked from a feature in input file.
     # Arguments:
     #    data: xcdat dataset
@@ -17,11 +17,7 @@ def region_from_file(data,rgn_path,feature,attr=None):
     print("Reading region from file.")
     try:
         regions_df = gpd.read_file(rgn_path)
-        if attr is not None:
-            regions = regionmask.from_geopandas(regions_df,names=attr)
-        else:
-            print("Attribute name not provided.")
-            regions = regionmask.from_geopandas(regions_df)
+        regions = regionmask.from_geopandas(regions_df,names=attr)
         mask = regions.mask(lon, lat)
         # Can't match mask by name, rather index of name
         val = list(regions_df[attr]).index(feature)

From b128ded8dd6476babfaa35e18c217ab9bad77f24 Mon Sep 17 00:00:00 2001
From: lee1043 <lee1043@llnl.gov>
Date: Mon, 18 Sep 2023 19:05:55 -0700
Subject: [PATCH 059/133] typo fix

---
 pcmdi_metrics/io/default_regions_define.py | 8 ++++----
 1 file changed, 4 insertions(+), 4 deletions(-)

diff --git a/pcmdi_metrics/io/default_regions_define.py b/pcmdi_metrics/io/default_regions_define.py
index a3654e6c2..b5362a2a7 100755
--- a/pcmdi_metrics/io/default_regions_define.py
+++ b/pcmdi_metrics/io/default_regions_define.py
@@ -24,10 +24,10 @@ def load_regions_specs():
         "ocean_NHEX": {"value": 0, "domain": {"latitude": (30.0, 90)}},
         "ocean_SHEX": {"value": 0, "domain": {"latitude": (-90.0, -30)}},
         "ocean_TROPICS": {"value": 0, "domain": {"latitude": (30.0, 30)}},
-        "ocean_50S50N" : {"value":0.,'domain': {latitude=(-50.,50)}},
-        "ocean_50S20S" : {"value":0.,'domain': {latitude=(-50.,-20)}},
-        "ocean_20S20N": {"value":0.,'domain': {latitude=(-20.,20)}},
-        "ocean_20N50N" : {"value":0.,'domain': {latitude=(20.,50)}},
+        "ocean_50S50N" : {"value":0.,'domain': {"latitude": (-50., 50)}},
+        "ocean_50S20S" : {"value":0.,'domain': {"latitude": (-50., -20)}},
+        "ocean_20S20N": {"value":0.,'domain': {"latitude": (-20., 20)}},
+        "ocean_20N50N" : {"value":0.,'domain': {"latitude": (20., 50)}},
         # Modes of variability
         "NAM": {"domain": {"latitude": (20.0, 90), "longitude": (-180, 180)}},
         "NAO": {"domain": {"latitude": (20.0, 80), "longitude": (-90, 40)}},

From 575e01633887b30f1600aa0beca9795fcae76fb1 Mon Sep 17 00:00:00 2001
From: lee1043 <lee1043@llnl.gov>
Date: Mon, 18 Sep 2023 19:25:09 -0700
Subject: [PATCH 060/133] Separate JSON: one JSON per run, instead of per
 model, to be better prepared for parallel processing

---
 .../mean_climate/mean_climate_driver.py       | 24 +++++++++----------
 1 file changed, 12 insertions(+), 12 deletions(-)

diff --git a/pcmdi_metrics/mean_climate/mean_climate_driver.py b/pcmdi_metrics/mean_climate/mean_climate_driver.py
index 58d34cea3..7cf6ac950 100755
--- a/pcmdi_metrics/mean_climate/mean_climate_driver.py
+++ b/pcmdi_metrics/mean_climate/mean_climate_driver.py
@@ -291,18 +291,18 @@
                             print('compute metrics start')
                             result_dict["RESULTS"][model][ref][run][region] = compute_metrics(varname, ds_test_dict[region], ds_ref_dict[region], debug=debug)
 
-                        # write individual JSON
-                        # --- single simulation, obs (need to accumulate later) / single variable
-                        json_filename_tmp = "_".join([model, var, target_grid, regrid_tool, "metrics", ref])
-                        mean_climate_metrics_to_json(
-                            os.path.join(metrics_output_path, var),
-                            json_filename_tmp,
-                            result_dict,
-                            model=model,
-                            run=run,
-                            cmec_flag=cmec,
-                            debug=debug
-                        )
+                            # write individual JSON
+                            # --- single simulation, obs (need to accumulate later) / single variable
+                            json_filename_tmp = "_".join([var, model, run, target_grid, regrid_tool, "metrics", ref])
+                            mean_climate_metrics_to_json(
+                                os.path.join(metrics_output_path, var),
+                                json_filename_tmp,
+                                result_dict,
+                                model=model,
+                                run=run,
+                                cmec_flag=cmec,
+                                debug=debug
+                            )
  
                     except Exception as e:
                         if debug:

From 1ee929d8fb4dcb8338494a6f6710d327d177b0d1 Mon Sep 17 00:00:00 2001
From: lee1043 <lee1043@llnl.gov>
Date: Mon, 18 Sep 2023 19:45:20 -0700
Subject: [PATCH 061/133] bug fix

---
 .../mean_climate/lib/mean_climate_metrics_to_json.py     | 9 +++++----
 1 file changed, 5 insertions(+), 4 deletions(-)

diff --git a/pcmdi_metrics/mean_climate/lib/mean_climate_metrics_to_json.py b/pcmdi_metrics/mean_climate/lib/mean_climate_metrics_to_json.py
index b614f3559..0e42f7ec7 100644
--- a/pcmdi_metrics/mean_climate/lib/mean_climate_metrics_to_json.py
+++ b/pcmdi_metrics/mean_climate/lib/mean_climate_metrics_to_json.py
@@ -21,10 +21,11 @@ def mean_climate_metrics_to_json(
         for m in models_in_dict:
             if m == model:
                 for ref in list(json_dict["RESULTS"][m].keys()):
-                    runs_in_model_dict = list(json_dict["RESULTS"][m][ref].keys())
-                    for r in runs_in_model_dict:
-                        if (r != run) and (run is not None):
-                            del json_dict["RESULTS"][m][ref][r]
+                    if ref != "units":
+                        runs_in_model_dict = list(json_dict["RESULTS"][m][ref].keys())
+                        for r in runs_in_model_dict:
+                            if (r != run) and (run is not None):
+                                del json_dict["RESULTS"][m][ref][r]
             else:
                 del json_dict["RESULTS"][m]
     # Write selected dict to JSON

From 638e88d250acc1519a1b0da59b356711eb8bc6c4 Mon Sep 17 00:00:00 2001
From: lee1043 <lee1043@llnl.gov>
Date: Mon, 18 Sep 2023 23:00:56 -0700
Subject: [PATCH 062/133] improve for parallel run

---
 .../lib/create_mean_climate_parser.py         |  9 +++++++
 .../mean_climate/mean_climate_driver.py       | 27 ++++++++++++-------
 2 files changed, 27 insertions(+), 9 deletions(-)

diff --git a/pcmdi_metrics/mean_climate/lib/create_mean_climate_parser.py b/pcmdi_metrics/mean_climate/lib/create_mean_climate_parser.py
index 06633b1e0..ba542bdf1 100644
--- a/pcmdi_metrics/mean_climate/lib/create_mean_climate_parser.py
+++ b/pcmdi_metrics/mean_climate/lib/create_mean_climate_parser.py
@@ -266,4 +266,13 @@ def create_mean_climate_parser():
         required=False,
     )
 
+    parser.add_argument(
+        "--parallel",
+        type=bool,
+        dest="parallel",
+        default=False,
+        help="Option for running code in parallel mode: True / False (default)",
+        required=False,
+    )
+
     return parser
diff --git a/pcmdi_metrics/mean_climate/mean_climate_driver.py b/pcmdi_metrics/mean_climate/mean_climate_driver.py
index 7cf6ac950..a2860f70b 100755
--- a/pcmdi_metrics/mean_climate/mean_climate_driver.py
+++ b/pcmdi_metrics/mean_climate/mean_climate_driver.py
@@ -50,6 +50,7 @@
 custom_obs = parameter.custom_observations
 debug = parameter.debug
 cmec = parameter.cmec
+parallel = parameter.parallel
 
 if metrics_output_path is not None:
     metrics_output_path = parameter.metrics_output_path.replace('%(case_id)', case_id)
@@ -146,6 +147,9 @@
 # -------------
 # variable loop
 # -------------
+if isinstance(vars, str):
+    vars = [vars]
+
 for var in vars:
 
     if '_' in var or '-' in var:
@@ -310,12 +314,17 @@
                         print('error occured for ', model, run)
                         print(e)
 
-    # write collective JSON --- all models / all obs / single variable
-    json_filename = "_".join([var, target_grid, regrid_tool, "metrics"])
-    mean_climate_metrics_to_json(
-        metrics_output_path,
-        json_filename,
-        result_dict,
-        cmec_flag=cmec,
-    )
-    print('pmp mean clim driver completed')
+    # ========================================================================
+    # Dictionary to JSON: collective JSON at the end of model_realization loop
+    # ------------------------------------------------------------------------
+    if not parallel:
+        # write collective JSON --- all models / all obs / single variable
+        json_filename = "_".join([var, target_grid, regrid_tool, "metrics"])
+        mean_climate_metrics_to_json(
+            metrics_output_path,
+            json_filename,
+            result_dict,
+            cmec_flag=cmec,
+        )
+
+print('pmp mean clim driver completed')

From 53419708d324ff77e42e307656a744748830cf5d Mon Sep 17 00:00:00 2001
From: lee1043 <lee1043@llnl.gov>
Date: Thu, 21 Sep 2023 17:17:58 -0700
Subject: [PATCH 063/133] update

---
 .../mean_climate/mean_climate_driver.py       |  6 +--
 pcmdi_metrics/mjo/lib/mjo_metric_calc.py      | 44 ++++++++++++-------
 pcmdi_metrics/mjo/param/myParam_mjo.py        |  2 +-
 pcmdi_metrics/mjo/scripts/parallel_driver.py  |  4 +-
 pcmdi_metrics/mjo/scripts/run.sh              |  7 +--
 share/DefArgsCIA.json                         |  2 +-
 6 files changed, 38 insertions(+), 27 deletions(-)

diff --git a/pcmdi_metrics/mean_climate/mean_climate_driver.py b/pcmdi_metrics/mean_climate/mean_climate_driver.py
index a2860f70b..1a9fd6c62 100755
--- a/pcmdi_metrics/mean_climate/mean_climate_driver.py
+++ b/pcmdi_metrics/mean_climate/mean_climate_driver.py
@@ -287,7 +287,7 @@
 
                             if debug:
                                 print('ds_test_tmp:', ds_test_tmp)
-                                ds_test_dict[region].to_netcdf('_'.join([var, 'model', model, run, region + '.nc']))
+                                ds_test_dict[region].to_netcdf('_'.join([var, 'model', model, run, region, case_id + '.nc']))
                                 if model == test_data_set[0] and run == realizations[0]:
                                     ds_ref_dict[region].to_netcdf('_'.join([var, 'ref', region + '.nc']))
 
@@ -297,7 +297,7 @@
 
                             # write individual JSON
                             # --- single simulation, obs (need to accumulate later) / single variable
-                            json_filename_tmp = "_".join([var, model, run, target_grid, regrid_tool, "metrics", ref])
+                            json_filename_tmp = "_".join([var, model, run, target_grid, regrid_tool, "metrics", ref, case_id])
                             mean_climate_metrics_to_json(
                                 os.path.join(metrics_output_path, var),
                                 json_filename_tmp,
@@ -319,7 +319,7 @@
     # ------------------------------------------------------------------------
     if not parallel:
         # write collective JSON --- all models / all obs / single variable
-        json_filename = "_".join([var, target_grid, regrid_tool, "metrics"])
+        json_filename = "_".join([var, target_grid, regrid_tool, "metrics", case_id])
         mean_climate_metrics_to_json(
             metrics_output_path,
             json_filename,
diff --git a/pcmdi_metrics/mjo/lib/mjo_metric_calc.py b/pcmdi_metrics/mjo/lib/mjo_metric_calc.py
index ea6962071..49f9acc58 100644
--- a/pcmdi_metrics/mjo/lib/mjo_metric_calc.py
+++ b/pcmdi_metrics/mjo/lib/mjo_metric_calc.py
@@ -167,21 +167,35 @@ def mjo_metric_ewr_calculation(
     if plot:
         os.makedirs(outdir(output_type="graphics"), exist_ok=True)
         fout = os.path.join(outdir(output_type="graphics"), output_filename)
-        title = (
-            mip.upper()
-            + ": "
-            + model
-            + " ("
-            + run
-            + ") \n"
-            + var.capitalize()
-            + ", "
-            + season
-            + " "
-            + str(startYear)
-            + "-"
-            + str(endYear)
-        )
+        if model == 'obs':
+            title = (
+                " OBS ("
+                + run
+                + ") \n"
+                + var.capitalize()
+                + ", "
+                + season
+                + " "
+                + str(startYear)
+                + "-"
+                + str(endYear)
+            )
+        else:
+            title = (
+                mip.upper()
+                + ": "
+                + model
+                + " ("
+                + run
+                + ") \n"
+                + var.capitalize()
+                + ", "
+                + season
+                + " "
+                + str(startYear)
+                + "-"
+                + str(endYear)
+            )
         if cmmGrid:
             title += ", common grid (2.5x2.5deg)"
         plot_power(OEE, title, fout, ewr)
diff --git a/pcmdi_metrics/mjo/param/myParam_mjo.py b/pcmdi_metrics/mjo/param/myParam_mjo.py
index fddc93746..4456f66de 100644
--- a/pcmdi_metrics/mjo/param/myParam_mjo.py
+++ b/pcmdi_metrics/mjo/param/myParam_mjo.py
@@ -30,7 +30,7 @@ def find_latest(path):
 # Observation
 # -------------------------------------------------
 reference_data_name = "GPCP-1-3"
-reference_data_path = "/p/user_pub/PCMDIobs/PCMDIobs2/atmos/day/pr/GPCP-1-3/gn/v20200707/pr_day_GPCP-1-3_BE_gn_v20200707_19961002-20170101.nc"  # noqa
+reference_data_path = "/p/user_pub/PCMDIobs/obs4MIPs_legacy/PCMDIobs2/atmos/day/pr/GPCP-1-3/gn/v20200924/pr_day_GPCP-1-3_BE_gn_v20200924_19961002-20170101.nc"  # noqa
 
 varOBS = "pr"
 ObsUnitsAdjust = (True, "multiply", 86400.0, "mm d-1")  # kg m-2 s-1 to mm day-1
diff --git a/pcmdi_metrics/mjo/scripts/parallel_driver.py b/pcmdi_metrics/mjo/scripts/parallel_driver.py
index d43cc9db5..17bcf20cb 100755
--- a/pcmdi_metrics/mjo/scripts/parallel_driver.py
+++ b/pcmdi_metrics/mjo/scripts/parallel_driver.py
@@ -97,10 +97,10 @@
 # =================================================
 # Generates list of command
 # -------------------------------------------------
-param_file = "../doc/myParam_mjo.py"
+param_file = "../param/myParam_mjo.py"
 
 if debug:
-    param_file = "../doc/myParam_test.py"
+    param_file = "../param/myParam_test.py"
     print("number of models (debug mode):", len(models))
 
 cmds_list = list()
diff --git a/pcmdi_metrics/mjo/scripts/run.sh b/pcmdi_metrics/mjo/scripts/run.sh
index fcca06219..8911b0897 100755
--- a/pcmdi_metrics/mjo/scripts/run.sh
+++ b/pcmdi_metrics/mjo/scripts/run.sh
@@ -1,9 +1,6 @@
 #!/bin/sh
 set -a
 
-# grim: pmp_nightly_20190628
-# gates: cdat82_20191107_py37, pmp_nightly_20190912
-
 #parallel=no
 parallel=yes
 
@@ -17,7 +14,7 @@ mkdir -p log
 if [ $parallel == no ]; then
     echo 'parallel no'
     for mip in $mips; do
-        python -u mjo_metrics_driver.py -p ../doc/myParam_mjo.py --mip ${mip} >& log/log.${mip}.txt &
+        mjo_metrics_driver.py -p ../param/myParam_mjo.py --mip ${mip} >& log/log.${mip}.txt &
         disown
     done
 elif [ $parallel == yes ]; then
@@ -25,7 +22,7 @@ elif [ $parallel == yes ]; then
     modnames="all"
     realization="all"
     for mip in $mips; do
-        python -u ./parallel_driver.py -p ../doc/myParam_mjo.py --mip ${mip} --num_workers $num_workers --modnames $modnames --realization $realization  >& log/log.parallel.${mip}.txt &
+        python -u ./parallel_driver.py -p ../param/myParam_mjo.py --mip ${mip} --num_workers $num_workers --modnames $modnames --realization $realization  >& log/log.parallel.${mip}.txt &
         disown
     done
 fi
diff --git a/share/DefArgsCIA.json b/share/DefArgsCIA.json
index 8507f33ba..cd33a055d 100644
--- a/share/DefArgsCIA.json
+++ b/share/DefArgsCIA.json
@@ -163,4 +163,4 @@
         ],
         "help":"A list of variables to be processed"
     }
-}
+}
\ No newline at end of file

From b3914789c9070e0abe6f38e21a5c78a21104a34d Mon Sep 17 00:00:00 2001
From: lee1043 <lee1043@llnl.gov>
Date: Thu, 21 Sep 2023 17:19:44 -0700
Subject: [PATCH 064/133] clean up

---
 pcmdi_metrics/mjo/scripts/parallel_driver.py | 1 -
 1 file changed, 1 deletion(-)

diff --git a/pcmdi_metrics/mjo/scripts/parallel_driver.py b/pcmdi_metrics/mjo/scripts/parallel_driver.py
index 17bcf20cb..f8179a838 100755
--- a/pcmdi_metrics/mjo/scripts/parallel_driver.py
+++ b/pcmdi_metrics/mjo/scripts/parallel_driver.py
@@ -148,7 +148,6 @@
     for r, run in enumerate(runs_list):
         # command line for queue
         cmd = [
-            "python",
             "mjo_metrics_driver.py",
             "-p",
             param_file,

From 93dbc974f74cd70be91d02d81f4a4c4f183a3706 Mon Sep 17 00:00:00 2001
From: lee1043 <lee1043@llnl.gov>
Date: Sun, 24 Sep 2023 19:39:06 -0700
Subject: [PATCH 065/133] pre-commit fix

---
 share/DefArgsCIA.json | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/share/DefArgsCIA.json b/share/DefArgsCIA.json
index cd33a055d..8507f33ba 100644
--- a/share/DefArgsCIA.json
+++ b/share/DefArgsCIA.json
@@ -163,4 +163,4 @@
         ],
         "help":"A list of variables to be processed"
     }
-}
\ No newline at end of file
+}

From 740e2ef640ccb545342c82857f14cb251e8414f8 Mon Sep 17 00:00:00 2001
From: Ana Ordonez <ordonez4@llnl.gov>
Date: Mon, 25 Sep 2023 13:38:27 -0700
Subject: [PATCH 066/133] Update variability_across_timescales_PS_driver.py

---
 .../variability_across_timescales_PS_driver.py         | 10 +++++-----
 1 file changed, 5 insertions(+), 5 deletions(-)

diff --git a/pcmdi_metrics/precip_variability/variability_across_timescales_PS_driver.py b/pcmdi_metrics/precip_variability/variability_across_timescales_PS_driver.py
index c606be499..c5fd773b0 100644
--- a/pcmdi_metrics/precip_variability/variability_across_timescales_PS_driver.py
+++ b/pcmdi_metrics/precip_variability/variability_across_timescales_PS_driver.py
@@ -3,8 +3,6 @@
 import glob
 import os
 
-from genutil import StringConstructor
-
 from pcmdi_metrics.mean_climate.lib.pmp_parser import PMPParser
 from pcmdi_metrics.precip_variability.lib import (
     AddParserArgument,
@@ -38,9 +36,11 @@
 outdir = StringConstructor(
     str(outdir_template(output_type="%(output_type)", mip=mip, case_id=case_id))
 )
+outdir_template = outdir_template.replace("%(mip)",mip).replace("%(case_id)",case_id)
 for output_type in ["graphics", "diagnostic_results", "metrics_results"]:
-    os.makedirs(outdir(output_type=output_type), exist_ok=True)
-    print(outdir(output_type=output_type))
+    outdir = outdir_template.replace("%(output_type)",output_type)
+    os.makedirs(outdir, exist_ok=True)
+    print(outdir)
 
 # Check data in advance
 file_list = sorted(glob.glob(os.path.join(modpath, mod)))
@@ -57,5 +57,5 @@
 syr = prd[0]
 eyr = prd[1]
 precip_variability_across_timescale(
-        file_list, syr, eyr, dfrq, mip, dat, var, fac, nperseg, noverlap, outdir, cmec
+        file_list, syr, eyr, dfrq, mip, dat, var, fac, nperseg, noverlap, outdir_template, cmec
     )

From fa5040d47bfd392f39022aa4e64f920b2784a19e Mon Sep 17 00:00:00 2001
From: Ana Ordonez <ordonez4@llnl.gov>
Date: Mon, 25 Sep 2023 13:40:01 -0700
Subject: [PATCH 067/133] Remove genutil constructor

---
 .../lib/lib_variability_across_timescales.py                | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/pcmdi_metrics/precip_variability/lib/lib_variability_across_timescales.py b/pcmdi_metrics/precip_variability/lib/lib_variability_across_timescales.py
index 5f54506c7..74352ec9a 100644
--- a/pcmdi_metrics/precip_variability/lib/lib_variability_across_timescales.py
+++ b/pcmdi_metrics/precip_variability/lib/lib_variability_across_timescales.py
@@ -63,7 +63,7 @@ def precip_variability_across_timescale(
     # Write data (nc file)
     outfilename = "PS_pr." + str(dfrq) + "_regrid.180x90_" + dat + ".nc"
     custom_dataset = xr.merge([freqs, ps, rn, sig95])
-    custom_dataset.to_netcdf(path=os.path.join(outdir(output_type="diagnostic_results"), outfilename))
+    custom_dataset.to_netcdf(path=os.path.join(outdir.replace("%(output_type)","diagnostic_results"), outfilename))
         
     # Power spectum of anomaly
     freqs, ps, rn, sig95 = Powerspectrum(anom, nperseg, noverlap)
@@ -72,7 +72,7 @@ def precip_variability_across_timescale(
     # Write data (nc file)
     outfilename = "PS_pr." + str(dfrq) + "_regrid.180x90_" + dat + "_unforced.nc"
     custom_dataset = xr.merge([freqs, ps, rn, sig95])
-    custom_dataset.to_netcdf(path=os.path.join(outdir(output_type="diagnostic_results"), outfilename))
+    custom_dataset.to_netcdf(path=os.path.join(outdir.replace("%(output_type)","diagnostic_results"), outfilename))
         
     # Write data (json file)
     psdmfm["RESULTS"][dat] = {}
@@ -83,7 +83,7 @@ def precip_variability_across_timescale(
         "PS_pr." + str(dfrq) + "_regrid.180x90_area.freq.mean_" + dat + ".json"
     )
     JSON = pcmdi_metrics.io.base.Base(
-        outdir(output_type="metrics_results"), outfilename
+        outdir.replace("%(output_type)","metrics_results"), outfilename
     )
     JSON.write(
         psdmfm,

From 5f44c39e603a53a5cffbf7ef5a9245ce25d915f1 Mon Sep 17 00:00:00 2001
From: Ana Ordonez <ordonez4@llnl.gov>
Date: Mon, 25 Sep 2023 13:53:02 -0700
Subject: [PATCH 068/133] Remove string constructor

---
 .../variability_across_timescales_PS_driver.py                 | 3 ---
 1 file changed, 3 deletions(-)

diff --git a/pcmdi_metrics/precip_variability/variability_across_timescales_PS_driver.py b/pcmdi_metrics/precip_variability/variability_across_timescales_PS_driver.py
index c5fd773b0..5b1ee951d 100644
--- a/pcmdi_metrics/precip_variability/variability_across_timescales_PS_driver.py
+++ b/pcmdi_metrics/precip_variability/variability_across_timescales_PS_driver.py
@@ -33,9 +33,6 @@
 # Create output directory
 case_id = param.case_id
 outdir_template = param.process_templated_argument("results_dir")
-outdir = StringConstructor(
-    str(outdir_template(output_type="%(output_type)", mip=mip, case_id=case_id))
-)
 outdir_template = outdir_template.replace("%(mip)",mip).replace("%(case_id)",case_id)
 for output_type in ["graphics", "diagnostic_results", "metrics_results"]:
     outdir = outdir_template.replace("%(output_type)",output_type)

From 34c54c6546cd108c07ea008e86db187ce591c8f2 Mon Sep 17 00:00:00 2001
From: Ana Ordonez <ordonez4@llnl.gov>
Date: Mon, 25 Sep 2023 13:59:26 -0700
Subject: [PATCH 069/133] Remove process templated arg

---
 .../variability_across_timescales_PS_driver.py                  | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/pcmdi_metrics/precip_variability/variability_across_timescales_PS_driver.py b/pcmdi_metrics/precip_variability/variability_across_timescales_PS_driver.py
index 5b1ee951d..aeebb6856 100644
--- a/pcmdi_metrics/precip_variability/variability_across_timescales_PS_driver.py
+++ b/pcmdi_metrics/precip_variability/variability_across_timescales_PS_driver.py
@@ -32,7 +32,7 @@
 
 # Create output directory
 case_id = param.case_id
-outdir_template = param.process_templated_argument("results_dir")
+outdir_template = param.results_dir
 outdir_template = outdir_template.replace("%(mip)",mip).replace("%(case_id)",case_id)
 for output_type in ["graphics", "diagnostic_results", "metrics_results"]:
     outdir = outdir_template.replace("%(output_type)",output_type)

From 68a9a2577ba71958727954cb5e998b42e1d07e44 Mon Sep 17 00:00:00 2001
From: Ana Ordonez <ordonez4@llnl.gov>
Date: Mon, 25 Sep 2023 14:05:57 -0700
Subject: [PATCH 070/133] Force str type

---
 .../variability_across_timescales_PS_driver.py                  | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/pcmdi_metrics/precip_variability/variability_across_timescales_PS_driver.py b/pcmdi_metrics/precip_variability/variability_across_timescales_PS_driver.py
index aeebb6856..45d4e4ee1 100644
--- a/pcmdi_metrics/precip_variability/variability_across_timescales_PS_driver.py
+++ b/pcmdi_metrics/precip_variability/variability_across_timescales_PS_driver.py
@@ -33,7 +33,7 @@
 # Create output directory
 case_id = param.case_id
 outdir_template = param.results_dir
-outdir_template = outdir_template.replace("%(mip)",mip).replace("%(case_id)",case_id)
+outdir_template = outdir_template.replace("%(mip)",str(mip)).replace("%(case_id)",str(case_id))
 for output_type in ["graphics", "diagnostic_results", "metrics_results"]:
     outdir = outdir_template.replace("%(output_type)",output_type)
     os.makedirs(outdir, exist_ok=True)

From 5694e9a897a98bd2ca278d5ffd221506a65909eb Mon Sep 17 00:00:00 2001
From: mzelinka <zelinka1@llnl.gov>
Date: Thu, 28 Sep 2023 14:31:24 -0700
Subject: [PATCH 071/133] added new files and removed obsolete files

---
 .../cloud_feedback/lib/bony_analysis.py       |  121 --
 .../cloud_feedback/lib/cal_CloudRadKernel.py  | 1029 -----------------
 .../cloud_feedback/lib/compute_ECS.py         |  184 ---
 3 files changed, 1334 deletions(-)
 delete mode 100644 pcmdi_metrics/cloud_feedback/lib/bony_analysis.py
 delete mode 100755 pcmdi_metrics/cloud_feedback/lib/cal_CloudRadKernel.py
 delete mode 100755 pcmdi_metrics/cloud_feedback/lib/compute_ECS.py

diff --git a/pcmdi_metrics/cloud_feedback/lib/bony_analysis.py b/pcmdi_metrics/cloud_feedback/lib/bony_analysis.py
deleted file mode 100644
index 019237a73..000000000
--- a/pcmdi_metrics/cloud_feedback/lib/bony_analysis.py
+++ /dev/null
@@ -1,121 +0,0 @@
-import cdutil
-import MV2 as MV
-import numpy as np
-
-
-def bony_sorting_part1(w500, binedges):
-
-    A, B, C = w500.shape
-    dx = np.diff(binedges)[0]
-    # Compute composite:
-    OKwaps = nanarray((A, B, C, 2 + len(binedges)))  # add 2 for the exceedances
-    xx = 0
-    for x in binedges:
-        xx += 1
-        w500_bin = MV.masked_less(w500, x)
-        OKwaps[..., xx] = MV.masked_greater_equal(w500_bin, x + dx)
-
-    # do the first wap bin:
-    OKwaps[..., 0] = MV.masked_greater_equal(w500, binedges[0])
-    # do the last wap bin:
-    OKwaps[..., -1] = MV.masked_less(w500, binedges[-1] + dx)
-
-    return OKwaps  # [month, lat, lon, wapbin]
-
-
-def bony_sorting_part2(OKwaps, data, OKland, WTS, binedges):
-
-    binary_waps = MV.where(OKwaps.mask, 0, 1)  # zeros and ones
-    binary_land = MV.where(OKland.mask, 0, 1)  # zeros and ones
-    if np.ndim(data.mask) == 0:
-        binary_data = MV.array(np.ones(data.shape))
-    else:
-        binary_data = MV.where(data.mask, 0, 1)  # zeros and ones
-
-    # this function maps data from [time, ...?, lat, lon] to [time, ...?, wapbin]
-    sh = list(data.shape[:-2])
-    sh.append(3 + len(binedges))  # add 2 for the exceedances, 1 for land
-    DATA = nanarray((sh))  # add 2 for the exceedances, 1 for land
-    DATA = np.moveaxis(np.moveaxis(DATA, 0, -1), -2, 0)
-
-    sh = list()
-    sh = np.append(
-        data.shape[0], 3 + len(binedges)
-    )  # add 2 for the exceedances, 1 for land
-    CNTS = nanarray((sh[-1::-1]))  # add 2 for the exceedances, 1 for land
-
-    binary_waps2 = np.moveaxis(binary_waps, -1, 0)  # bring the wap bins to index 0
-    A1b = np.moveaxis(
-        np.moveaxis(binary_waps2 * WTS, 0, -1), 0, -1
-    )  # zero for wap outside range, frac area for wap inside range
-    CNTS[:-1, :] = np.sum(
-        np.sum(A1b, -3), -3
-    )  # fractional area of this bin includes regions where data is undefined
-
-    # reshape stuff to be [...?, lat, lon, time] to capitalize on broadcasting
-    WTS2 = np.moveaxis(WTS, 0, -1)
-    binary_data2 = np.moveaxis(binary_data, 0, -1)
-    data2 = np.moveaxis(data, 0, -1)
-    for xx in range(2 + len(binedges)):
-        A2 = (
-            binary_data2 * A1b[..., xx, :]
-        )  # zero for wap outside range or undefined data, frac area for wap inside range
-        denom = np.ma.sum(np.ma.sum(A2, -2), -2)
-        numer = np.sum(np.sum(data2 * A2, -2), -2)
-        DATA[xx, :] = (
-            numer / denom
-        )  # bin-avg data is computed where both data and wap are defined
-
-    # now do the land-only average:
-    A1b = np.moveaxis(
-        binary_land * WTS, 0, -1
-    )  # zero for ocean points, frac area for land points
-    CNTS[-1, :] = np.sum(
-        np.sum(A1b, -2), -2
-    )  # fractional area of this bin includes regions where data is undefined
-    A2 = (
-        binary_data2 * A1b
-    )  # zero for ocean points or undefined data, frac area for land points
-    denom = np.ma.sum(np.ma.sum(A2, -2), -2)
-    numer = np.sum(np.sum(data2 * A2, -2), -2)
-    DATA[-1, :] = (
-        numer / denom
-    )  # bin-avg data is computed where both data and wap are defined
-
-    # Ensure that the area matrix has zeros rather than masked points
-    CNTS[CNTS.mask] = 0
-
-    if np.allclose(0.5, np.sum(CNTS, 0)) is False:
-        print(
-            "sum of fractional counts over all wapbins does not equal 0.5 (tropical fraction)"
-        )
-        # moot
-
-    # DATA contains area-weighted averages within each bin
-    # CNTS contains fractional areas represented by each bin
-    # so summing (DATA*CNTS) over all regimes should recover the tropical contribution to the global mean
-    DATA2 = np.moveaxis(DATA, 0, -1)
-    CNTS2 = np.moveaxis(CNTS, 0, -1)
-    v1 = np.moveaxis(np.sum(DATA2 * CNTS2, -1), -1, 0)
-    v2a = 0.5 * cdutil.averager(data, axis="xy", weights="weighted")
-    v2b = np.moveaxis(np.ma.sum(np.ma.sum(WTS2 * data2, -2), -2), -1, 0)
-
-    if np.allclose(v1, v2a) is False or np.allclose(v1, v2b) is False:
-        print("Cannot reconstruct tropical average via summing regimes")
-
-    DATA3 = np.moveaxis(np.moveaxis(DATA, 0, -1), -2, 0)
-
-    return DATA3, CNTS2  # [time, wapbin]
-
-
-def nanarray(vector):
-    """
-    this generates a masked array with the size given by vector
-    example: vector = (90, 144, 28)
-    similar to this=NaN*ones(x, y, z) in matlab
-    """
-
-    this = MV.zeros(vector)
-    this = MV.masked_where(this == 0, this)
-
-    return this
diff --git a/pcmdi_metrics/cloud_feedback/lib/cal_CloudRadKernel.py b/pcmdi_metrics/cloud_feedback/lib/cal_CloudRadKernel.py
deleted file mode 100755
index 3b6e08b3f..000000000
--- a/pcmdi_metrics/cloud_feedback/lib/cal_CloudRadKernel.py
+++ /dev/null
@@ -1,1029 +0,0 @@
-#!/usr/bin/env python
-# coding: utf-8
-
-# =============================================
-# Performs the cloud feedback and cloud error
-# metric calculations in preparation for comparing
-# to expert-assessed values from Sherwood et al (2020)
-# =============================================
-
-from datetime import date
-
-# IMPORT STUFF:
-# =====================
-import cdms2 as cdms
-import cdutil
-import MV2 as MV
-import numpy as np
-from genutil import grower
-
-import pcmdi_metrics.cloud_feedback.lib.bony_analysis as BA
-import pcmdi_metrics.cloud_feedback.lib.zelinka_analysis as ZA
-
-
-# datadir='../data/'
-datadir = "./data/"
-
-# Define a python dictionary containing the sections of the histogram to consider
-# These are the same as in Zelinka et al, GRL, 2016
-sections = ["ALL", "HI680", "LO680"]
-Psections = [slice(0, 7), slice(2, 7), slice(0, 2)]
-sec_dic = dict(zip(sections, Psections))
-
-TR = cdutil.region.domain(latitude=(-30.0, 30.0))
-
-# 10 hPa/dy wide bins:
-width = 10
-binedges = np.arange(-100, 100, width)
-x1 = np.arange(binedges[0] - width, binedges[-1] + 2 * width, width)
-binmids = x1 + width / 2.0
-cutoff = np.int(
-    len(binmids) / 2
-)  # [:cutoff] = ascent; [cutoff:-1] = descent; [-1] = land
-
-# Load in the Zelinka et al 2012 kernels:
-f = cdms.open(datadir + "cloud_kernels2.nc")
-LWkernel = f("LWkernel")
-SWkernel = f("SWkernel")
-f.close()
-LWkernel = MV.masked_where(np.isnan(LWkernel), LWkernel)
-SWkernel = MV.masked_where(np.isnan(SWkernel), SWkernel)
-albcs = np.arange(
-    0.0, 1.5, 0.5
-)  # the clear-sky albedos over which the kernel is computed
-
-# Define the cloud kernel axis attributes
-lats = cdms.createAxis(LWkernel.getLatitude()[:])
-lats.id = "lat"
-lats.units = "degrees_N"
-lats.designateLatitude()
-lons = cdms.createAxis(np.arange(1.25, 360, 2.5))
-lons.id = "lon"
-lons.units = "degrees_E"
-lons.designateLongitude()
-kern_grid = cdms.createGenericGrid(lats, lons)
-kern_grid.getLatitude().id = "lat"
-kern_grid.getLongitude().id = "lon"
-
-
-##########################################################
-##### Load in ISCCP HGG clisccp climo annual cycle  ######
-##########################################################
-f = cdms.open(datadir + "AC_clisccp_ISCCP_HGG_198301-200812.nc", "r")
-# f.history='Written by /work/zelinka1/scripts/load_ISCCP_HGG.py on feedback.llnl.gov'
-# f.comment='Monthly-resolved climatological annual cycle over 198301-200812'
-obs_clisccp_AC = f("AC_clisccp")
-f.close()
-
-
-f = cdms.open(datadir + "AC_clisccp_wap_ISCCP_HGG_198301-200812.nc", "r")
-# f.history='Written by /work/zelinka1/scripts/load_ISCCP_HGG.py on feedback.llnl.gov'
-# f.comment='Monthly-resolved climatological annual cycle over 198301-200812, in omega500 space'
-obs_clisccp_AC_wap = f("AC_clisccp_wap")
-obs_N_AC_wap = f("AC_N_wap")
-f.close()
-
-
-###########################################################################
-def apply_land_mask_v2(data):
-    """
-    apply land mask (data):
-    this will read in and reshape the land-sea mask to match data
-    """
-    # Call the cdutil function to generate a mask, 0 for ocean, 1 for land.
-    mask = cdutil.generateLandSeaMask(data)
-
-    # Match dimension using "grower" function
-    data, mask2 = grower(data, mask)
-
-    ocean_data = MV.masked_where(mask2, data)
-    land_data = MV.masked_where(mask2 == 0.0, data)
-
-    return (ocean_data, land_data)
-
-
-###########################################################################
-def apply_land_mask_v3(data, ocn_mask, lnd_mask):
-    """
-    apply land mask (data):
-    this will read in and reshape the land-sea mask to match data
-    """
-
-    ocean_data = MV.masked_where(ocn_mask.mask, data)
-    land_data = MV.masked_where(lnd_mask.mask, data)
-
-    return (ocean_data, land_data)
-
-
-###############################################################################################
-def compute_fbk(ctl, fut, DT):
-    DR = fut - ctl
-    fbk = DR / DT
-    baseline = ctl
-    return fbk, baseline
-
-
-###############################################################################################
-def do_klein_calcs(
-    ctl_clisccp,
-    LWK,
-    SWK,
-    obs_clisccp_AC,
-    ctl_clisccp_wap,
-    LWK_wap,
-    SWK_wap,
-    obs_clisccp_AC_wap,
-    obs_N_AC_wap,
-):
-    KEM_dict = {}  # dictionary to contain all computed Klein error metrics
-    for sec in sections:
-        KEM_dict[sec] = {}
-        PP = sec_dic[sec]
-        C1 = ctl_clisccp[:, :, PP, :]
-        Klw = LWK[:, :, PP, :]
-        Ksw = SWK[:, :, PP, :]
-
-        obs_C1 = obs_clisccp_AC[:, :, PP, :]
-        ocn_obs_C1, lnd_obs_C1 = apply_land_mask_v2(obs_C1)
-        ocn_C1, lnd_C1 = apply_land_mask_v2(C1)
-
-        WTS = get_area_wts(obs_C1[:, 0, 0, :])  # summing this over lat and lon = 1
-        for region in [
-            "eq90",
-            "eq60",
-            "eq30",
-            "30-60",
-            "30-80",
-            "40-70",
-            "Arctic",
-        ]:  # assessed feedback regions + Klein region (eq60)
-            KEM_dict[sec][region] = {}
-            obs_C1_dom = select_regions(obs_C1, region)
-            ocn_obs_C1_dom = select_regions(ocn_obs_C1, region)
-            lnd_obs_C1_dom = select_regions(lnd_obs_C1, region)
-            C1_dom = select_regions(C1, region)
-            ocn_C1_dom = select_regions(ocn_C1, region)
-            lnd_C1_dom = select_regions(lnd_C1, region)
-            Klw_dom = select_regions(Klw, region)
-            Ksw_dom = select_regions(Ksw, region)
-            WTS_dom = select_regions(WTS, region)
-            for sfc in ["all", "ocn", "lnd", "ocn_asc", "ocn_dsc"]:
-                KEM_dict[sec][region][sfc] = {}
-                if sfc == "all":
-                    (E_TCA, E_ctpt, E_LW, E_SW, E_NET) = klein_metrics(
-                        obs_C1_dom, C1_dom, Klw_dom, Ksw_dom, WTS_dom
-                    )
-                elif sfc == "ocn":
-                    (E_TCA, E_ctpt, E_LW, E_SW, E_NET) = klein_metrics(
-                        ocn_obs_C1_dom, ocn_C1_dom, Klw_dom, Ksw_dom, WTS_dom
-                    )
-                elif sfc == "lnd":
-                    (E_TCA, E_ctpt, E_LW, E_SW, E_NET) = klein_metrics(
-                        lnd_obs_C1_dom, lnd_C1_dom, Klw_dom, Ksw_dom, WTS_dom
-                    )
-                else:
-                    continue
-                KEM_dict[sec][region][sfc]["E_TCA"] = E_TCA
-                KEM_dict[sec][region][sfc]["E_ctpt"] = E_ctpt
-                KEM_dict[sec][region][sfc]["E_LW"] = E_LW
-                KEM_dict[sec][region][sfc]["E_SW"] = E_SW
-                KEM_dict[sec][region][sfc]["E_NET"] = E_NET
-
-        C1 = ctl_clisccp_wap[:, :, PP, :-1]
-        obs_C1 = obs_clisccp_AC_wap[:, :, PP, :-1]  # ignore the land bin
-        WTS = obs_N_AC_wap[:, :-1]  # ignore the land bin
-        Klw = LWK_wap[:, :, PP, :-1]  # ignore the land bin
-        Ksw = SWK_wap[:, :, PP, :-1]
-        (E_TCA, E_ctpt, E_LW, E_SW, E_NET) = klein_metrics(
-            obs_C1[..., :cutoff],
-            C1[..., :cutoff],
-            Klw[..., :cutoff],
-            Ksw[..., :cutoff],
-            WTS[:, :cutoff],
-        )
-        KEM_dict[sec]["eq30"]["ocn_asc"]["E_TCA"] = E_TCA
-        KEM_dict[sec]["eq30"]["ocn_asc"]["E_ctpt"] = E_ctpt
-        KEM_dict[sec]["eq30"]["ocn_asc"]["E_LW"] = E_LW
-        KEM_dict[sec]["eq30"]["ocn_asc"]["E_SW"] = E_SW
-        KEM_dict[sec]["eq30"]["ocn_asc"]["E_NET"] = E_NET
-        (E_TCA, E_ctpt, E_LW, E_SW, E_NET) = klein_metrics(
-            obs_C1[..., cutoff:-1],
-            C1[..., cutoff:-1],
-            Klw[..., cutoff:-1],
-            Ksw[..., cutoff:-1],
-            WTS[:, cutoff:-1],
-        )
-        KEM_dict[sec]["eq30"]["ocn_dsc"]["E_TCA"] = E_TCA
-        KEM_dict[sec]["eq30"]["ocn_dsc"]["E_ctpt"] = E_ctpt
-        KEM_dict[sec]["eq30"]["ocn_dsc"]["E_LW"] = E_LW
-        KEM_dict[sec]["eq30"]["ocn_dsc"]["E_SW"] = E_SW
-        KEM_dict[sec]["eq30"]["ocn_dsc"]["E_NET"] = E_NET
-    # end for sec in sections:
-    KEM_dict["metadata"] = {}
-    meta = {
-        "date_modified": str(date.today()),
-        "author": "Mark D. Zelinka <zelinka1@llnl.gov>",
-    }
-    KEM_dict["metadata"] = meta
-
-    return KEM_dict
-
-
-###########################################################################
-def do_obscuration_calcs(CTL, FUT, Klw, Ksw, DT):
-
-    (L_R_bar, dobsc, dunobsc, dobsc_cov) = obscuration_terms3(CTL, FUT)
-
-    # Get unobscured low-cloud feedbacks and those caused by change in obscuration
-    ZEROS = np.zeros(L_R_bar.shape)
-    dummy, L_R_bar_base = compute_fbk(L_R_bar, L_R_bar, DT)
-    dobsc_fbk, dummy = compute_fbk(ZEROS, dobsc, DT)
-    dunobsc_fbk, dummy = compute_fbk(ZEROS, dunobsc, DT)
-    dobsc_cov_fbk, dummy = compute_fbk(ZEROS, dobsc_cov, DT)
-    obsc_output = obscuration_feedback_terms_general(
-        L_R_bar_base, dobsc_fbk, dunobsc_fbk, dobsc_cov_fbk, Klw, Ksw
-    )
-
-    return obsc_output
-
-
-###########################################################################
-def get_amip_data(filename, var, lev=None):
-    # load in cmip data using the appropriate function for the experiment/mip
-
-    print("        " + var)
-
-    tslice = (
-        "1983-01-01",
-        "2008-12-31",
-    )  # we only want this portion of the amip run (overlap with all AMIPs and ISCCP)
-    f = cdms.open(filename[var])
-    if lev:
-        data = f(var, time=tslice, level=lev, squeeze=1)
-    else:
-        data = f(var, time=tslice, squeeze=1)
-    f.close()
-    # Compute climatological monthly means
-    dummy, avg = monthly_anomalies(data)
-    avg.setAxisList(data[:12, :].getAxisList())
-    # Regrid to cloud kernel grid
-    data = avg.regrid(kern_grid, regridTool="esmf", regridMethod="linear")
-
-    return data
-
-
-###########################################################################
-def get_area_wts(data):
-    # Create map of area weights
-    lats = data.getLatitude()
-    lons = data.getLongitude()
-    A, B, C = data.shape
-    coslat = np.cos(lats[:] * np.pi / 180)
-    coslat2 = coslat / np.sum(coslat)
-    area_wts = MV.array(
-        np.moveaxis(np.tile(coslat2 / C, [A, C, 1]), 1, 2)
-    )  # summing this over lat and lon = 1
-    area_wts.setAxis(1, lats)
-    area_wts.setAxis(2, lons)
-    return area_wts
-
-
-###########################################################################
-def get_CRK_data(filenames):
-    # Read in data, regrid and map kernels to lat/lon
-
-    # Load in regridded monthly mean climatologies from control and perturbed simulation
-    print("    amip")
-    ctl_tas = get_amip_data(filenames["amip"], "tas")
-    ctl_rsdscs = get_amip_data(filenames["amip"], "rsdscs")
-    ctl_rsuscs = get_amip_data(filenames["amip"], "rsuscs")
-    ctl_wap = get_amip_data(filenames["amip"], "wap", 50000)
-    ctl_clisccp = get_amip_data(filenames["amip"], "clisccp")
-
-    print("    amip-p4K")
-    fut_tas = get_amip_data(filenames["amip-p4K"], "tas")
-    # fut_rsdscs = get_amip_data(filenames["amip-p4K"], "rsdscs")
-    # fut_rsuscs = get_amip_data(filenames["amip-p4K"], "rsuscs")
-    fut_wap = get_amip_data(filenames["amip-p4K"], "wap", 50000)
-    fut_clisccp = get_amip_data(filenames["amip-p4K"], "clisccp")
-
-    # Make sure wap is in hPa/day
-    fut_wap = 36 * 24 * fut_wap  # Pa s-1 --> hPa/day
-    ctl_wap = 36 * 24 * ctl_wap  # Pa s-1 --> hPa/day
-
-    # Make sure clisccp is in percent
-    sumclisccp1 = np.ma.sum(np.ma.sum(ctl_clisccp, 2), 1)
-    if np.max(sumclisccp1) <= 1.0:
-        ctl_clisccp = ctl_clisccp * 100.0
-        fut_clisccp = fut_clisccp * 100.0
-
-    # Give clisccp axes info
-    AXL = ctl_tas.getAxisList()
-    ctl_clisccp.setAxis(0, AXL[0])
-    ctl_clisccp.setAxis(3, AXL[1])
-    ctl_clisccp.setAxis(4, AXL[2])
-    fut_clisccp.setAxisList(ctl_clisccp.getAxisList())
-
-    # Compute clear-sky surface albedo
-    ctl_albcs = ctl_rsuscs / ctl_rsdscs  # (12, 90, 144)
-    ctl_albcs = MV.where(
-        ctl_albcs > 1.0, 1, ctl_albcs
-    )  # where(condition, x, y) is x where condition is true, y otherwise
-    ctl_albcs = MV.where(ctl_albcs < 0.0, 0, ctl_albcs)
-
-    # The first month may not be January:
-    yrs, mos = get_plottable_time(ctl_albcs)
-    choose = mos - 1  # mos runs from 1-12, so subtract 1 to make an index
-
-    # Use control albcs to map SW kernel to appropriate longitudes
-    SWkernel_map = ZA.map_SWkern_to_lon(SWkernel, ctl_albcs)
-    # LW kernel does not depend on albcs, just repeat the final dimension over longitudes:
-    LWkernel_map = np.tile(
-        np.tile(LWkernel[:, :, :, :, 0], (1, 1, 1, 1, 1)), (144, 1, 1, 1, 1)
-    )(order=[1, 2, 3, 4, 0])
-    LWkernel_map = MV.take(LWkernel_map, choose, axis=0)
-    LWkernel_map.setAxisList(ctl_clisccp.getAxisList())
-    SWkernel_map.setAxisList(ctl_clisccp.getAxisList())
-
-    # global mean delta tas
-    anom_tas = fut_tas - ctl_tas
-    avgdtas0 = cdutil.averager(
-        anom_tas, axis="xy", weights="weighted"
-    )  # global average
-    avgdtas0.setAxis(0, ctl_tas.getAxis(0))
-
-    # compute annual averages
-    avgdtas = YEAR(avgdtas0)
-
-    print("Sort into omega500 bins")
-    ctl_wap_ocean, ctl_wap_land = apply_land_mask_v2(TR.select(ctl_wap))
-    fut_wap_ocean, fut_wap_land = apply_land_mask_v2(TR.select(fut_wap))
-    ctl_OKwaps = BA.bony_sorting_part1(ctl_wap_ocean, binedges)
-    fut_OKwaps = BA.bony_sorting_part1(fut_wap_ocean, binedges)
-
-    area_wts = get_area_wts(ctl_tas)  # summing this over lat and lon = 1
-    WTS = TR.select(area_wts)
-    TCC = TR.select(ctl_clisccp)
-    TFC = TR.select(fut_clisccp)
-    TLK = TR.select(LWkernel_map)
-    TSK = TR.select(SWkernel_map)
-
-    ctl_clisccp_wap, ctl_N = BA.bony_sorting_part2(
-        ctl_OKwaps, TCC, ctl_wap_land, WTS, binedges
-    )
-    fut_clisccp_wap, fut_N = BA.bony_sorting_part2(
-        fut_OKwaps, TFC, fut_wap_land, WTS, binedges
-    )
-    LWK_wap, P_wapbin = BA.bony_sorting_part2(
-        ctl_OKwaps, TLK, ctl_wap_land, WTS, binedges
-    )
-    SWK_wap, P_wapbin = BA.bony_sorting_part2(
-        ctl_OKwaps, TSK, ctl_wap_land, WTS, binedges
-    )
-
-    return (
-        ctl_clisccp,
-        fut_clisccp,
-        LWkernel_map,
-        SWkernel_map,
-        avgdtas,
-        ctl_clisccp_wap,
-        fut_clisccp_wap,
-        LWK_wap,
-        SWK_wap,
-        ctl_N,
-        fut_N,
-    )
-
-
-###########################################################################
-def get_plottable_time(X):
-    # Function stolen / modified from Kate Marvel
-    years = [x.year + (x.month - 1) / 12.0 for x in X.getTime().asComponentTime()]
-    months = [x.month for x in X.getTime().asComponentTime()]
-    return (np.array(years), np.array(months))
-
-
-###########################################################################
-def klein_metrics(obs_clisccp, gcm_clisccp, LWkern, SWkern, WTS):
-
-    ########################################################
-    ######### Compute Klein et al (2013) metrics ###########
-    ########################################################
-
-    # Remove the thinnest optical depth bin from models/kernels so as to compare properly with obs:
-    gcm_clisccp = gcm_clisccp[:, 1:, :, :]
-    LWkern = LWkern[:, 1:, :, :]
-    SWkern = SWkern[:, 1:, :, :]
-
-    ## Compute Cloud Fraction Histogram Anomalies w.r.t. observations
-    clisccp_bias = gcm_clisccp - obs_clisccp
-
-    ## Multiply Anomalies by Kernels
-    SW = SWkern * clisccp_bias
-    LW = LWkern * clisccp_bias
-    NET = SW + LW
-
-    ########################################################
-    # E_TCA (TOTAL CLOUD AMOUNT METRIC)
-    ########################################################
-    # take only clouds with tau>1.3
-    WTS_dom = WTS / 12
-    WTS_dom = WTS_dom / np.sum(
-        WTS_dom
-    )  # np.sum(WTS_dom) = 1, so weighted sums give area-weighted avg, NOT scaled by fraction of planet
-    obs_clisccp_dom = obs_clisccp[:, 1:, :]
-    gcm_clisccp_dom = gcm_clisccp[:, 1:, :]
-
-    # sum over CTP and TAU:
-    gcm_cltisccp_dom = MV.sum(MV.sum(gcm_clisccp_dom, 1), 1)  # (time, lat, lon)
-    obs_cltisccp_dom = MV.sum(MV.sum(obs_clisccp_dom, 1), 1)  # (time, lat, lon)
-
-    # 1) Denominator (Eq. 3 in Klein et al. (2013))
-    avg = np.sum(obs_cltisccp_dom * WTS_dom)  # (scalar)
-    anom1 = obs_cltisccp_dom - avg  # anomaly of obs from its spatio-temporal mean
-    # 2) Numerator -- Model minus ISCCP
-    anom2 = gcm_cltisccp_dom - obs_cltisccp_dom  # (time, lat, lon)
-
-    E_TCA_denom = np.ma.sqrt(np.ma.sum(WTS_dom * anom1**2))  # (scalar)
-    E_TCA_numer2 = np.ma.sqrt(np.ma.sum(WTS_dom * anom2**2))  # (scalar)
-
-    E_TCA = E_TCA_numer2 / E_TCA_denom
-
-    ########################################################
-    # CLOUD PROPERTY METRICS
-    ########################################################
-    # take only clouds with tau>3.6
-    clisccp_bias_dom = clisccp_bias[:, 2:, :]
-    obs_clisccp_dom = obs_clisccp[:, 2:, :]
-    gcm_clisccp_dom = gcm_clisccp[:, 2:, :]
-    LWkernel_dom = LWkern[:, 2:, :]
-    SWkernel_dom = SWkern[:, 2:, :]
-    NETkernel_dom = SWkernel_dom + LWkernel_dom
-
-    # Compute anomaly of obs histogram from its spatio-temporal mean
-    this = np.moveaxis(obs_clisccp_dom, 0, 2)  # [TAU,CTP,month,space]
-    if np.ndim(WTS_dom) == 2:  # working in wap space
-        avg_obs_clisccp_dom = np.ma.sum(np.ma.sum(this * WTS_dom, -1), -1)  # (TAU,CTP)
-    else:  # working in lat/lon space
-        avg_obs_clisccp_dom = np.ma.sum(
-            np.ma.sum(np.ma.sum(this * WTS_dom, -1), -1), -1
-        )  # (TAU,CTP)
-    this = np.moveaxis(np.moveaxis(obs_clisccp_dom, 1, -1), 1, -1) - avg_obs_clisccp_dom
-    anom_obs_clisccp_dom = np.moveaxis(np.moveaxis(this, -1, 1), -1, 1)
-
-    ## Compute radiative impacts of cloud fraction anomalies
-    gcm_NET_bias = NET[:, 2:, :]
-    obs_NET_bias = anom_obs_clisccp_dom * NETkernel_dom
-    gcm_SW_bias = SW[:, 2:, :]
-    obs_SW_bias = anom_obs_clisccp_dom * SWkernel_dom
-    gcm_LW_bias = LW[:, 2:, :]
-    obs_LW_bias = anom_obs_clisccp_dom * LWkernel_dom
-
-    ## Aggregate high, mid, and low clouds over medium and thick ISCCP ranges
-    CTPmids = obs_clisccp.getAxis(2)[:]
-    Psec_name = ["LO", "MID", "HI"]
-    Psec_bnds = ((1100, 680), (680, 440), (440, 10))
-    Psec_dic = dict(zip(Psec_name, Psec_bnds))
-    Tsec_name = ["MED", "THICK"]
-    Tsections = [slice(0, 2), slice(2, 4)]
-    Tsec_dic = dict(zip(Tsec_name, Tsections))
-
-    agg_obs_NET_bias = np.zeros(gcm_SW_bias.shape)
-    agg_gcm_NET_bias = np.zeros(gcm_SW_bias.shape)
-    agg_obs_SW_bias = np.zeros(gcm_SW_bias.shape)
-    agg_gcm_SW_bias = np.zeros(gcm_SW_bias.shape)
-    agg_obs_LW_bias = np.zeros(gcm_SW_bias.shape)
-    agg_gcm_LW_bias = np.zeros(gcm_SW_bias.shape)
-    agg_obs_clisccp_bias = np.zeros(gcm_SW_bias.shape)
-    agg_gcm_clisccp_bias = np.zeros(gcm_SW_bias.shape)
-
-    obs_NET_bias = MV.where(obs_NET_bias.mask, 0, obs_NET_bias)
-    gcm_NET_bias = MV.where(gcm_NET_bias.mask, 0, gcm_NET_bias)
-    obs_SW_bias = MV.where(obs_SW_bias.mask, 0, obs_SW_bias)
-    gcm_SW_bias = MV.where(gcm_SW_bias.mask, 0, gcm_SW_bias)
-    obs_LW_bias = MV.where(obs_LW_bias.mask, 0, obs_LW_bias)
-    gcm_LW_bias = MV.where(gcm_LW_bias.mask, 0, gcm_LW_bias)
-    anom_obs_clisccp_dom = MV.where(anom_obs_clisccp_dom.mask, 0, anom_obs_clisccp_dom)
-    clisccp_bias_dom = MV.where(clisccp_bias_dom.mask, 0, clisccp_bias_dom)
-
-    tt = -1
-    for Tsec in Tsec_name:
-        tt += 1
-        TT = Tsec_dic[Tsec]
-        pp = -1
-        for Psec in Psec_name:
-            pbot, ptop = Psec_dic[Psec]
-            PP = np.where(np.logical_and(CTPmids <= pbot, CTPmids > ptop))[0]
-            if len(CTPmids[PP]) > 0:
-                pp += 1
-                agg_obs_NET_bias[:, tt, pp, :] = np.ma.sum(
-                    np.ma.sum(np.array(obs_NET_bias)[:, TT, PP, :], 1), 1
-                )
-                agg_gcm_NET_bias[:, tt, pp, :] = np.ma.sum(
-                    np.ma.sum(np.array(gcm_NET_bias)[:, TT, PP, :], 1), 1
-                )
-                agg_obs_SW_bias[:, tt, pp, :] = np.ma.sum(
-                    np.ma.sum(np.array(obs_SW_bias)[:, TT, PP, :], 1), 1
-                )
-                agg_gcm_SW_bias[:, tt, pp, :] = np.ma.sum(
-                    np.ma.sum(np.array(gcm_SW_bias)[:, TT, PP, :], 1), 1
-                )
-                agg_obs_LW_bias[:, tt, pp, :] = np.ma.sum(
-                    np.ma.sum(np.array(obs_LW_bias)[:, TT, PP, :], 1), 1
-                )
-                agg_gcm_LW_bias[:, tt, pp, :] = np.ma.sum(
-                    np.ma.sum(np.array(gcm_LW_bias)[:, TT, PP, :], 1), 1
-                )
-                agg_obs_clisccp_bias[:, tt, pp, :] = np.ma.sum(
-                    np.ma.sum(np.array(anom_obs_clisccp_dom)[:, TT, PP, :], 1), 1
-                )
-                agg_gcm_clisccp_bias[:, tt, pp, :] = np.ma.sum(
-                    np.ma.sum(np.array(clisccp_bias_dom)[:, TT, PP, :], 1), 1
-                )
-    NP = pp + 1
-    NT = tt + 1
-
-    ## Compute E_ctp-tau -- Cloud properties error
-    ctot1 = np.ma.sum(np.ma.sum(agg_gcm_clisccp_bias**2, 1), 1) / (NT * NP)
-    ctot2 = np.ma.sum(np.ma.sum(agg_obs_clisccp_bias**2, 1), 1) / (NT * NP)
-
-    ## Compute E_LW -- LW-relevant cloud properties error
-    ctot3 = np.ma.sum(np.ma.sum(agg_gcm_LW_bias**2, 1), 1) / (NT * NP)
-    ctot4 = np.ma.sum(np.ma.sum(agg_obs_LW_bias**2, 1), 1) / (NT * NP)
-
-    ## Compute E_SW -- SW-relevant cloud properties error
-    ctot5 = np.ma.sum(np.ma.sum(agg_gcm_SW_bias**2, 1), 1) / (NT * NP)
-    ctot6 = np.ma.sum(np.ma.sum(agg_obs_SW_bias**2, 1), 1) / (NT * NP)
-
-    ## Compute E_NET -- NET-relevant cloud properties error
-    ctot7 = np.ma.sum(np.ma.sum(agg_gcm_NET_bias**2, 1), 1) / (NT * NP)
-    ctot8 = np.ma.sum(np.ma.sum(agg_obs_NET_bias**2, 1), 1) / (NT * NP)
-
-    # compute one metric
-    E_ctpt_numer = np.ma.sqrt(np.ma.sum(WTS_dom * ctot1))  # (scalar)
-    E_ctpt_denom = np.ma.sqrt(np.ma.sum(WTS_dom * ctot2))  # (scalar)
-    E_LW_numer = np.ma.sqrt(np.ma.sum(WTS_dom * ctot3))  # (scalar)
-    E_LW_denom = np.ma.sqrt(np.ma.sum(WTS_dom * ctot4))  # (scalar)
-    E_SW_numer = np.ma.sqrt(np.ma.sum(WTS_dom * ctot5))  # (scalar)
-    E_SW_denom = np.ma.sqrt(np.ma.sum(WTS_dom * ctot6))  # (scalar)
-    E_NET_numer = np.ma.sqrt(np.ma.sum(WTS_dom * ctot7))  # (scalar)
-    E_NET_denom = np.ma.sqrt(np.ma.sum(WTS_dom * ctot8))  # (scalar)
-
-    E_ctpt = E_ctpt_numer / E_ctpt_denom
-    E_LW = E_LW_numer / E_LW_denom
-    E_SW = E_SW_numer / E_SW_denom
-    E_NET = E_NET_numer / E_NET_denom
-
-    return (E_TCA, E_ctpt, E_LW, E_SW, E_NET)
-
-
-###########################################################################
-def monthly_anomalies(data):
-    """
-    Compute departures from the climatological annual cycle
-    usage:
-    anom,avg = monthly_anomalies(data)
-    """
-    anomdata = nanarray(data.shape)
-    avgdata = nanarray(data[:12].shape)
-    for i in range(12):
-        avgdata[i] = MV.average(data[i::12], 0)
-        anomdata[i::12] = data[i::12] - avgdata[i]
-
-    try:
-        avgdata.setAxisList(data[:12].getAxisList())
-        anomdata.setAxisList(data.getAxisList())
-    except Exception:
-        pass
-
-    return (anomdata, avgdata)
-
-
-###########################################################################
-def nanarray(vector):
-    """
-    this generates a masked array with the size given by vector
-    example: vector = (90,144,28)
-    similar to this=NaN*ones(x,y,z) in matlab
-    """
-
-    this = MV.zeros(vector)
-    this = MV.masked_where(this == 0, this)
-
-    return this
-
-
-###########################################################################
-def obscuration_feedback_terms_general(
-    L_R_bar0, dobsc_fbk, dunobsc_fbk, dobsc_cov_fbk, Klw, Ksw
-):
-    """
-    Estimate unobscured low cloud feedback,
-    the low cloud feedback arising solely from changes in obscuration by upper-level clouds,
-    and the covariance term
-
-    This function takes in a (month,tau,CTP,lat,lon) matrix
-
-    Klw and Ksw contain just the low bins
-
-    the following terms are generated in obscuration_terms():
-    dobsc = L_R_bar0 * F_prime
-    dunobsc = L_R_prime * F_bar
-    dobsc_cov = (L_R_prime * F_prime) - climo(L_R_prime * F_prime)
-    """
-
-    Klw_low = Klw
-    Ksw_low = Ksw
-    L_R_bar0 = 100 * L_R_bar0
-    dobsc_fbk = 100 * dobsc_fbk
-    dunobsc_fbk = 100 * dunobsc_fbk
-    dobsc_cov_fbk = 100 * dobsc_cov_fbk
-
-    LWdobsc_fbk = MV.sum(MV.sum(Klw_low * dobsc_fbk, 1), 1)
-    LWdunobsc_fbk = MV.sum(MV.sum(Klw_low * dunobsc_fbk, 1), 1)
-    LWdobsc_cov_fbk = MV.sum(MV.sum(Klw_low * dobsc_cov_fbk, 1), 1)
-
-    SWdobsc_fbk = MV.sum(MV.sum(Ksw_low * dobsc_fbk, 1), 1)
-    SWdunobsc_fbk = MV.sum(MV.sum(Ksw_low * dunobsc_fbk, 1), 1)
-    SWdobsc_cov_fbk = MV.sum(MV.sum(Ksw_low * dobsc_cov_fbk, 1), 1)
-
-    ###########################################################################
-    # Further break down the true and apparent low cloud-induced radiation anomalies into components
-    ###########################################################################
-    # No need to break down dobsc_fbk, as that is purely an amount component.
-
-    # Break down dunobsc_fbk:
-    C_ctl = L_R_bar0
-    dC = dunobsc_fbk
-    C_fut = C_ctl + dC
-
-    obsc_fbk_output = ZA.KT_decomposition_general(C_ctl, C_fut, Klw_low, Ksw_low)
-
-    obsc_fbk_output["LWdobsc_fbk"] = LWdobsc_fbk
-    obsc_fbk_output["LWdunobsc_fbk"] = LWdunobsc_fbk
-    obsc_fbk_output["LWdobsc_cov_fbk"] = LWdobsc_cov_fbk
-    obsc_fbk_output["SWdobsc_fbk"] = SWdobsc_fbk
-    obsc_fbk_output["SWdunobsc_fbk"] = SWdunobsc_fbk
-    obsc_fbk_output["SWdobsc_cov_fbk"] = SWdobsc_cov_fbk
-
-    return obsc_fbk_output
-
-
-###########################################################################
-def obscuration_terms3(c1, c2):
-    """
-    USE THIS VERSION FOR DIFFERENCES OF 2 CLIMATOLOGIES (E.G. AMIP4K, 2xCO2 SLAB RUNS)
-
-    Compute the components required for the obscuration-affected low cloud feedback
-    These are the terms shown in Eq 4 of Scott et al (2020) DOI: 10.1175/JCLI-D-19-1028.1
-    L_prime = dunobsc + dobsc + dobsc_cov, where
-    dunobsc = L_R_prime * F_bar     (delta unobscured low clouds, i.e., true low cloud feedback)
-    dobsc = L_R_bar * F_prime       (delta obscuration by upper level clouds)
-    dobsc_cov = (L_R_prime * F_prime) - climo(L_R_prime * F_prime)  (covariance term)
-    """
-    # c is [mo,tau,ctp,lat,lon]
-    # c is in percent
-
-    # AX = c2.getAxisList()
-
-    c1 = MV.masked_where(c2.mask, c1)
-    c2 = MV.masked_where(c1.mask, c2)
-
-    # SPLICE c1 and c2:
-    # MAKE SURE c1 and c2 are the same size!!!
-    if c1.shape != c2.shape:
-        raise RuntimeError("c1 and c2 are NOT the same size!!!")
-
-    c12 = np.ma.append(c1, c2, axis=0)
-
-    midpt = len(c1)
-
-    U12 = MV.sum(MV.sum(c12[:, :, 2:, :], 1), 1) / 100.0
-
-    L12 = c12[:, :, :2, :] / 100.0
-
-    F12 = 1.0 - U12
-    F12 = MV.masked_less(F12, 0)
-
-    F12b = MV.array(np.expand_dims(np.expand_dims(F12, axis=1), axis=1))
-    F12b = MV.masked_where(L12[:, :1, :1, :].mask, F12b)
-
-    L_R12 = L12 / F12b
-    sum_L_R12 = MV.sum(MV.sum(L_R12, 1), 1)
-    sum_L_R12b = MV.array(np.expand_dims(np.expand_dims(sum_L_R12, axis=1), axis=1))
-    sum_L_R12c = np.broadcast_to(sum_L_R12b, L_R12.shape)
-    this = MV.masked_outside(sum_L_R12c, 0, 1)
-    L_R12 = MV.masked_where(this.mask, L_R12)
-
-    L_R12 = MV.masked_where(sum_L_R12c > 1, L_R12)
-
-    L_R_prime, L_R_bar = monthly_anomalies(L_R12)
-    F_prime, F_bar = monthly_anomalies(F12b)
-    L_prime, L_bar = monthly_anomalies(L12)
-
-    # Cannot have negative cloud fractions:
-    L_R_bar[L_R_bar < 0] = 0
-    F_bar[F_bar < 0] = 0
-
-    # rep_L_bar = tile_uneven(L_bar, L12)
-    rep_L_R_bar = tile_uneven(L_R_bar, L_R12)
-    rep_F_bar = tile_uneven(F_bar, F12b)
-
-    # Cannot have negative cloud fractions:
-    L_R_bar[L_R_bar < 0] = 0
-    F_bar[F_bar < 0] = 0
-
-    dobsc = rep_L_R_bar * F_prime
-    dunobsc = L_R_prime * rep_F_bar
-    prime_prime = L_R_prime * F_prime
-
-    dobsc_cov, climo_prime_prime = monthly_anomalies(prime_prime)
-
-    # Re-scale these anomalies by 2, since we have computed all anomalies w.r.t.
-    # the ctl+pert average rather than w.r.t. the ctl average
-    dobsc *= 2
-    dunobsc *= 2
-    dobsc_cov *= 2
-
-    return (rep_L_R_bar[midpt:], dobsc[midpt:], dunobsc[midpt:], dobsc_cov[midpt:])
-
-
-###########################################################################
-def regional_breakdown(data, OCN, LND, area_wts, AX, norm=False):
-    # Compute spatial averages over various geographical regions, for ocean, land, and both
-    # if norm=False (the default), these averages are scaled by the fractional area of the planet over which they occur
-    # if norm=True, these are simply area-weighted averages
-
-    ocn_area_wts, lnd_area_wts = apply_land_mask_v3(area_wts, OCN, LND)
-    mx = np.arange(
-        10, 101, 10
-    )  # max latitude of region (i.e., from -mx to mx); last one is for Arctic
-    denom = 1
-    reg_dict = {}
-    sections = list(data.keys())
-    surfaces = ["all", "ocn", "lnd"]
-    for r in mx:
-        if r == 100:
-            region = "Arctic"
-            domain = cdutil.region.domain(latitude=(70, 90))
-        else:
-            region = "eq" + str(r)
-            domain = cdutil.region.domain(latitude=(-r, r))
-        reg_dict[region] = {}
-        for sfc in surfaces:
-            reg_dict[region][sfc] = {}
-            for sec in sections:
-                reg_dict[region][sfc][sec] = {}
-                DATA = data[sec]
-                names = list(DATA.keys())
-                for name in names:
-                    # reg_dict[region][sfc][sec][name] = {}
-                    fbk = DATA[name]
-                    fbk.setAxisList(AX)
-                    ocn_fbk, lnd_fbk = apply_land_mask_v3(fbk, OCN, LND)
-                    if sfc == "ocn":
-                        wtd_fbk = np.ma.sum(
-                            np.ma.sum(domain.select(ocn_fbk * area_wts), 1), 1
-                        )
-                        if norm:
-                            denom = np.ma.sum(
-                                np.ma.sum(domain.select(ocn_area_wts), 1), 1
-                            )
-                    elif sfc == "lnd":
-                        wtd_fbk = np.ma.sum(
-                            np.ma.sum(domain.select(lnd_fbk * area_wts), 1), 1
-                        )
-                        if norm:
-                            denom = np.ma.sum(
-                                np.ma.sum(domain.select(lnd_area_wts), 1), 1
-                            )
-                    elif sfc == "all":
-                        wtd_fbk = np.ma.sum(
-                            np.ma.sum(domain.select(fbk * area_wts), 1), 1
-                        )
-                        if norm:
-                            denom = np.ma.sum(np.ma.sum(domain.select(area_wts), 1), 1)
-                    wtd_fbk = wtd_fbk / denom
-                    reg_dict[region][sfc][sec][name] = np.ma.average(wtd_fbk, 0)
-
-    # reserve spots in the dictionary for asc/dsc feedbacks
-    reg_dict["eq30"]["ocn_asc"] = {}
-    reg_dict["eq30"]["ocn_dsc"] = {}
-    for sec in sections:
-        reg_dict["eq30"]["ocn_asc"][sec] = {}
-        reg_dict["eq30"]["ocn_dsc"][sec] = {}
-
-    return reg_dict
-
-
-###########################################################################
-def select_regions(field, region):
-    if region == "eq90":
-        inds = np.where((np.abs(lats[:]) < 90))
-    elif region == "eq60":
-        inds = np.where((np.abs(lats[:]) < 60))
-    elif region == "eq30":
-        inds = np.where((np.abs(lats[:]) < 30))
-    elif region == "30-60":
-        inds = np.where((np.abs(lats[:]) < 60) & (np.abs(lats[:]) > 30))
-    elif region == "30-80":
-        inds = np.where((np.abs(lats[:]) < 80) & (np.abs(lats[:]) > 30))
-    elif region == "40-70":
-        inds = np.where((np.abs(lats[:]) < 70) & (np.abs(lats[:]) > 40))
-    elif region == "Arctic":
-        inds = np.where((lats[:] > 70))
-    field_dom = MV.take(field, inds[0], axis=-2)
-    return field_dom
-
-
-###########################################################################
-def tile_uneven(data, data_to_match):
-    """extend data to match size of data_to_match even if not a multiple of 12"""
-
-    A12 = len(data_to_match) // 12
-    ind = np.arange(
-        12,
-    )
-    rep_ind = np.tile(ind, (A12 + 1))[
-        : int(len(data_to_match))
-    ]  # int() added for python3
-
-    rep_data = MV.array(np.array(data)[rep_ind])
-
-    return rep_data
-
-
-###########################################################################
-def YEAR(data):
-    """
-    Compute annual means without forcing it to be Jan through Dec
-    """
-
-    A = data.shape[0]
-    anndata0 = nanarray(data.shape)
-    cnt = -1
-    for i in np.arange(0, A, 12):
-        if len(data[i : i + 12]) == 12:  # only take full 12-month periods
-            cnt += 1
-            anndata0[cnt] = MV.average(data[i : i + 12], 0)
-    B = cnt + 1
-    anndata = anndata0[:B]
-    if type(anndata) != float:
-        anndata.setAxisList(data[: B * 12 : 12].getAxisList())
-
-    return anndata
-
-
-###############################################################################################
-def CloudRadKernel(filenames):
-
-    print("Load in data")
-    (
-        ctl_clisccp,
-        fut_clisccp,
-        LWK,
-        SWK,
-        dTs,
-        ctl_clisccp_wap,
-        fut_clisccp_wap,
-        LWK_wap,
-        SWK_wap,
-        ctl_N,
-        fut_N,
-    ) = get_CRK_data(filenames)
-
-    # Create a dummy variable so we don't have keep calling the land mask function:
-    dummy = ctl_clisccp[:12, 0, 0, :]  # 12,90,144
-    OCN, LND = apply_land_mask_v2(dummy)
-    area_wts = get_area_wts(
-        ctl_clisccp[:12, 0, 0, :]
-    )  # summing this over lat and lon = 1
-
-    print("Compute Klein et al error metrics")
-    ###########################################################################
-    # Compute Klein et al cloud error metrics and their breakdown into components
-    ###########################################################################
-    KEM_dict = do_klein_calcs(
-        ctl_clisccp,
-        LWK,
-        SWK,
-        obs_clisccp_AC,
-        ctl_clisccp_wap,
-        LWK_wap,
-        SWK_wap,
-        obs_clisccp_AC_wap,
-        obs_N_AC_wap,
-    )
-    # [sec][flavor][region][all / ocn / lnd / ocn_asc / ocn_dsc]
-
-    print("Compute feedbacks")
-    ###########################################################################
-    # Compute cloud feedbacks and their breakdown into components
-    ###########################################################################
-    clisccp_fbk, clisccp_base = compute_fbk(ctl_clisccp, fut_clisccp, dTs)
-    dummy, LWK_base = compute_fbk(LWK, LWK, dTs)
-    dummy, SWK_base = compute_fbk(SWK, SWK, dTs)
-
-    AX = ctl_clisccp[:12, 0, 0, :].getAxisList()
-
-    # The following performs the amount/altitude/optical depth decomposition of
-    # Zelinka et al., J Climate (2012b), as modified in Zelinka et al., J. Climate (2013)
-    output = {}
-    output_wap = {}
-    for sec in sections:
-        print("    for section " + sec)
-        # [sec][flavor][region][all / ocn / lnd / ocn_asc / ocn_dsc]
-
-        PP = sec_dic[sec]
-
-        C1 = clisccp_base[:, :, PP, :]
-        C2 = C1 + clisccp_fbk[:, :, PP, :]
-        Klw = LWK_base[:, :, PP, :]
-        Ksw = SWK_base[:, :, PP, :]
-
-        output[sec] = ZA.KT_decomposition_general(C1, C2, Klw, Ksw)
-
-        dummy, LWK_wap_base = compute_fbk(LWK_wap, LWK_wap, dTs)
-        dummy, SWK_wap_base = compute_fbk(SWK_wap, SWK_wap, dTs)
-
-        Klw = LWK_wap_base[:, :, PP, :-1]  # ignore the land bin
-        Ksw = SWK_wap_base[:, :, PP, :-1]
-        C1 = ctl_clisccp_wap[:, :, PP, :-1]
-        C2 = fut_clisccp_wap[:, :, PP, :-1]
-        N1 = ctl_N[:, :-1]
-        N2 = fut_N[:, :-1]
-
-        # no breakdown (this is identical to within + between + covariance)
-        C1b = np.moveaxis(np.moveaxis(C1, 1, 0), 2, 1)  # [TAU,CTP,month,regime]
-        C1N1 = np.moveaxis(C1b * N1, 2, 0)  # [month,TAU,CTP,regime]
-        C2b = np.moveaxis(np.moveaxis(C2, 1, 0), 2, 1)  # [TAU,CTP,month,regime]
-        C2N2 = np.moveaxis(C2b * N2, 2, 0)  # [month,TAU,CTP,regime]
-        pert, C_base = compute_fbk(C1N1, C2N2, dTs)
-        output_wap[sec] = ZA.KT_decomposition_general(C_base, C_base + pert, Klw, Ksw)
-
-    # end for sec in sections
-
-    ###########################################################################
-    # Compute obscuration feedback components
-    ###########################################################################
-    sec = "LO680"  # this should already be true, but just in case...
-    PP = sec_dic[sec]
-    PP = sec_dic[sec]
-    print("Get Obscuration Terms")
-    CTL, FUT = ctl_clisccp, fut_clisccp
-    LWK = LWK_base[:, :, PP, :]
-    SWK = SWK_base[:, :, PP, :]
-    obsc_output = {}
-    obsc_output[sec] = do_obscuration_calcs(CTL, FUT, LWK, SWK, dTs)
-
-    # Do this for the omega-regimes too:
-    C1 = np.moveaxis(ctl_clisccp_wap, 0, -1)
-    N1 = np.moveaxis(ctl_N, 0, -1)
-    CTL = np.moveaxis(C1 * N1, -1, 0)[..., :-1]  # ignore the land bin
-    C2 = np.moveaxis(fut_clisccp_wap, 0, -1)
-    N2 = np.moveaxis(fut_N, 0, -1)
-    FUT = np.moveaxis(C2 * N2, -1, 0)[..., :-1]  # ignore the land bin
-    LWK = LWK_wap_base[:, :, PP, :-1]  # ignore the land bin
-    SWK = SWK_wap_base[:, :, PP, :-1]  # ignore the land bin
-    obsc_output_wap = {}
-    obsc_output_wap[sec] = do_obscuration_calcs(CTL, FUT, LWK, SWK, dTs)
-
-    ###########################################################################
-    # Compute regional averages (weighted by fraction of globe); place in dictionary
-    ###########################################################################
-    print("Compute regional averages")
-    # [region][sfc][sec][name]
-    obsc_fbk_dict = regional_breakdown(obsc_output, OCN, LND, area_wts, AX)
-    fbk_dict = regional_breakdown(output, OCN, LND, area_wts, AX)
-
-    # Put all the ascending and descending region quantities in a dictionary
-    names = list(output_wap["ALL"].keys())
-    for sec in sections:
-        for name in names:
-            fbk_dict["eq30"]["ocn_asc"][sec][name] = np.ma.average(
-                np.sum((output_wap[sec][name])[:, :cutoff], 1), 0
-            )
-            fbk_dict["eq30"]["ocn_dsc"][sec][name] = np.ma.average(
-                np.sum((output_wap[sec][name])[:, cutoff:], 1), 0
-            )
-
-    sec = "LO680"
-    names = list(obsc_output_wap[sec].keys())
-    for name in names:
-        obsc_fbk_dict["eq30"]["ocn_asc"][sec][name] = np.ma.average(
-            np.sum((obsc_output_wap[sec][name])[:, :cutoff], 1), 0
-        )
-        obsc_fbk_dict["eq30"]["ocn_dsc"][sec][name] = np.ma.average(
-            np.sum((obsc_output_wap[sec][name])[:, cutoff:], 1), 0
-        )
-
-    meta = {
-        "date_modified": str(date.today()),
-        "author": "Mark D. Zelinka <zelinka1@llnl.gov>",
-    }
-    fbk_dict["metadata"] = meta
-    obsc_fbk_dict["metadata"] = meta
-
-    # [sec][flavor][region][all / ocn / lnd / ocn_asc / ocn_dsc]
-    return (fbk_dict, obsc_fbk_dict, KEM_dict)
diff --git a/pcmdi_metrics/cloud_feedback/lib/compute_ECS.py b/pcmdi_metrics/cloud_feedback/lib/compute_ECS.py
deleted file mode 100755
index 64d093683..000000000
--- a/pcmdi_metrics/cloud_feedback/lib/compute_ECS.py
+++ /dev/null
@@ -1,184 +0,0 @@
-#!/usr/bin/env cdat
-"""
-Use Gregory approach to estimate ECS
-"""
-
-import cdms2
-import cdtime
-import cdutil
-import MV2
-import numpy as np
-from scipy import stats
-
-
-###########################################################################
-def get_anom_abrupt(ab_path, pi_path, var):
-    """
-    this function returns the array of var values with
-    PI-control 21 yr running mean-smoothed values from
-    coincident times removed (to deal with drift not removed
-    from the ctl simulation which is ostensibly also happening
-    in the forced runs.
-    """
-
-    f = cdms2.open(ab_path)
-
-    # Get info about branch times, etc.
-    source = ab_path.split("/")[-1].split(".")[1]
-    if source == "GFDL-CM4":
-        branch_time_in_parent = 91250  # https://errata.es-doc.org/static/view.html?uid=2f6b5963-f87e-b2df-a5b0-2f12b6b68d32
-    else:
-        branch_time_in_parent = np.float(f.branch_time_in_parent)
-    # branch_time_in_child = f.branch_time_in_child
-    parent_time_units = f.parent_time_units
-    child_time_units = f[var].getTime().units
-    ab_st = f.branch_time_in_child
-
-    # FOR NOW WE ONLY WANT UP TO 150 YEARS OF ABRUPT SIMULATION:
-    r = cdtime.reltime(ab_st, child_time_units)
-    FN = r.add(
-        12 * 150, cdtime.Months
-    )  # add 121 rather than 120 because it uses day 1 rather than day 15
-    ab_fn = FN.value
-
-    # Get abrupt data
-    data = f(var, time=(ab_st, ab_fn))
-    f.close()
-
-    # Determine the appropriate overlapping piControl period
-    true_st = branch_time_in_parent
-    true_fn = true_st + data.getTime()[-1]
-    r = cdtime.reltime(true_fn, parent_time_units)
-    true_fn = r.add(
-        15, cdtime.Days
-    ).value  # add 15 days on to take it to the end of the month
-
-    # we want to grab data starting 10 yrs prior to true_st
-    r = cdtime.reltime(true_st, parent_time_units)
-    extend_st = r.sub(120, cdtime.Months).value
-    # we want to grab data ending 10 yrs beyond to true_fn
-    r = cdtime.reltime(true_fn, parent_time_units)
-    extend_fn = r.add(
-        121, cdtime.Months
-    ).value  # add 121 rather than 120 because it uses day 1 rather than day 15
-
-    # Get the smoothed piControl data
-    pi_data_smooth = get_smooth(var, pi_path, extend_st, extend_fn, true_st, true_fn)
-
-    # Compute the anomaly
-    LA = len(data)
-    LP = len(pi_data_smooth)
-    endpt = np.min((LA, LP, 150 * 12))  # take no data beyond year 150
-    anom = data[:endpt, :] - pi_data_smooth[:endpt, :]
-    anom.setAxisList(data[:endpt, :].getAxisList())
-    cdutil.setTimeBoundsMonthly(anom)
-
-    return anom
-
-
-###########################################################################
-
-
-###########################################################################
-def get_smooth(var, pi_xml, extend_st, extend_fn, true_st, true_fn):
-
-    f = cdms2.open(pi_xml)
-    pi_data = f(var, time=(extend_st, extend_fn))
-    f.close()
-
-    # Compute 21-yr running mean
-    avgpi_data = running_mean(pi_data, 21)
-
-    # Now just hang onto portion of piControl that overlaps with abrupt
-    pi_data_smooth = avgpi_data.subRegion(time=(true_st, true_fn))
-
-    return pi_data_smooth
-
-
-def global_avg(DATA):
-    GLavg_DATA = cdutil.averager(DATA, axis="xy", weights="weighted")
-    return GLavg_DATA
-
-
-###########################################################################
-
-
-###########################################################################
-def annual_avg(data):
-    """
-    Compute annual means without forcing it to be Jan through Dec
-    """
-
-    A = data.shape[0]
-    anndata0 = np.empty(data.shape)
-    anndata0[:] = np.nan
-    cnt = -1
-    for i in np.arange(0, A, 12):
-        try:
-            LD = len(data[i : i + 12])
-        except Exception:
-            continue
-        if LD == 12:  # only take full 12-month periods
-            cnt += 1
-            anndata0[cnt] = np.ma.average(data[i : i + 12], 0)
-
-    B = cnt + 1
-    anndata = MV2.array(anndata0[:B])  # convert to a cdms2 transient variable
-    if type(anndata) != float:
-        anndata.setAxisList(data[: B * 12 : 12].getAxisList())
-
-    return anndata
-
-
-###########################################################################
-
-###########################################################################
-def running_mean(data, N):
-
-    # Compute N-yr running mean
-    back = N / 2
-    forward = N / 2 + 1
-    avgdata = np.empty(data.shape)
-    avgdata[:] = np.nan
-    for month in range(12):
-        subset = data[month::12, :]
-        avgsubset = np.empty(subset.shape)
-        avgsubset[:] = np.nan
-        LS = len(subset)
-        for ii in range(LS):
-            start = np.max((ii - back, 0))
-            finish = np.min((ii + forward, LS))
-            avgsubset[ii, :] = np.ma.average(subset[int(start) : int(finish), :], 0)
-        avgdata[month::12, :] = avgsubset
-
-    avgdata = MV2.array(avgdata)  # convert to a cdms2 transient variable
-    avgdata.setAxisList(data.getAxisList())
-
-    return avgdata
-
-
-###########################################################################
-
-
-def compute_ECS(filenames):
-    data = {}
-    fields = ["tas", "rlut", "rsut", "rsdt"]  # needed for Gregory plots
-    for var in fields:
-        ab_path = filenames["abrupt-4xCO2"][var]
-        pi_path = filenames["piControl"][var]
-        ab_anom = get_anom_abrupt(ab_path, pi_path, var)
-        # globally and annually average:
-        data[var] = annual_avg(global_avg(ab_anom))
-
-    rndt = data["rsdt"] - data["rsut"] - data["rlut"]
-    tas = data["tas"]
-
-    scale = np.log2(4.0)
-    x = tas[:150]
-    y = rndt[:150]
-    m, b, r, p, std_err = stats.linregress(x, y)
-    ERF150 = b / scale
-    LAM150 = m
-    ecs = -ERF150 / LAM150
-
-    return ecs

From 3c17e89d9702d88a5877058a936acce624a849eb Mon Sep 17 00:00:00 2001
From: mzelinka <zelinka1@llnl.gov>
Date: Thu, 28 Sep 2023 15:22:25 -0700
Subject: [PATCH 072/133] a few additional changes

---
 .../data/cmip56_forcing_feedback_ecs.json     | 1911 -----------------
 .../lib/cal_CloudRadKernel_xr.py              | 1238 +++++++++++
 .../cloud_feedback/lib/compute_ECS_xr.py      |  143 ++
 .../cloud_feedback/lib/get_assessment.py      |  217 ++
 .../cloud_feedback/lib/organize_jsons.py      |  113 +-
 5 files changed, 1646 insertions(+), 1976 deletions(-)
 delete mode 100644 pcmdi_metrics/cloud_feedback/data/cmip56_forcing_feedback_ecs.json
 create mode 100644 pcmdi_metrics/cloud_feedback/lib/cal_CloudRadKernel_xr.py
 create mode 100644 pcmdi_metrics/cloud_feedback/lib/compute_ECS_xr.py
 create mode 100644 pcmdi_metrics/cloud_feedback/lib/get_assessment.py

diff --git a/pcmdi_metrics/cloud_feedback/data/cmip56_forcing_feedback_ecs.json b/pcmdi_metrics/cloud_feedback/data/cmip56_forcing_feedback_ecs.json
deleted file mode 100644
index 397f8c722..000000000
--- a/pcmdi_metrics/cloud_feedback/data/cmip56_forcing_feedback_ecs.json
+++ /dev/null
@@ -1,1911 +0,0 @@
-{
-    "CMIP5":{
-        "ACCESS1-0":{
-            "r1i1p1":{
-                "ALB":0.47332693471892917,
-                "CLD":0.40194106706454624,
-                "ECS":3.8521236403427412,
-                "ERF2x":2.944747049241059,
-                "ERR":0.24803573876881158,
-                "LR":-0.4214726297658267,
-                "LR*":-0.013736797144021434,
-                "LWCLD":0.32798272293513436,
-                "NET":-0.7644476979921265,
-                "PL":-3.23246651792142,
-                "PL*":-1.8969973255310337,
-                "RH":0.024557892049811014,
-                "SWCLD":0.07395834412941184,
-                "WV":1.7661877091428333
-            }
-        },
-        "ACCESS1-3":{
-            "r1i1p1":{
-                "ALB":0.45691517520531866,
-                "CLD":0.5878139719471649,
-                "ECS":3.547449104125713,
-                "ERF2x":2.8770583390064104,
-                "ERR":0.08603537171168041,
-                "LR":-0.4107991154357168,
-                "LR*":-0.014420749416059876,
-                "LWCLD":0.10873970256426756,
-                "NET":-0.8110217383134173,
-                "PL":-3.269380814344435,
-                "PL*":-1.9039103934630022,
-                "RH":-0.02179461432556406,
-                "SWCLD":0.479074269382897,
-                "WV":1.7383936726025706
-            }
-        },
-        "BNU-ESM":{
-            "r1i1p1":{
-                "ALB":0.7146772383536569,
-                "CLD":0.10203203046317082,
-                "ECS":4.039457626299429,
-                "ERF2x":3.7209706155366318,
-                "ERR":0.04075811217635983,
-                "LR":-0.11933247628476837,
-                "LR*":0.12084901933868648,
-                "LWCLD":0.331446719991803,
-                "NET":-0.9211559966146828,
-                "PL":-3.1994797857708246,
-                "PL*":-1.9268318472181258,
-                "RH":0.029560793246773864,
-                "SWCLD":-0.22941468952863192,
-                "WV":1.5401888844477227
-            }
-        },
-        "CCSM4":{
-            "r1i1p1":{
-                "ALB":0.5529314159811028,
-                "CLD":0.17786283590263388,
-                "ECS":2.940467480572202,
-                "ERF2x":3.475876391595876,
-                "ERR":-0.016782848199297096,
-                "LR":-0.2186415452190655,
-                "LR*":0.08227678495036718,
-                "LWCLD":0.2648418607553671,
-                "NET":-1.1820829220391467,
-                "PL":-3.2578691521501506,
-                "PL*":-1.9411821660341009,
-                "RH":-0.03563879897925686,
-                "SWCLD":-0.08697902485273334,
-                "WV":1.5804163716456294
-            }
-        },
-        "CNRM-CM5":{
-            "r1i1p1":{
-                "ALB":0.5032910997056891,
-                "CLD":0.09974008227894655,
-                "ECS":3.2518128798411747,
-                "ERF2x":3.6906899714629793,
-                "ERR":0.22345748518248154,
-                "LR":-0.3176316324830886,
-                "LR*":0.0029241977929269697,
-                "LWCLD":0.3124174874930869,
-                "NET":-1.1349638210557922,
-                "PL":-3.258867717404402,
-                "PL*":-1.9188395537828176,
-                "RH":-0.0436705338328495,
-                "SWCLD":-0.21267740521414022,
-                "WV":1.6150468616645812
-            }
-        },
-        "CSIRO-Mk3-6-0":{
-            "r1i1p1":{
-                "ALB":0.43515687283153276,
-                "CLD":0.5935751474004058,
-                "ECS":4.085779330952333,
-                "ERF2x":2.596086293985563,
-                "ERR":0.32198838776191996,
-                "LR":-0.49031521603109424,
-                "LR*":-0.041109888495848165,
-                "LWCLD":0.042780779753097936,
-                "NET":-0.6353956206882213,
-                "PL":-3.3236138813147185,
-                "PL*":-1.9138471793735672,
-                "RH":-0.029541854849050614,
-                "SWCLD":0.5507943676473083,
-                "WV":1.8278130686637328
-            }
-        },
-        "CanESM2":{
-            "r1i1p1":{
-                "ALB":0.4474451835965679,
-                "CLD":0.45177652507170374,
-                "ECS":3.7018135696766965,
-                "ERF2x":3.812724070043131,
-                "ERR":-0.016973343164847554,
-                "LR":-0.4360652730761489,
-                "LR*":-0.028629191709793778,
-                "LWCLD":0.7396595779439362,
-                "NET":-1.029961125345413,
-                "PL":-3.34121557656095,
-                "PL*":-1.9486227368438034,
-                "RH":0.06704386157127774,
-                "SWCLD":-0.28788305287223215,
-                "WV":1.8650713587882621
-            }
-        },
-        "FGOALS-g2":{
-            "r1i1p1":{
-                "ALB":0.657623186151965,
-                "CLD":0.3046287054866826,
-                "ECS":3.3687490377140836,
-                "ERF2x":2.838264318103892,
-                "ERR":-0.03953335152657711,
-                "LR":-0.16452507396447594,
-                "LR*":0.10084021204680037,
-                "LWCLD":0.3686933391149449,
-                "NET":-0.8425276820352993,
-                "PL":-3.2466544557401775,
-                "PL*":-1.9429346880281895,
-                "RH":0.07879647354406255,
-                "SWCLD":-0.06406463362826244,
-                "WV":1.645933307557284
-            }
-        },
-        "FGOALS-s2":{
-            "r1i1p1":{
-                "ALB":0.5837993663974269,
-                "CLD":-0.05888018475553496,
-                "ECS":4.183485463739026,
-                "ERF2x":3.818635892879149,
-                "ERR":0.4373930926241443,
-                "LR":-0.39316763101879837,
-                "LR*":-0.007985257331473641,
-                "LWCLD":0.35547803993812305,
-                "NET":-0.9127881346732851,
-                "PL":-3.254990319230712,
-                "PL*":-1.9211733176010157,
-                "RH":0.05664055387663938,
-                "SWCLD":-0.414358224693658,
-                "WV":1.773057541310189
-            }
-        },
-        "GFDL-CM3":{
-            "r1i1p1":{
-                "ALB":0.46940601962253486,
-                "CLD":0.8339421743296541,
-                "ECS":3.9517628150395643,
-                "ERF2x":3.0101970952835613,
-                "ERR":0.020554228737618807,
-                "LR":-0.5959147328381047,
-                "LR*":-0.09826775485159973,
-                "LWCLD":0.2338333534183598,
-                "NET":-0.7617352650385278,
-                "PL":-3.2385247995366107,
-                "PL*":-1.8720145931777925,
-                "RH":-0.11344102120443181,
-                "SWCLD":0.6001088209112944,
-                "WV":1.7488018446463796
-            }
-        },
-        "GFDL-ESM2G":{
-            "r1i1p1":{
-                "ALB":0.3418470628486172,
-                "CLD":0.17597021057290965,
-                "ECS":2.426655608221552,
-                "ERF2x":2.9948607375853786,
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-                "LR":-0.766262693725254,
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-                "LWCLD":0.5795621261912118,
-                "NET":-1.234151532437787,
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-                "RH":0.055044324118207914,
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-                "WV":2.0186955348290323
-            }
-        },
-        "GFDL-ESM2M":{
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-                "ERF2x":3.3537662632188487,
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-                "LR":-0.8359546282256621,
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-                "LWCLD":0.5616430031728484,
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-                "SWCLD":-0.4903555617349872,
-                "WV":2.0483744740590155
-            }
-        },
-        "GISS-E2-H":{
-            "r1i1p1":{
-                "ALB":0.3626843786255222,
-                "CLD":-0.1856181427328397,
-                "ECS":2.3055077136773545,
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-                "LR":-0.45613535507197644,
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-                "LWCLD":0.5375773986340467,
-                "NET":-1.6571675198826161,
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-                "PL*":-1.9011064456216544,
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-                "WV":1.8052016527271009
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-        "GISS-E2-R":{
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-        "HadGEM2-ES":{
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-            }
-        },
-        "bcc-csm1-1":{
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-            "r1i1p1":"/p/user_pub/work/zelinka1/cmip5/amip/metadata_MIROC-ESM.r1i1p1.2021-02-25.json"
-        },
-        "MIROC5":{
-            "r1i1p1":"/p/user_pub/work/zelinka1/cmip5/amip/metadata_MIROC5.r1i1p1.2021-02-26.json"
-        },
-        "MIROC6":{
-            "r1i1p1f1":"/p/user_pub/work/zelinka1/cmip6/amip/metadata_MIROC6.r1i1p1f1.2021-03-04.json"
-        },
-        "MPI-ESM-1-2-HAM":{
-            "r1i1p1f1":"/p/user_pub/work/zelinka1/cmip6/abrupt-4xCO2/metadata_MPI-ESM-1-2-HAM.r1i1p1f1.2021-02-24.json"
-        },
-        "MPI-ESM-LR":{
-            "r1i1p1":"/p/user_pub/work/zelinka1/cmip5/amip/metadata_MPI-ESM-LR.r1i1p1.2021-02-26.json"
-        },
-        "MPI-ESM-MR":{
-            "r1i1p1":"/p/user_pub/work/zelinka1/cmip5/amip/metadata_MPI-ESM-MR.r1i1p1.2021-02-26.json"
-        },
-        "MPI-ESM-P":{
-            "r1i1p1":"/p/user_pub/work/zelinka1/cmip5/abrupt4xCO2/metadata_MPI-ESM-P.r1i1p1.2021-02-22.json"
-        },
-        "MPI-ESM1-2-HR":{
-            "r1i1p1f1":"/p/user_pub/work/zelinka1/cmip6/abrupt-4xCO2/metadata_MPI-ESM1-2-HR.r1i1p1f1.2021-02-23.json"
-        },
-        "MPI-ESM1-2-LR":{
-            "r1i1p1f1":"/p/user_pub/work/zelinka1/cmip6/abrupt-4xCO2/metadata_MPI-ESM1-2-LR.r1i1p1f1.2021-02-23.json"
-        },
-        "MRI-CGCM3":{
-            "r1i1p1":"/p/user_pub/work/zelinka1/cmip5/abrupt4xCO2/metadata_MRI-CGCM3.r1i1p1.2021-02-22.json"
-        },
-        "MRI-ESM2-0":{
-            "r10i1p1f1":"/p/user_pub/work/zelinka1/cmip6/abrupt-4xCO2/metadata_MRI-ESM2-0.r10i1p1f1.2020-07-03.json",
-            "r13i1p1f1":"/p/user_pub/work/zelinka1/cmip6/abrupt-4xCO2/metadata_MRI-ESM2-0.r13i1p1f1.2020-07-03.json",
-            "r1i1p1f1":"/p/user_pub/work/zelinka1/cmip6/amip-piForcing/metadata_MRI-ESM2-0.r1i1p1f1.2021-03-03.json",
-            "r1i2p1f1":"/p/user_pub/work/zelinka1/cmip6/abrupt-4xCO2/metadata_MRI-ESM2-0.r1i2p1f1.2020-07-03.json",
-            "r4i1p1f1":"/p/user_pub/work/zelinka1/cmip6/abrupt-4xCO2/metadata_MRI-ESM2-0.r4i1p1f1.2020-07-03.json",
-            "r7i1p1f1":"/p/user_pub/work/zelinka1/cmip6/abrupt-4xCO2/metadata_MRI-ESM2-0.r7i1p1f1.2020-07-03.json"
-        },
-        "NESM3":{
-            "r1i1p1f1":"/p/user_pub/work/zelinka1/cmip6/abrupt-4xCO2/metadata_NESM3.r1i1p1f1.2021-02-22.json"
-        },
-        "NorCPM1":{
-            "r1i1p1f1":"/p/user_pub/work/zelinka1/cmip6/abrupt-4xCO2/metadata_NorCPM1.r1i1p1f1.2021-02-22.json"
-        },
-        "NorESM1-M":{
-            "r1i1p1":"/p/user_pub/work/zelinka1/cmip5/abrupt4xCO2/metadata_NorESM1-M.r1i1p1.2021-02-22.json"
-        },
-        "NorESM1-ME":{
-            "r1i1p1":"/p/user_pub/work/zelinka1/cmip5/abrupt4xCO2/metadata_NorESM1-ME.r1i1p1.2021-02-22.json"
-        },
-        "NorESM2-LM":{
-            "r1i1p1f1":"/p/user_pub/work/zelinka1/cmip6/amip/metadata_NorESM2-LM.r1i1p1f1.2021-03-04.json"
-        },
-        "NorESM2-MM":{
-            "r1i1p1f1":"/p/user_pub/work/zelinka1/cmip6/abrupt-4xCO2/metadata_NorESM2-MM.r1i1p1f1.2021-02-22.json"
-        },
-        "SAM0-UNICON":{
-            "r1i1p1f1":"/p/user_pub/work/zelinka1/cmip6/abrupt-4xCO2/metadata_SAM0-UNICON.r1i1p1f1.2021-02-22.json"
-        },
-        "TaiESM1":{
-            "r1i1p1f1":"/p/user_pub/work/zelinka1/cmip6/abrupt-4xCO2/metadata_TaiESM1.r1i1p1f1.2021-02-22.json"
-        },
-        "UKESM1-0-LL":{
-            "r1i1p1f2":"/p/user_pub/work/zelinka1/cmip6/abrupt-4xCO2/metadata_UKESM1-0-LL.r1i1p1f2.2021-02-22.json"
-        },
-        "bcc-csm1-1":{
-            "r1i1p1":"/p/user_pub/work/zelinka1/cmip5/abrupt4xCO2/metadata_bcc-csm1-1.r1i1p1.2021-02-23.json"
-        },
-        "bcc-csm1-1-m":{
-            "r1i1p1":"/p/user_pub/work/zelinka1/cmip5/abrupt4xCO2/metadata_bcc-csm1-1-m.r1i1p1.2021-02-22.json"
-        },
-        "inmcm4":{
-            "r1i1p1":"/p/user_pub/work/zelinka1/cmip5/abrupt4xCO2/metadata_inmcm4.r1i1p1.2021-02-23.json"
-        }
-    }
-}
diff --git a/pcmdi_metrics/cloud_feedback/lib/cal_CloudRadKernel_xr.py b/pcmdi_metrics/cloud_feedback/lib/cal_CloudRadKernel_xr.py
new file mode 100644
index 000000000..60af64dd1
--- /dev/null
+++ b/pcmdi_metrics/cloud_feedback/lib/cal_CloudRadKernel_xr.py
@@ -0,0 +1,1238 @@
+#!/usr/bin/env python
+# coding: utf-8
+
+# =============================================
+# Performs the cloud feedback and cloud error
+# metric calculations in preparation for comparing
+# to expert-assessed values from Sherwood et al (2020)
+# =============================================
+
+# IMPORT STUFF:
+import cftime
+import xarray as xr
+import xcdat as xc
+import numpy as np
+from global_land_mask import globe
+from datetime import date
+
+# =============================================
+# define necessary information
+# =============================================
+
+datadir = "../data/"
+
+# Define a python dictionary containing the sections of the histogram to consider
+# These are the same as in Zelinka et al, GRL, 2016
+sections = ["ALL", "HI680", "LO680"]
+Psections = [slice(0, 7), slice(2, 7), slice(0, 2)]
+sec_dic = dict(zip(sections, Psections))
+
+# 10 hPa/dy wide bins:
+width = 10
+binedges = np.arange(-100, 100, width)
+x1 = np.arange(binedges[0] - width, binedges[-1] + 2 * width, width)
+binmids = x1 + width / 2.0
+cutoff = int(len(binmids) / 2)
+# [:cutoff] = ascent; [cutoff:-1] = descent; [-1] = land
+
+# load land sea mask (90x144):
+lat = np.arange(-89, 90, 2.0)
+lon = np.arange(1.25, 360, 2.5) - 180
+lon_grid, lat_grid = np.meshgrid(lon, lat)
+land_mask = globe.is_land(lat_grid, lon_grid)
+land_mask = xr.DataArray(
+    data=land_mask,
+    dims=["lat", "lon"],
+    coords=dict(lon=lon, lat=lat),
+)
+E = land_mask.sel(lon=slice(0, 180)).copy()
+W = land_mask.sel(lon=slice(-180, 0)).copy()
+W["lon"] = W["lon"] + 360
+land_mask = xr.concat((E, W), dim="lon")
+land_mask = xr.where(land_mask, 1.0, 0.0)
+ocean_mask = xr.where(land_mask == 1.0, 0.0, 1.0)
+del E, W
+
+
+###########################################################################
+def get_amip_data(filename, var, lev=None):
+    # load in cmip data using the appropriate function for the experiment/mip
+    print('  '+var)#, end=",")
+
+    tslice = slice(
+        "1983-01-01", "2008-12-31"
+    )  # we only want this portion of the amip run (overlap with all AMIPs and ISCCP)
+
+    f = xc.open_mfdataset(
+        filename[var], chunks={"lat": -1, "lon": -1, "time": -1}
+    ).load()
+    if lev:
+        f = f.sel(time=tslice, plev=lev)
+        f = f.drop_vars(["plev", "plev_bnds"])
+    else:
+        f = f.sel(time=tslice)
+
+    # Compute climatological monthly means
+    avg = f.temporal.climatology(var, freq="month", weighted=True)
+    # Regrid to cloud kernel grid
+    output_grid = xc.create_grid(land_mask.lat.values, land_mask.lon.values)
+    output = avg.regridder.horizontal(
+        var, output_grid, tool="xesmf", method="bilinear", extrap_method="inverse_dist"
+    )
+
+    return output
+
+
+###########################################################################
+def get_model_data(filepath):
+    # Read in data, regrid
+
+    # Load in regridded monthly mean climatologies from control and perturbed simulation
+    variables = ["tas", "rsdscs", "rsuscs", "wap", "clisccp"]
+    print("amip")
+    exp = "amip"
+    ctl = []
+    for var in variables:
+        if "wap" in var:
+            ctl.append(get_amip_data(filepath[exp], var, 50000))
+        else:
+            ctl.append(get_amip_data(filepath[exp], var))
+    ctl = xr.merge(ctl)
+
+    print("amip-p4K")
+    exp = "amip-p4K"
+    fut = []
+    for var in variables:
+        if "wap" in var:
+            fut.append(get_amip_data(filepath[exp], var, 50000))
+        else:
+            fut.append(get_amip_data(filepath[exp], var))
+    fut = xr.merge(fut)
+
+    # set tau,plev to consistent field
+    ctl["clisccp"] = ctl["clisccp"].transpose("time", "tau", "plev", "lat", "lon")
+    fut["clisccp"] = fut["clisccp"].transpose("time", "tau", "plev", "lat", "lon")
+    ctl["tau"] = np.arange(7)
+    ctl["plev"] = np.arange(7)
+    fut["tau"] = np.arange(7)
+    fut["plev"] = np.arange(7)
+
+    # Make sure wap is in hPa/day
+    ctl["wap"] = 36 * 24 * ctl["wap"]  # Pa s-1 --> hPa/day
+    fut["wap"] = 36 * 24 * fut["wap"]  # Pa s-1 --> hPa/day
+
+    # Make sure clisccp is in percent
+    sumclisccp1 = ctl["clisccp"].sum(dim=["tau", "plev"])
+    if np.max(sumclisccp1) <= 1.0:
+        ctl["clisccp"] = ctl["clisccp"] * 100.0
+        fut["clisccp"] = fut["clisccp"] * 100.0
+
+    return (ctl, fut)
+
+
+###########################################################################
+def get_CRK_data(filepath):
+    # Read in data, regrid
+
+    # Load in regridded monthly mean climatologies from control and perturbed simulation
+    print("get data")
+    ctl, fut = get_model_data(filepath)
+    print("get LW and SW kernel")
+    LWK, SWK = get_kernel_regrid(ctl)
+
+    # global mean and annual average delta tas
+    avgdtas0 = fut["tas"] - ctl["tas"]
+    avgdtas0 = xc_to_dataset(avgdtas0)
+    avgdtas0 = avgdtas0.spatial.average("data", axis=["X", "Y"])["data"]
+    dTs = avgdtas0.mean()
+
+    print("Sort into omega500 bins")
+    ctl_wap_ocean = (ctl["wap"] * ocean_mask).sel(lat=slice(-30, 30))
+    ctl_wap_land = (ctl["wap"] * land_mask).sel(lat=slice(-30, 30))
+    fut_wap_ocean = (fut["wap"] * ocean_mask).sel(lat=slice(-30, 30))
+    fut_wap_land = (fut["wap"] * land_mask).sel(lat=slice(-30, 30))
+
+    ctl_OKwaps = bony_sorting_part1(ctl_wap_ocean, binedges)
+    fut_OKwaps = bony_sorting_part1(fut_wap_ocean, binedges)
+
+    area_wts = get_area_wts(ctl["tas"])  # summing this over lat and lon = 1
+    WTS = area_wts.sel(lat=slice(-30, 30))
+    TCC = ctl.clisccp.sel(lat=slice(-30, 30))
+    TFC = fut.clisccp.sel(lat=slice(-30, 30))
+    TLK = LWK.sel(lat=slice(-30, 30))
+    TSK = SWK.sel(lat=slice(-30, 30))
+
+    ctl_clisccp_wap, ctl_N = bony_sorting_part2(
+        ctl_OKwaps, TCC, ctl_wap_land, WTS, binedges
+    )
+    fut_clisccp_wap, fut_N = bony_sorting_part2(
+        fut_OKwaps, TFC, fut_wap_land, WTS, binedges
+    )
+    LWK_wap, P_wapbin = bony_sorting_part2(ctl_OKwaps, TLK, ctl_wap_land, WTS, binedges)
+    SWK_wap, P_wapbin = bony_sorting_part2(ctl_OKwaps, TSK, ctl_wap_land, WTS, binedges)
+
+    return (
+        ctl.clisccp,
+        fut.clisccp,
+        LWK,
+        SWK,
+        dTs,
+        area_wts,
+        ctl_clisccp_wap,
+        fut_clisccp_wap,
+        LWK_wap,
+        SWK_wap,
+        ctl_N,
+        fut_N,
+    )
+
+
+###########################################################################
+def get_kernel_regrid(ctl):
+    # Read in data and map kernels to lat/lon
+
+    
+    f = xc.open_mfdataset(datadir + "cloud_kernels2.nc", decode_times=False)
+    f = f.rename({"mo": "time", "tau_midpt": "tau", "p_midpt": "plev"})
+    f["time"] = ctl["time"].copy()
+    f["tau"] = np.arange(7)
+    f["plev"] = np.arange(7)  # set tau,plev to consistent field
+    LWkernel = f["LWkernel"].isel(albcs=0).squeeze()  # two kernels file are different
+    SWkernel = f["SWkernel"]
+    del f
+
+    # Compute clear-sky surface albedo
+    ctl_albcs = ctl.rsuscs / ctl.rsdscs  # (12, 90, 144)
+    ctl_albcs = ctl_albcs.fillna(0.0)
+    ctl_albcs = ctl_albcs.where(~np.isinf(ctl_albcs), 0.0)
+    ctl_albcs = xr.where(
+        ctl_albcs > 1.0, 1, ctl_albcs
+    )  # where(condition, x, y) is x where condition is true, y otherwise
+    ctl_albcs = xr.where(ctl_albcs < 0.0, 0, ctl_albcs)
+
+    # LW kernel does not depend on albcs, just repeat the final dimension over longitudes:
+    LWK = LWkernel.expand_dims(dim=dict(lon=land_mask.lon), axis=4)
+
+    # Use control albcs to map SW kernel to appropriate longitudes
+    SWK = map_SWkern_to_lon(SWkernel, LWK, ctl_albcs)
+
+    return LWK, SWK
+
+
+###########################################################################
+def map_SWkern_to_lon(SWkernel, LWK, albcsmap):
+    """revised from zelinka_analysis.py"""
+
+    from scipy.interpolate import interp1d
+
+    ## Map each location's clear-sky surface albedo to the correct albedo bin
+    # Ksw is size 12,7,7,lats,3
+    # albcsmap is size 12,lats,lons
+    albcs = np.arange(0.0, 1.5, 0.5)
+    SWkernel_map = LWK.copy(data=nanarray(LWK.shape))
+
+    for T in range(len(LWK.time)):
+        for LAT in range(len(LWK.lat)):
+            alon = albcsmap[T, LAT, :].copy()  # a longitude array
+            if sum(~np.isnan(alon)) >= 1:  # at least 1 unmasked value
+                if len(SWkernel[T, :, :, LAT, :] > 0) == 0:
+                    SWkernel_map[T, :, :, LAT, :] = 0
+                else:
+                    f = interp1d(albcs, SWkernel[T, :, :, LAT, :], axis=2)
+                    SWkernel_map[T, :, :, LAT, :] = f(alon.values)
+            else:
+                continue
+
+    return SWkernel_map
+
+
+###########################################################################
+def nanarray(vector):
+    # this generates a masked array with the size given by vector
+    # example: vector = (90,144,28)
+    # similar to this=NaN*ones(x,y,z) in matlab
+    # used in "map_SWkern_to_lon"
+    this = np.nan * np.ones(vector)
+    return this
+
+
+###########################################################################
+def get_area_wts(idata):
+    wgt1 = np.cos(np.deg2rad(idata.lat)) * (idata * 0 + 1)
+    wgt1 = wgt1 / wgt1.sum(dim=["lat", "lon"])
+    wgt1 = wgt1.transpose("time", "lat", "lon")
+
+    return wgt1
+
+
+###########################################################################
+def compute_fbk(ctl, fut, DT):
+    DR = fut - ctl
+    fbk = DR / DT
+    baseline = ctl
+    return fbk, baseline
+
+
+###########################################################################
+def bony_sorting_part1(w500, binedges):
+    """revised from bony_analysis.py"""
+
+    w500 = xr.where(w500 == 0, np.nan, w500)
+    A, B, C = w500.shape
+    dx = np.diff(binedges)[0]
+    # Compute composite:
+    # add 2 for the exceedances
+    OKwaps = xr.DataArray(
+        data=nanarray((A, B, C, 2 + len(binedges))),
+        dims=["time", "lat", "lon", "dwap"],
+        coords=dict(
+            time=w500.time,
+            lat=w500.lat,
+            lon=w500.lon,
+            dwap=np.arange(2 + len(binedges)),
+        ),
+    )
+    xx = 0
+    for x in binedges:
+        xx += 1
+        w500_bin = xr.where(w500 >= x, w500, np.nan)
+        OKwaps[:, :, :, xx] = xr.where(w500_bin < x + dx, w500_bin, np.nan)
+
+    # do the first wap bin:
+    OKwaps[:, :, :, 0] = xr.where(w500 < binedges[0], w500, np.nan)
+    # do the last wap bin:
+    OKwaps[:, :, :, -1] = xr.where(w500 >= binedges[-1] + dx, w500, np.nan)
+
+    return OKwaps  # [month,lat,lon,wapbin]
+
+
+###########################################################################
+def bony_sorting_part2(OKwaps, data, OKland, WTS, binedges):
+    """revised from bony_analysis.py"""
+
+    # OKwaps # xarray(12, 30, 144, 22)
+    # data # xarray(12, 7, 7, 30, 144)
+    # OKland # xarray(12, 30, 144)
+    # WTS # xarray(12,30,144)
+    # binedges # numpy(20)
+
+    # zeros and ones
+    binary_waps = xr.where(np.isnan(OKwaps), 0.0, 1.0)  # xarray(12, 30, 144, 22)
+    binary_land = xr.where(OKland == 0, 0.0, 1.0)  # xarray(12, 30, 144)
+    binary_data = xr.where(np.isnan(data), 0.0, 1.0)  # xarray(12,7,7,30,144)
+
+    # this function maps data from [time,...?,lat,lon] to [time,...?,wapbin]
+    sh = list(data.shape[:-2])
+    sh.append(3 + len(binedges))
+    # add 2 for the exceedances, 1 for land, sh = [12, 7, 7, 23]
+    DATA = xr.DataArray(
+        data=nanarray((sh)),
+        dims=["time", "tau", "plev", "dwap"],
+        coords=dict(time=data.time, tau=data.tau, plev=data.plev, dwap=np.arange(23)),
+    )
+    sh = list()
+    sh = np.append(data.shape[0], 3 + len(binedges))  # sh = [12,23]
+    CNTS = xr.DataArray(
+        data=nanarray((sh)),
+        dims=["time", "dwap"],
+        coords=dict(time=data.time, dwap=np.arange(23)),
+    )
+    # xarray(12, 23)
+
+    A1b = binary_waps * WTS
+    # zero for wap outside range, frac area for wap inside range
+    # xarray(12,30,144,22)
+    CNTS[:, :-1] = A1b.sum(dim=["lat", "lon"])
+    # for ocean region
+    # fractional area of this bin includes regions where data is undefined
+    # xarray(12,23)
+
+    for xx in range(2 + len(binedges)):
+        A2 = binary_data * A1b.isel(dwap=xx)  # xarray(12,7,7,30,144)
+        # zero for wap outside range or undefined data,
+        # frac area for wap inside range
+        denom = (A2).sum(dim=["lat", "lon"])  # xarray(12,7,7)
+        numer = (data * A2).sum(dim=["lat", "lon"])  # xarray(12,7,7)
+        DATA[:, :, :, xx] = numer / denom
+        # bin-avg data is computed where both data and wap are defined
+
+    # now do the land-only average:
+    A1b = binary_land * WTS  # xarray(12,30,144)
+    # zero for ocean points, frac area for land points
+    CNTS[:, -1] = (A1b).sum(dim=["lat", "lon"])  # xarray(12,23)
+    # fractional area of this bin includes regions where data is undefined
+    A2 = binary_data * A1b  # xarray(12,7,7,30,144)
+    # zero for ocean points or undefined data, frac area for land points
+    denom = (A2).sum(dim=["lat", "lon"])  # xarray(12,7,7)
+    numer = (data * A2).sum(dim=["lat", "lon"])  # xarray(12,7,7)
+    DATA[:, :, :, -1] = numer / denom
+    # bin-avg data is computed where both data and wap are defined
+
+    # Ensure that the area matrix has zeros rather than masked points
+    CNTS = CNTS.where(~np.isnan(CNTS), 0.0)
+
+    if np.allclose(0.5, np.sum(CNTS.values, 1)) == False:
+        print(
+            "sum of fractional counts over all wapbins does not equal 0.5 (tropical fraction)"
+        )
+        # moot
+
+    # DATA contains area-weighted averages within each bin
+    # CNTS contains fractional areas represented by each bin
+    # so summing (DATA*CNTS) over all regimes should recover
+    # the tropical contribution to the global mean
+    v1 = (DATA * CNTS).sum("dwap")
+    # v2a = 0.5*gavg(data)
+    v2b = (data * WTS).sum(dim=["lat", "lon"]).transpose("time", "tau", "plev")
+
+    # if np.allclose(v1.values,v2a.values)==False or np.allclose(v1.values,v2b.values)==False:
+    if np.allclose(v1.values, v2b.values) == False:
+        print("Cannot reconstruct tropical average via summing regimes")
+
+    return DATA, CNTS  # [time,wapbin]
+
+
+###########################################################################
+def KT_decomposition_general(c1, c2, Klw, Ksw):
+    """
+    this function takes in a (month,TAU,CTP,lat,lon) matrix and performs the
+    decomposition of Zelinka et al 2013 doi:10.1175/JCLI-D-12-00555.1
+    """
+
+    sum_c = c1.sum(dim=["tau", "plev"])  # Eq. B2
+    dc = c2 - c1
+    sum_dc = dc.sum(dim=["tau", "plev"])
+    dc_prop = c1 * (sum_dc / sum_c)
+    dc_star = dc - dc_prop  # Eq. B1
+    C_ratio = c1 / sum_c
+
+    # LW components
+    Klw0 = (Klw * c1 / sum_c).sum(dim=["tau", "plev"])  # Eq. B4
+    Klw_prime = Klw - Klw0  # Eq. B3
+    Klw_p_prime = (Klw_prime * (C_ratio.sum(dim="plev"))).sum(dim="tau")  # Eq. B7
+    Klw_t_prime = (Klw_prime * (C_ratio.sum(dim="tau"))).sum(dim="plev")  # Eq. B8
+    Klw_resid_prime = Klw_prime - Klw_p_prime - Klw_t_prime  # Eq. B9
+    dRlw_true = (Klw * dc).sum(dim=["tau", "plev"])  # LW total
+    dRlw_prop = Klw0 * sum_dc  # LW amount component
+    dRlw_dctp = (Klw_p_prime * (dc_star.sum(dim="tau"))).sum(
+        dim="plev"
+    )  # LW altitude component
+    dRlw_dtau = (Klw_t_prime * (dc_star.sum(dim="plev"))).sum(
+        dim="tau"
+    )  # LW tauical depth component
+    dRlw_resid = (Klw_resid_prime * dc_star).sum(dim=["tau", "plev"])  # LW residual
+    # dRlw_sum = dRlw_prop + dRlw_dctp + dRlw_dtau + dRlw_resid           # sum of LW components -- should equal LW total
+
+    # SW components
+    Ksw0 = (Ksw * c1 / sum_c).sum(dim=["tau", "plev"])  # Eq. B4
+    Ksw_prime = Ksw - Ksw0  # Eq. B3
+    Ksw_p_prime = (Ksw_prime * (C_ratio.sum(dim="plev"))).sum(dim="tau")  # Eq. B7
+    Ksw_t_prime = (Ksw_prime * (C_ratio.sum(dim="tau"))).sum(dim="plev")  # Eq. B8
+    Ksw_resid_prime = Ksw_prime - Ksw_p_prime - Ksw_t_prime  # Eq. B9
+    dRsw_true = (Ksw * dc).sum(dim=["tau", "plev"])  # SW total
+    dRsw_prop = Ksw0 * sum_dc  # SW amount component
+    dRsw_dctp = (Ksw_p_prime * (dc_star.sum(dim="tau"))).sum(
+        dim="plev"
+    )  # SW altitude component
+    dRsw_dtau = (Ksw_t_prime * (dc_star.sum(dim="plev"))).sum(
+        dim="tau"
+    )  # SW tauical depth component
+    dRsw_resid = (Ksw_resid_prime * dc_star).sum(dim=["tau", "plev"])  # SW residual
+    # dRsw_sum = dRsw_prop + dRsw_dctp + dRsw_dtau + dRsw_resid
+
+    # Set SW fields to zero where the sun is down
+    dRsw_true = xr.where(Ksw0 == 0, 0, dRsw_true)
+    dRsw_prop = xr.where(Ksw0 == 0, 0, dRsw_prop)
+    dRsw_dctp = xr.where(Ksw0 == 0, 0, dRsw_dctp)
+    dRsw_dtau = xr.where(Ksw0 == 0, 0, dRsw_dtau)
+    dRsw_resid = xr.where(Ksw0 == 0, 0, dRsw_resid)
+
+    if "dwap" in c1.dims:
+        output = {}
+        output["LWcld_tot"] = dRlw_true.transpose("time", "dwap")
+        output["LWcld_amt"] = dRlw_prop.transpose("time", "dwap")
+        output["LWcld_alt"] = dRlw_dctp.transpose("time", "dwap")
+        output["LWcld_tau"] = dRlw_dtau.transpose("time", "dwap")
+        output["LWcld_err"] = dRlw_resid.transpose("time", "dwap")
+        output["SWcld_tot"] = dRsw_true.transpose("time", "dwap")
+        output["SWcld_amt"] = dRsw_prop.transpose("time", "dwap")
+        output["SWcld_alt"] = dRsw_dctp.transpose("time", "dwap")
+        output["SWcld_tau"] = dRsw_dtau.transpose("time", "dwap")
+        output["SWcld_err"] = dRsw_resid.transpose("time", "dwap")
+
+    else:
+        output = {}
+        output["LWcld_tot"] = dRlw_true.transpose("time", "lat", "lon")
+        output["LWcld_amt"] = dRlw_prop.transpose("time", "lat", "lon")
+        output["LWcld_alt"] = dRlw_dctp.transpose("time", "lat", "lon")
+        output["LWcld_tau"] = dRlw_dtau.transpose("time", "lat", "lon")
+        output["LWcld_err"] = dRlw_resid.transpose("time", "lat", "lon")
+        output["SWcld_tot"] = dRsw_true.transpose("time", "lat", "lon")
+        output["SWcld_amt"] = dRsw_prop.transpose("time", "lat", "lon")
+        output["SWcld_alt"] = dRsw_dctp.transpose("time", "lat", "lon")
+        output["SWcld_tau"] = dRsw_dtau.transpose("time", "lat", "lon")
+        output["SWcld_err"] = dRsw_resid.transpose("time", "lat", "lon")
+
+    return output
+
+
+###########################################################################
+def do_obscuration_calcs(CTL, FUT, Klw, Ksw, DT):
+    (L_R_bar, dobsc, dunobsc, dobsc_cov) = obscuration_terms3(CTL, FUT)
+
+    # Get unobscured low-cloud feedbacks and those caused by change in obscuration
+    ZEROS = np.zeros(L_R_bar.shape)
+    dummy, L_R_bar_base = compute_fbk(L_R_bar, L_R_bar, DT)
+    dobsc_fbk, dummy = compute_fbk(ZEROS, dobsc, DT)
+    dunobsc_fbk, dummy = compute_fbk(ZEROS, dunobsc, DT)
+    dobsc_cov_fbk, dummy = compute_fbk(ZEROS, dobsc_cov, DT)
+    obsc_output = obscuration_feedback_terms_general(
+        L_R_bar_base, dobsc_fbk, dunobsc_fbk, dobsc_cov_fbk, Klw, Ksw
+    )
+
+    return obsc_output
+
+
+###########################################################################
+def obscuration_feedback_terms_general(
+    L_R_bar0, dobsc_fbk, dunobsc_fbk, dobsc_cov_fbk, Klw, Ksw
+):
+    """
+    Estimate unobscured low cloud feedback,
+    the low cloud feedback arising solely from changes in obscuration by upper-level clouds,
+    and the covariance term
+
+    This function takes in a (month,tau,CTP,lat,lon) matrix
+
+    Klw and Ksw contain just the low bins
+
+    the following terms are generated in obscuration_terms():
+    dobsc = L_R_bar0 * F_prime
+    dunobsc = L_R_prime * F_bar
+    dobsc_cov = (L_R_prime * F_prime) - climo(L_R_prime * F_prime)
+    """
+
+    Klw_low = Klw
+    Ksw_low = Ksw
+    L_R_bar0 = 100 * L_R_bar0
+    dobsc_fbk = 100 * dobsc_fbk
+    dunobsc_fbk = 100 * dunobsc_fbk
+    dobsc_cov_fbk = 100 * dobsc_cov_fbk
+
+    LWdobsc_fbk = (Klw_low * dobsc_fbk).sum(dim=["tau", "plev"])
+    LWdunobsc_fbk = (Klw_low * dunobsc_fbk).sum(dim=["tau", "plev"])
+    LWdobsc_cov_fbk = (Klw_low * dobsc_cov_fbk).sum(dim=["tau", "plev"])
+
+    SWdobsc_fbk = (Ksw_low * dobsc_fbk).sum(dim=["tau", "plev"])
+    SWdunobsc_fbk = (Ksw_low * dunobsc_fbk).sum(dim=["tau", "plev"])
+    SWdobsc_cov_fbk = (Ksw_low * dobsc_cov_fbk).sum(dim=["tau", "plev"])
+
+    ###########################################################################
+    # Further break down the true and apparent low cloud-induced radiation anomalies into components
+    ###########################################################################
+    # No need to break down dobsc_fbk, as that is purely an amount component.
+
+    # Break down dunobsc_fbk:
+    C_ctl = L_R_bar0
+    dC = dunobsc_fbk
+    C_fut = C_ctl + dC
+
+    obsc_fbk_output = KT_decomposition_general(C_ctl, C_fut, Klw_low, Ksw_low)
+
+    obsc_fbk_output["LWdobsc_fbk"] = LWdobsc_fbk
+    obsc_fbk_output["LWdunobsc_fbk"] = LWdunobsc_fbk
+    obsc_fbk_output["LWdobsc_cov_fbk"] = LWdobsc_cov_fbk
+    obsc_fbk_output["SWdobsc_fbk"] = SWdobsc_fbk
+    obsc_fbk_output["SWdunobsc_fbk"] = SWdunobsc_fbk
+    obsc_fbk_output["SWdobsc_cov_fbk"] = SWdobsc_cov_fbk
+
+    return obsc_fbk_output
+
+
+###########################################################################
+def obscuration_terms3(c1, c2):
+    """
+    USE THIS VERSION FOR DIFFERENCES OF 2 CLIMATOLOGIES (E.G. AMIP4K, 2xCO2 SLAB RUNS)
+
+    Compute the components required for the obscuration-affected low cloud feedback
+    These are the terms shown in Eq 4 of Scott et al (2020) DOI: 10.1175/JCLI-D-19-1028.1
+    L_prime = dunobsc + dobsc + dobsc_cov, where
+    dunobsc = L_R_prime * F_bar     (delta unobscured low clouds, i.e., true low cloud feedback)
+    dobsc = L_R_bar * F_prime       (delta obscuration by upper level clouds)
+    dobsc_cov = (L_R_prime * F_prime) - climo(L_R_prime * F_prime)  (covariance term)
+    """
+    # c is [mo,tau,ctp,lat,lon]
+    # c is in percent
+
+    # SPLICE c1 and c2:
+    # MAKE SURE c1 and c2 are the same size!!!
+    if c1.shape != c2.shape:
+        raise RuntimeError("c1 and c2 are NOT the same size!!!")
+
+    c12 = xr.concat([c1, c2], dim="time")
+    c12["time"] = xr.cftime_range(start="1983-01", periods=len(c12.time), freq="MS")
+
+    midpt = len(c1)
+
+    U12 = (c12[:, :, 2:, :]).sum(
+        dim=["tau", "plev"]
+    ) / 100.0  # [time, lat, lon] or [time, dwap]
+
+    L12 = c12[:, :, :2, :] / 100.0
+
+    F12 = 1.0 - U12
+    F12 = xr.where(F12 >= 0, F12, np.nan)
+
+    F12b = F12.expand_dims(dim=dict(tau=c12.tau), axis=1)
+    F12b = F12b.expand_dims(dim=dict(plev=c12.plev), axis=2)
+
+    L_R12 = L12 / F12  # L12/F12b
+    sum_L_R12 = (L_R12).sum(dim=["tau", "plev"])
+    sum_L_R12c = sum_L_R12.expand_dims(dim=dict(tau=L12.tau), axis=1)
+    sum_L_R12c = sum_L_R12c.expand_dims(dim=dict(plev=L12.plev), axis=2)
+    this = sum_L_R12c.where(sum_L_R12c <= 1, np.nan)
+    this = this.where(this >= 0, np.nan)
+    L_R12 = L_R12.where(~np.isnan(this), np.nan)
+    L_R12 = L_R12.where(sum_L_R12c <= 1, np.nan)
+
+    L_R_prime, L_R_bar = monthly_anomalies(L_R12)
+    F_prime, F_bar = monthly_anomalies(F12b)
+    L_prime, L_bar = monthly_anomalies(L12)
+
+    # Cannot have negative cloud fractions:
+    L_R_bar = L_R_bar.where(L_R_bar >= 0, 0.0)
+    F_bar = F_bar.where(F_bar >= 0, 0.0)
+
+    rep_L_bar = tile_uneven(L_bar, L12)
+    rep_L_R_bar = tile_uneven(L_R_bar, L_R12)
+    rep_F_bar = tile_uneven(F_bar, F12b)
+
+    # Cannot have negative cloud fractions:
+    L_R_bar = L_R_bar.where(L_R_bar >= 0, 0.0)
+    F_bar = F_bar.where(F_bar >= 0, 0.0)
+
+    dobsc = rep_L_R_bar * F_prime
+    dunobsc = L_R_prime * rep_F_bar
+    prime_prime = L_R_prime * F_prime
+
+    dobsc_cov, climo_prime_prime = monthly_anomalies(prime_prime)
+
+    # Re-scale these anomalies by 2, since we have computed all anomalies w.r.t.
+    # the ctl+pert average rather than w.r.t. the ctl average
+    dobsc *= 2
+    dunobsc *= 2
+    dobsc_cov *= 2
+
+    # change time dimension
+    Time = c1.time.copy()
+    rep_L_R_bar_new = rep_L_R_bar[midpt:]
+    rep_L_R_bar_new["time"] = Time
+    dobsc_new = dobsc[midpt:]
+    dobsc_new["time"] = Time
+    dunobsc_new = dunobsc[midpt:]
+    dunobsc_new["time"] = Time
+    dobsc_cov_new = dobsc_cov[midpt:]
+    dobsc_cov_new["time"] = Time
+
+    return (rep_L_R_bar_new, dobsc_new, dunobsc_new, dobsc_cov_new)
+
+
+###########################################################################
+def xc_to_dataset(idata):
+    idata = idata.to_dataset(name="data")
+    if "height" in idata.coords:
+        idata = idata.drop("height")
+    idata = idata.bounds.add_missing_bounds()
+    return idata
+
+
+###########################################################################
+def monthly_anomalies(idata):
+    """
+    Compute departures from the climatological annual cycle
+    usage:
+    anom,avg = monthly_anomalies(data)
+    """
+    idata = xc_to_dataset(idata)
+    idata["time"].encoding["calendar"] = "noleap"
+    clim = idata.temporal.climatology("data", freq="month", weighted=True)
+    anom = idata.temporal.departures("data", freq="month", weighted=True)
+
+    return (anom["data"], clim["data"])
+
+
+###########################################################################
+def tile_uneven(data, data_to_match):
+    """extend data to match size of data_to_match even if not a multiple of 12"""
+
+    A12 = len(data_to_match) // 12
+    ind = np.arange(
+        12,
+    )
+    rep_ind = np.tile(ind, (A12 + 1))[
+        : int(len(data_to_match))
+    ]  # int() added for python3
+
+    rep_data = (data).isel(time=rep_ind)
+    rep_data["time"] = data_to_match.time.copy()
+
+    return rep_data
+
+
+###########################################################################
+def select_regions(field, region):
+    if region == "eq90":
+        field_dom = field.sel(lat=slice(-90, 90))
+    elif region == "eq60":
+        field_dom = field.sel(lat=slice(-60, 60))
+    elif region == "eq30":
+        field_dom = field.sel(lat=slice(-30, 30))
+    elif region == "30-60":
+        field1 = field.sel(lat=slice(-60, -30))
+        field2 = field.sel(lat=slice(30, 60))
+        field_dom = xr.concat([field1, field2], dim="lat")
+    elif region == "30-80":
+        field1 = field.sel(lat=slice(-80, -30))
+        field2 = field.sel(lat=slice(30, 80))
+        field_dom = xr.concat([field1, field2], dim="lat")
+    elif region == "40-70":
+        field1 = field.sel(lat=slice(-70, -40))
+        field2 = field.sel(lat=slice(40, 70))
+        field_dom = xr.concat([field1, field2], dim="lat")
+    elif region == "Arctic":
+        field_dom = field.sel(lat=slice(70, 90))
+    return field_dom
+
+
+###########################################################################
+def do_klein_calcs(
+    ctl_clisccp,
+    LWK,
+    SWK,
+    WTS,
+    ctl_clisccp_wap,
+    LWK_wap,
+    SWK_wap,
+    obs_clisccp_AC,
+    obs_clisccp_AC_wap,
+    obs_N_AC_wap,
+):
+    KEM_dict = {}  # dictionary to contain all computed Klein error metrics
+    for sec in sections:
+        KEM_dict[sec] = {}
+        PP = sec_dic[sec]
+        C1 = ctl_clisccp[:, :, PP, :]
+        Klw = LWK[:, :, PP, :]
+        Ksw = SWK[:, :, PP, :]
+
+        obs_C1 = obs_clisccp_AC[:, :, PP, :]
+        ocn_obs_C1 = obs_C1 * ocean_mask
+        lnd_obs_C1 = obs_C1 * land_mask
+        ocn_C1 = C1 * ocean_mask
+        lnd_C1 = C1 * land_mask
+
+        # set ocean or land to nan
+        ocn_obs_C1 = ocn_obs_C1.where(ocn_obs_C1 != 0.0, np.nan)
+        lnd_obs_C1 = lnd_obs_C1.where(lnd_obs_C1 != 0.0, np.nan)
+        ocn_C1 = ocn_C1.where(ocn_C1 != 0.0, np.nan)
+        lnd_C1 = lnd_C1.where(lnd_C1 != 0.0, np.nan)
+
+        # assessed feedback regions + Klein region (eq60)
+        for region in ["eq90", "eq60", "eq30", "30-60", "30-80", "40-70", "Arctic"]:
+            KEM_dict[sec][region] = {}
+            obs_C1_dom = select_regions(obs_C1, region)
+            ocn_obs_C1_dom = select_regions(ocn_obs_C1, region)
+            lnd_obs_C1_dom = select_regions(lnd_obs_C1, region)
+            C1_dom = select_regions(C1, region)
+            ocn_C1_dom = select_regions(ocn_C1, region)
+            lnd_C1_dom = select_regions(lnd_C1, region)
+            Klw_dom = select_regions(Klw, region)
+            Ksw_dom = select_regions(Ksw, region)
+            WTS_dom = select_regions(WTS, region)
+            for sfc in ["all", "ocn", "lnd", "ocn_asc", "ocn_dsc"]:
+                KEM_dict[sec][region][sfc] = {}
+                if sfc == "all":
+                    (E_TCA, E_ctpt, E_LW, E_SW, E_NET) = klein_metrics(
+                        obs_C1_dom, C1_dom, Klw_dom, Ksw_dom, WTS_dom
+                    )
+                elif sfc == "ocn":
+                    (E_TCA, E_ctpt, E_LW, E_SW, E_NET) = klein_metrics(
+                        ocn_obs_C1_dom, ocn_C1_dom, Klw_dom, Ksw_dom, WTS_dom
+                    )
+                elif sfc == "lnd":
+                    (E_TCA, E_ctpt, E_LW, E_SW, E_NET) = klein_metrics(
+                        lnd_obs_C1_dom, lnd_C1_dom, Klw_dom, Ksw_dom, WTS_dom
+                    )
+                else:
+                    continue
+                KEM_dict[sec][region][sfc]["E_TCA"] = E_TCA
+                KEM_dict[sec][region][sfc]["E_ctpt"] = E_ctpt
+                KEM_dict[sec][region][sfc]["E_LW"] = E_LW
+                KEM_dict[sec][region][sfc]["E_SW"] = E_SW
+                KEM_dict[sec][region][sfc]["E_NET"] = E_NET
+
+        C1 = ctl_clisccp_wap[:, :, PP, :-1]
+        obs_C1 = obs_clisccp_AC_wap[:, :, PP, :-1]  # ignore the land bin
+        WTS_wap = obs_N_AC_wap[:, :-1]  # ignore the land bin
+        Klw_wap = LWK_wap[:, :, PP, :-1]  # ignore the land bin
+        Ksw_wap = SWK_wap[:, :, PP, :-1]
+        (E_TCA, E_ctpt, E_LW, E_SW, E_NET) = klein_metrics(
+            obs_C1[..., :cutoff],
+            C1[..., :cutoff],
+            Klw_wap[..., :cutoff],
+            Ksw_wap[..., :cutoff],
+            WTS_wap[:, :cutoff],
+        )
+        KEM_dict[sec]["eq30"]["ocn_asc"]["E_TCA"] = E_TCA
+        KEM_dict[sec]["eq30"]["ocn_asc"]["E_ctpt"] = E_ctpt
+        KEM_dict[sec]["eq30"]["ocn_asc"]["E_LW"] = E_LW
+        KEM_dict[sec]["eq30"]["ocn_asc"]["E_SW"] = E_SW
+        KEM_dict[sec]["eq30"]["ocn_asc"]["E_NET"] = E_NET
+
+        ### this part is changed ###
+        (E_TCA, E_ctpt, E_LW, E_SW, E_NET) = klein_metrics(
+            obs_C1[..., cutoff:],
+            C1[..., cutoff:],
+            Klw_wap[..., cutoff:],
+            Ksw_wap[..., cutoff:],
+            WTS_wap[:, cutoff:],
+        )
+        KEM_dict[sec]["eq30"]["ocn_dsc"]["E_TCA"] = E_TCA
+        KEM_dict[sec]["eq30"]["ocn_dsc"]["E_ctpt"] = E_ctpt
+        KEM_dict[sec]["eq30"]["ocn_dsc"]["E_LW"] = E_LW
+        KEM_dict[sec]["eq30"]["ocn_dsc"]["E_SW"] = E_SW
+        KEM_dict[sec]["eq30"]["ocn_dsc"]["E_NET"] = E_NET
+
+    # end for sec in sections:
+    KEM_dict["metadata"] = {}
+    meta = {
+        "date_modified": str(date.today()),
+        "author": "Mark D. Zelinka <zelinka1@llnl.gov> and Li-Wei Chao <chao5@llnl.gov>",
+    }
+    KEM_dict["metadata"] = meta
+
+    return KEM_dict
+
+
+###########################################################################
+def klein_metrics(obs_clisccp, gcm_clisccp, LWkern, SWkern, WTS):
+    ########################################################
+    ######### Compute Klein et al (2013) metrics ###########
+    ########################################################
+
+    # Remove the thinnest optical depth bin from models/kernels so as to compare properly with obs:
+    gcm_clisccp = gcm_clisccp[:, 1:, :, :]  # 6 tau bins
+    LWkern = LWkern[:, 1:, :, :]
+    SWkern = SWkern[:, 1:, :, :]
+
+    obs_clisccp["tau"] = np.arange(6)
+    gcm_clisccp["tau"] = np.arange(6)  # tau start from 1 so need to redefine tau
+    LWkern["tau"] = np.arange(6)
+    SWkern["tau"] = np.arange(6)
+
+    ## Compute Cloud Fraction Histogram Anomalies w.r.t. observations
+    clisccp_bias = gcm_clisccp - obs_clisccp
+
+    ## Multiply Anomalies by Kernels
+    SW = SWkern * clisccp_bias
+    LW = LWkern * clisccp_bias
+    NET = SW + LW
+
+    ########################################################
+    # E_TCA (TOTAL CLOUD AMOUNT METRIC)
+    ########################################################
+    # take only clouds with tau>1.3
+    WTS_domavg = WTS / 12
+    WTS_domavg = WTS_domavg / np.sum(WTS_domavg)
+    # np.sum(WTS_dom) = 1, so weighted sums give area-weighted avg, NOT scaled by fraction of planet
+    obs_clisccp_TCA = obs_clisccp[:, 1:, :]  # 5 tau bins
+    gcm_clisccp_TCA = gcm_clisccp[:, 1:, :]  # 5 tau bins
+
+    # sum over CTP and TAU:
+    gcm_cltisccp_TCA = gcm_clisccp_TCA.sum(dim=["tau", "plev"])  # (time, lat, lon)
+    obs_cltisccp_TCA = obs_clisccp_TCA.sum(dim=["tau", "plev"])  # (time, lat, lon)
+
+    gcm_cltisccp_TCA = gcm_cltisccp_TCA.where(
+        gcm_cltisccp_TCA != 0.0, np.nan
+    )  # sum of nan will give 0, so make it nan again
+    obs_cltisccp_TCA = obs_cltisccp_TCA.where(obs_cltisccp_TCA != 0.0, np.nan)
+
+    # 1) Denominator (Eq. 3 in Klein et al. (2013))
+    avg = (obs_cltisccp_TCA * WTS_domavg).sum()  # (scalar)
+    anom1 = obs_cltisccp_TCA - avg  # anomaly of obs from its spatio-temporal mean
+    # 2) Numerator -- Model minus ISCCP
+    anom2 = gcm_cltisccp_TCA - obs_cltisccp_TCA  # (time, lat, lon)
+
+    E_TCA_denom = np.sqrt((WTS_domavg * (anom1**2)).sum())  # (scalar)
+    E_TCA_numer2 = np.sqrt((WTS_domavg * (anom2**2)).sum())  # (scalar)
+
+    E_TCA = E_TCA_numer2 / E_TCA_denom
+
+    ########################################################
+    # CLOUD PROPERTY METRICS
+    ########################################################
+    # take only clouds with tau>3.6
+    clisccp_bias_CPM = clisccp_bias[:, 2:, :]  # 4 tau bins
+    obs_clisccp_CPM = obs_clisccp[:, 2:, :]  # 4 tau bins
+    gcm_clisccp_CPM = gcm_clisccp[:, 2:, :]  # 4 tau bins
+    LWkernel_CPM = LWkern[:, 2:, :]  # 4 tau bins
+    SWkernel_CPM = SWkern[:, 2:, :]  # 4 tau bins
+    NETkernel_CPM = SWkernel_CPM + LWkernel_CPM
+
+    # Compute anomaly of obs histogram from its spatio-temporal mean
+    if "dwap" in WTS_domavg.dims:  # working in wap space
+        avg_obs_clisccp_CPM = (obs_clisccp_CPM * WTS_domavg).sum(
+            dim=["time", "dwap"]
+        )  # (TAU,CTP)
+    else:  # working in lat/lon space
+        avg_obs_clisccp_CPM = (obs_clisccp_CPM * WTS_domavg).sum(
+            dim=["time", "lat", "lon"]
+        )  # (TAU,CTP)
+    anom_obs_clisccp_CPM = obs_clisccp_CPM - avg_obs_clisccp_CPM
+
+    ## Compute radiative impacts of cloud fraction anomalies
+    gcm_NET_bias = NET[:, 2:, :]  # 4 tau bins
+    obs_NET_bias = anom_obs_clisccp_CPM * NETkernel_CPM
+    gcm_SW_bias = SW[:, 2:, :]
+    obs_SW_bias = anom_obs_clisccp_CPM * SWkernel_CPM
+    gcm_LW_bias = LW[:, 2:, :]
+    obs_LW_bias = anom_obs_clisccp_CPM * LWkernel_CPM
+
+    ## Aggregate high, mid, and low clouds over medium and thick ISCCP ranges
+    # Psec_name = ['LO','MID','HI']
+    # Tsec_name = ['MED','THICK']
+    if len(obs_clisccp.plev) == 7:
+        Psec = [slice(0, 2), slice(2, 4), slice(4, 7)]
+    elif len(obs_clisccp.plev) == 2:
+        Psec = [slice(0, 2)]
+    elif len(obs_clisccp.plev) == 5:
+        Psec = [slice(0, 2), slice(2, 5)]
+
+    Tsec = [slice(0, 2), slice(2, 4)]
+
+    NP = len(Psec)
+    NT = len(Tsec)
+
+    agg_obs_NET_bias = gcm_NET_bias[:, :NT, :NP, :].copy() * 0.0
+    agg_gcm_NET_bias = gcm_NET_bias[:, :NT, :NP, :].copy() * 0.0
+    agg_obs_SW_bias = gcm_NET_bias[:, :NT, :NP, :].copy() * 0.0
+    agg_gcm_SW_bias = gcm_NET_bias[:, :NT, :NP, :].copy() * 0.0
+    agg_obs_LW_bias = gcm_NET_bias[:, :NT, :NP, :].copy() * 0.0
+    agg_gcm_LW_bias = gcm_NET_bias[:, :NT, :NP, :].copy() * 0.0
+    agg_obs_clisccp_bias = gcm_NET_bias[:, :NT, :NP, :].copy() * 0.0
+    agg_gcm_clisccp_bias = gcm_NET_bias[:, :NT, :NP, :].copy() * 0.0
+
+    obs_NET_bias = obs_NET_bias.where(~np.isnan(obs_NET_bias), 0)  # set nan to zero
+    gcm_NET_bias = gcm_NET_bias.where(~np.isnan(gcm_NET_bias), 0)
+    obs_SW_bias = obs_SW_bias.where(~np.isnan(obs_SW_bias), 0)
+    gcm_SW_bias = gcm_SW_bias.where(~np.isnan(gcm_SW_bias), 0)
+    obs_LW_bias = obs_LW_bias.where(~np.isnan(obs_LW_bias), 0)
+    gcm_LW_bias = gcm_LW_bias.where(~np.isnan(gcm_LW_bias), 0)
+    anom_obs_clisccp_CPM = anom_obs_clisccp_CPM.where(
+        ~np.isnan(anom_obs_clisccp_CPM), 0
+    )
+    clisccp_bias_CPM = clisccp_bias_CPM.where(~np.isnan(clisccp_bias_CPM), 0)
+
+    for tt, TT in enumerate(Tsec):
+        for pp, PP in enumerate(Psec):
+            agg_obs_NET_bias[:, tt, pp, :] = obs_NET_bias[:, TT, PP, :].sum(
+                dim=["tau", "plev"]
+            )
+            agg_gcm_NET_bias[:, tt, pp, :] = gcm_NET_bias[:, TT, PP, :].sum(
+                dim=["tau", "plev"]
+            )
+            agg_obs_SW_bias[:, tt, pp, :] = obs_SW_bias[:, TT, PP, :].sum(
+                dim=["tau", "plev"]
+            )
+            agg_gcm_SW_bias[:, tt, pp, :] = gcm_SW_bias[:, TT, PP, :].sum(
+                dim=["tau", "plev"]
+            )
+            agg_obs_LW_bias[:, tt, pp, :] = obs_LW_bias[:, TT, PP, :].sum(
+                dim=["tau", "plev"]
+            )
+            agg_gcm_LW_bias[:, tt, pp, :] = gcm_LW_bias[:, TT, PP, :].sum(
+                dim=["tau", "plev"]
+            )
+            agg_obs_clisccp_bias[:, tt, pp, :] = anom_obs_clisccp_CPM[:, TT, PP, :].sum(
+                dim=["tau", "plev"]
+            )
+            agg_gcm_clisccp_bias[:, tt, pp, :] = clisccp_bias_CPM[:, TT, PP, :].sum(
+                dim=["tau", "plev"]
+            )
+
+    ## Compute E_ctp-tau -- Cloud properties error
+    ctot1 = (agg_gcm_clisccp_bias**2).sum(dim=["tau", "plev"]) / (NT * NP)
+    ctot2 = (agg_obs_clisccp_bias**2).sum(dim=["tau", "plev"]) / (NT * NP)
+
+    ## Compute E_LW -- LW-relevant cloud properties error
+    ctot3 = (agg_gcm_LW_bias**2).sum(dim=["tau", "plev"]) / (NT * NP)
+    ctot4 = (agg_obs_LW_bias**2).sum(dim=["tau", "plev"]) / (NT * NP)
+
+    ## Compute E_SW -- SW-relevant cloud properties error
+    ctot5 = (agg_gcm_SW_bias**2).sum(dim=["tau", "plev"]) / (NT * NP)
+    ctot6 = (agg_obs_SW_bias**2).sum(dim=["tau", "plev"]) / (NT * NP)
+
+    ## Compute E_NET -- NET-relevant cloud properties error
+    ctot7 = (agg_gcm_NET_bias**2).sum(dim=["tau", "plev"]) / (NT * NP)
+    ctot8 = (agg_obs_NET_bias**2).sum(dim=["tau", "plev"]) / (NT * NP)
+
+    # compute one metric
+    E_ctpt_numer = np.sqrt((WTS_domavg * ctot1).sum())  # (scalar)
+    E_ctpt_denom = np.sqrt((WTS_domavg * ctot2).sum())  # (scalar)
+    E_LW_numer = np.sqrt((WTS_domavg * ctot3).sum())  # (scalar)
+    E_LW_denom = np.sqrt((WTS_domavg * ctot4).sum())  # (scalar)
+    E_SW_numer = np.sqrt((WTS_domavg * ctot5).sum())  # (scalar)
+    E_SW_denom = np.sqrt((WTS_domavg * ctot6).sum())  # (scalar)
+    E_NET_numer = np.sqrt((WTS_domavg * ctot7).sum())  # (scalar)
+    E_NET_denom = np.sqrt((WTS_domavg * ctot8).sum())  # (scalar)
+
+    E_ctpt = E_ctpt_numer / E_ctpt_denom
+    E_LW = E_LW_numer / E_LW_denom
+    E_SW = E_SW_numer / E_SW_denom
+    E_NET = E_NET_numer / E_NET_denom
+
+    return (E_TCA.values, E_ctpt.values, E_LW.values, E_SW.values, E_NET.values)
+
+
+###########################################################################
+def cal_Klein_metrics(
+    ctl_clisccp, LWK, SWK, area_wts, ctl_clisccp_wap, LWK_wap, SWK_wap
+):
+    ##########################################################
+    ##### Load in ISCCP HGG clisccp climo annual cycle  ######
+    ##########################################################
+
+    f = xc.open_dataset(datadir + "AC_clisccp_ISCCP_HGG_198301-200812.nc")
+    # f.history='Written by /work/zelinka1/scripts/load_ISCCP_HGG.py on feedback.llnl.gov'
+    # f.comment='Monthly-resolved climatological annual cycle over 198301-200812'
+    obs_clisccp_AC = f["AC_clisccp"]
+    obs_clisccp_AC = obs_clisccp_AC.rename({"tau_midpt": "tau", "p_midpt": "plev"})
+    obs_clisccp_AC["time"] = ctl_clisccp["time"].copy(deep=True)
+    obs_clisccp_AC["tau"] = np.arange(6)
+    obs_clisccp_AC["plev"] = np.arange(7)
+    del f
+
+    f = xc.open_dataset(datadir + "AC_clisccp_wap_ISCCP_HGG_198301-200812.nc")
+    # f.history='Written by /work/zelinka1/scripts/load_ISCCP_HGG.py on feedback.llnl.gov'
+    # f.comment='Monthly-resolved climatological annual cycle over 198301-200812, in omega500 space'
+    obs_clisccp_AC_wap = f["AC_clisccp_wap"]
+    obs_clisccp_AC_wap = obs_clisccp_AC_wap.rename(
+        {"tau_midpt": "tau", "p_midpt": "plev", "wap500_bin": "dwap"}
+    )
+    obs_clisccp_AC_wap["time"] = ctl_clisccp["time"].copy(deep=True)
+    obs_clisccp_AC_wap["tau"] = np.arange(6)
+    obs_clisccp_AC_wap["plev"] = np.arange(7)
+    obs_clisccp_AC_wap["dwap"] = np.arange(23)
+
+    obs_N_AC_wap = f["AC_N_wap"]
+    obs_N_AC_wap = obs_N_AC_wap.rename({"wap500_bin": "dwap"})
+    obs_N_AC_wap["time"] = ctl_clisccp["time"].copy(deep=True)
+    obs_N_AC_wap["dwap"] = np.arange(23)
+    del f
+
+    KEM_dict = do_klein_calcs(
+        ctl_clisccp,
+        LWK,
+        SWK,
+        area_wts,
+        ctl_clisccp_wap,
+        LWK_wap,
+        SWK_wap,
+        obs_clisccp_AC,
+        obs_clisccp_AC_wap,
+        obs_N_AC_wap,
+    )
+
+    return KEM_dict
+
+
+###########################################################################
+def regional_breakdown(data, OCN, LND, area_wts, norm=False):
+    # Compute spatial averages over various geographical regions, for ocean, land, and both
+    # if norm=False (the default), these averages are scaled by the fractional area of the planet over which they occur
+    # if norm=True, these are simply area-weighted averages
+
+    ocn_area_wts = area_wts * OCN
+    lnd_area_wts = area_wts * LND
+    mx = np.arange(
+        10, 101, 10
+    )  # max latitude of region (i.e., from -mx to mx); last one is for Arctic
+    denom = 1
+    reg_dict = {}
+    sections = list(data.keys())
+    surfaces = ["all", "ocn", "lnd"]
+    for r in mx:
+        if r == 100:
+            region = "Arctic"
+            domain = slice(70, 90)
+        else:
+            region = "eq" + str(r)
+            domain = slice(-r, r)
+        reg_dict[region] = {}
+        for sfc in surfaces:
+            reg_dict[region][sfc] = {}
+            for sec in sections:
+                reg_dict[region][sfc][sec] = {}
+                DATA = data[sec]
+                names = list(DATA.keys())
+                for name in names:
+                    # reg_dict[region][sfc][sec][name] = {}
+                    fbk = DATA[name]
+                    fbk_wts = fbk * area_wts
+                    if sfc == "ocn":
+                        wtd_fbk = (
+                            (fbk_wts * OCN).sel(lat=domain).sum(dim=["lat", "lon"])
+                        )
+                        if norm:
+                            denom = (
+                                (ocn_area_wts).sel(lat=domain).sum(dim=["lat", "lon"])
+                            )
+                    elif sfc == "lnd":
+                        wtd_fbk = (
+                            (fbk_wts * LND).sel(lat=domain).sum(dim=["lat", "lon"])
+                        )
+                        if norm:
+                            denom = (
+                                (lnd_area_wts).sel(lat=domain).sum(dim=["lat", "lon"])
+                            )
+                    elif sfc == "all":
+                        wtd_fbk = (fbk_wts).sel(lat=domain).sum(dim=["lat", "lon"])
+                        if norm:
+                            denom = (area_wts).sel(lat=domain).sum(dim=["lat", "lon"])
+                    wtd_fbk = wtd_fbk / denom
+                    reg_dict[region][sfc][sec][name] = (wtd_fbk).mean("time").values
+
+    # reserve spots in the dictionary for asc/dsc feedbacks
+    reg_dict["eq30"]["ocn_asc"] = {}
+    reg_dict["eq30"]["ocn_dsc"] = {}
+    for sec in sections:
+        reg_dict["eq30"]["ocn_asc"][sec] = {}
+        reg_dict["eq30"]["ocn_dsc"][sec] = {}
+
+    return reg_dict
+
+
+###########################################################################
+def CloudRadKernel(filepath):
+    (
+        ctl_clisccp,
+        fut_clisccp,
+        LWK,
+        SWK,
+        dTs,
+        area_wts,
+        ctl_clisccp_wap,
+        fut_clisccp_wap,
+        LWK_wap,
+        SWK_wap,
+        ctl_N,
+        fut_N,
+    ) = get_CRK_data(filepath)
+
+    OCN = ocean_mask.copy()
+    LND = land_mask.copy()
+
+    ##############################################################################
+    # Compute Klein et al cloud error metrics and their breakdown into components
+    ##############################################################################
+    print("Compute Klein et al error metrics")
+    KEM_dict = cal_Klein_metrics(
+        ctl_clisccp, LWK, SWK, area_wts, ctl_clisccp_wap, LWK_wap, SWK_wap
+    )
+    # [sec][region][sfc][ID], ID=E_TCA,E_ctpt,E_LW,E_SW,E_NET
+
+    ###########################################################################
+    # Compute cloud feedbacks and their breakdown into components
+    ###########################################################################
+    print("Compute feedbacks")
+    clisccp_fbk, clisccp_base = compute_fbk(ctl_clisccp, fut_clisccp, dTs)
+    # The following performs the amount/altitude/optical depth decomposition of
+    # Zelinka et al., J Climate (2012b), as modified in Zelinka et al., J. Climate (2013)
+    output = {}
+    output_wap = {}
+    for sec in sections:
+        print("    for section " + sec)
+        # [sec][flavor][region][all / ocn / lnd / ocn_asc / ocn_dsc]
+
+        PP = sec_dic[sec]
+
+        C1 = clisccp_base[:, :, PP, :]
+        C2 = C1 + clisccp_fbk[:, :, PP, :]
+        Klw = LWK[:, :, PP, :]
+        Ksw = SWK[:, :, PP, :]
+
+        output[sec] = KT_decomposition_general(C1, C2, Klw, Ksw)
+
+        Klw = LWK_wap[:, :, PP, :-1]  # ignore the land bin
+        Ksw = SWK_wap[:, :, PP, :-1]
+        C1 = ctl_clisccp_wap[:, :, PP, :-1]
+        C2 = fut_clisccp_wap[:, :, PP, :-1]
+        N1 = ctl_N[:, :-1]
+        N2 = fut_N[:, :-1]
+
+        # no breakdown (this is identical to within + between + covariance)
+        C1N1 = (C1 * N1).transpose(
+            "time", "tau", "plev", "dwap"
+        )  # [month,TAU,CTP,regime]
+        C2N2 = (C2 * N2).transpose(
+            "time", "tau", "plev", "dwap"
+        )  # [month,TAU,CTP,regime]
+        pert, C_base = compute_fbk(C1N1, C2N2, dTs)
+        output_wap[sec] = KT_decomposition_general(C_base, C_base + pert, Klw, Ksw)
+
+    ###########################################################################
+    # Compute obscuration feedback components
+    ###########################################################################
+    print("Get Obscuration Terms")
+    sec = "LO680"  # this should already be true, but just in case...
+    PP = sec_dic[sec]
+    obsc_output = {}
+    obsc_output[sec] = do_obscuration_calcs(
+        ctl_clisccp, fut_clisccp, LWK[:, :, PP, :], SWK[:, :, PP, :], dTs
+    )
+
+    # Do this for the omega-regimes too:
+    CTL = (ctl_clisccp_wap * ctl_N)[..., :-1]  # ignore the land bin
+    FUT = (fut_clisccp_wap * fut_N)[..., :-1]
+    LWK_wap_tmp = LWK_wap[:, :, PP, :-1]  # ignore the land bin
+    SWK_wap_tmp = SWK_wap[:, :, PP, :-1]  # ignore the land bin
+    obsc_output_wap = {}
+    obsc_output_wap[sec] = do_obscuration_calcs(CTL, FUT, LWK_wap_tmp, SWK_wap_tmp, dTs)
+
+    ###########################################################################
+    # Compute regional averages (weighted by fraction of globe); place in dictionary
+    ###########################################################################
+    print("Compute regional averages")
+    # [region][sfc][sec][name]
+    obsc_fbk_dict = regional_breakdown(obsc_output, OCN, LND, area_wts)
+    fbk_dict = regional_breakdown(output, OCN, LND, area_wts)
+
+    # Put all the ascending and descending region quantities in a dictionary
+    names = list(output_wap["ALL"].keys())
+    for sec in sections:
+        for name in names:
+            fbk_dict["eq30"]["ocn_asc"][sec][name] = (
+                (output_wap[sec][name][:, :cutoff]).sum("dwap").mean("time").values
+            )
+            fbk_dict["eq30"]["ocn_dsc"][sec][name] = (
+                (output_wap[sec][name][:, cutoff:]).sum("dwap").mean("time").values
+            )
+
+    sec = "LO680"
+    names = list(obsc_output_wap[sec].keys())
+    for name in names:
+        obsc_fbk_dict["eq30"]["ocn_asc"][sec][name] = (
+            (obsc_output_wap[sec][name][:, :cutoff]).sum("dwap").mean("time").values
+        )
+        obsc_fbk_dict["eq30"]["ocn_dsc"][sec][name] = (
+            (obsc_output_wap[sec][name][:, cutoff:]).sum("dwap").mean("time").values
+        )
+
+    meta = {
+        "date_modified": str(date.today()),
+        "author": "Mark D. Zelinka <zelinka1@llnl.gov> and Li-Wei Chao <chao5@llnl.gov>",
+    }
+    fbk_dict["metadata"] = meta
+    obsc_fbk_dict["metadata"] = meta
+
+    return (fbk_dict, obsc_fbk_dict, KEM_dict)
diff --git a/pcmdi_metrics/cloud_feedback/lib/compute_ECS_xr.py b/pcmdi_metrics/cloud_feedback/lib/compute_ECS_xr.py
new file mode 100644
index 000000000..b3db85e05
--- /dev/null
+++ b/pcmdi_metrics/cloud_feedback/lib/compute_ECS_xr.py
@@ -0,0 +1,143 @@
+# Compute ECS, ERF, and lambda from abrupt-4xCO2 experiments via Gregory approach
+
+import xarray as xr
+import xcdat
+import numpy as np
+from scipy import stats
+import cftime
+
+def dict_to_dataset(DICT):
+    # Convert a dictionary of Datasets to a single Dataset
+    data_vars={}
+    for var in list(DICT.keys()):
+        data_vars[var] = (["year"], DICT[var][var].data)
+    ds = xr.Dataset(
+        data_vars,
+        coords=dict(year=DICT[var].year),
+    )
+    return(ds)
+
+def get_branch_time(pi,ab):
+    # Determine when abrupt overlaps with piControl
+    # returns the following:
+    # 1) st: the branch time in cftime.datetime format
+    # 2) fn: the last abrupt year in cftime.datetime format
+    # 3) extensions beyond the ~150-year overlap period to allow for 21-year rolling mean:
+    #    3a) extend_st -- the date 10 years prior to st
+    #    3b) extend_fn -- the date 10 years after fn
+    
+    var = ab.variable_id
+    source = ab.source_id
+    experiment = ab.experiment_id
+    absub = ab.isel(time=slice(0,12*150))
+    lenab = int(absub[var].shape[0]/12)-1
+    btip = int(absub.attrs['branch_time_in_parent']) # timestep, not a date
+
+    if source=='GFDL-CM4' and (experiment=='1pctCO2' or experiment=='abrupt-4xCO2' or experiment=='historical'): 
+        btip = 91250 # https://errata.es-doc.org/static/view.html?uid=2f6b5963-f87e-b2df-a5b0-2f12b6b68d32    
+    ptu = absub.attrs['parent_time_units']
+    # print('  branch time in parent:   '+str(btip)+' '+ptu)
+    # print('  parent times:   '+str(pi.time.dt.year[0].values)+' - '+str(pi.time.dt.year[-1].values))
+    start = int(ptu.split(' ')[2][:4])
+    cal = pi.time.dt.calendar
+    # see http://cfconventions.org/cf-conventions/cf-conventions.html#calendar
+    if cal=='360_day':
+        dpy=360
+    elif cal=='noleap':
+        dpy=365
+    elif cal=='proleptic_gregorian' or cal=='standard' or cal=='julian':
+        dpy=365.25
+    else:
+        raise(Exception('Not sure how to handle '+cal+' calendar'))
+    if source=='CIESM' and (experiment=='abrupt-4xCO2' or experiment=='1pctCO2'):
+        # info from Yi Qin 10/1/20:
+        # "The branch_time for 4xCO2 should be the 101-yr of these published 500-yr piControl data, 
+        # but I wrongly regarded the “raw” piControl with piControl-spinup as the parent_case, which is a 1000-yr long run.
+        year0 = 101 
+    elif source=='KACE-1-0-G' and (experiment=='abrupt-4xCO2' or experiment=='1pctCO2'):
+        # info from Jae-Hee Lee (via Gavin Schmidt on 1/26/22):
+        # "We used year 2150 of piControl as the initial condition for the KACE-1-0-G 1pctCO2 runs. 
+        # so, you can assume that 1850 in the 1pctCO2 and 2150 in the piControl are the same
+        year0 = 2150         
+    else:
+        year0 = int((btip/dpy)+start)
+    year150 = int(year0+lenab)
+    st=cftime.datetime(year0, 1, 1).strftime("%Y-%m-%dT%H:%M:%S")
+    fn=cftime.datetime(year150, 12, 31,23,59,59).strftime("%Y-%m-%dT%H:%M:%S")
+    # Add on 10 years before st and after fn to assist in computing 21-year rolling means
+    extend_st=cftime.datetime(np.max((year0-10,1)), 1, 1).strftime("%Y-%m-%dT%H:%M:%S")
+    extend_fn=cftime.datetime(year150+10, 12, pi.time.dt.day[-1],23,59,59).strftime("%Y-%m-%dT%H:%M:%S") 
+
+    return(extend_st,extend_fn,st,fn)#,year0,year150)
+
+def get_data(pi,ab):  
+    # Return the piControl running mean and the abrupt deviation from this
+
+    var=ab.variable_id
+    extend_st,extend_fn,st,fn = get_branch_time(pi,ab)
+    absub = ab.isel(time=slice(0,12*150))
+    pisub = pi.sel(time=slice(extend_st,extend_fn))
+    if len(pisub.time)<1200:
+        print('len(time)<1200...skipping')
+        return None
+
+    bdate = pisub.time.dt.year.values 
+    # compute global means:
+    GLcntl = pisub.spatial.average(var)
+    GLpert = absub.spatial.average(var)
+    # compute annual means:
+    annpert = GLpert.groupby('time.year').mean('time')
+    anncntl = GLcntl.groupby('time.year').mean('time')
+    # compute 21-year centered rolling mean:
+    runcntl = anncntl.rolling(year = 21, min_periods = 1, center = True).mean()
+    # subset the rolling mean for the actual overlapping time:
+    runcntlsub = runcntl.sel(year=slice(int(st[:4]),int(fn[:4])))
+    # set the abrupt run's year coord to match the overlapping piCon's year coord
+    annpert=annpert.assign_coords(year=runcntlsub.coords['year'])
+    # compute abrupt anomalies from piControl running mean:
+    annanom = annpert - runcntlsub
+    
+    return(runcntl,annanom)
+
+def compute_abrupt_anoms(pifilepath,abfilepath):
+    # compute annual- anad global-mean tas and EEI anomalies in abrupt4xCO2 w.r.t. piControl climo, then compute ERF, lambda, and ECS via Gregory
+    cntl={}
+    anom={}
+    skip=False
+    variables = list(pifilepath.keys())
+    for var in variables:
+        pi=xcdat.open_mfdataset(pifilepath[var], use_cftime = True)
+        ab=xcdat.open_mfdataset(abfilepath[var], use_cftime = True)
+        
+        if len(ab.time)<140*12:
+            print('   len(ab.time)<140*12')
+            skip=True
+            break
+        output = get_data(pi,ab)
+        if output:
+            cntl[var],anom[var] = output
+        else:
+            skip=True
+            break
+    if skip:
+        # print('Skipping this model...')
+        return None
+
+    CNTL = dict_to_dataset(cntl)
+    ANOM = dict_to_dataset(anom)
+
+    CNTL['eei'] = CNTL['rsdt']-CNTL['rsut']-CNTL['rlut']
+    ANOM['eei'] = ANOM['rsdt']-ANOM['rsut']-ANOM['rlut']
+
+    x = ANOM['tas'].load()
+    y = ANOM['eei'].load()
+
+    return(x,y)
+
+def gregory_calcs(x,y):
+    m, b, r, p, std_err = stats.linregress(x,y)
+    ERF = b/2
+    lam = m
+    ECS = -ERF/lam
+    return (ERF,lam,ECS)
+
diff --git a/pcmdi_metrics/cloud_feedback/lib/get_assessment.py b/pcmdi_metrics/cloud_feedback/lib/get_assessment.py
new file mode 100644
index 000000000..4cc5c5be2
--- /dev/null
+++ b/pcmdi_metrics/cloud_feedback/lib/get_assessment.py
@@ -0,0 +1,217 @@
+#!/usr/bin/env python
+# coding: utf-8
+
+#=============================================
+# calculate assessed and unassessed feedbacks
+# revised from Zelinka et al (2022)
+# by Li-Wei Chao (2022/02/25)
+#=============================================
+
+import numpy as np
+
+# 10 hPa/dy wide bins:
+width = 10
+binedges=np.arange(-100,100,width)
+x1=np.arange(binedges[0]-width,binedges[-1]+2*width,width)
+binmids = x1+width/2.
+cutoff = int(len(binmids)/2) 
+
+def get_assessed_fbks(fbk_dict,obsc_fbk_dict):
+
+    # dictionary is structured: [region][sfc][sec][name]
+
+    assessed=[]
+    fbk_names=[]
+    
+    # 1) Global high cloud altitude
+    #===========================================
+    LW = fbk_dict['eq90']['all']['HI680']['LWcld_alt']
+    SW = fbk_dict['eq90']['all']['HI680']['SWcld_alt']
+    assessed.append(LW+SW)
+    fbk_names.append('High-Cloud Altitude')
+
+    # 2) Tropical Ocean Descent Low AMT + TAU
+    #===========================================
+    # Unobscured components only:
+    LW = obsc_fbk_dict['eq30']['ocn_dsc']['LO680']['LWcld_amt'] + obsc_fbk_dict['eq30']['ocn_dsc']['LO680']['LWcld_tau']
+    SW = obsc_fbk_dict['eq30']['ocn_dsc']['LO680']['SWcld_amt'] + obsc_fbk_dict['eq30']['ocn_dsc']['LO680']['SWcld_tau']
+    assessed.append(LW+SW)
+    fbk_names.append('Tropical Marine Low-Cloud')
+
+    # 3) Anvil
+    #===========================================
+    # Tropical oceanic ascent: High amt + high tau + delta obscuration-induced low
+    LW = fbk_dict['eq30']['ocn_asc']['HI680']['LWcld_amt'] + fbk_dict['eq30']['ocn_asc']['HI680']['LWcld_tau'] +  \
+          obsc_fbk_dict['eq30']['ocn_asc']['LO680']['LWdobsc_fbk']
+    SW = fbk_dict['eq30']['ocn_asc']['HI680']['SWcld_amt'] + fbk_dict['eq30']['ocn_asc']['HI680']['SWcld_tau'] +  \
+          obsc_fbk_dict['eq30']['ocn_asc']['LO680']['SWdobsc_fbk']
+    assessed.append(LW+SW)
+    fbk_names.append('Tropical Anvil Cloud')
+
+    # 4) Global land cloud amount
+    # !!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
+    # CHANGE
+    #===========================================
+    # unobscured low cloud amount + high cloud amount + ∆obscuration
+    LW = fbk_dict['eq90']['lnd']['HI680']['LWcld_amt'] + obsc_fbk_dict['eq90']['lnd']['LO680']['LWcld_amt'] +  \
+                                                         obsc_fbk_dict['eq90']['lnd']['LO680']['LWdobsc_fbk']
+    SW = fbk_dict['eq90']['lnd']['HI680']['SWcld_amt'] + obsc_fbk_dict['eq90']['lnd']['LO680']['SWcld_amt'] +  \
+                                                         obsc_fbk_dict['eq90']['lnd']['LO680']['SWdobsc_fbk']
+    assessed.append(LW+SW)
+    fbk_names.append('Land Cloud Amount')
+
+    # 5) Middle latitude cloud amount feedback
+    #===========================================
+    # Using the unobscured components:
+    LW = obsc_fbk_dict['eq60']['ocn']['LO680']['LWcld_amt'] - obsc_fbk_dict['eq30']['ocn']['LO680']['LWcld_amt']
+    SW = obsc_fbk_dict['eq60']['ocn']['LO680']['SWcld_amt'] - obsc_fbk_dict['eq30']['ocn']['LO680']['SWcld_amt']
+    assessed.append(LW+SW)
+    fbk_names.append('Middle Latitude Marine Low-Cloud Amount')
+
+    # 6) Extratropical cloud optical depth feedback
+    #===========================================
+    # Using the unobscured components:
+    LW = obsc_fbk_dict['eq70']['all']['LO680']['LWcld_tau'] - obsc_fbk_dict['eq40']['all']['LO680']['LWcld_tau']
+    SW = obsc_fbk_dict['eq70']['all']['LO680']['SWcld_tau'] - obsc_fbk_dict['eq40']['all']['LO680']['SWcld_tau']
+    assessed.append(LW+SW)
+    fbk_names.append('High Latitude Low-Cloud Optical Depth')
+
+    # 7) true_total
+    #===========================================
+    LW = fbk_dict['eq90']['all']['ALL']['LWcld_tot']
+    SW = fbk_dict['eq90']['all']['ALL']['SWcld_tot']
+    true_total = LW+SW
+
+    sum_assessed = np.array(assessed).sum()
+    imply_not_assessed = true_total - sum_assessed 
+    assessed.append(imply_not_assessed)
+    assessed.append(sum_assessed)
+    assessed.append(true_total)
+    
+    fbk_names.append('Implied Unassessed Cloud Feedbacks')
+    fbk_names.append('Sum of Assessed Cloud Feedbacks')
+    fbk_names.append('Total Cloud Feedback')
+    
+    return(np.array(assessed),fbk_names) # size [fbk_types]  
+
+
+
+def get_unassessed_fbks(fbk_dict,obsc_fbk_dict):
+
+    # dictionary is structured: [region][sfc][sec][name]
+
+    fbk_names=[]
+    unassessed=[]
+        
+    # 1) Global unobscured low altitude (Complement to global high cloud altitude)
+    #===========================================
+    LW = obsc_fbk_dict['eq90']['all']['LO680']['LWcld_alt']
+    SW = obsc_fbk_dict['eq90']['all']['LO680']['SWcld_alt']
+    unassessed.append(LW+SW)
+    fbk_names.append('Low-Cloud Altitude')
+
+    # 2) Tropical Ocean Descent High AMT+TAU (Complement to Tropical Ocean Descent Low AMT+TAU)
+    #===========================================
+    # High AMT+TAU + ∆obscuration
+    LW = fbk_dict['eq30']['ocn_dsc']['HI680']['LWcld_amt'] + fbk_dict['eq30']['ocn_dsc']['HI680']['LWcld_tau'] + \
+                                                        obsc_fbk_dict['eq30']['ocn_dsc']['LO680']['LWdobsc_fbk']
+    SW = fbk_dict['eq30']['ocn_dsc']['HI680']['SWcld_amt'] + fbk_dict['eq30']['ocn_dsc']['HI680']['SWcld_tau'] + \
+                                                        obsc_fbk_dict['eq30']['ocn_dsc']['LO680']['SWdobsc_fbk']
+    unassessed.append(LW+SW)
+    fbk_names.append('Tropical Marine Subsidence\nHigh-Cloud Amount + Optical Depth')
+    
+    # 3) Complement to the Anvil
+    #===========================================
+    # Tropical oceanic ascent: unobscured low amt + low tau (complement to High amt + high tau + delta obscuration-induced low)
+    LW = obsc_fbk_dict['eq30']['ocn_asc']['LO680']['LWcld_amt'] + obsc_fbk_dict['eq30']['ocn_asc']['LO680']['LWcld_tau']
+    SW = obsc_fbk_dict['eq30']['ocn_asc']['LO680']['SWcld_amt'] + obsc_fbk_dict['eq30']['ocn_asc']['LO680']['SWcld_tau']
+    unassessed.append(LW+SW)
+    fbk_names.append('Tropical Marine Ascent\nLow-Cloud Amount + Optical Depth')
+    
+    # 4) Tropical land high+low optical depth (Complement to Global land cloud amount)
+    #===========================================
+    LW = fbk_dict['eq30']['lnd']['HI680']['LWcld_tau'] + obsc_fbk_dict['eq30']['lnd']['LO680']['LWcld_tau']
+    SW = fbk_dict['eq30']['lnd']['HI680']['SWcld_tau'] + obsc_fbk_dict['eq30']['lnd']['LO680']['SWcld_tau']
+    unassessed.append(LW+SW)
+    fbk_names.append('Tropical Land Cloud Optical Depth')
+
+    # 5) Complement to middle latitude cloud amount feedback
+    #===========================================
+    # 60-90 Ocean unobscured Low AMT
+    #===========================================
+    LW = obsc_fbk_dict['eq90']['ocn']['LO680']['LWcld_amt'] - obsc_fbk_dict['eq60']['ocn']['LO680']['LWcld_amt']
+    SW = obsc_fbk_dict['eq90']['ocn']['LO680']['SWcld_amt'] - obsc_fbk_dict['eq60']['ocn']['LO680']['SWcld_amt']
+    unassessed.append(LW+SW)
+    fbk_names.append('60-90 Marine Low-Cloud Amount')
+
+    # 6) Complement to Extratropical cloud optical depth feedback
+    #===========================================
+    # 30-40/70-90 Ocean+Land unobscured Low TAU
+    #===========================================
+    LW = obsc_fbk_dict['eq40']['all']['LO680']['LWcld_tau'] - obsc_fbk_dict['eq30']['all']['LO680']['LWcld_tau'] + \
+         obsc_fbk_dict['eq90']['all']['LO680']['LWcld_tau'] - obsc_fbk_dict['eq70']['all']['LO680']['LWcld_tau']
+    SW = obsc_fbk_dict['eq40']['all']['LO680']['SWcld_tau'] - obsc_fbk_dict['eq30']['all']['LO680']['SWcld_tau'] + \
+         obsc_fbk_dict['eq90']['all']['LO680']['SWcld_tau'] - obsc_fbk_dict['eq70']['all']['LO680']['SWcld_tau']
+    unassessed.append(LW+SW)
+    fbk_names.append('30-40 / 70-90 Low-Cloud Optical Depth')
+   
+    # 7) 30-90 Ocean+Land High TAU
+    #===========================================
+    LW = fbk_dict['eq90']['all']['HI680']['LWcld_tau'] - fbk_dict['eq30']['all']['HI680']['LWcld_tau']
+    SW = fbk_dict['eq90']['all']['HI680']['SWcld_tau'] - fbk_dict['eq30']['all']['HI680']['SWcld_tau']
+    unassessed.append(LW+SW)
+    fbk_names.append('30-90 High-Cloud Optical Depth')
+   
+    # 8) 30-90 Ocean High AMT + ∆obscuration
+    #===========================================
+    LW = fbk_dict['eq90']['ocn']['HI680']['LWcld_amt'] - fbk_dict['eq30']['ocn']['HI680']['LWcld_amt'] + \
+    obsc_fbk_dict['eq90']['ocn']['LO680']['LWdobsc_fbk']  - obsc_fbk_dict['eq30']['ocn']['LO680']['LWdobsc_fbk']
+    SW = fbk_dict['eq90']['ocn']['HI680']['SWcld_amt'] - fbk_dict['eq30']['ocn']['HI680']['SWcld_amt'] + \
+    obsc_fbk_dict['eq90']['ocn']['LO680']['SWdobsc_fbk']  - obsc_fbk_dict['eq30']['ocn']['LO680']['SWdobsc_fbk']
+    unassessed.append(LW+SW)
+    fbk_names.append('30-90 Marine High-Cloud Amount')
+        
+    # 9) Obscuration covariance term
+    #===========================================
+    LW = obsc_fbk_dict['eq90']['all']['LO680']['LWdobsc_cov_fbk']
+    SW = obsc_fbk_dict['eq90']['all']['LO680']['SWdobsc_cov_fbk']
+    unassessed.append(LW+SW)
+    fbk_names.append('Obscuration Covariance')
+
+    # 10) Global Residual
+    #===========================================
+    LW = obsc_fbk_dict['eq90']['all']['LO680']['LWcld_err'] + fbk_dict['eq90']['all']['HI680']['LWcld_err']
+    SW = obsc_fbk_dict['eq90']['all']['LO680']['SWcld_err'] + fbk_dict['eq90']['all']['HI680']['SWcld_err']
+    unassessed.append(LW+SW)
+    fbk_names.append('Zelinka Decomposition Residual')
+    
+    # 11) ocean ascent/descent Residual
+    #===========================================
+    LW_A = obsc_fbk_dict['eq30']['ocn']['LO680']['LWcld_amt'] + obsc_fbk_dict['eq30']['ocn']['LO680']['LWcld_tau'] +\
+    fbk_dict['eq30']['ocn']['HI680']['LWcld_amt'] + fbk_dict['eq30']['ocn']['HI680']['LWcld_tau'] +\
+    obsc_fbk_dict['eq30']['ocn']['LO680']['LWdobsc_fbk']
+    SW_A = obsc_fbk_dict['eq30']['ocn']['LO680']['SWcld_amt'] + obsc_fbk_dict['eq30']['ocn']['LO680']['SWcld_tau'] +\
+    fbk_dict['eq30']['ocn']['HI680']['SWcld_amt'] + fbk_dict['eq30']['ocn']['HI680']['SWcld_tau'] +\
+    obsc_fbk_dict['eq30']['ocn']['LO680']['SWdobsc_fbk']
+    
+    LW_B = obsc_fbk_dict['eq30']['ocn_asc']['LO680']['LWcld_amt'] + obsc_fbk_dict['eq30']['ocn_asc']['LO680']['LWcld_tau'] +\
+    fbk_dict['eq30']['ocn_asc']['HI680']['LWcld_amt'] + fbk_dict['eq30']['ocn_asc']['HI680']['LWcld_tau'] +\
+    obsc_fbk_dict['eq30']['ocn_asc']['LO680']['LWdobsc_fbk'] +\
+    obsc_fbk_dict['eq30']['ocn_dsc']['LO680']['LWcld_amt'] + obsc_fbk_dict['eq30']['ocn_dsc']['LO680']['LWcld_tau'] +\
+    fbk_dict['eq30']['ocn_dsc']['HI680']['LWcld_amt'] + fbk_dict['eq30']['ocn_dsc']['HI680']['LWcld_tau'] +\
+    obsc_fbk_dict['eq30']['ocn_dsc']['LO680']['LWdobsc_fbk']
+    SW_B = obsc_fbk_dict['eq30']['ocn_asc']['LO680']['SWcld_amt'] + obsc_fbk_dict['eq30']['ocn_asc']['LO680']['SWcld_tau'] +\
+    fbk_dict['eq30']['ocn_asc']['HI680']['SWcld_amt'] + fbk_dict['eq30']['ocn_asc']['HI680']['SWcld_tau'] +\
+    obsc_fbk_dict['eq30']['ocn_asc']['LO680']['SWdobsc_fbk'] +\
+    obsc_fbk_dict['eq30']['ocn_dsc']['LO680']['SWcld_amt'] + obsc_fbk_dict['eq30']['ocn_dsc']['LO680']['SWcld_tau'] +\
+    fbk_dict['eq30']['ocn_dsc']['HI680']['SWcld_amt'] + fbk_dict['eq30']['ocn_dsc']['HI680']['SWcld_tau'] +\
+    obsc_fbk_dict['eq30']['ocn_dsc']['LO680']['SWdobsc_fbk']
+    unassessed.append(LW_A+SW_A-LW_B-SW_B)
+    fbk_names.append('Bony Omega Space Residual')
+    
+    sum_unassessed = np.array(unassessed).sum()
+    unassessed.append(sum_unassessed)
+    fbk_names.append('Sum of Unassessed Cloud Feedbacks')
+    
+    return(np.array(unassessed),fbk_names) # size [fbk_types]
+
diff --git a/pcmdi_metrics/cloud_feedback/lib/organize_jsons.py b/pcmdi_metrics/cloud_feedback/lib/organize_jsons.py
index 6084ca433..75cd06bdc 100644
--- a/pcmdi_metrics/cloud_feedback/lib/organize_jsons.py
+++ b/pcmdi_metrics/cloud_feedback/lib/organize_jsons.py
@@ -2,101 +2,84 @@
 # Compute NET = LW+SW feedbacks
 # Append this dictionary to the existing json containing feedbacks and error metrics
 
-import json
-from datetime import date
-import os
-
+from datetime import date 
+import urllib.request
 import numpy as np
+import json
 
 meta = {
-    "date_modified": str(date.today()),
-    "author": "Mark D. Zelinka <zelinka1@llnl.gov>",
+"date_modified" :   str(date.today()),
+"author"        :   "Mark D. Zelinka <zelinka1@llnl.gov>",
 }
 
+# Location of the cloud feedback and error metric jsons:
+urlpath = 'https://raw.githubusercontent.com/mzelinka/assessed-cloud-fbks/main/data/'
 
-def organize_fbk_jsons(new_dict, new_obsc_dict, mo, ripf, datadir=None):
-
-    if datadir is None:
-        datadir = "./data/"
+def organize_fbk_jsons(new_dict,new_obsc_dict,mo,ripf):
 
-    # Load in the existing file containing pre-computed CMIP6 feedbacks
-    file = os.path.join(datadir, "cmip6_amip-p4K_cld_fbks.json")
-    f = open(file, "r")
-    old_dict = json.load(f)
-    f.close()
+    # Load in the existing file containing pre-computed CMIP6 feedbacks    
+    fname = 'cmip6_amip-p4K_cld_fbks.json'
+    with urllib.request.urlopen(urlpath+fname) as url:
+        old_dict = json.load(url)   
 
-    old_dict[mo] = {}
-    old_dict[mo][ripf] = {}
+    old_dict[mo]={} 
+    old_dict[mo][ripf]={}
     old_dict[mo][ripf] = new_dict
-    old_dict["metadata"] = meta
+    old_dict['metadata'] = meta
 
     # Load in the existing file containing pre-computed CMIP6 obscuration-related feedbacks
-    file = os.path.join(datadir, "cmip6_amip-p4K_cld_obsc_fbks.json")
-    f = open(file, "r")
-    old_obsc_dict = json.load(f)
-    f.close()
+    fname = 'cmip6_amip-p4K_cld_obsc_fbks.json'
+    with urllib.request.urlopen(urlpath+fname) as url:
+        old_obsc_dict = json.load(url)
 
-    old_obsc_dict[mo] = {}
-    old_obsc_dict[mo][ripf] = {}
+    old_obsc_dict[mo]={} 
+    old_obsc_dict[mo][ripf]={}
     old_obsc_dict[mo][ripf] = new_obsc_dict
-    old_obsc_dict["metadata"] = meta
+    old_obsc_dict['metadata'] = meta
+    
+    return(old_dict,old_obsc_dict) # now updated to include info from input dictionary 
 
-    return (
-        old_dict,
-        old_obsc_dict,
-    )  # now updated to include info from input dictionary
 
-
-def organize_err_jsons(new_dict, mo, ripf, datadir=None):
-
-    if datadir is None:
-        datadir = "./data/"    
+def organize_err_jsons(new_dict,mo,ripf):
 
     # Load in the existing file containing pre-computed CMIP6 error metrics
-    file = os.path.join(datadir, "cmip6_amip_cld_errs.json")
-    f = open(file, "r")
-    old_dict = json.load(f)
-    f.close()
-
-    names = ["E_TCA", "E_ctpt", "E_LW", "E_SW", "E_NET"]
-    old_dict[mo] = {}
-    old_dict[mo][ripf] = {}
-    for region in new_dict["ALL"].keys():
-        old_dict[mo][ripf][region] = {}
-        for sfc in ["all", "ocn", "lnd", "ocn_asc", "ocn_dsc"]:
-            old_dict[mo][ripf][region][sfc] = {}
-            for sec in ["ALL", "HI680", "LO680"]:
-                old_dict[mo][ripf][region][sfc][sec] = {}
+    fname = 'cmip6_amip_cld_errs.json'
+    with urllib.request.urlopen(urlpath+fname) as url:
+        old_dict = json.load(url)
+
+    names = ['E_TCA','E_ctpt','E_LW','E_SW','E_NET']
+    old_dict[mo]={} 
+    old_dict[mo][ripf]={}
+    for region in new_dict['ALL'].keys():
+        old_dict[mo][ripf][region]={}
+        for sfc in ['all','ocn','lnd','ocn_asc','ocn_dsc']:
+            old_dict[mo][ripf][region][sfc]={}
+            for sec in ['ALL','HI680','LO680']:
+                old_dict[mo][ripf][region][sfc][sec]={}
                 for name in names:
                     # place in a natural order (e.g., Tropical marine low cloud amount = region/sfc/sec/name)
                     try:
-                        old_dict[mo][ripf][region][sfc][sec][name] = new_dict[sec][
-                            region
-                        ][sfc][name]
-                    except Exception:
+                        old_dict[mo][ripf][region][sfc][sec][name] = new_dict[sec][region][sfc][name] 
+                    except:
                         old_dict[mo][ripf][region][sfc][sec][name] = np.nan
                 # end name loop
             # end section loop:
         # end sfc type loop
-    # end region loop
-    old_dict["metadata"] = meta
-
-    return old_dict  # now updated to include info from input dictionary
+    # end region loop   
+    old_dict['metadata'] = meta
 
+    return(old_dict) # now updated to include info from input dictionary 
 
-def organize_ecs_jsons(new_ecs, mo, ripf, datadir=None):
 
-    if datadir is None:
-        datadir = "./data/"
+def organize_ecs_jsons(new_ecs,mo,ripf):
 
     ##################################################################
     # READ IN GREGORY ECS VALUES DERIVED IN ZELINKA ET AL (2020) GRL #
     ##################################################################
-    f = open(os.path.join(datadir, "cmip56_forcing_feedback_ecs.json"), "r")
-    old_dict = json.load(f)
-    f.close()
+    with urllib.request.urlopen('https://raw.githubusercontent.com/mzelinka/cmip56_forcing_feedback_ecs/master/cmip56_forcing_feedback_ecs.json') as url:
+        old_dict = json.load(url) 
 
-    if new_ecs is not None:
-        old_dict["CMIP6"][mo][ripf]["ECS"] = new_ecs
+    if new_ecs!=None:
+        old_dict['CMIP6'][mo][ripf]['ECS'] = new_ecs
 
-    return old_dict  # now updated to include info from input dictionary
+    return(old_dict) # now updated to include info from input dictionary 

From 9cc0f1f5510db31d57cf393899460873cc7d618c Mon Sep 17 00:00:00 2001
From: mzelinka <zelinka1@llnl.gov>
Date: Thu, 28 Sep 2023 15:34:25 -0700
Subject: [PATCH 073/133] python scripts reformatted with black

---
 .../lib/cal_CloudRadKernel_xr.py              |   3 +-
 .../cloud_feedback/lib/compute_ECS_xr.py      | 154 ++++---
 .../cloud_feedback/lib/get_assessment.py      | 397 +++++++++++-------
 .../cloud_feedback/lib/organize_jsons.py      |  89 ++--
 4 files changed, 380 insertions(+), 263 deletions(-)

diff --git a/pcmdi_metrics/cloud_feedback/lib/cal_CloudRadKernel_xr.py b/pcmdi_metrics/cloud_feedback/lib/cal_CloudRadKernel_xr.py
index 60af64dd1..1dfffbbf9 100644
--- a/pcmdi_metrics/cloud_feedback/lib/cal_CloudRadKernel_xr.py
+++ b/pcmdi_metrics/cloud_feedback/lib/cal_CloudRadKernel_xr.py
@@ -57,7 +57,7 @@
 ###########################################################################
 def get_amip_data(filename, var, lev=None):
     # load in cmip data using the appropriate function for the experiment/mip
-    print('  '+var)#, end=",")
+    print("  " + var)  # , end=",")
 
     tslice = slice(
         "1983-01-01", "2008-12-31"
@@ -191,7 +191,6 @@ def get_CRK_data(filepath):
 def get_kernel_regrid(ctl):
     # Read in data and map kernels to lat/lon
 
-    
     f = xc.open_mfdataset(datadir + "cloud_kernels2.nc", decode_times=False)
     f = f.rename({"mo": "time", "tau_midpt": "tau", "p_midpt": "plev"})
     f["time"] = ctl["time"].copy()
diff --git a/pcmdi_metrics/cloud_feedback/lib/compute_ECS_xr.py b/pcmdi_metrics/cloud_feedback/lib/compute_ECS_xr.py
index b3db85e05..954032339 100644
--- a/pcmdi_metrics/cloud_feedback/lib/compute_ECS_xr.py
+++ b/pcmdi_metrics/cloud_feedback/lib/compute_ECS_xr.py
@@ -6,18 +6,20 @@
 from scipy import stats
 import cftime
 
+
 def dict_to_dataset(DICT):
     # Convert a dictionary of Datasets to a single Dataset
-    data_vars={}
+    data_vars = {}
     for var in list(DICT.keys()):
         data_vars[var] = (["year"], DICT[var][var].data)
     ds = xr.Dataset(
         data_vars,
         coords=dict(year=DICT[var].year),
     )
-    return(ds)
+    return ds
+
 
-def get_branch_time(pi,ab):
+def get_branch_time(pi, ab):
     # Determine when abrupt overlaps with piControl
     # returns the following:
     # 1) st: the branch time in cftime.datetime format
@@ -25,99 +27,111 @@ def get_branch_time(pi,ab):
     # 3) extensions beyond the ~150-year overlap period to allow for 21-year rolling mean:
     #    3a) extend_st -- the date 10 years prior to st
     #    3b) extend_fn -- the date 10 years after fn
-    
+
     var = ab.variable_id
     source = ab.source_id
     experiment = ab.experiment_id
-    absub = ab.isel(time=slice(0,12*150))
-    lenab = int(absub[var].shape[0]/12)-1
-    btip = int(absub.attrs['branch_time_in_parent']) # timestep, not a date
+    absub = ab.isel(time=slice(0, 12 * 150))
+    lenab = int(absub[var].shape[0] / 12) - 1
+    btip = int(absub.attrs["branch_time_in_parent"])  # timestep, not a date
 
-    if source=='GFDL-CM4' and (experiment=='1pctCO2' or experiment=='abrupt-4xCO2' or experiment=='historical'): 
-        btip = 91250 # https://errata.es-doc.org/static/view.html?uid=2f6b5963-f87e-b2df-a5b0-2f12b6b68d32    
-    ptu = absub.attrs['parent_time_units']
+    if source == "GFDL-CM4" and (
+        experiment == "1pctCO2"
+        or experiment == "abrupt-4xCO2"
+        or experiment == "historical"
+    ):
+        btip = 91250  # https://errata.es-doc.org/static/view.html?uid=2f6b5963-f87e-b2df-a5b0-2f12b6b68d32
+    ptu = absub.attrs["parent_time_units"]
     # print('  branch time in parent:   '+str(btip)+' '+ptu)
     # print('  parent times:   '+str(pi.time.dt.year[0].values)+' - '+str(pi.time.dt.year[-1].values))
-    start = int(ptu.split(' ')[2][:4])
+    start = int(ptu.split(" ")[2][:4])
     cal = pi.time.dt.calendar
     # see http://cfconventions.org/cf-conventions/cf-conventions.html#calendar
-    if cal=='360_day':
-        dpy=360
-    elif cal=='noleap':
-        dpy=365
-    elif cal=='proleptic_gregorian' or cal=='standard' or cal=='julian':
-        dpy=365.25
+    if cal == "360_day":
+        dpy = 360
+    elif cal == "noleap":
+        dpy = 365
+    elif cal == "proleptic_gregorian" or cal == "standard" or cal == "julian":
+        dpy = 365.25
     else:
-        raise(Exception('Not sure how to handle '+cal+' calendar'))
-    if source=='CIESM' and (experiment=='abrupt-4xCO2' or experiment=='1pctCO2'):
+        raise (Exception("Not sure how to handle " + cal + " calendar"))
+    if source == "CIESM" and (experiment == "abrupt-4xCO2" or experiment == "1pctCO2"):
         # info from Yi Qin 10/1/20:
-        # "The branch_time for 4xCO2 should be the 101-yr of these published 500-yr piControl data, 
+        # "The branch_time for 4xCO2 should be the 101-yr of these published 500-yr piControl data,
         # but I wrongly regarded the “raw” piControl with piControl-spinup as the parent_case, which is a 1000-yr long run.
-        year0 = 101 
-    elif source=='KACE-1-0-G' and (experiment=='abrupt-4xCO2' or experiment=='1pctCO2'):
+        year0 = 101
+    elif source == "KACE-1-0-G" and (
+        experiment == "abrupt-4xCO2" or experiment == "1pctCO2"
+    ):
         # info from Jae-Hee Lee (via Gavin Schmidt on 1/26/22):
-        # "We used year 2150 of piControl as the initial condition for the KACE-1-0-G 1pctCO2 runs. 
+        # "We used year 2150 of piControl as the initial condition for the KACE-1-0-G 1pctCO2 runs.
         # so, you can assume that 1850 in the 1pctCO2 and 2150 in the piControl are the same
-        year0 = 2150         
+        year0 = 2150
     else:
-        year0 = int((btip/dpy)+start)
-    year150 = int(year0+lenab)
-    st=cftime.datetime(year0, 1, 1).strftime("%Y-%m-%dT%H:%M:%S")
-    fn=cftime.datetime(year150, 12, 31,23,59,59).strftime("%Y-%m-%dT%H:%M:%S")
+        year0 = int((btip / dpy) + start)
+    year150 = int(year0 + lenab)
+    st = cftime.datetime(year0, 1, 1).strftime("%Y-%m-%dT%H:%M:%S")
+    fn = cftime.datetime(year150, 12, 31, 23, 59, 59).strftime("%Y-%m-%dT%H:%M:%S")
     # Add on 10 years before st and after fn to assist in computing 21-year rolling means
-    extend_st=cftime.datetime(np.max((year0-10,1)), 1, 1).strftime("%Y-%m-%dT%H:%M:%S")
-    extend_fn=cftime.datetime(year150+10, 12, pi.time.dt.day[-1],23,59,59).strftime("%Y-%m-%dT%H:%M:%S") 
+    extend_st = cftime.datetime(np.max((year0 - 10, 1)), 1, 1).strftime(
+        "%Y-%m-%dT%H:%M:%S"
+    )
+    extend_fn = cftime.datetime(
+        year150 + 10, 12, pi.time.dt.day[-1], 23, 59, 59
+    ).strftime("%Y-%m-%dT%H:%M:%S")
+
+    return (extend_st, extend_fn, st, fn)  # ,year0,year150)
 
-    return(extend_st,extend_fn,st,fn)#,year0,year150)
 
-def get_data(pi,ab):  
+def get_data(pi, ab):
     # Return the piControl running mean and the abrupt deviation from this
 
-    var=ab.variable_id
-    extend_st,extend_fn,st,fn = get_branch_time(pi,ab)
-    absub = ab.isel(time=slice(0,12*150))
-    pisub = pi.sel(time=slice(extend_st,extend_fn))
-    if len(pisub.time)<1200:
-        print('len(time)<1200...skipping')
+    var = ab.variable_id
+    extend_st, extend_fn, st, fn = get_branch_time(pi, ab)
+    absub = ab.isel(time=slice(0, 12 * 150))
+    pisub = pi.sel(time=slice(extend_st, extend_fn))
+    if len(pisub.time) < 1200:
+        print("len(time)<1200...skipping")
         return None
 
-    bdate = pisub.time.dt.year.values 
+    bdate = pisub.time.dt.year.values
     # compute global means:
     GLcntl = pisub.spatial.average(var)
     GLpert = absub.spatial.average(var)
     # compute annual means:
-    annpert = GLpert.groupby('time.year').mean('time')
-    anncntl = GLcntl.groupby('time.year').mean('time')
+    annpert = GLpert.groupby("time.year").mean("time")
+    anncntl = GLcntl.groupby("time.year").mean("time")
     # compute 21-year centered rolling mean:
-    runcntl = anncntl.rolling(year = 21, min_periods = 1, center = True).mean()
+    runcntl = anncntl.rolling(year=21, min_periods=1, center=True).mean()
     # subset the rolling mean for the actual overlapping time:
-    runcntlsub = runcntl.sel(year=slice(int(st[:4]),int(fn[:4])))
+    runcntlsub = runcntl.sel(year=slice(int(st[:4]), int(fn[:4])))
     # set the abrupt run's year coord to match the overlapping piCon's year coord
-    annpert=annpert.assign_coords(year=runcntlsub.coords['year'])
+    annpert = annpert.assign_coords(year=runcntlsub.coords["year"])
     # compute abrupt anomalies from piControl running mean:
     annanom = annpert - runcntlsub
-    
-    return(runcntl,annanom)
 
-def compute_abrupt_anoms(pifilepath,abfilepath):
+    return (runcntl, annanom)
+
+
+def compute_abrupt_anoms(pifilepath, abfilepath):
     # compute annual- anad global-mean tas and EEI anomalies in abrupt4xCO2 w.r.t. piControl climo, then compute ERF, lambda, and ECS via Gregory
-    cntl={}
-    anom={}
-    skip=False
+    cntl = {}
+    anom = {}
+    skip = False
     variables = list(pifilepath.keys())
     for var in variables:
-        pi=xcdat.open_mfdataset(pifilepath[var], use_cftime = True)
-        ab=xcdat.open_mfdataset(abfilepath[var], use_cftime = True)
-        
-        if len(ab.time)<140*12:
-            print('   len(ab.time)<140*12')
-            skip=True
+        pi = xcdat.open_mfdataset(pifilepath[var], use_cftime=True)
+        ab = xcdat.open_mfdataset(abfilepath[var], use_cftime=True)
+
+        if len(ab.time) < 140 * 12:
+            print("   len(ab.time)<140*12")
+            skip = True
             break
-        output = get_data(pi,ab)
+        output = get_data(pi, ab)
         if output:
-            cntl[var],anom[var] = output
+            cntl[var], anom[var] = output
         else:
-            skip=True
+            skip = True
             break
     if skip:
         # print('Skipping this model...')
@@ -126,18 +140,18 @@ def compute_abrupt_anoms(pifilepath,abfilepath):
     CNTL = dict_to_dataset(cntl)
     ANOM = dict_to_dataset(anom)
 
-    CNTL['eei'] = CNTL['rsdt']-CNTL['rsut']-CNTL['rlut']
-    ANOM['eei'] = ANOM['rsdt']-ANOM['rsut']-ANOM['rlut']
+    CNTL["eei"] = CNTL["rsdt"] - CNTL["rsut"] - CNTL["rlut"]
+    ANOM["eei"] = ANOM["rsdt"] - ANOM["rsut"] - ANOM["rlut"]
 
-    x = ANOM['tas'].load()
-    y = ANOM['eei'].load()
+    x = ANOM["tas"].load()
+    y = ANOM["eei"].load()
 
-    return(x,y)
+    return (x, y)
 
-def gregory_calcs(x,y):
-    m, b, r, p, std_err = stats.linregress(x,y)
-    ERF = b/2
-    lam = m
-    ECS = -ERF/lam
-    return (ERF,lam,ECS)
 
+def gregory_calcs(x, y):
+    m, b, r, p, std_err = stats.linregress(x, y)
+    ERF = b / 2
+    lam = m
+    ECS = -ERF / lam
+    return (ERF, lam, ECS)
diff --git a/pcmdi_metrics/cloud_feedback/lib/get_assessment.py b/pcmdi_metrics/cloud_feedback/lib/get_assessment.py
index 4cc5c5be2..ec38cac70 100644
--- a/pcmdi_metrics/cloud_feedback/lib/get_assessment.py
+++ b/pcmdi_metrics/cloud_feedback/lib/get_assessment.py
@@ -1,217 +1,316 @@
 #!/usr/bin/env python
 # coding: utf-8
 
-#=============================================
+# =============================================
 # calculate assessed and unassessed feedbacks
 # revised from Zelinka et al (2022)
 # by Li-Wei Chao (2022/02/25)
-#=============================================
+# =============================================
 
 import numpy as np
 
 # 10 hPa/dy wide bins:
 width = 10
-binedges=np.arange(-100,100,width)
-x1=np.arange(binedges[0]-width,binedges[-1]+2*width,width)
-binmids = x1+width/2.
-cutoff = int(len(binmids)/2) 
+binedges = np.arange(-100, 100, width)
+x1 = np.arange(binedges[0] - width, binedges[-1] + 2 * width, width)
+binmids = x1 + width / 2.0
+cutoff = int(len(binmids) / 2)
 
-def get_assessed_fbks(fbk_dict,obsc_fbk_dict):
 
+def get_assessed_fbks(fbk_dict, obsc_fbk_dict):
     # dictionary is structured: [region][sfc][sec][name]
 
-    assessed=[]
-    fbk_names=[]
-    
+    assessed = []
+    fbk_names = []
+
     # 1) Global high cloud altitude
-    #===========================================
-    LW = fbk_dict['eq90']['all']['HI680']['LWcld_alt']
-    SW = fbk_dict['eq90']['all']['HI680']['SWcld_alt']
-    assessed.append(LW+SW)
-    fbk_names.append('High-Cloud Altitude')
+    # ===========================================
+    LW = fbk_dict["eq90"]["all"]["HI680"]["LWcld_alt"]
+    SW = fbk_dict["eq90"]["all"]["HI680"]["SWcld_alt"]
+    assessed.append(LW + SW)
+    fbk_names.append("High-Cloud Altitude")
 
     # 2) Tropical Ocean Descent Low AMT + TAU
-    #===========================================
+    # ===========================================
     # Unobscured components only:
-    LW = obsc_fbk_dict['eq30']['ocn_dsc']['LO680']['LWcld_amt'] + obsc_fbk_dict['eq30']['ocn_dsc']['LO680']['LWcld_tau']
-    SW = obsc_fbk_dict['eq30']['ocn_dsc']['LO680']['SWcld_amt'] + obsc_fbk_dict['eq30']['ocn_dsc']['LO680']['SWcld_tau']
-    assessed.append(LW+SW)
-    fbk_names.append('Tropical Marine Low-Cloud')
+    LW = (
+        obsc_fbk_dict["eq30"]["ocn_dsc"]["LO680"]["LWcld_amt"]
+        + obsc_fbk_dict["eq30"]["ocn_dsc"]["LO680"]["LWcld_tau"]
+    )
+    SW = (
+        obsc_fbk_dict["eq30"]["ocn_dsc"]["LO680"]["SWcld_amt"]
+        + obsc_fbk_dict["eq30"]["ocn_dsc"]["LO680"]["SWcld_tau"]
+    )
+    assessed.append(LW + SW)
+    fbk_names.append("Tropical Marine Low-Cloud")
 
     # 3) Anvil
-    #===========================================
+    # ===========================================
     # Tropical oceanic ascent: High amt + high tau + delta obscuration-induced low
-    LW = fbk_dict['eq30']['ocn_asc']['HI680']['LWcld_amt'] + fbk_dict['eq30']['ocn_asc']['HI680']['LWcld_tau'] +  \
-          obsc_fbk_dict['eq30']['ocn_asc']['LO680']['LWdobsc_fbk']
-    SW = fbk_dict['eq30']['ocn_asc']['HI680']['SWcld_amt'] + fbk_dict['eq30']['ocn_asc']['HI680']['SWcld_tau'] +  \
-          obsc_fbk_dict['eq30']['ocn_asc']['LO680']['SWdobsc_fbk']
-    assessed.append(LW+SW)
-    fbk_names.append('Tropical Anvil Cloud')
+    LW = (
+        fbk_dict["eq30"]["ocn_asc"]["HI680"]["LWcld_amt"]
+        + fbk_dict["eq30"]["ocn_asc"]["HI680"]["LWcld_tau"]
+        + obsc_fbk_dict["eq30"]["ocn_asc"]["LO680"]["LWdobsc_fbk"]
+    )
+    SW = (
+        fbk_dict["eq30"]["ocn_asc"]["HI680"]["SWcld_amt"]
+        + fbk_dict["eq30"]["ocn_asc"]["HI680"]["SWcld_tau"]
+        + obsc_fbk_dict["eq30"]["ocn_asc"]["LO680"]["SWdobsc_fbk"]
+    )
+    assessed.append(LW + SW)
+    fbk_names.append("Tropical Anvil Cloud")
 
     # 4) Global land cloud amount
     # !!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
     # CHANGE
-    #===========================================
+    # ===========================================
     # unobscured low cloud amount + high cloud amount + ∆obscuration
-    LW = fbk_dict['eq90']['lnd']['HI680']['LWcld_amt'] + obsc_fbk_dict['eq90']['lnd']['LO680']['LWcld_amt'] +  \
-                                                         obsc_fbk_dict['eq90']['lnd']['LO680']['LWdobsc_fbk']
-    SW = fbk_dict['eq90']['lnd']['HI680']['SWcld_amt'] + obsc_fbk_dict['eq90']['lnd']['LO680']['SWcld_amt'] +  \
-                                                         obsc_fbk_dict['eq90']['lnd']['LO680']['SWdobsc_fbk']
-    assessed.append(LW+SW)
-    fbk_names.append('Land Cloud Amount')
+    LW = (
+        fbk_dict["eq90"]["lnd"]["HI680"]["LWcld_amt"]
+        + obsc_fbk_dict["eq90"]["lnd"]["LO680"]["LWcld_amt"]
+        + obsc_fbk_dict["eq90"]["lnd"]["LO680"]["LWdobsc_fbk"]
+    )
+    SW = (
+        fbk_dict["eq90"]["lnd"]["HI680"]["SWcld_amt"]
+        + obsc_fbk_dict["eq90"]["lnd"]["LO680"]["SWcld_amt"]
+        + obsc_fbk_dict["eq90"]["lnd"]["LO680"]["SWdobsc_fbk"]
+    )
+    assessed.append(LW + SW)
+    fbk_names.append("Land Cloud Amount")
 
     # 5) Middle latitude cloud amount feedback
-    #===========================================
+    # ===========================================
     # Using the unobscured components:
-    LW = obsc_fbk_dict['eq60']['ocn']['LO680']['LWcld_amt'] - obsc_fbk_dict['eq30']['ocn']['LO680']['LWcld_amt']
-    SW = obsc_fbk_dict['eq60']['ocn']['LO680']['SWcld_amt'] - obsc_fbk_dict['eq30']['ocn']['LO680']['SWcld_amt']
-    assessed.append(LW+SW)
-    fbk_names.append('Middle Latitude Marine Low-Cloud Amount')
+    LW = (
+        obsc_fbk_dict["eq60"]["ocn"]["LO680"]["LWcld_amt"]
+        - obsc_fbk_dict["eq30"]["ocn"]["LO680"]["LWcld_amt"]
+    )
+    SW = (
+        obsc_fbk_dict["eq60"]["ocn"]["LO680"]["SWcld_amt"]
+        - obsc_fbk_dict["eq30"]["ocn"]["LO680"]["SWcld_amt"]
+    )
+    assessed.append(LW + SW)
+    fbk_names.append("Middle Latitude Marine Low-Cloud Amount")
 
     # 6) Extratropical cloud optical depth feedback
-    #===========================================
+    # ===========================================
     # Using the unobscured components:
-    LW = obsc_fbk_dict['eq70']['all']['LO680']['LWcld_tau'] - obsc_fbk_dict['eq40']['all']['LO680']['LWcld_tau']
-    SW = obsc_fbk_dict['eq70']['all']['LO680']['SWcld_tau'] - obsc_fbk_dict['eq40']['all']['LO680']['SWcld_tau']
-    assessed.append(LW+SW)
-    fbk_names.append('High Latitude Low-Cloud Optical Depth')
+    LW = (
+        obsc_fbk_dict["eq70"]["all"]["LO680"]["LWcld_tau"]
+        - obsc_fbk_dict["eq40"]["all"]["LO680"]["LWcld_tau"]
+    )
+    SW = (
+        obsc_fbk_dict["eq70"]["all"]["LO680"]["SWcld_tau"]
+        - obsc_fbk_dict["eq40"]["all"]["LO680"]["SWcld_tau"]
+    )
+    assessed.append(LW + SW)
+    fbk_names.append("High Latitude Low-Cloud Optical Depth")
 
     # 7) true_total
-    #===========================================
-    LW = fbk_dict['eq90']['all']['ALL']['LWcld_tot']
-    SW = fbk_dict['eq90']['all']['ALL']['SWcld_tot']
-    true_total = LW+SW
+    # ===========================================
+    LW = fbk_dict["eq90"]["all"]["ALL"]["LWcld_tot"]
+    SW = fbk_dict["eq90"]["all"]["ALL"]["SWcld_tot"]
+    true_total = LW + SW
 
     sum_assessed = np.array(assessed).sum()
-    imply_not_assessed = true_total - sum_assessed 
+    imply_not_assessed = true_total - sum_assessed
     assessed.append(imply_not_assessed)
     assessed.append(sum_assessed)
     assessed.append(true_total)
-    
-    fbk_names.append('Implied Unassessed Cloud Feedbacks')
-    fbk_names.append('Sum of Assessed Cloud Feedbacks')
-    fbk_names.append('Total Cloud Feedback')
-    
-    return(np.array(assessed),fbk_names) # size [fbk_types]  
 
+    fbk_names.append("Implied Unassessed Cloud Feedbacks")
+    fbk_names.append("Sum of Assessed Cloud Feedbacks")
+    fbk_names.append("Total Cloud Feedback")
 
+    return (np.array(assessed), fbk_names)  # size [fbk_types]
 
-def get_unassessed_fbks(fbk_dict,obsc_fbk_dict):
 
+def get_unassessed_fbks(fbk_dict, obsc_fbk_dict):
     # dictionary is structured: [region][sfc][sec][name]
 
-    fbk_names=[]
-    unassessed=[]
-        
+    fbk_names = []
+    unassessed = []
+
     # 1) Global unobscured low altitude (Complement to global high cloud altitude)
-    #===========================================
-    LW = obsc_fbk_dict['eq90']['all']['LO680']['LWcld_alt']
-    SW = obsc_fbk_dict['eq90']['all']['LO680']['SWcld_alt']
-    unassessed.append(LW+SW)
-    fbk_names.append('Low-Cloud Altitude')
+    # ===========================================
+    LW = obsc_fbk_dict["eq90"]["all"]["LO680"]["LWcld_alt"]
+    SW = obsc_fbk_dict["eq90"]["all"]["LO680"]["SWcld_alt"]
+    unassessed.append(LW + SW)
+    fbk_names.append("Low-Cloud Altitude")
 
     # 2) Tropical Ocean Descent High AMT+TAU (Complement to Tropical Ocean Descent Low AMT+TAU)
-    #===========================================
+    # ===========================================
     # High AMT+TAU + ∆obscuration
-    LW = fbk_dict['eq30']['ocn_dsc']['HI680']['LWcld_amt'] + fbk_dict['eq30']['ocn_dsc']['HI680']['LWcld_tau'] + \
-                                                        obsc_fbk_dict['eq30']['ocn_dsc']['LO680']['LWdobsc_fbk']
-    SW = fbk_dict['eq30']['ocn_dsc']['HI680']['SWcld_amt'] + fbk_dict['eq30']['ocn_dsc']['HI680']['SWcld_tau'] + \
-                                                        obsc_fbk_dict['eq30']['ocn_dsc']['LO680']['SWdobsc_fbk']
-    unassessed.append(LW+SW)
-    fbk_names.append('Tropical Marine Subsidence\nHigh-Cloud Amount + Optical Depth')
-    
+    LW = (
+        fbk_dict["eq30"]["ocn_dsc"]["HI680"]["LWcld_amt"]
+        + fbk_dict["eq30"]["ocn_dsc"]["HI680"]["LWcld_tau"]
+        + obsc_fbk_dict["eq30"]["ocn_dsc"]["LO680"]["LWdobsc_fbk"]
+    )
+    SW = (
+        fbk_dict["eq30"]["ocn_dsc"]["HI680"]["SWcld_amt"]
+        + fbk_dict["eq30"]["ocn_dsc"]["HI680"]["SWcld_tau"]
+        + obsc_fbk_dict["eq30"]["ocn_dsc"]["LO680"]["SWdobsc_fbk"]
+    )
+    unassessed.append(LW + SW)
+    fbk_names.append("Tropical Marine Subsidence\nHigh-Cloud Amount + Optical Depth")
+
     # 3) Complement to the Anvil
-    #===========================================
+    # ===========================================
     # Tropical oceanic ascent: unobscured low amt + low tau (complement to High amt + high tau + delta obscuration-induced low)
-    LW = obsc_fbk_dict['eq30']['ocn_asc']['LO680']['LWcld_amt'] + obsc_fbk_dict['eq30']['ocn_asc']['LO680']['LWcld_tau']
-    SW = obsc_fbk_dict['eq30']['ocn_asc']['LO680']['SWcld_amt'] + obsc_fbk_dict['eq30']['ocn_asc']['LO680']['SWcld_tau']
-    unassessed.append(LW+SW)
-    fbk_names.append('Tropical Marine Ascent\nLow-Cloud Amount + Optical Depth')
-    
+    LW = (
+        obsc_fbk_dict["eq30"]["ocn_asc"]["LO680"]["LWcld_amt"]
+        + obsc_fbk_dict["eq30"]["ocn_asc"]["LO680"]["LWcld_tau"]
+    )
+    SW = (
+        obsc_fbk_dict["eq30"]["ocn_asc"]["LO680"]["SWcld_amt"]
+        + obsc_fbk_dict["eq30"]["ocn_asc"]["LO680"]["SWcld_tau"]
+    )
+    unassessed.append(LW + SW)
+    fbk_names.append("Tropical Marine Ascent\nLow-Cloud Amount + Optical Depth")
+
     # 4) Tropical land high+low optical depth (Complement to Global land cloud amount)
-    #===========================================
-    LW = fbk_dict['eq30']['lnd']['HI680']['LWcld_tau'] + obsc_fbk_dict['eq30']['lnd']['LO680']['LWcld_tau']
-    SW = fbk_dict['eq30']['lnd']['HI680']['SWcld_tau'] + obsc_fbk_dict['eq30']['lnd']['LO680']['SWcld_tau']
-    unassessed.append(LW+SW)
-    fbk_names.append('Tropical Land Cloud Optical Depth')
+    # ===========================================
+    LW = (
+        fbk_dict["eq30"]["lnd"]["HI680"]["LWcld_tau"]
+        + obsc_fbk_dict["eq30"]["lnd"]["LO680"]["LWcld_tau"]
+    )
+    SW = (
+        fbk_dict["eq30"]["lnd"]["HI680"]["SWcld_tau"]
+        + obsc_fbk_dict["eq30"]["lnd"]["LO680"]["SWcld_tau"]
+    )
+    unassessed.append(LW + SW)
+    fbk_names.append("Tropical Land Cloud Optical Depth")
 
     # 5) Complement to middle latitude cloud amount feedback
-    #===========================================
+    # ===========================================
     # 60-90 Ocean unobscured Low AMT
-    #===========================================
-    LW = obsc_fbk_dict['eq90']['ocn']['LO680']['LWcld_amt'] - obsc_fbk_dict['eq60']['ocn']['LO680']['LWcld_amt']
-    SW = obsc_fbk_dict['eq90']['ocn']['LO680']['SWcld_amt'] - obsc_fbk_dict['eq60']['ocn']['LO680']['SWcld_amt']
-    unassessed.append(LW+SW)
-    fbk_names.append('60-90 Marine Low-Cloud Amount')
+    # ===========================================
+    LW = (
+        obsc_fbk_dict["eq90"]["ocn"]["LO680"]["LWcld_amt"]
+        - obsc_fbk_dict["eq60"]["ocn"]["LO680"]["LWcld_amt"]
+    )
+    SW = (
+        obsc_fbk_dict["eq90"]["ocn"]["LO680"]["SWcld_amt"]
+        - obsc_fbk_dict["eq60"]["ocn"]["LO680"]["SWcld_amt"]
+    )
+    unassessed.append(LW + SW)
+    fbk_names.append("60-90 Marine Low-Cloud Amount")
 
     # 6) Complement to Extratropical cloud optical depth feedback
-    #===========================================
+    # ===========================================
     # 30-40/70-90 Ocean+Land unobscured Low TAU
-    #===========================================
-    LW = obsc_fbk_dict['eq40']['all']['LO680']['LWcld_tau'] - obsc_fbk_dict['eq30']['all']['LO680']['LWcld_tau'] + \
-         obsc_fbk_dict['eq90']['all']['LO680']['LWcld_tau'] - obsc_fbk_dict['eq70']['all']['LO680']['LWcld_tau']
-    SW = obsc_fbk_dict['eq40']['all']['LO680']['SWcld_tau'] - obsc_fbk_dict['eq30']['all']['LO680']['SWcld_tau'] + \
-         obsc_fbk_dict['eq90']['all']['LO680']['SWcld_tau'] - obsc_fbk_dict['eq70']['all']['LO680']['SWcld_tau']
-    unassessed.append(LW+SW)
-    fbk_names.append('30-40 / 70-90 Low-Cloud Optical Depth')
-   
+    # ===========================================
+    LW = (
+        obsc_fbk_dict["eq40"]["all"]["LO680"]["LWcld_tau"]
+        - obsc_fbk_dict["eq30"]["all"]["LO680"]["LWcld_tau"]
+        + obsc_fbk_dict["eq90"]["all"]["LO680"]["LWcld_tau"]
+        - obsc_fbk_dict["eq70"]["all"]["LO680"]["LWcld_tau"]
+    )
+    SW = (
+        obsc_fbk_dict["eq40"]["all"]["LO680"]["SWcld_tau"]
+        - obsc_fbk_dict["eq30"]["all"]["LO680"]["SWcld_tau"]
+        + obsc_fbk_dict["eq90"]["all"]["LO680"]["SWcld_tau"]
+        - obsc_fbk_dict["eq70"]["all"]["LO680"]["SWcld_tau"]
+    )
+    unassessed.append(LW + SW)
+    fbk_names.append("30-40 / 70-90 Low-Cloud Optical Depth")
+
     # 7) 30-90 Ocean+Land High TAU
-    #===========================================
-    LW = fbk_dict['eq90']['all']['HI680']['LWcld_tau'] - fbk_dict['eq30']['all']['HI680']['LWcld_tau']
-    SW = fbk_dict['eq90']['all']['HI680']['SWcld_tau'] - fbk_dict['eq30']['all']['HI680']['SWcld_tau']
-    unassessed.append(LW+SW)
-    fbk_names.append('30-90 High-Cloud Optical Depth')
-   
+    # ===========================================
+    LW = (
+        fbk_dict["eq90"]["all"]["HI680"]["LWcld_tau"]
+        - fbk_dict["eq30"]["all"]["HI680"]["LWcld_tau"]
+    )
+    SW = (
+        fbk_dict["eq90"]["all"]["HI680"]["SWcld_tau"]
+        - fbk_dict["eq30"]["all"]["HI680"]["SWcld_tau"]
+    )
+    unassessed.append(LW + SW)
+    fbk_names.append("30-90 High-Cloud Optical Depth")
+
     # 8) 30-90 Ocean High AMT + ∆obscuration
-    #===========================================
-    LW = fbk_dict['eq90']['ocn']['HI680']['LWcld_amt'] - fbk_dict['eq30']['ocn']['HI680']['LWcld_amt'] + \
-    obsc_fbk_dict['eq90']['ocn']['LO680']['LWdobsc_fbk']  - obsc_fbk_dict['eq30']['ocn']['LO680']['LWdobsc_fbk']
-    SW = fbk_dict['eq90']['ocn']['HI680']['SWcld_amt'] - fbk_dict['eq30']['ocn']['HI680']['SWcld_amt'] + \
-    obsc_fbk_dict['eq90']['ocn']['LO680']['SWdobsc_fbk']  - obsc_fbk_dict['eq30']['ocn']['LO680']['SWdobsc_fbk']
-    unassessed.append(LW+SW)
-    fbk_names.append('30-90 Marine High-Cloud Amount')
-        
+    # ===========================================
+    LW = (
+        fbk_dict["eq90"]["ocn"]["HI680"]["LWcld_amt"]
+        - fbk_dict["eq30"]["ocn"]["HI680"]["LWcld_amt"]
+        + obsc_fbk_dict["eq90"]["ocn"]["LO680"]["LWdobsc_fbk"]
+        - obsc_fbk_dict["eq30"]["ocn"]["LO680"]["LWdobsc_fbk"]
+    )
+    SW = (
+        fbk_dict["eq90"]["ocn"]["HI680"]["SWcld_amt"]
+        - fbk_dict["eq30"]["ocn"]["HI680"]["SWcld_amt"]
+        + obsc_fbk_dict["eq90"]["ocn"]["LO680"]["SWdobsc_fbk"]
+        - obsc_fbk_dict["eq30"]["ocn"]["LO680"]["SWdobsc_fbk"]
+    )
+    unassessed.append(LW + SW)
+    fbk_names.append("30-90 Marine High-Cloud Amount")
+
     # 9) Obscuration covariance term
-    #===========================================
-    LW = obsc_fbk_dict['eq90']['all']['LO680']['LWdobsc_cov_fbk']
-    SW = obsc_fbk_dict['eq90']['all']['LO680']['SWdobsc_cov_fbk']
-    unassessed.append(LW+SW)
-    fbk_names.append('Obscuration Covariance')
+    # ===========================================
+    LW = obsc_fbk_dict["eq90"]["all"]["LO680"]["LWdobsc_cov_fbk"]
+    SW = obsc_fbk_dict["eq90"]["all"]["LO680"]["SWdobsc_cov_fbk"]
+    unassessed.append(LW + SW)
+    fbk_names.append("Obscuration Covariance")
 
     # 10) Global Residual
-    #===========================================
-    LW = obsc_fbk_dict['eq90']['all']['LO680']['LWcld_err'] + fbk_dict['eq90']['all']['HI680']['LWcld_err']
-    SW = obsc_fbk_dict['eq90']['all']['LO680']['SWcld_err'] + fbk_dict['eq90']['all']['HI680']['SWcld_err']
-    unassessed.append(LW+SW)
-    fbk_names.append('Zelinka Decomposition Residual')
-    
+    # ===========================================
+    LW = (
+        obsc_fbk_dict["eq90"]["all"]["LO680"]["LWcld_err"]
+        + fbk_dict["eq90"]["all"]["HI680"]["LWcld_err"]
+    )
+    SW = (
+        obsc_fbk_dict["eq90"]["all"]["LO680"]["SWcld_err"]
+        + fbk_dict["eq90"]["all"]["HI680"]["SWcld_err"]
+    )
+    unassessed.append(LW + SW)
+    fbk_names.append("Zelinka Decomposition Residual")
+
     # 11) ocean ascent/descent Residual
-    #===========================================
-    LW_A = obsc_fbk_dict['eq30']['ocn']['LO680']['LWcld_amt'] + obsc_fbk_dict['eq30']['ocn']['LO680']['LWcld_tau'] +\
-    fbk_dict['eq30']['ocn']['HI680']['LWcld_amt'] + fbk_dict['eq30']['ocn']['HI680']['LWcld_tau'] +\
-    obsc_fbk_dict['eq30']['ocn']['LO680']['LWdobsc_fbk']
-    SW_A = obsc_fbk_dict['eq30']['ocn']['LO680']['SWcld_amt'] + obsc_fbk_dict['eq30']['ocn']['LO680']['SWcld_tau'] +\
-    fbk_dict['eq30']['ocn']['HI680']['SWcld_amt'] + fbk_dict['eq30']['ocn']['HI680']['SWcld_tau'] +\
-    obsc_fbk_dict['eq30']['ocn']['LO680']['SWdobsc_fbk']
-    
-    LW_B = obsc_fbk_dict['eq30']['ocn_asc']['LO680']['LWcld_amt'] + obsc_fbk_dict['eq30']['ocn_asc']['LO680']['LWcld_tau'] +\
-    fbk_dict['eq30']['ocn_asc']['HI680']['LWcld_amt'] + fbk_dict['eq30']['ocn_asc']['HI680']['LWcld_tau'] +\
-    obsc_fbk_dict['eq30']['ocn_asc']['LO680']['LWdobsc_fbk'] +\
-    obsc_fbk_dict['eq30']['ocn_dsc']['LO680']['LWcld_amt'] + obsc_fbk_dict['eq30']['ocn_dsc']['LO680']['LWcld_tau'] +\
-    fbk_dict['eq30']['ocn_dsc']['HI680']['LWcld_amt'] + fbk_dict['eq30']['ocn_dsc']['HI680']['LWcld_tau'] +\
-    obsc_fbk_dict['eq30']['ocn_dsc']['LO680']['LWdobsc_fbk']
-    SW_B = obsc_fbk_dict['eq30']['ocn_asc']['LO680']['SWcld_amt'] + obsc_fbk_dict['eq30']['ocn_asc']['LO680']['SWcld_tau'] +\
-    fbk_dict['eq30']['ocn_asc']['HI680']['SWcld_amt'] + fbk_dict['eq30']['ocn_asc']['HI680']['SWcld_tau'] +\
-    obsc_fbk_dict['eq30']['ocn_asc']['LO680']['SWdobsc_fbk'] +\
-    obsc_fbk_dict['eq30']['ocn_dsc']['LO680']['SWcld_amt'] + obsc_fbk_dict['eq30']['ocn_dsc']['LO680']['SWcld_tau'] +\
-    fbk_dict['eq30']['ocn_dsc']['HI680']['SWcld_amt'] + fbk_dict['eq30']['ocn_dsc']['HI680']['SWcld_tau'] +\
-    obsc_fbk_dict['eq30']['ocn_dsc']['LO680']['SWdobsc_fbk']
-    unassessed.append(LW_A+SW_A-LW_B-SW_B)
-    fbk_names.append('Bony Omega Space Residual')
-    
+    # ===========================================
+    LW_A = (
+        obsc_fbk_dict["eq30"]["ocn"]["LO680"]["LWcld_amt"]
+        + obsc_fbk_dict["eq30"]["ocn"]["LO680"]["LWcld_tau"]
+        + fbk_dict["eq30"]["ocn"]["HI680"]["LWcld_amt"]
+        + fbk_dict["eq30"]["ocn"]["HI680"]["LWcld_tau"]
+        + obsc_fbk_dict["eq30"]["ocn"]["LO680"]["LWdobsc_fbk"]
+    )
+    SW_A = (
+        obsc_fbk_dict["eq30"]["ocn"]["LO680"]["SWcld_amt"]
+        + obsc_fbk_dict["eq30"]["ocn"]["LO680"]["SWcld_tau"]
+        + fbk_dict["eq30"]["ocn"]["HI680"]["SWcld_amt"]
+        + fbk_dict["eq30"]["ocn"]["HI680"]["SWcld_tau"]
+        + obsc_fbk_dict["eq30"]["ocn"]["LO680"]["SWdobsc_fbk"]
+    )
+
+    LW_B = (
+        obsc_fbk_dict["eq30"]["ocn_asc"]["LO680"]["LWcld_amt"]
+        + obsc_fbk_dict["eq30"]["ocn_asc"]["LO680"]["LWcld_tau"]
+        + fbk_dict["eq30"]["ocn_asc"]["HI680"]["LWcld_amt"]
+        + fbk_dict["eq30"]["ocn_asc"]["HI680"]["LWcld_tau"]
+        + obsc_fbk_dict["eq30"]["ocn_asc"]["LO680"]["LWdobsc_fbk"]
+        + obsc_fbk_dict["eq30"]["ocn_dsc"]["LO680"]["LWcld_amt"]
+        + obsc_fbk_dict["eq30"]["ocn_dsc"]["LO680"]["LWcld_tau"]
+        + fbk_dict["eq30"]["ocn_dsc"]["HI680"]["LWcld_amt"]
+        + fbk_dict["eq30"]["ocn_dsc"]["HI680"]["LWcld_tau"]
+        + obsc_fbk_dict["eq30"]["ocn_dsc"]["LO680"]["LWdobsc_fbk"]
+    )
+    SW_B = (
+        obsc_fbk_dict["eq30"]["ocn_asc"]["LO680"]["SWcld_amt"]
+        + obsc_fbk_dict["eq30"]["ocn_asc"]["LO680"]["SWcld_tau"]
+        + fbk_dict["eq30"]["ocn_asc"]["HI680"]["SWcld_amt"]
+        + fbk_dict["eq30"]["ocn_asc"]["HI680"]["SWcld_tau"]
+        + obsc_fbk_dict["eq30"]["ocn_asc"]["LO680"]["SWdobsc_fbk"]
+        + obsc_fbk_dict["eq30"]["ocn_dsc"]["LO680"]["SWcld_amt"]
+        + obsc_fbk_dict["eq30"]["ocn_dsc"]["LO680"]["SWcld_tau"]
+        + fbk_dict["eq30"]["ocn_dsc"]["HI680"]["SWcld_amt"]
+        + fbk_dict["eq30"]["ocn_dsc"]["HI680"]["SWcld_tau"]
+        + obsc_fbk_dict["eq30"]["ocn_dsc"]["LO680"]["SWdobsc_fbk"]
+    )
+    unassessed.append(LW_A + SW_A - LW_B - SW_B)
+    fbk_names.append("Bony Omega Space Residual")
+
     sum_unassessed = np.array(unassessed).sum()
     unassessed.append(sum_unassessed)
-    fbk_names.append('Sum of Unassessed Cloud Feedbacks')
-    
-    return(np.array(unassessed),fbk_names) # size [fbk_types]
+    fbk_names.append("Sum of Unassessed Cloud Feedbacks")
 
+    return (np.array(unassessed), fbk_names)  # size [fbk_types]
diff --git a/pcmdi_metrics/cloud_feedback/lib/organize_jsons.py b/pcmdi_metrics/cloud_feedback/lib/organize_jsons.py
index 75cd06bdc..e09af655f 100644
--- a/pcmdi_metrics/cloud_feedback/lib/organize_jsons.py
+++ b/pcmdi_metrics/cloud_feedback/lib/organize_jsons.py
@@ -2,84 +2,89 @@
 # Compute NET = LW+SW feedbacks
 # Append this dictionary to the existing json containing feedbacks and error metrics
 
-from datetime import date 
+from datetime import date
 import urllib.request
 import numpy as np
 import json
 
 meta = {
-"date_modified" :   str(date.today()),
-"author"        :   "Mark D. Zelinka <zelinka1@llnl.gov>",
+    "date_modified": str(date.today()),
+    "author": "Mark D. Zelinka <zelinka1@llnl.gov>",
 }
 
 # Location of the cloud feedback and error metric jsons:
-urlpath = 'https://raw.githubusercontent.com/mzelinka/assessed-cloud-fbks/main/data/'
+urlpath = "https://raw.githubusercontent.com/mzelinka/assessed-cloud-fbks/main/data/"
 
-def organize_fbk_jsons(new_dict,new_obsc_dict,mo,ripf):
 
-    # Load in the existing file containing pre-computed CMIP6 feedbacks    
-    fname = 'cmip6_amip-p4K_cld_fbks.json'
-    with urllib.request.urlopen(urlpath+fname) as url:
-        old_dict = json.load(url)   
+def organize_fbk_jsons(new_dict, new_obsc_dict, mo, ripf):
+    # Load in the existing file containing pre-computed CMIP6 feedbacks
+    fname = "cmip6_amip-p4K_cld_fbks.json"
+    with urllib.request.urlopen(urlpath + fname) as url:
+        old_dict = json.load(url)
 
-    old_dict[mo]={} 
-    old_dict[mo][ripf]={}
+    old_dict[mo] = {}
+    old_dict[mo][ripf] = {}
     old_dict[mo][ripf] = new_dict
-    old_dict['metadata'] = meta
+    old_dict["metadata"] = meta
 
     # Load in the existing file containing pre-computed CMIP6 obscuration-related feedbacks
-    fname = 'cmip6_amip-p4K_cld_obsc_fbks.json'
-    with urllib.request.urlopen(urlpath+fname) as url:
+    fname = "cmip6_amip-p4K_cld_obsc_fbks.json"
+    with urllib.request.urlopen(urlpath + fname) as url:
         old_obsc_dict = json.load(url)
 
-    old_obsc_dict[mo]={} 
-    old_obsc_dict[mo][ripf]={}
+    old_obsc_dict[mo] = {}
+    old_obsc_dict[mo][ripf] = {}
     old_obsc_dict[mo][ripf] = new_obsc_dict
-    old_obsc_dict['metadata'] = meta
-    
-    return(old_dict,old_obsc_dict) # now updated to include info from input dictionary 
+    old_obsc_dict["metadata"] = meta
 
+    return (
+        old_dict,
+        old_obsc_dict,
+    )  # now updated to include info from input dictionary
 
-def organize_err_jsons(new_dict,mo,ripf):
 
+def organize_err_jsons(new_dict, mo, ripf):
     # Load in the existing file containing pre-computed CMIP6 error metrics
-    fname = 'cmip6_amip_cld_errs.json'
-    with urllib.request.urlopen(urlpath+fname) as url:
+    fname = "cmip6_amip_cld_errs.json"
+    with urllib.request.urlopen(urlpath + fname) as url:
         old_dict = json.load(url)
 
-    names = ['E_TCA','E_ctpt','E_LW','E_SW','E_NET']
-    old_dict[mo]={} 
-    old_dict[mo][ripf]={}
-    for region in new_dict['ALL'].keys():
-        old_dict[mo][ripf][region]={}
-        for sfc in ['all','ocn','lnd','ocn_asc','ocn_dsc']:
-            old_dict[mo][ripf][region][sfc]={}
-            for sec in ['ALL','HI680','LO680']:
-                old_dict[mo][ripf][region][sfc][sec]={}
+    names = ["E_TCA", "E_ctpt", "E_LW", "E_SW", "E_NET"]
+    old_dict[mo] = {}
+    old_dict[mo][ripf] = {}
+    for region in new_dict["ALL"].keys():
+        old_dict[mo][ripf][region] = {}
+        for sfc in ["all", "ocn", "lnd", "ocn_asc", "ocn_dsc"]:
+            old_dict[mo][ripf][region][sfc] = {}
+            for sec in ["ALL", "HI680", "LO680"]:
+                old_dict[mo][ripf][region][sfc][sec] = {}
                 for name in names:
                     # place in a natural order (e.g., Tropical marine low cloud amount = region/sfc/sec/name)
                     try:
-                        old_dict[mo][ripf][region][sfc][sec][name] = new_dict[sec][region][sfc][name] 
+                        old_dict[mo][ripf][region][sfc][sec][name] = new_dict[sec][
+                            region
+                        ][sfc][name]
                     except:
                         old_dict[mo][ripf][region][sfc][sec][name] = np.nan
                 # end name loop
             # end section loop:
         # end sfc type loop
-    # end region loop   
-    old_dict['metadata'] = meta
-
-    return(old_dict) # now updated to include info from input dictionary 
+    # end region loop
+    old_dict["metadata"] = meta
 
+    return old_dict  # now updated to include info from input dictionary
 
-def organize_ecs_jsons(new_ecs,mo,ripf):
 
+def organize_ecs_jsons(new_ecs, mo, ripf):
     ##################################################################
     # READ IN GREGORY ECS VALUES DERIVED IN ZELINKA ET AL (2020) GRL #
     ##################################################################
-    with urllib.request.urlopen('https://raw.githubusercontent.com/mzelinka/cmip56_forcing_feedback_ecs/master/cmip56_forcing_feedback_ecs.json') as url:
-        old_dict = json.load(url) 
+    with urllib.request.urlopen(
+        "https://raw.githubusercontent.com/mzelinka/cmip56_forcing_feedback_ecs/master/cmip56_forcing_feedback_ecs.json"
+    ) as url:
+        old_dict = json.load(url)
 
-    if new_ecs!=None:
-        old_dict['CMIP6'][mo][ripf]['ECS'] = new_ecs
+    if new_ecs != None:
+        old_dict["CMIP6"][mo][ripf]["ECS"] = new_ecs
 
-    return(old_dict) # now updated to include info from input dictionary 
+    return old_dict  # now updated to include info from input dictionary

From 1dd76090fc42ec51e4b6f040c00aff4f03c1bd31 Mon Sep 17 00:00:00 2001
From: mzelinka <zelinka1@llnl.gov>
Date: Thu, 28 Sep 2023 15:41:03 -0700
Subject: [PATCH 074/133] removed json files

---
 .../data/cmip6_amip-p4K_cld_fbks.json         | 19246 ----------
 .../data/cmip6_amip-p4K_cld_obsc_fbks.json    | 10243 ------
 .../data/cmip6_amip_cld_errs.json             | 30413 ----------------
 3 files changed, 59902 deletions(-)
 delete mode 100644 pcmdi_metrics/cloud_feedback/data/cmip6_amip-p4K_cld_fbks.json
 delete mode 100644 pcmdi_metrics/cloud_feedback/data/cmip6_amip-p4K_cld_obsc_fbks.json
 delete mode 100644 pcmdi_metrics/cloud_feedback/data/cmip6_amip_cld_errs.json

diff --git a/pcmdi_metrics/cloud_feedback/data/cmip6_amip-p4K_cld_fbks.json b/pcmdi_metrics/cloud_feedback/data/cmip6_amip-p4K_cld_fbks.json
deleted file mode 100644
index d6c648bc7..000000000
--- a/pcmdi_metrics/cloud_feedback/data/cmip6_amip-p4K_cld_fbks.json
+++ /dev/null
@@ -1,19246 +0,0 @@
-{
-    "BCC-CSM2-MR":{
-        "r1i1p1f1":{
-            "Arctic":{
-                "all":{
-                    "ALL":{
-                        "LWcld_alt":0.01736601462044245,
-                        "LWcld_amt":0.005578603643865574,
-                        "LWcld_err":0.001058468848164741,
-                        "LWcld_tau":0.004190984627621649,
-                        "LWcld_tot":0.028194071740094414,
-                        "SWcld_alt":-0.0031488020753567213,
-                        "SWcld_amt":-0.009813368285190536,
-                        "SWcld_err":-5.235374250502992e-05,
-                        "SWcld_tau":-0.008645889772614405,
-                        "SWcld_tot":-0.021660413875666692
-                    },
-                    "HI680":{
-                        "LWcld_alt":0.005501961996136769,
-                        "LWcld_amt":0.016269683349328896,
-                        "LWcld_err":0.001243412171403002,
-                        "LWcld_tau":0.004274891722428344,
-                        "LWcld_tot":0.027289949239297007,
-                        "SWcld_alt":-0.00028872074303987666,
-                        "SWcld_amt":-0.02567913825009927,
-                        "SWcld_err":-9.729719181808683e-05,
-                        "SWcld_tau":-0.0023363278601203756,
-                        "SWcld_tot":-0.02840148404507761
-                    },
-                    "LO680":{
-                        "LWcld_alt":0.0015819243534378321,
-                        "LWcld_amt":-0.001104250101945886,
-                        "LWcld_err":7.093049167543916e-05,
-                        "LWcld_tau":0.00035551775763002265,
-                        "LWcld_tot":0.0009041225007974081,
-                        "SWcld_alt":-0.0005933518834793746,
-                        "SWcld_amt":0.011857308368286724,
-                        "SWcld_err":-2.650348970961765e-06,
-                        "SWcld_tau":-0.004520235966425475,
-                        "SWcld_tot":0.006741070169410911
-                    }
-                },
-                "lnd":{
-                    "ALL":{
-                        "LWcld_alt":0.004014786536066434,
-                        "LWcld_amt":0.002077181416846371,
-                        "LWcld_err":0.0003073463564219882,
-                        "LWcld_tau":0.0016740253992780696,
-                        "LWcld_tot":0.008073339708612863,
-                        "SWcld_alt":-0.0006246044617613591,
-                        "SWcld_amt":-0.0015283590417269005,
-                        "SWcld_err":-1.3662828568557365e-05,
-                        "SWcld_tau":-0.002157694277647145,
-                        "SWcld_tot":-0.004324320609703961
-                    },
-                    "HI680":{
-                        "LWcld_alt":0.002092680169881406,
-                        "LWcld_amt":0.0036372115854268893,
-                        "LWcld_err":0.0003527555154751063,
-                        "LWcld_tau":0.001680566971458224,
-                        "LWcld_tot":0.007763214242241626,
-                        "SWcld_alt":-0.00016158410944624318,
-                        "SWcld_amt":-0.003923938778870627,
-                        "SWcld_err":-2.5482138824300946e-05,
-                        "SWcld_tau":-0.0008534789234920216,
-                        "SWcld_tot":-0.004964483950633192
-                    },
-                    "LO680":{
-                        "LWcld_alt":0.00032141984621856227,
-                        "LWcld_amt":-0.00013179912399993254,
-                        "LWcld_err":1.6664826467236148e-05,
-                        "LWcld_tau":0.00010383991768537157,
-                        "LWcld_tot":0.0003101254663712375,
-                        "SWcld_alt":-0.00012459391310555643,
-                        "SWcld_amt":0.0018616275126714888,
-                        "SWcld_err":-5.014125563361057e-06,
-                        "SWcld_tau":-0.0010918561330733407,
-                        "SWcld_tot":0.0006401633409292304
-                    }
-                },
-                "ocn":{
-                    "ALL":{
-                        "LWcld_alt":0.01492995668623442,
-                        "LWcld_amt":0.004008568012825165,
-                        "LWcld_err":0.000847346932485002,
-                        "LWcld_tau":0.0028979578308546377,
-                        "LWcld_tot":0.022683829462399222,
-                        "SWcld_alt":-0.002810452347718701,
-                        "SWcld_amt":-0.009177133633026413,
-                        "SWcld_err":-4.3450345073293445e-05,
-                        "SWcld_tau":-0.007274185474295844,
-                        "SWcld_tot":-0.01930522180011425
-                    },
-                    "HI680":{
-                        "LWcld_alt":0.003909460189540525,
-                        "LWcld_amt":0.014111533886568266,
-                        "LWcld_err":0.0010036941260554413,
-                        "LWcld_tau":0.0029829512711908772,
-                        "LWcld_tot":0.022007639473355108,
-                        "SWcld_alt":-0.00015338397386998584,
-                        "SWcld_amt":-0.024089666584874027,
-                        "SWcld_err":-8.066025224997559e-05,
-                        "SWcld_tau":-0.001695242378457479,
-                        "SWcld_tot":-0.02601895318945147
-                    },
-                    "LO680":{
-                        "LWcld_alt":0.0014043158120772543,
-                        "LWcld_amt":-0.0010728373508501249,
-                        "LWcld_err":6.0713891724152025e-05,
-                        "LWcld_tau":0.00028399763609283495,
-                        "LWcld_tot":0.0006761899890441169,
-                        "SWcld_alt":-0.000522699050690125,
-                        "SWcld_amt":0.011073617980004934,
-                        "SWcld_err":2.1228357768573125e-06,
-                        "SWcld_tau":-0.0038393103757544495,
-                        "SWcld_tot":0.006713731389337218
-                    }
-                },
-                "ocn_asc":{
-                    "ALL":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN
-                    },
-                    "HI680":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN
-                    },
-                    "LO680":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN
-                    }
-                },
-                "ocn_dsc":{
-                    "ALL":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN
-                    },
-                    "HI680":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN
-                    },
-                    "LO680":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN
-                    }
-                }
-            },
-            "eq10":{
-                "all":{
-                    "ALL":{
-                        "LWcld_alt":0.01561779312013341,
-                        "LWcld_amt":-0.07371780949808564,
-                        "LWcld_err":0.02729297097765038,
-                        "LWcld_tau":0.05075026435156477,
-                        "LWcld_tot":0.019943218951262928,
-                        "SWcld_alt":-0.0034059137054348603,
-                        "SWcld_amt":0.10325419695045364,
-                        "SWcld_err":-0.005723351519133494,
-                        "SWcld_tau":-0.07420473597048201,
-                        "SWcld_tot":0.019920195755403287
-                    },
-                    "HI680":{
-                        "LWcld_alt":0.011027999848244282,
-                        "LWcld_amt":-0.06522550369005416,
-                        "LWcld_err":0.00699857452446678,
-                        "LWcld_tau":0.06641962684917699,
-                        "LWcld_tot":0.0192206975318339,
-                        "SWcld_alt":-0.00047415914445932404,
-                        "SWcld_amt":0.06083358686535628,
-                        "SWcld_err":9.258843845375979e-05,
-                        "SWcld_tau":-0.08294249783408131,
-                        "SWcld_tot":-0.02249048167473058
-                    },
-                    "LO680":{
-                        "LWcld_alt":0.004601090104634963,
-                        "LWcld_amt":-0.003371711879326064,
-                        "LWcld_err":-9.217229915296897e-05,
-                        "LWcld_tau":-0.00041468450672689554,
-                        "LWcld_tot":0.0007225214194290349,
-                        "SWcld_alt":-0.0027623451158011775,
-                        "SWcld_amt":0.03711390916199502,
-                        "SWcld_err":-1.9152173132837794e-05,
-                        "SWcld_tau":0.008078265557072853,
-                        "SWcld_tot":0.04241067743013386
-                    }
-                },
-                "lnd":{
-                    "ALL":{
-                        "LWcld_alt":0.010597077433089528,
-                        "LWcld_amt":-0.04053880488234871,
-                        "LWcld_err":0.0030421259331316884,
-                        "LWcld_tau":-0.01214245416516685,
-                        "LWcld_tot":-0.03904205568129433,
-                        "SWcld_alt":-0.0012990808994298205,
-                        "SWcld_amt":0.05496938846724156,
-                        "SWcld_err":-0.0006639211422168428,
-                        "SWcld_tau":0.018898449711505932,
-                        "SWcld_tot":0.07190483613710083
-                    },
-                    "HI680":{
-                        "LWcld_alt":0.005895907192666291,
-                        "LWcld_amt":-0.03634982997004163,
-                        "LWcld_err":0.0017257491903705806,
-                        "LWcld_tau":-0.009405549990407817,
-                        "LWcld_tot":-0.03813372357741258,
-                        "SWcld_alt":-0.0001699318131193545,
-                        "SWcld_amt":0.04089492685608514,
-                        "SWcld_err":7.370903072295781e-05,
-                        "SWcld_tau":0.014587997863868371,
-                        "SWcld_tot":0.055386701937557105
-                    },
-                    "LO680":{
-                        "LWcld_alt":0.0011871217445162576,
-                        "LWcld_amt":-0.00209790632596867,
-                        "LWcld_err":1.2458273464275201e-05,
-                        "LWcld_tau":-1.0005795893633815e-05,
-                        "LWcld_tot":-0.0009083321038817708,
-                        "SWcld_alt":-0.000745236892410464,
-                        "SWcld_amt":0.01721786892327898,
-                        "SWcld_err":-5.271954630320933e-06,
-                        "SWcld_tau":5.077412330551691e-05,
-                        "SWcld_tot":0.01651813419954371
-                    }
-                },
-                "ocn":{
-                    "ALL":{
-                        "LWcld_alt":0.005020715687043879,
-                        "LWcld_amt":-0.033179004615736925,
-                        "LWcld_err":0.024250845044518698,
-                        "LWcld_tau":0.06289271851673162,
-                        "LWcld_tot":0.05898527463255726,
-                        "SWcld_alt":-0.00210683280600504,
-                        "SWcld_amt":0.04828480848321209,
-                        "SWcld_err":-0.00505943037691665,
-                        "SWcld_tau":-0.09310318568198793,
-                        "SWcld_tot":-0.05198464038169754
-                    },
-                    "HI680":{
-                        "LWcld_alt":0.005132092655577992,
-                        "LWcld_amt":-0.028875673720012537,
-                        "LWcld_err":0.0052728253340962004,
-                        "LWcld_tau":0.07582517683958481,
-                        "LWcld_tot":0.05735442110924646,
-                        "SWcld_alt":-0.00030422733133996947,
-                        "SWcld_amt":0.019938660009271157,
-                        "SWcld_err":1.8879407730802012e-05,
-                        "SWcld_tau":-0.09753049569794968,
-                        "SWcld_tot":-0.07787718361228768
-                    },
-                    "LO680":{
-                        "LWcld_alt":0.003413968360118705,
-                        "LWcld_amt":-0.0012738055533573938,
-                        "LWcld_err":-0.00010463057261724417,
-                        "LWcld_tau":-0.0004046787108332618,
-                        "LWcld_tot":0.0016308535233108053,
-                        "SWcld_alt":-0.0020171082233907144,
-                        "SWcld_amt":0.019896040238716047,
-                        "SWcld_err":-1.3880218502516866e-05,
-                        "SWcld_tau":0.008027491433767337,
-                        "SWcld_tot":0.025892543230590145
-                    }
-                },
-                "ocn_asc":{
-                    "ALL":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN
-                    },
-                    "HI680":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN
-                    },
-                    "LO680":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN
-                    }
-                },
-                "ocn_dsc":{
-                    "ALL":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN
-                    },
-                    "HI680":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN
-                    },
-                    "LO680":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN
-                    }
-                }
-            },
-            "eq20":{
-                "all":{
-                    "ALL":{
-                        "LWcld_alt":0.0115340554200797,
-                        "LWcld_amt":-0.12687893543350348,
-                        "LWcld_err":0.04246373202731949,
-                        "LWcld_tau":0.09176577776674444,
-                        "LWcld_tot":0.018884629780640108,
-                        "SWcld_alt":-0.0034754874099999105,
-                        "SWcld_amt":0.163237448632506,
-                        "SWcld_err":-0.00877814422447265,
-                        "SWcld_tau":-0.14676988321563475,
-                        "SWcld_tot":0.004213933782398673
-                    },
-                    "HI680":{
-                        "LWcld_alt":0.013653240312735298,
-                        "LWcld_amt":-0.12157300136633331,
-                        "LWcld_err":0.010233241766494292,
-                        "LWcld_tau":0.11166169109247741,
-                        "LWcld_tot":0.013975171805373653,
-                        "SWcld_alt":-0.0001146906988494306,
-                        "SWcld_amt":0.11449281021171487,
-                        "SWcld_err":0.0003019876922214205,
-                        "SWcld_tau":-0.15285929202433834,
-                        "SWcld_tot":-0.03817918481925147
-                    },
-                    "LO680":{
-                        "LWcld_alt":0.009075115533977704,
-                        "LWcld_amt":-0.003132975869114995,
-                        "LWcld_err":-0.00032038452952378604,
-                        "LWcld_tau":-0.0007122972027853703,
-                        "LWcld_tot":0.004909457975266454,
-                        "SWcld_alt":-0.005190292542406193,
-                        "SWcld_amt":0.03557553854019439,
-                        "SWcld_err":8.030328432855367e-05,
-                        "SWcld_tau":0.011927569548069818,
-                        "SWcld_tot":0.042393118830186566
-                    }
-                },
-                "lnd":{
-                    "ALL":{
-                        "LWcld_alt":0.018286045452931138,
-                        "LWcld_amt":-0.0824247193630603,
-                        "LWcld_err":0.007453254392803036,
-                        "LWcld_tau":-0.01838243243078488,
-                        "LWcld_tot":-0.07506785194811098,
-                        "SWcld_alt":-0.002231800627157473,
-                        "SWcld_amt":0.10320400786851952,
-                        "SWcld_err":-0.001337420229025075,
-                        "SWcld_tau":0.027532173226264268,
-                        "SWcld_tot":0.12716696023860127
-                    },
-                    "HI680":{
-                        "LWcld_alt":0.010288507055829493,
-                        "LWcld_amt":-0.07483541682670782,
-                        "LWcld_err":0.0043757894535236,
-                        "LWcld_tau":-0.012773625984392372,
-                        "LWcld_tot":-0.07294474630174709,
-                        "SWcld_alt":-0.0004012179357396686,
-                        "SWcld_amt":0.07944325249322949,
-                        "SWcld_err":-2.803907549056007e-05,
-                        "SWcld_tau":0.01913049864597675,
-                        "SWcld_tot":0.09814449412797603
-                    },
-                    "LO680":{
-                        "LWcld_alt":0.001590593787680844,
-                        "LWcld_amt":-0.0036961077685742304,
-                        "LWcld_err":1.983400347452355e-05,
-                        "LWcld_tau":-3.7425711657936006e-05,
-                        "LWcld_tot":-0.002123105646363896,
-                        "SWcld_alt":-0.000976346400439282,
-                        "SWcld_amt":0.029628168220733508,
-                        "SWcld_err":-8.544972122136044e-06,
-                        "SWcld_tau":0.00037918949098956134,
-                        "SWcld_tot":0.029022466339161654
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-                },
-                "ocn":{
-                    "ALL":{
-                        "LWcld_alt":-0.006751990032851436,
-                        "LWcld_amt":-0.044454216070443225,
-                        "LWcld_err":0.03501047763451646,
-                        "LWcld_tau":0.11014821019752928,
-                        "LWcld_tot":0.0939524817287511,
-                        "SWcld_alt":-0.0012436867828424381,
-                        "SWcld_amt":0.06003344076398648,
-                        "SWcld_err":-0.007440723995447575,
-                        "SWcld_tau":-0.17430205644189903,
-                        "SWcld_tot":-0.12295302645620255
-                    },
-                    "HI680":{
-                        "LWcld_alt":0.003364733256905804,
-                        "LWcld_amt":-0.04673758453962553,
-                        "LWcld_err":0.005857452312970693,
-                        "LWcld_tau":0.12443531707686978,
-                        "LWcld_tot":0.08691991810712076,
-                        "SWcld_alt":0.000286527236890238,
-                        "SWcld_amt":0.03504955771848539,
-                        "SWcld_err":0.0003300267677119806,
-                        "SWcld_tau":-0.17198979067031508,
-                        "SWcld_tot":-0.1363236789472275
-                    },
-                    "LO680":{
-                        "LWcld_alt":0.0074845217462968594,
-                        "LWcld_amt":0.0005631318994592348,
-                        "LWcld_err":-0.00034021853299830954,
-                        "LWcld_tau":-0.0006748714911274343,
-                        "LWcld_tot":0.00703256362163035,
-                        "SWcld_alt":-0.004213946141966911,
-                        "SWcld_amt":0.005947370319460879,
-                        "SWcld_err":8.884825645068972e-05,
-                        "SWcld_tau":0.011548380057080254,
-                        "SWcld_tot":0.013370652491024917
-                    }
-                },
-                "ocn_asc":{
-                    "ALL":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN
-                    },
-                    "HI680":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN
-                    },
-                    "LO680":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN
-                    }
-                },
-                "ocn_dsc":{
-                    "ALL":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN
-                    },
-                    "HI680":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN
-                    },
-                    "LO680":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN
-                    }
-                }
-            },
-            "eq30":{
-                "all":{
-                    "ALL":{
-                        "LWcld_alt":0.03566891836652721,
-                        "LWcld_amt":-0.16289371780552828,
-                        "LWcld_err":0.05713315568446766,
-                        "LWcld_tau":0.09057680282681024,
-                        "LWcld_tot":0.020485159072276837,
-                        "SWcld_alt":-0.006803621740945682,
-                        "SWcld_amt":0.19720943066506366,
-                        "SWcld_err":-0.01106110065885318,
-                        "SWcld_tau":-0.15275090240959457,
-                        "SWcld_tot":0.026593805855670213
-                    },
-                    "HI680":{
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-                        "LWcld_amt":-0.15440377854635648,
-                        "LWcld_err":0.02063329606797258,
-                        "LWcld_tau":0.12062647275442251,
-                        "LWcld_tot":0.013858129846562303,
-                        "SWcld_alt":-0.00023096367342005276,
-                        "SWcld_amt":0.15021422257668907,
-                        "SWcld_err":-0.0003099974004573831,
-                        "SWcld_tau":-0.1747015217899375,
-                        "SWcld_tot":-0.025028260287125886
-                    },
-                    "LO680":{
-                        "LWcld_alt":0.01346429051131884,
-                        "LWcld_amt":-0.004549437986368436,
-                        "LWcld_err":-0.0007937922094432759,
-                        "LWcld_tau":-0.0014940331162016259,
-                        "LWcld_tot":0.006627029225714531,
-                        "SWcld_alt":-0.007349488213699423,
-                        "SWcld_amt":0.04238921469469812,
-                        "SWcld_err":0.00025497712906457867,
-                        "SWcld_tau":0.01632736915068973,
-                        "SWcld_tot":0.05162207276075301
-                    }
-                },
-                "lnd":{
-                    "ALL":{
-                        "LWcld_alt":0.0313800409661007,
-                        "LWcld_amt":-0.1247018305305548,
-                        "LWcld_err":0.010428573994331154,
-                        "LWcld_tau":-0.028001221071369306,
-                        "LWcld_tot":-0.11089443664149227,
-                        "SWcld_alt":-0.0035076008384706332,
-                        "SWcld_amt":0.1554524980992885,
-                        "SWcld_err":-0.001776955023703655,
-                        "SWcld_tau":0.040791162202818344,
-                        "SWcld_tot":0.19095910443993258
-                    },
-                    "HI680":{
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-                        "LWcld_amt":-0.1132897897230528,
-                        "LWcld_err":0.007571431147475914,
-                        "LWcld_tau":-0.021168820844088693,
-                        "LWcld_tot":-0.10713671236677619,
-                        "SWcld_alt":-0.0010013172645648401,
-                        "SWcld_amt":0.11992035201186252,
-                        "SWcld_err":-0.000248404475261726,
-                        "SWcld_tau":0.029469184812578538,
-                        "SWcld_tot":0.1481398150846145
-                    },
-                    "LO680":{
-                        "LWcld_alt":0.0020199156173792377,
-                        "LWcld_amt":-0.0057527786781573975,
-                        "LWcld_err":2.228215671382649e-05,
-                        "LWcld_tau":-4.714539706075742e-05,
-                        "LWcld_tot":-0.003757724274716061,
-                        "SWcld_alt":-0.0011997900604508607,
-                        "SWcld_amt":0.04367271834371602,
-                        "SWcld_err":-6.355894580209252e-06,
-                        "SWcld_tau":0.0003527235845900247,
-                        "SWcld_tot":0.04281929597327497
-                    }
-                },
-                "ocn":{
-                    "ALL":{
-                        "LWcld_alt":0.004288877400426519,
-                        "LWcld_amt":-0.03819188727497347,
-                        "LWcld_err":0.046704581690136505,
-                        "LWcld_tau":0.11857802389817952,
-                        "LWcld_tot":0.13137959571376912,
-                        "SWcld_alt":-0.0032960209024750485,
-                        "SWcld_amt":0.04175693256577514,
-                        "SWcld_err":-0.009284145635149529,
-                        "SWcld_tau":-0.19354206461241288,
-                        "SWcld_tot":-0.16436529858426233
-                    },
-                    "HI680":{
-                        "LWcld_alt":0.0072516725176342825,
-                        "LWcld_amt":-0.041113988823303645,
-                        "LWcld_err":0.01306186492049667,
-                        "LWcld_tau":0.1417952935985112,
-                        "LWcld_tot":0.12099484221333852,
-                        "SWcld_alt":0.0007703535911447873,
-                        "SWcld_amt":0.03029387056482652,
-                        "SWcld_err":-6.159292519565704e-05,
-                        "SWcld_tau":-0.2041707066025161,
-                        "SWcld_tot":-0.1731680753717404
-                    },
-                    "LO680":{
-                        "LWcld_alt":0.011444374893939601,
-                        "LWcld_amt":0.001203340691788961,
-                        "LWcld_err":-0.0008160743661571025,
-                        "LWcld_tau":-0.0014468877191408677,
-                        "LWcld_tot":0.010384753500430591,
-                        "SWcld_alt":-0.006149698153248562,
-                        "SWcld_amt":-0.0012835036490178925,
-                        "SWcld_err":0.00026133302364478783,
-                        "SWcld_tau":0.0159746455660997,
-                        "SWcld_tot":0.008802776787478036
-                    }
-                },
-                "ocn_asc":{
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-                        "LWcld_alt":0.007055194322999131,
-                        "LWcld_amt":0.06002695175612636,
-                        "LWcld_err":0.028131774698426846,
-                        "LWcld_tau":0.07228084945440572,
-                        "LWcld_tot":0.16749477023195805,
-                        "SWcld_alt":-1.6710131014746257e-05,
-                        "SWcld_amt":-0.0842158073202345,
-                        "SWcld_err":-0.004513795817137946,
-                        "SWcld_tau":-0.13915416292844013,
-                        "SWcld_tot":-0.2279004761968273
-                    },
-                    "HI680":{
-                        "LWcld_alt":0.010332263588664242,
-                        "LWcld_amt":0.052753336221654575,
-                        "LWcld_err":0.019240096971904697,
-                        "LWcld_tau":0.07899083140750322,
-                        "LWcld_tot":0.16131652818972672,
-                        "SWcld_alt":-0.0003153915100651799,
-                        "SWcld_amt":-0.0544837778356107,
-                        "SWcld_err":-0.0012979401438883557,
-                        "SWcld_tau":-0.13142609288663648,
-                        "SWcld_tot":-0.1875232023762007
-                    },
-                    "LO680":{
-                        "LWcld_alt":0.0032699035692136205,
-                        "LWcld_amt":0.003243866702252456,
-                        "LWcld_err":-0.00012302526633794914,
-                        "LWcld_tau":-0.00021250296289679482,
-                        "LWcld_tot":0.006178242042231333,
-                        "SWcld_alt":-0.00178193909843204,
-                        "SWcld_amt":-0.03492610660620656,
-                        "SWcld_err":-7.79742434523937e-05,
-                        "SWcld_tau":-0.003591253872535616,
-                        "SWcld_tot":-0.040377273820626614
-                    }
-                },
-                "ocn_dsc":{
-                    "ALL":{
-                        "LWcld_alt":-0.09078598036697515,
-                        "LWcld_amt":-0.0345989231410466,
-                        "LWcld_err":0.027341146888524886,
-                        "LWcld_tau":0.0662720185874973,
-                        "LWcld_tot":-0.03177173803199954,
-                        "SWcld_alt":0.016518508215238575,
-                        "SWcld_amt":0.1504165326410527,
-                        "SWcld_err":-0.004391559605987555,
-                        "SWcld_tau":-0.11751349332941913,
-                        "SWcld_tot":0.045029987920884564
-                    },
-                    "HI680":{
-                        "LWcld_alt":-0.015074321914467207,
-                        "LWcld_amt":-0.06844683962417453,
-                        "LWcld_err":-0.0057409424012132395,
-                        "LWcld_tau":0.05447127831903465,
-                        "LWcld_tot":-0.03479082562082031,
-                        "SWcld_alt":0.002864439476467534,
-                        "SWcld_amt":0.07604180988093966,
-                        "SWcld_err":0.0022642027147129424,
-                        "SWcld_tau":-0.06907473285678793,
-                        "SWcld_tot":0.012095719215332193
-                    },
-                    "LO680":{
-                        "LWcld_alt":0.011000989801685736,
-                        "LWcld_amt":-0.003982932481815496,
-                        "LWcld_err":-0.0008784741553983921,
-                        "LWcld_tau":-0.0031204955756510776,
-                        "LWcld_tot":0.0030190875888207705,
-                        "SWcld_alt":-0.005828111127020616,
-                        "SWcld_amt":0.001023285095391676,
-                        "SWcld_err":0.0004446574500840848,
-                        "SWcld_tau":0.037294437287097214,
-                        "SWcld_tot":0.03293426870555236
-                    }
-                }
-            },
-            "eq40":{
-                "all":{
-                    "ALL":{
-                        "LWcld_alt":0.09234484427093646,
-                        "LWcld_amt":-0.22369895562454425,
-                        "LWcld_err":0.07111242065909361,
-                        "LWcld_tau":0.08321264716413575,
-                        "LWcld_tot":0.02297095646962159,
-                        "SWcld_alt":-0.013725367901945708,
-                        "SWcld_amt":0.29206664865987114,
-                        "SWcld_err":-0.013769306252383826,
-                        "SWcld_tau":-0.12419904927016302,
-                        "SWcld_tot":0.14037292523537856
-                    },
-                    "HI680":{
-                        "LWcld_alt":0.07172973956788554,
-                        "LWcld_amt":-0.20802584472807492,
-                        "LWcld_err":0.0315641003112261,
-                        "LWcld_tau":0.12317037424074517,
-                        "LWcld_tot":0.018438369391781867,
-                        "SWcld_alt":-0.003546663200855478,
-                        "SWcld_amt":0.22250275134831451,
-                        "SWcld_err":-0.0013199815895394331,
-                        "SWcld_tau":-0.16956632247123873,
-                        "SWcld_tot":0.04806978408668083
-                    },
-                    "LO680":{
-                        "LWcld_alt":0.017507207990310764,
-                        "LWcld_amt":-0.008763045567113653,
-                        "LWcld_err":-0.001138547426015234,
-                        "LWcld_tau":-0.0030730330828479813,
-                        "LWcld_tot":0.004532587077839718,
-                        "SWcld_alt":-0.009305419342705156,
-                        "SWcld_amt":0.06954481623341575,
-                        "SWcld_err":0.0003793243363851011,
-                        "SWcld_tau":0.031684429684037445,
-                        "SWcld_tot":0.09230315091113313
-                    }
-                },
-                "lnd":{
-                    "ALL":{
-                        "LWcld_alt":0.04797279902799404,
-                        "LWcld_amt":-0.15493697081409522,
-                        "LWcld_err":0.012484934103432281,
-                        "LWcld_tau":-0.029622271567552327,
-                        "LWcld_tot":-0.12410150925022116,
-                        "SWcld_alt":-0.005287392433906075,
-                        "SWcld_amt":0.1965452484067488,
-                        "SWcld_err":-0.0022188462034743455,
-                        "SWcld_tau":0.044492438332525426,
-                        "SWcld_tot":0.23353144810189383
-                    },
-                    "HI680":{
-                        "LWcld_alt":0.03181730885645235,
-                        "LWcld_amt":-0.13849279161221026,
-                        "LWcld_err":0.009960320497576917,
-                        "LWcld_tau":-0.02161112186956346,
-                        "LWcld_tot":-0.11832628412774444,
-                        "SWcld_alt":-0.0019906311334820397,
-                        "SWcld_amt":0.1505716296817218,
-                        "SWcld_err":-0.0005193502518726357,
-                        "SWcld_tau":0.03089181215380789,
-                        "SWcld_tot":0.17895346045017502
-                    },
-                    "LO680":{
-                        "LWcld_alt":0.002235442487304839,
-                        "LWcld_amt":-0.008006232183575284,
-                        "LWcld_err":2.8393451240398448e-05,
-                        "LWcld_tau":-3.2834040952500855e-05,
-                        "LWcld_tot":-0.005775225122476731,
-                        "SWcld_alt":-0.0012952752228562775,
-                        "SWcld_amt":0.05588959442421867,
-                        "SWcld_err":-8.849233125278472e-06,
-                        "SWcld_tau":-7.472554082866461e-06,
-                        "SWcld_tot":0.054577997414154235
-                    }
-                },
-                "ocn":{
-                    "ALL":{
-                        "LWcld_alt":0.044372045242942415,
-                        "LWcld_amt":-0.06876198481044905,
-                        "LWcld_err":0.05862748655566132,
-                        "LWcld_tau":0.11283491873168809,
-                        "LWcld_tot":0.14707246571984275,
-                        "SWcld_alt":-0.008437975468039637,
-                        "SWcld_amt":0.0955214002531223,
-                        "SWcld_err":-0.01155046004890948,
-                        "SWcld_tau":-0.16869148760268846,
-                        "SWcld_tot":-0.09315852286651527
-                    },
-                    "HI680":{
-                        "LWcld_alt":0.039912430711433175,
-                        "LWcld_amt":-0.06953305311586468,
-                        "LWcld_err":0.02160377981364918,
-                        "LWcld_tau":0.14478149611030866,
-                        "LWcld_tot":0.13676465351952632,
-                        "SWcld_alt":-0.0015560320673734375,
-                        "SWcld_amt":0.0719311216665927,
-                        "SWcld_err":-0.0008006313376667972,
-                        "SWcld_tau":-0.20045813462504666,
-                        "SWcld_tot":-0.13088367636349416
-                    },
-                    "LO680":{
-                        "LWcld_alt":0.01527176550300593,
-                        "LWcld_amt":-0.0007568133835383677,
-                        "LWcld_err":-0.0011669408772556325,
-                        "LWcld_tau":-0.00304019904189548,
-                        "LWcld_tot":0.01030781220031645,
-                        "SWcld_alt":-0.00801014411984888,
-                        "SWcld_amt":0.013655221809197088,
-                        "SWcld_err":0.0003881735695103797,
-                        "SWcld_tau":0.031691902238120316,
-                        "SWcld_tot":0.03772515349697891
-                    }
-                },
-                "ocn_asc":{
-                    "ALL":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN
-                    },
-                    "HI680":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN
-                    },
-                    "LO680":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN
-                    }
-                },
-                "ocn_dsc":{
-                    "ALL":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN
-                    },
-                    "HI680":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN
-                    },
-                    "LO680":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN
-                    }
-                }
-            },
-            "eq50":{
-                "all":{
-                    "ALL":{
-                        "LWcld_alt":0.17294879399983928,
-                        "LWcld_amt":-0.2642374164222468,
-                        "LWcld_err":0.07343111765111691,
-                        "LWcld_tau":0.07445447913991045,
-                        "LWcld_tot":0.05659697436861977,
-                        "SWcld_alt":-0.022590269083040636,
-                        "SWcld_amt":0.38639689680178124,
-                        "SWcld_err":-0.015084443357309322,
-                        "SWcld_tau":-0.08508486329956085,
-                        "SWcld_tot":0.2636373210618704
-                    },
-                    "HI680":{
-                        "LWcld_alt":0.13597408616299747,
-                        "LWcld_amt":-0.23397055548491308,
-                        "LWcld_err":0.034552318170731365,
-                        "LWcld_tau":0.11861823948180844,
-                        "LWcld_tot":0.05517408833062413,
-                        "SWcld_alt":-0.008800096446398164,
-                        "SWcld_amt":0.2735731647963488,
-                        "SWcld_err":-0.0017556593318892328,
-                        "SWcld_tau":-0.14494858708044533,
-                        "SWcld_tot":0.11806882193761609
-                    },
-                    "LO680":{
-                        "LWcld_alt":0.02143633377181432,
-                        "LWcld_amt":-0.014913450679319233,
-                        "LWcld_err":-0.0011926333646789485,
-                        "LWcld_tau":-0.003907368853326311,
-                        "LWcld_tot":0.0014228860379956432,
-                        "SWcld_alt":-0.011126213010069245,
-                        "SWcld_amt":0.11366776289621221,
-                        "SWcld_err":0.00040956812187738836,
-                        "SWcld_tau":0.04261739087866937,
-                        "SWcld_tot":0.14556850888668973
-                    }
-                },
-                "lnd":{
-                    "ALL":{
-                        "LWcld_alt":0.07001426630091147,
-                        "LWcld_amt":-0.16900569976907875,
-                        "LWcld_err":0.011997169276611897,
-                        "LWcld_tau":-0.03105996893461035,
-                        "LWcld_tot":-0.11805423312616577,
-                        "SWcld_alt":-0.007936218373132638,
-                        "SWcld_amt":0.22734315797586777,
-                        "SWcld_err":-0.0023792613608861492,
-                        "SWcld_tau":0.05073951272783258,
-                        "SWcld_tot":0.2677671909696816
-                    },
-                    "HI680":{
-                        "LWcld_alt":0.0472280063511975,
-                        "LWcld_amt":-0.1458139313882977,
-                        "LWcld_err":0.010755609867693599,
-                        "LWcld_tau":-0.023000747976109886,
-                        "LWcld_tot":-0.11083106314551648,
-                        "SWcld_alt":-0.003356630358373206,
-                        "SWcld_amt":0.165609589136392,
-                        "SWcld_err":-0.0006757048770893019,
-                        "SWcld_tau":0.03773239208228526,
-                        "SWcld_tot":0.19930964598321474
-                    },
-                    "LO680":{
-                        "LWcld_alt":0.00263899482643083,
-                        "LWcld_amt":-0.010339591762112507,
-                        "LWcld_err":5.5786497203644715e-05,
-                        "LWcld_tau":0.0004216352943229456,
-                        "LWcld_tot":-0.007223169980649269,
-                        "SWcld_alt":-0.0014313368634871293,
-                        "SWcld_amt":0.07286618792280024,
-                        "SWcld_err":-1.1209443151039053e-05,
-                        "SWcld_tau":-0.0029660868672598466,
-                        "SWcld_tot":0.06845755474890222
-                    }
-                },
-                "ocn":{
-                    "ALL":{
-                        "LWcld_alt":0.1029345276989278,
-                        "LWcld_amt":-0.09523171665316808,
-                        "LWcld_err":0.061433948374505,
-                        "LWcld_tau":0.10551444807452078,
-                        "LWcld_tot":0.1746512074947855,
-                        "SWcld_alt":-0.014654050709908002,
-                        "SWcld_amt":0.15905373882591342,
-                        "SWcld_err":-0.012705181996423174,
-                        "SWcld_tau":-0.13582437602739342,
-                        "SWcld_tot":-0.004129869907811184
-                    },
-                    "HI680":{
-                        "LWcld_alt":0.08874607981179994,
-                        "LWcld_amt":-0.0881566240966154,
-                        "LWcld_err":0.023796708303037758,
-                        "LWcld_tau":0.14161898745791832,
-                        "LWcld_tot":0.16600515147614062,
-                        "SWcld_alt":-0.005443466088024957,
-                        "SWcld_amt":0.1079635756599568,
-                        "SWcld_err":-0.0010799544547999303,
-                        "SWcld_tau":-0.1826809791627306,
-                        "SWcld_tot":-0.08124082404559868
-                    },
-                    "LO680":{
-                        "LWcld_alt":0.01879733894538349,
-                        "LWcld_amt":-0.0045738589172067265,
-                        "LWcld_err":-0.0012484198618825935,
-                        "LWcld_tau":-0.004329004147649257,
-                        "LWcld_tot":0.008646056018644911,
-                        "SWcld_alt":-0.009694876146582118,
-                        "SWcld_amt":0.040801574973411976,
-                        "SWcld_err":0.0004207775650284274,
-                        "SWcld_tau":0.04558347774592921,
-                        "SWcld_tot":0.0771109541377875
-                    }
-                },
-                "ocn_asc":{
-                    "ALL":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN
-                    },
-                    "HI680":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN
-                    },
-                    "LO680":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN
-                    }
-                },
-                "ocn_dsc":{
-                    "ALL":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN
-                    },
-                    "HI680":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN
-                    },
-                    "LO680":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN
-                    }
-                }
-            },
-            "eq60":{
-                "all":{
-                    "ALL":{
-                        "LWcld_alt":0.25141811529142283,
-                        "LWcld_amt":-0.268155468056504,
-                        "LWcld_err":0.07679727050091488,
-                        "LWcld_tau":0.07873977427845234,
-                        "LWcld_tot":0.13879969201428613,
-                        "SWcld_alt":-0.0320481341711088,
-                        "SWcld_amt":0.39961368840254435,
-                        "SWcld_err":-0.016118548538054202,
-                        "SWcld_tau":-0.09810883187812602,
-                        "SWcld_tot":0.2533381738152554
-                    },
-                    "HI680":{
-                        "LWcld_alt":0.18587166373044958,
-                        "LWcld_amt":-0.20953785304692718,
-                        "LWcld_err":0.03801778812961062,
-                        "LWcld_tau":0.1258735798677193,
-                        "LWcld_tot":0.14022517868085227,
-                        "SWcld_alt":-0.012755332064895299,
-                        "SWcld_amt":0.23491810243936662,
-                        "SWcld_err":-0.0020579959494872583,
-                        "SWcld_tau":-0.1513123813247557,
-                        "SWcld_tot":0.0687923931002284
-                    },
-                    "LO680":{
-                        "LWcld_alt":0.024720646991086694,
-                        "LWcld_amt":-0.021637420424651502,
-                        "LWcld_err":-0.0010379715589614227,
-                        "LWcld_tau":-0.0034707468375457166,
-                        "LWcld_tot":-0.001425486666566127,
-                        "SWcld_alt":-0.012750949133554498,
-                        "SWcld_amt":0.16110000162783464,
-                        "SWcld_err":0.0003612867155696593,
-                        "SWcld_tau":0.03583545126761264,
-                        "SWcld_tot":0.18454579047746242
-                    }
-                },
-                "lnd":{
-                    "ALL":{
-                        "LWcld_alt":0.08888557455476868,
-                        "LWcld_amt":-0.17017918715743555,
-                        "LWcld_err":0.011806534043105941,
-                        "LWcld_tau":-0.027299526539076677,
-                        "LWcld_tot":-0.09678660509863761,
-                        "SWcld_alt":-0.010253117128440148,
-                        "SWcld_amt":0.23689307149764452,
-                        "SWcld_err":-0.0024210151625899644,
-                        "SWcld_tau":0.048658404865171125,
-                        "SWcld_tot":0.27287734407178554
-                    },
-                    "HI680":{
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-                        "LWcld_amt":-0.14256809265501888,
-                        "LWcld_err":0.011402045739741894,
-                        "LWcld_tau":-0.019750949735903033,
-                        "LWcld_tot":-0.08969538307213394,
-                        "SWcld_alt":-0.004573603640473741,
-                        "SWcld_amt":0.16367410137923669,
-                        "SWcld_err":-0.0007639006412949433,
-                        "SWcld_tau":0.03909996816086445,
-                        "SWcld_tot":0.1974365652583325
-                    },
-                    "LO680":{
-                        "LWcld_alt":0.0030007945719723386,
-                        "LWcld_amt":-0.011354573546725017,
-                        "LWcld_err":0.00010628096018090785,
-                        "LWcld_tau":0.0011562708245623076,
-                        "LWcld_tot":-0.007091222026503645,
-                        "SWcld_alt":-0.001599708005729794,
-                        "SWcld_amt":0.08434463767521937,
-                        "SWcld_err":-9.662660185333333e-06,
-                        "SWcld_tau":-0.007294478433415764,
-                        "SWcld_tot":0.07544078857588848
-                    }
-                },
-                "ocn":{
-                    "ALL":{
-                        "LWcld_alt":0.16253254073665416,
-                        "LWcld_amt":-0.09797628089906839,
-                        "LWcld_err":0.06499073645780894,
-                        "LWcld_tau":0.106039300817529,
-                        "LWcld_tot":0.23558629711292375,
-                        "SWcld_alt":-0.02179501704266865,
-                        "SWcld_amt":0.1627206169048999,
-                        "SWcld_err":-0.013697533375464236,
-                        "SWcld_tau":-0.14676723674329714,
-                        "SWcld_tot":-0.019539170256530142
-                    },
-                    "HI680":{
-                        "LWcld_alt":0.12465005015140346,
-                        "LWcld_amt":-0.06696976039190829,
-                        "LWcld_err":0.026615742389868715,
-                        "LWcld_tau":0.14562452960362232,
-                        "LWcld_tot":0.22992056175298625,
-                        "SWcld_alt":-0.008181728424421556,
-                        "SWcld_amt":0.07124400106012993,
-                        "SWcld_err":-0.001294095308192315,
-                        "SWcld_tau":-0.19041234948562014,
-                        "SWcld_tot":-0.12864417215810406
-                    },
-                    "LO680":{
-                        "LWcld_alt":0.021719852419114352,
-                        "LWcld_amt":-0.010282846877926483,
-                        "LWcld_err":-0.0011442525191423308,
-                        "LWcld_tau":-0.004627017662108024,
-                        "LWcld_tot":0.005665735359937518,
-                        "SWcld_alt":-0.011151241127824703,
-                        "SWcld_amt":0.07675536395261524,
-                        "SWcld_err":0.00037094937575499263,
-                        "SWcld_tau":0.043129929701028406,
-                        "SWcld_tot":0.10910500190157392
-                    }
-                },
-                "ocn_asc":{
-                    "ALL":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN
-                    },
-                    "HI680":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN
-                    },
-                    "LO680":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN
-                    }
-                },
-                "ocn_dsc":{
-                    "ALL":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN
-                    },
-                    "HI680":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN
-                    },
-                    "LO680":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN
-                    }
-                }
-            },
-            "eq70":{
-                "all":{
-                    "ALL":{
-                        "LWcld_alt":0.3054070831864147,
-                        "LWcld_amt":-0.2604615837178705,
-                        "LWcld_err":0.08004188716439052,
-                        "LWcld_tau":0.08953421717987625,
-                        "LWcld_tot":0.21452160381281093,
-                        "SWcld_alt":-0.03854848494603099,
-                        "SWcld_amt":0.3799003460152434,
-                        "SWcld_err":-0.016461121469508462,
-                        "SWcld_tau":-0.126460578511733,
-                        "SWcld_tot":0.19843016108797096
-                    },
-                    "HI680":{
-                        "LWcld_alt":0.21687611014634037,
-                        "LWcld_amt":-0.1808190370220012,
-                        "LWcld_err":0.041553140273046525,
-                        "LWcld_tau":0.1379803849186658,
-                        "LWcld_tot":0.21559059831605154,
-                        "SWcld_alt":-0.014880258274875777,
-                        "SWcld_amt":0.18667047890827118,
-                        "SWcld_err":-0.0023259046554837417,
-                        "SWcld_tau":-0.16629304262811484,
-                        "SWcld_tot":0.003171273349796848
-                    },
-                    "LO680":{
-                        "LWcld_alt":0.027163062457184767,
-                        "LWcld_amt":-0.024587944880040898,
-                        "LWcld_err":-0.0009134231010472777,
-                        "LWcld_tau":-0.002730694142842986,
-                        "LWcld_tot":-0.0010689945032405738,
-                        "SWcld_alt":-0.0139076912300791,
-                        "SWcld_amt":0.18451456358467996,
-                        "SWcld_err":0.0004019736333721559,
-                        "SWcld_tau":0.02425005151263651,
-                        "SWcld_tot":0.1952588975006095
-                    }
-                },
-                "lnd":{
-                    "ALL":{
-                        "LWcld_alt":0.10670417117851998,
-                        "LWcld_amt":-0.16581140091351682,
-                        "LWcld_err":0.012631776776752535,
-                        "LWcld_tau":-0.021126672351508714,
-                        "LWcld_tot":-0.06760212530975303,
-                        "SWcld_alt":-0.012616080585940178,
-                        "SWcld_amt":0.23284241678360032,
-                        "SWcld_err":-0.002491359040799701,
-                        "SWcld_tau":0.04036608638034182,
-                        "SWcld_tot":0.25810106353720225
-                    },
-                    "HI680":{
-                        "LWcld_alt":0.07333697597508178,
-                        "LWcld_amt":-0.13322704705858826,
-                        "LWcld_err":0.012556010432686462,
-                        "LWcld_tau":-0.013630603580525244,
-                        "LWcld_tot":-0.0609646642313453,
-                        "SWcld_alt":-0.005496175716339233,
-                        "SWcld_amt":0.15041367941875108,
-                        "SWcld_err":-0.0008525833699117648,
-                        "SWcld_tau":0.03549432897112639,
-                        "SWcld_tot":0.17955924930362646
-                    },
-                    "LO680":{
-                        "LWcld_alt":0.0037203010436093766,
-                        "LWcld_amt":-0.01217573935455724,
-                        "LWcld_err":0.00015779126763376902,
-                        "LWcld_tau":0.0016601808014005386,
-                        "LWcld_tot":-0.0066374610784077385,
-                        "SWcld_alt":-0.0019491019099857323,
-                        "SWcld_amt":0.09242411875602345,
-                        "SWcld_err":-1.724104066702642e-05,
-                        "SWcld_tau":-0.011915951809359499,
-                        "SWcld_tot":0.0785418239960112
-                    }
-                },
-                "ocn":{
-                    "ALL":{
-                        "LWcld_alt":0.1987029120078947,
-                        "LWcld_amt":-0.09465018280435362,
-                        "LWcld_err":0.06741011038763799,
-                        "LWcld_tau":0.11066088953138496,
-                        "LWcld_tot":0.282123729122564,
-                        "SWcld_alt":-0.02593240436009081,
-                        "SWcld_amt":0.1470579292316431,
-                        "SWcld_err":-0.01396976242870876,
-                        "SWcld_tau":-0.16682666489207484,
-                        "SWcld_tot":-0.05967090244923132
-                    },
-                    "HI680":{
-                        "LWcld_alt":0.14353913417125863,
-                        "LWcld_amt":-0.047591989963412906,
-                        "LWcld_err":0.02899712984036007,
-                        "LWcld_tau":0.15161098849919102,
-                        "LWcld_tot":0.2765552625473969,
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-                        "LWcld_tot":0.0040733196102226135,
-                        "SWcld_alt":-0.0004358143374282962,
-                        "SWcld_amt":0.0006095920258490195,
-                        "SWcld_err":4.3075791457682246e-05,
-                        "SWcld_tau":-0.0019144623712054414,
-                        "SWcld_tot":-0.0016976088913270356
-                    },
-                    "HI680":{
-                        "LWcld_alt":0.0010038272593058878,
-                        "LWcld_amt":0.0011058110580451734,
-                        "LWcld_err":-0.0001352849161264384,
-                        "LWcld_tau":0.0022382410266237403,
-                        "LWcld_tot":0.004212594427848363,
-                        "SWcld_alt":-0.00013001848348783828,
-                        "SWcld_amt":-0.002097021994135845,
-                        "SWcld_err":1.245716525368966e-05,
-                        "SWcld_tau":-0.0012643815466481323,
-                        "SWcld_tot":-0.0034789648590181255
-                    },
-                    "LO680":{
-                        "LWcld_alt":0.00011451751570158902,
-                        "LWcld_amt":-0.0004537980490846978,
-                        "LWcld_err":-1.2569889786681707e-05,
-                        "LWcld_tau":0.0002125230482280677,
-                        "LWcld_tot":-0.00013927481762574968,
-                        "SWcld_alt":-4.776398069047883e-05,
-                        "SWcld_amt":0.002766630893066822,
-                        "SWcld_err":9.592061574908504e-06,
-                        "SWcld_tau":-0.0009471020313555934,
-                        "SWcld_tot":0.0017813569425956589
-                    }
-                },
-                "ocn":{
-                    "ALL":{
-                        "LWcld_alt":0.00820717201405493,
-                        "LWcld_amt":9.194959543956392e-05,
-                        "LWcld_err":0.00029052980099555584,
-                        "LWcld_tau":0.002834929962878929,
-                        "LWcld_tot":0.01142458137336898,
-                        "SWcld_alt":-0.0019916129882116716,
-                        "SWcld_amt":-0.00039275999517173393,
-                        "SWcld_err":-4.337054633181707e-05,
-                        "SWcld_tau":-0.01032616401752051,
-                        "SWcld_tot":-0.012753907547235733
-                    },
-                    "HI680":{
-                        "LWcld_alt":0.001506308529615437,
-                        "LWcld_amt":0.006900904154667994,
-                        "LWcld_err":0.0005803957528084298,
-                        "LWcld_tau":0.003228959261308109,
-                        "LWcld_tot":0.012216567698399971,
-                        "SWcld_alt":-0.00010023868326536779,
-                        "SWcld_amt":-0.015940190648758606,
-                        "SWcld_err":-0.00011725452772842407,
-                        "SWcld_tau":-0.004100358199625022,
-                        "SWcld_tot":-0.020258042059377424
-                    },
-                    "LO680":{
-                        "LWcld_alt":0.0008119016914190567,
-                        "LWcld_amt":-0.0020881839153249026,
-                        "LWcld_err":-0.00012258709360160066,
-                        "LWcld_tau":0.0006068829924764538,
-                        "LWcld_tot":-0.0007919863250309927,
-                        "SWcld_alt":-0.0004215455648411158,
-                        "SWcld_amt":0.014372897896071475,
-                        "SWcld_err":9.212210658805773e-05,
-                        "SWcld_tau":-0.006539339925676734,
-                        "SWcld_tot":0.007504134512141687
-                    }
-                },
-                "ocn_asc":{
-                    "ALL":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN
-                    },
-                    "HI680":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN
-                    },
-                    "LO680":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN
-                    }
-                },
-                "ocn_dsc":{
-                    "ALL":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN
-                    },
-                    "HI680":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN
-                    },
-                    "LO680":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN
-                    }
-                }
-            },
-            "eq10":{
-                "all":{
-                    "ALL":{
-                        "LWcld_alt":0.15750293642694088,
-                        "LWcld_amt":-0.05679177153069212,
-                        "LWcld_err":-0.015963382574370886,
-                        "LWcld_tau":-0.035718521870748754,
-                        "LWcld_tot":0.049029260451129104,
-                        "SWcld_alt":-0.02091146166108263,
-                        "SWcld_amt":0.11023771905664363,
-                        "SWcld_err":-0.0040710361266050885,
-                        "SWcld_tau":0.04540161773882078,
-                        "SWcld_tot":0.13065683900777667
-                    },
-                    "HI680":{
-                        "LWcld_alt":0.11834050689688114,
-                        "LWcld_amt":-0.031015954413566558,
-                        "LWcld_err":-0.011595225964161753,
-                        "LWcld_tau":-0.01872917084344942,
-                        "LWcld_tot":0.05700015567570341,
-                        "SWcld_alt":-0.011301843742423266,
-                        "SWcld_amt":0.04492001718380911,
-                        "SWcld_err":-0.0027003412090113844,
-                        "SWcld_tau":0.00740308385169217,
-                        "SWcld_tot":0.03832091608406663
-                    },
-                    "LO680":{
-                        "LWcld_alt":0.002741820458917495,
-                        "LWcld_amt":-0.010571244101933178,
-                        "LWcld_err":6.521807288147703e-05,
-                        "LWcld_tau":-0.00020668965444009473,
-                        "LWcld_tot":-0.007970895224574299,
-                        "SWcld_alt":-0.001389481617696878,
-                        "SWcld_amt":0.08379491099559165,
-                        "SWcld_err":-4.8767987849773914e-05,
-                        "SWcld_tau":0.009979261533665048,
-                        "SWcld_tot":0.09233592292371005
-                    }
-                },
-                "lnd":{
-                    "ALL":{
-                        "LWcld_alt":0.04803444891345097,
-                        "LWcld_amt":-0.03447874247144831,
-                        "LWcld_err":-0.01328093750609212,
-                        "LWcld_tau":-0.026723487174144925,
-                        "LWcld_tot":-0.02644871823823439,
-                        "SWcld_alt":-0.005207530050665768,
-                        "SWcld_amt":0.057611743591433444,
-                        "SWcld_err":0.0008061844876912209,
-                        "SWcld_tau":0.035653511199908776,
-                        "SWcld_tot":0.08886390922836768
-                    },
-                    "HI680":{
-                        "LWcld_alt":0.040329713658844364,
-                        "LWcld_amt":-0.028759210112754202,
-                        "LWcld_err":-0.011322218378888646,
-                        "LWcld_tau":-0.024992639458503413,
-                        "LWcld_tot":-0.024744354291301885,
-                        "SWcld_alt":-0.0033479255718489773,
-                        "SWcld_amt":0.04254817714942937,
-                        "SWcld_err":0.0007012203084908556,
-                        "SWcld_tau":0.032660764314958665,
-                        "SWcld_tot":0.07256223620102992
-                    },
-                    "LO680":{
-                        "LWcld_alt":0.0005922247758319274,
-                        "LWcld_amt":-0.0023526637204538273,
-                        "LWcld_err":2.5586579503085687e-05,
-                        "LWcld_tau":3.048841818630467e-05,
-                        "LWcld_tot":-0.0017043639469325094,
-                        "SWcld_alt":-0.00033143322131651965,
-                        "SWcld_amt":0.017616013824371623,
-                        "SWcld_err":-2.4152102480734705e-05,
-                        "SWcld_tau":-0.0009587554732366005,
-                        "SWcld_tot":0.01630167302733776
-                    }
-                },
-                "ocn":{
-                    "ALL":{
-                        "LWcld_alt":0.10946848751348992,
-                        "LWcld_amt":-0.022313029059243806,
-                        "LWcld_err":-0.002682445068278766,
-                        "LWcld_tau":-0.008995034696603827,
-                        "LWcld_tot":0.07547797868936351,
-                        "SWcld_alt":-0.015703931610416864,
-                        "SWcld_amt":0.052625975465210174,
-                        "SWcld_err":-0.00487722061429631,
-                        "SWcld_tau":0.00974810653891199,
-                        "SWcld_tot":0.041792929779408995
-                    },
-                    "HI680":{
-                        "LWcld_alt":0.07801079323803677,
-                        "LWcld_amt":-0.002256744300812351,
-                        "LWcld_err":-0.00027300758527310794,
-                        "LWcld_tau":0.006263468615053987,
-                        "LWcld_tot":0.08174450996700529,
-                        "SWcld_alt":-0.007953918170574288,
-                        "SWcld_amt":0.002371840034379742,
-                        "SWcld_err":-0.00340156151750224,
-                        "SWcld_tau":-0.0252576804632665,
-                        "SWcld_tot":-0.034241320116963285
-                    },
-                    "LO680":{
-                        "LWcld_alt":0.0021495956830855675,
-                        "LWcld_amt":-0.008218580381479351,
-                        "LWcld_err":3.963149337839136e-05,
-                        "LWcld_tau":-0.00023717807262639944,
-                        "LWcld_tot":-0.006266531277641791,
-                        "SWcld_alt":-0.0010580483963803582,
-                        "SWcld_amt":0.06617889717122004,
-                        "SWcld_err":-2.4615885369039212e-05,
-                        "SWcld_tau":0.010938017006901647,
-                        "SWcld_tot":0.0760342498963723
-                    }
-                },
-                "ocn_asc":{
-                    "ALL":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN
-                    },
-                    "HI680":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN
-                    },
-                    "LO680":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN
-                    }
-                },
-                "ocn_dsc":{
-                    "ALL":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN
-                    },
-                    "HI680":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN
-                    },
-                    "LO680":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN
-                    }
-                }
-            },
-            "eq20":{
-                "all":{
-                    "ALL":{
-                        "LWcld_alt":0.27718245782735873,
-                        "LWcld_amt":-0.0989426622924305,
-                        "LWcld_err":-0.021282130387215662,
-                        "LWcld_tau":-0.03967983929649285,
-                        "LWcld_tot":0.11727782585121972,
-                        "SWcld_alt":-0.03823967248136666,
-                        "SWcld_amt":0.16903926378089038,
-                        "SWcld_err":-0.007446419497275828,
-                        "SWcld_tau":0.045930905902804776,
-                        "SWcld_tot":0.1692840777050527
-                    },
-                    "HI680":{
-                        "LWcld_alt":0.18729627447497457,
-                        "LWcld_amt":-0.04918726323636779,
-                        "LWcld_err":-0.013114751070751064,
-                        "LWcld_tau":0.0059202094350921846,
-                        "LWcld_tot":0.13091446960294795,
-                        "SWcld_alt":-0.01688053292574627,
-                        "SWcld_amt":0.06800837599071745,
-                        "SWcld_err":-0.004670082791028944,
-                        "SWcld_tau":-0.046026207375429534,
-                        "SWcld_tot":0.00043155289851270806
-                    },
-                    "LO680":{
-                        "LWcld_alt":0.0065904676986499955,
-                        "LWcld_amt":-0.019806041618184837,
-                        "LWcld_err":2.3420225586039634e-05,
-                        "LWcld_tau":-0.000444567708985863,
-                        "LWcld_tot":-0.01363664375172821,
-                        "SWcld_alt":-0.0031789122956779707,
-                        "SWcld_amt":0.15357246132865707,
-                        "SWcld_err":4.691477927660632e-05,
-                        "SWcld_tau":0.018412220752861776,
-                        "SWcld_tot":0.16885268456511748
-                    }
-                },
-                "lnd":{
-                    "ALL":{
-                        "LWcld_alt":0.07360706747844876,
-                        "LWcld_amt":-0.06324877418768089,
-                        "LWcld_err":-0.01903501287302674,
-                        "LWcld_tau":-0.0396781641880832,
-                        "LWcld_tot":-0.04835488377034208,
-                        "SWcld_alt":-0.0075196368251830665,
-                        "SWcld_amt":0.09351453682314463,
-                        "SWcld_err":0.001053529089170387,
-                        "SWcld_tau":0.04913652499276936,
-                        "SWcld_tot":0.1361849540799013
-                    },
-                    "HI680":{
-                        "LWcld_alt":0.06107128932242758,
-                        "LWcld_amt":-0.05418234458031163,
-                        "LWcld_err":-0.016759114704519757,
-                        "LWcld_tau":-0.03565486920324435,
-                        "LWcld_tot":-0.04552503916564818,
-                        "SWcld_alt":-0.004683016121339241,
-                        "SWcld_amt":0.07210356286585093,
-                        "SWcld_err":0.0011199507387866165,
-                        "SWcld_tau":0.04288185474012455,
-                        "SWcld_tot":0.11142235222342288
-                    },
-                    "LO680":{
-                        "LWcld_alt":0.0008587179152271053,
-                        "LWcld_amt":-0.003763369753121446,
-                        "LWcld_err":2.6203818834983467e-05,
-                        "LWcld_tau":4.852576315900653e-05,
-                        "LWcld_tot":-0.0028298446046938932,
-                        "SWcld_alt":-0.0004673329038750065,
-                        "SWcld_amt":0.026626475434610126,
-                        "SWcld_err":-2.2087328939430013e-05,
-                        "SWcld_tau":-0.0013742935867397364,
-                        "SWcld_tot":0.02476276161505596
-                    }
-                },
-                "ocn":{
-                    "ALL":{
-                        "LWcld_alt":0.20357539034891003,
-                        "LWcld_amt":-0.035693888104749626,
-                        "LWcld_err":-0.0022471175141889205,
-                        "LWcld_tau":-1.675108409657002e-06,
-                        "LWcld_tot":0.16563270962156182,
-                        "SWcld_alt":-0.030720035656183586,
-                        "SWcld_amt":0.07552472695774574,
-                        "SWcld_err":-0.008499948586446218,
-                        "SWcld_tau":-0.00320561908996459,
-                        "SWcld_tot":0.03309912362515134
-                    },
-                    "HI680":{
-                        "LWcld_alt":0.12622498515254701,
-                        "LWcld_amt":0.0049950813439438545,
-                        "LWcld_err":0.003644363633768697,
-                        "LWcld_tau":0.041575078638336545,
-                        "LWcld_tot":0.17643950876859613,
-                        "SWcld_alt":-0.012197516804407033,
-                        "SWcld_amt":-0.004095186875133479,
-                        "SWcld_err":-0.00579003352981556,
-                        "SWcld_tau":-0.08890806211555408,
-                        "SWcld_tot":-0.11099079932491017
-                    },
-                    "LO680":{
-                        "LWcld_alt":0.00573174978342289,
-                        "LWcld_amt":-0.016042671865063394,
-                        "LWcld_err":-2.7835932489438293e-06,
-                        "LWcld_tau":-0.0004930934721448695,
-                        "LWcld_tot":-0.010806799147034314,
-                        "SWcld_alt":-0.0027115793918029638,
-                        "SWcld_amt":0.12694598589404693,
-                        "SWcld_err":6.900210821603635e-05,
-                        "SWcld_tau":0.019786514339601515,
-                        "SWcld_tot":0.14408992295006148
-                    }
-                },
-                "ocn_asc":{
-                    "ALL":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN
-                    },
-                    "HI680":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN
-                    },
-                    "LO680":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN
-                    }
-                },
-                "ocn_dsc":{
-                    "ALL":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN
-                    },
-                    "HI680":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN
-                    },
-                    "LO680":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN
-                    }
-                }
-            },
-            "eq30":{
-                "all":{
-                    "ALL":{
-                        "LWcld_alt":0.3761341071697967,
-                        "LWcld_amt":-0.13742880009328057,
-                        "LWcld_err":-0.03797795967367204,
-                        "LWcld_tau":-0.042894801565092754,
-                        "LWcld_tot":0.15783254583775136,
-                        "SWcld_alt":-0.05409028619737647,
-                        "SWcld_amt":0.23258663568861895,
-                        "SWcld_err":-0.006846430017796649,
-                        "SWcld_tau":0.06729920816311549,
-                        "SWcld_tot":0.23894912763656131
-                    },
-                    "HI680":{
-                        "LWcld_alt":0.2384740978814159,
-                        "LWcld_amt":-0.06500122411610866,
-                        "LWcld_err":-0.018393489877098147,
-                        "LWcld_tau":0.02513825581019082,
-                        "LWcld_tot":0.18021763969839988,
-                        "SWcld_alt":-0.02123200208708209,
-                        "SWcld_amt":0.08697702114043941,
-                        "SWcld_err":-0.0051422829481824256,
-                        "SWcld_tau":-0.07375329281665449,
-                        "SWcld_tot":-0.013150556711479603
-                    },
-                    "LO680":{
-                        "LWcld_alt":0.009830519799726092,
-                        "LWcld_amt":-0.03190979520531891,
-                        "LWcld_err":7.938306664060323e-05,
-                        "LWcld_tau":-0.00038542076169221926,
-                        "LWcld_tot":-0.022385093860648547,
-                        "SWcld_alt":-0.004578073408380795,
-                        "SWcld_amt":0.23263232681324575,
-                        "SWcld_err":4.574509475134935e-05,
-                        "SWcld_tau":0.024000152821934417,
-                        "SWcld_tot":0.2521001513215508
-                    }
-                },
-                "lnd":{
-                    "ALL":{
-                        "LWcld_alt":0.09537788859000347,
-                        "LWcld_amt":-0.09059560524529514,
-                        "LWcld_err":-0.025177868743046377,
-                        "LWcld_tau":-0.046383433748451046,
-                        "LWcld_tot":-0.06677901914678909,
-                        "SWcld_alt":-0.009411914645112611,
-                        "SWcld_amt":0.13621440808917065,
-                        "SWcld_err":0.001531086902663194,
-                        "SWcld_tau":0.06027754004516686,
-                        "SWcld_tot":0.1886111203918881
-                    },
-                    "HI680":{
-                        "LWcld_alt":0.08033105001166592,
-                        "LWcld_amt":-0.07904836038636456,
-                        "LWcld_err":-0.021303410525437712,
-                        "LWcld_tau":-0.04266743061663312,
-                        "LWcld_tot":-0.06268815151676949,
-                        "SWcld_alt":-0.006074767148651451,
-                        "SWcld_amt":0.10550413505119792,
-                        "SWcld_err":0.0014787788149631927,
-                        "SWcld_tau":0.05388311380891386,
-                        "SWcld_tot":0.1547912605264235
-                    },
-                    "LO680":{
-                        "LWcld_alt":0.0011561679472049792,
-                        "LWcld_amt":-0.005403619620078671,
-                        "LWcld_err":2.5758688223427083e-05,
-                        "LWcld_tau":0.00013060611463476445,
-                        "LWcld_tot":-0.004090867630019603,
-                        "SWcld_alt":-0.0006096540551130514,
-                        "SWcld_amt":0.036330838990101644,
-                        "SWcld_err":-1.8076873922239647e-05,
-                        "SWcld_tau":-0.001882781222091931,
-                        "SWcld_tot":0.033820326838974415
-                    }
-                },
-                "ocn":{
-                    "ALL":{
-                        "LWcld_alt":0.2807562185797932,
-                        "LWcld_amt":-0.04683319484798542,
-                        "LWcld_err":-0.01280009093062566,
-                        "LWcld_tau":0.0034886321833582823,
-                        "LWcld_tot":0.2246115649845404,
-                        "SWcld_alt":-0.04467837155226386,
-                        "SWcld_amt":0.09637222759944829,
-                        "SWcld_err":-0.008377516920459844,
-                        "SWcld_tau":0.007021668117948633,
-                        "SWcld_tot":0.05033800724467324
-                    },
-                    "HI680":{
-                        "LWcld_alt":0.15814304786974998,
-                        "LWcld_amt":0.014047136270255896,
-                        "LWcld_err":0.0029099206483395677,
-                        "LWcld_tau":0.06780568642682394,
-                        "LWcld_tot":0.24290579121516934,
-                        "SWcld_alt":-0.015157234938430637,
-                        "SWcld_amt":-0.018527113910758494,
-                        "SWcld_err":-0.006621061763145618,
-                        "SWcld_tau":-0.12763640662556835,
-                        "SWcld_tot":-0.16794181723790313
-                    },
-                    "LO680":{
-                        "LWcld_alt":0.008674351852521113,
-                        "LWcld_amt":-0.02650617558524024,
-                        "LWcld_err":5.362437841717614e-05,
-                        "LWcld_tau":-0.0005160268763269837,
-                        "LWcld_tot":-0.018294226230628943,
-                        "SWcld_alt":-0.003968419353267743,
-                        "SWcld_amt":0.19630148782314413,
-                        "SWcld_err":6.382196867358901e-05,
-                        "SWcld_tau":0.02588293404402635,
-                        "SWcld_tot":0.21827982448257635
-                    }
-                },
-                "ocn_asc":{
-                    "ALL":{
-                        "LWcld_alt":0.1331308960833464,
-                        "LWcld_amt":0.06553018929942706,
-                        "LWcld_err":0.0074875684882440945,
-                        "LWcld_tau":0.02945212916345589,
-                        "LWcld_tot":0.2356007830344735,
-                        "SWcld_alt":-0.016011103825287035,
-                        "SWcld_amt":-0.10822758219367457,
-                        "SWcld_err":-0.006179891324272335,
-                        "SWcld_tau":-0.09128742300398913,
-                        "SWcld_tot":-0.22170600034722301
-                    },
-                    "HI680":{
-                        "LWcld_alt":0.1063556713690617,
-                        "LWcld_amt":0.07770626704982941,
-                        "LWcld_err":0.007966587590732729,
-                        "LWcld_tau":0.04071952290950702,
-                        "LWcld_tot":0.23274804891913084,
-                        "SWcld_alt":-0.009766782144492258,
-                        "SWcld_amt":-0.09914940999805805,
-                        "SWcld_err":-0.0040219187059072495,
-                        "SWcld_tau":-0.1063366972065596,
-                        "SWcld_tot":-0.21927480805501717
-                    },
-                    "LO680":{
-                        "LWcld_alt":0.0026861392676342122,
-                        "LWcld_amt":-0.0001240534793379479,
-                        "LWcld_err":3.8117501022997295e-05,
-                        "LWcld_tau":0.0002525308260233213,
-                        "LWcld_tot":0.002852734115342583,
-                        "SWcld_alt":-0.0013250630470561823,
-                        "SWcld_amt":0.0010951192796605426,
-                        "SWcld_err":1.7498002758822817e-05,
-                        "SWcld_tau":-0.0022187465275690997,
-                        "SWcld_tot":-0.002431192292205918
-                    }
-                },
-                "ocn_dsc":{
-                    "ALL":{
-                        "LWcld_alt":0.07776140406670258,
-                        "LWcld_amt":-0.044440024021462916,
-                        "LWcld_err":-0.019561344808237364,
-                        "LWcld_tau":-0.020903532065834975,
-                        "LWcld_tot":-0.0071434968288326475,
-                        "SWcld_alt":-0.009425679699959024,
-                        "SWcld_amt":0.19252422600933294,
-                        "SWcld_err":0.0003262929009313237,
-                        "SWcld_tau":0.06915656385170389,
-                        "SWcld_tot":0.25258140306200916
-                    },
-                    "HI680":{
-                        "LWcld_alt":0.04827950814365323,
-                        "LWcld_amt":-0.015435466818555774,
-                        "LWcld_err":-0.0007275446368961025,
-                        "LWcld_tau":-0.017943998349301982,
-                        "LWcld_tot":0.014172498338899374,
-                        "SWcld_alt":-0.004194963931986542,
-                        "SWcld_amt":0.02067683351573493,
-                        "SWcld_err":-0.0010518952135660628,
-                        "SWcld_tau":0.030519126064256486,
-                        "SWcld_tot":0.04594910043443882
-                    },
-                    "LO680":{
-                        "LWcld_alt":0.006343494997955196,
-                        "LWcld_amt":-0.02667708096941517,
-                        "LWcld_err":-9.456094866807335e-05,
-                        "LWcld_tau":-0.0008878482476039816,
-                        "LWcld_tot":-0.021315995167732023,
-                        "SWcld_alt":-0.0030027694312907576,
-                        "SWcld_amt":0.178176598521274,
-                        "SWcld_err":0.0003258950043874904,
-                        "SWcld_tau":0.031132578533199607,
-                        "SWcld_tot":0.20663230262757035
-                    }
-                }
-            },
-            "eq40":{
-                "all":{
-                    "ALL":{
-                        "LWcld_alt":0.4883000563768902,
-                        "LWcld_amt":-0.21468164331179837,
-                        "LWcld_err":-0.06453153941016589,
-                        "LWcld_tau":-0.05681198900731082,
-                        "LWcld_tot":0.15227488464761513,
-                        "SWcld_alt":-0.07378276455549461,
-                        "SWcld_amt":0.405412103382331,
-                        "SWcld_err":-0.0030618886103266764,
-                        "SWcld_tau":0.10455429477125038,
-                        "SWcld_tot":0.43312174498776007
-                    },
-                    "HI680":{
-                        "LWcld_alt":0.2975751431319888,
-                        "LWcld_amt":-0.09304588955242142,
-                        "LWcld_err":-0.021505462078168534,
-                        "LWcld_tau":0.017773781873661736,
-                        "LWcld_tot":0.20079757337506057,
-                        "SWcld_alt":-0.02788887017712492,
-                        "SWcld_amt":0.12156934479289229,
-                        "SWcld_err":-0.005435649184406995,
-                        "SWcld_tau":-0.05856279440520265,
-                        "SWcld_tot":0.029682031026157716
-                    },
-                    "LO680":{
-                        "LWcld_alt":0.011841674643300293,
-                        "LWcld_amt":-0.06274008197425952,
-                        "LWcld_err":0.00020743859092279778,
-                        "LWcld_tau":0.0021680334446516707,
-                        "LWcld_tot":-0.048522688727445445,
-                        "SWcld_alt":-0.005371742816874779,
-                        "SWcld_amt":0.4020936594019539,
-                        "SWcld_err":-4.312248892901425e-05,
-                        "SWcld_tau":0.006761447891444482,
-                        "SWcld_tot":0.40344024198759465
-                    }
-                },
-                "lnd":{
-                    "ALL":{
-                        "LWcld_alt":0.11463557497014078,
-                        "LWcld_amt":-0.11525385288371302,
-                        "LWcld_err":-0.030396351915577876,
-                        "LWcld_tau":-0.05001067387976213,
-                        "LWcld_tot":-0.08102530370891221,
-                        "SWcld_alt":-0.011759171701574164,
-                        "SWcld_amt":0.17295054892935857,
-                        "SWcld_err":0.002079382750045185,
-                        "SWcld_tau":0.06460563577053884,
-                        "SWcld_tot":0.2278763957483684
-                    },
-                    "HI680":{
-                        "LWcld_alt":0.09556785375444445,
-                        "LWcld_amt":-0.09933885335998903,
-                        "LWcld_err":-0.024288437237511184,
-                        "LWcld_tau":-0.046472731496194686,
-                        "LWcld_tot":-0.07453216833925046,
-                        "SWcld_alt":-0.007636601553966228,
-                        "SWcld_amt":0.13071470001886923,
-                        "SWcld_err":0.0017883104212064215,
-                        "SWcld_tau":0.05820093383782546,
-                        "SWcld_tot":0.18306734272393488
-                    },
-                    "LO680":{
-                        "LWcld_alt":0.0013182978444663217,
-                        "LWcld_amt":-0.008418348310230191,
-                        "LWcld_err":2.1911136661975787e-05,
-                        "LWcld_tau":0.0005847573915008278,
-                        "LWcld_tot":-0.006493135369661751,
-                        "SWcld_alt":-0.0006824509941298486,
-                        "SWcld_amt":0.050227493076827806,
-                        "SWcld_err":-1.607374120471397e-05,
-                        "SWcld_tau":-0.004719387291067466,
-                        "SWcld_tot":0.04480958105042577
-                    }
-                },
-                "ocn":{
-                    "ALL":{
-                        "LWcld_alt":0.3736644814067494,
-                        "LWcld_amt":-0.09942779042808536,
-                        "LWcld_err":-0.03413518749458802,
-                        "LWcld_tau":-0.006801315127548686,
-                        "LWcld_tot":0.2333001883565273,
-                        "SWcld_alt":-0.06202359285392045,
-                        "SWcld_amt":0.23246155445297245,
-                        "SWcld_err":-0.005141271360371862,
-                        "SWcld_tau":0.03994865900071154,
-                        "SWcld_tot":0.20524534923939167
-                    },
-                    "HI680":{
-                        "LWcld_alt":0.2020072893775443,
-                        "LWcld_amt":0.006292963807567618,
-                        "LWcld_err":0.0027829751593426533,
-                        "LWcld_tau":0.06424651336985643,
-                        "LWcld_tot":0.275329741714311,
-                        "SWcld_alt":-0.02025226862315869,
-                        "SWcld_amt":-0.009145355225976955,
-                        "SWcld_err":-0.007223959605613416,
-                        "SWcld_tau":-0.11676372824302807,
-                        "SWcld_tot":-0.15338531169777714
-                    },
-                    "LO680":{
-                        "LWcld_alt":0.01052337679883397,
-                        "LWcld_amt":-0.05432173366402934,
-                        "LWcld_err":0.00018552745426082202,
-                        "LWcld_tau":0.001583276053150843,
-                        "LWcld_tot":-0.042029553357783696,
-                        "SWcld_alt":-0.004689291822744931,
-                        "SWcld_amt":0.3518661663251261,
-                        "SWcld_err":-2.7048747724300288e-05,
-                        "SWcld_tau":0.01148083518251195,
-                        "SWcld_tot":0.3586306609371688
-                    }
-                },
-                "ocn_asc":{
-                    "ALL":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN
-                    },
-                    "HI680":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN
-                    },
-                    "LO680":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN
-                    }
-                },
-                "ocn_dsc":{
-                    "ALL":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN
-                    },
-                    "HI680":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN
-                    },
-                    "LO680":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN
-                    }
-                }
-            },
-            "eq50":{
-                "all":{
-                    "ALL":{
-                        "LWcld_alt":0.5823636143119523,
-                        "LWcld_amt":-0.2585892959119187,
-                        "LWcld_err":-0.09207542249763136,
-                        "LWcld_tau":-0.054323635208719245,
-                        "LWcld_tot":0.17737526069368295,
-                        "SWcld_alt":-0.0899835893484869,
-                        "SWcld_amt":0.533968203788861,
-                        "SWcld_err":0.0015504997512758815,
-                        "SWcld_tau":0.08703277265470423,
-                        "SWcld_tot":0.5325678868463543
-                    },
-                    "HI680":{
-                        "LWcld_alt":0.3531298003327774,
-                        "LWcld_amt":-0.09020779148422804,
-                        "LWcld_err":-0.028757039127399023,
-                        "LWcld_tau":0.008002146992902257,
-                        "LWcld_tot":0.24216711671405255,
-                        "SWcld_alt":-0.03401297427516941,
-                        "SWcld_amt":0.11054475693942077,
-                        "SWcld_err":-0.004909222797359532,
-                        "SWcld_tau":-0.03244550493300389,
-                        "SWcld_tot":0.039177054933887914
-                    },
-                    "LO680":{
-                        "LWcld_alt":0.014668949456250135,
-                        "LWcld_amt":-0.08763571102973215,
-                        "LWcld_err":-0.0005208003787365336,
-                        "LWcld_tau":0.008695459363909612,
-                        "LWcld_tot":-0.06479185602036962,
-                        "SWcld_alt":-0.006710143539930046,
-                        "SWcld_amt":0.5472273483810227,
-                        "SWcld_err":0.00034345123445703114,
-                        "SWcld_tau":-0.04746929613709113,
-                        "SWcld_tot":0.4933913599384585
-                    }
-                },
-                "lnd":{
-                    "ALL":{
-                        "LWcld_alt":0.1341550377262096,
-                        "LWcld_amt":-0.12776498589804183,
-                        "LWcld_err":-0.03722054780549245,
-                        "LWcld_tau":-0.04933689746487186,
-                        "LWcld_tot":-0.08016739344219652,
-                        "SWcld_alt":-0.014483837930192664,
-                        "SWcld_amt":0.19866422656214766,
-                        "SWcld_err":0.0029569122494652127,
-                        "SWcld_tau":0.06365532330268935,
-                        "SWcld_tot":0.2507926241841096
-                    },
-                    "HI680":{
-                        "LWcld_alt":0.10966239664080484,
-                        "LWcld_amt":-0.10630462678142054,
-                        "LWcld_err":-0.027678063941549803,
-                        "LWcld_tau":-0.04761490718138933,
-                        "LWcld_tot":-0.07193520126355488,
-                        "SWcld_alt":-0.009177484216512795,
-                        "SWcld_amt":0.1418151222730172,
-                        "SWcld_err":0.002164285362188461,
-                        "SWcld_tau":0.059521720459199755,
-                        "SWcld_tot":0.1943236438778926
-                    },
-                    "LO680":{
-                        "LWcld_alt":0.001441421031698989,
-                        "LWcld_amt":-0.011447822488743037,
-                        "LWcld_err":6.100774897817642e-06,
-                        "LWcld_tau":0.0017678619355652608,
-                        "LWcld_tot":-0.008232192178641658,
-                        "SWcld_alt":-0.000729780524050911,
-                        "SWcld_amt":0.06777178609176891,
-                        "SWcld_err":-7.967824623930263e-06,
-                        "SWcld_tau":-0.010564529410884954,
-                        "SWcld_tot":0.05646950833220912
-                    }
-                },
-                "ocn":{
-                    "ALL":{
-                        "LWcld_alt":0.4482085765857426,
-                        "LWcld_amt":-0.13082431001387687,
-                        "LWcld_err":-0.05485487469213893,
-                        "LWcld_tau":-0.004986737743847379,
-                        "LWcld_tot":0.25754265413587946,
-                        "SWcld_alt":-0.07549975141829424,
-                        "SWcld_amt":0.3353039772267134,
-                        "SWcld_err":-0.0014064124981893304,
-                        "SWcld_tau":0.023377449352014888,
-                        "SWcld_tot":0.2817752626622447
-                    },
-                    "HI680":{
-                        "LWcld_alt":0.24346740369197262,
-                        "LWcld_amt":0.016096835297192493,
-                        "LWcld_err":-0.001078975185849224,
-                        "LWcld_tau":0.0556170541742916,
-                        "LWcld_tot":0.31410231797760746,
-                        "SWcld_alt":-0.02483549005865661,
-                        "SWcld_amt":-0.03127036533359646,
-                        "SWcld_err":-0.007073508159547993,
-                        "SWcld_tau":-0.09196722539220364,
-                        "SWcld_tot":-0.15514658894400474
-                    },
-                    "LO680":{
-                        "LWcld_alt":0.013227528424551147,
-                        "LWcld_amt":-0.07618788854098911,
-                        "LWcld_err":-0.0005269011536343514,
-                        "LWcld_tau":0.006927597428344351,
-                        "LWcld_tot":-0.05655966384172797,
-                        "SWcld_alt":-0.005980363015879135,
-                        "SWcld_amt":0.47945556228925373,
-                        "SWcld_err":0.0003514190590809614,
-                        "SWcld_tau":-0.03690476672620616,
-                        "SWcld_tot":0.43692185160624936
-                    }
-                },
-                "ocn_asc":{
-                    "ALL":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN
-                    },
-                    "HI680":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN
-                    },
-                    "LO680":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN
-                    }
-                },
-                "ocn_dsc":{
-                    "ALL":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN
-                    },
-                    "HI680":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN
-                    },
-                    "LO680":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN
-                    }
-                }
-            },
-            "eq60":{
-                "all":{
-                    "ALL":{
-                        "LWcld_alt":0.6497748548366101,
-                        "LWcld_amt":-0.2710144673241391,
-                        "LWcld_err":-0.10385232649849808,
-                        "LWcld_tau":-0.03579107686206432,
-                        "LWcld_tot":0.23911698415190855,
-                        "SWcld_alt":-0.10143536757727434,
-                        "SWcld_amt":0.5761229261611758,
-                        "SWcld_err":0.0037298340388370707,
-                        "SWcld_tau":-0.00018930809416049915,
-                        "SWcld_tot":0.4782280845285782
-                    },
-                    "HI680":{
-                        "LWcld_alt":0.39085498971516003,
-                        "LWcld_amt":-0.06369790244180586,
-                        "LWcld_err":-0.030985808724288095,
-                        "LWcld_tau":0.016245549551716067,
-                        "LWcld_tot":0.31241682810078203,
-                        "SWcld_alt":-0.037948984451983823,
-                        "SWcld_amt":0.05880669097209711,
-                        "SWcld_err":-0.004773112426885666,
-                        "SWcld_tau":-0.04573644466667992,
-                        "SWcld_tot":-0.02965185057345232
-                    },
-                    "LO680":{
-                        "LWcld_alt":0.015867946149979658,
-                        "LWcld_amt":-0.10394673404402925,
-                        "LWcld_err":-0.001588974453313162,
-                        "LWcld_tau":0.016367671830549893,
-                        "LWcld_tot":-0.07329984394887352,
-                        "SWcld_alt":-0.007696037999368246,
-                        "SWcld_amt":0.6361151386795765,
-                        "SWcld_err":0.0009381377852232635,
-                        "SWcld_tau":-0.1214767753374089,
-                        "SWcld_tot":0.5078804631280226
-                    }
-                },
-                "lnd":{
-                    "ALL":{
-                        "LWcld_alt":0.15218914770841493,
-                        "LWcld_amt":-0.1316543070635377,
-                        "LWcld_err":-0.0410435494394885,
-                        "LWcld_tau":-0.04302244818978074,
-                        "LWcld_tot":-0.063531156984392,
-                        "SWcld_alt":-0.01740886758965911,
-                        "SWcld_amt":0.21185085935181194,
-                        "SWcld_err":0.0035396191231736487,
-                        "SWcld_tau":0.05449282295826227,
-                        "SWcld_tot":0.25247443384358875
-                    },
-                    "HI680":{
-                        "LWcld_alt":0.1213943147568363,
-                        "LWcld_amt":-0.10432682200718851,
-                        "LWcld_err":-0.02913234786904674,
-                        "LWcld_tau":-0.0422599524081391,
-                        "LWcld_tot":-0.054324807527538044,
-                        "SWcld_alt":-0.010596300802693475,
-                        "SWcld_amt":0.13987643744273662,
-                        "SWcld_err":0.0023311318661818035,
-                        "SWcld_tau":0.05482059427512987,
-                        "SWcld_tot":0.18643186278135482
-                    },
-                    "LO680":{
-                        "LWcld_alt":0.0015311749731526242,
-                        "LWcld_amt":-0.014112336104399726,
-                        "LWcld_err":-5.861066258421837e-05,
-                        "LWcld_tau":0.0034331757690380204,
-                        "LWcld_tot":-0.009206349456853983,
-                        "SWcld_alt":-0.0007566180646080219,
-                        "SWcld_amt":0.08639039143826759,
-                        "SWcld_err":1.4193804856380563e-05,
-                        "SWcld_tau":-0.01960486809028982,
-                        "SWcld_tot":0.06604309908822613
-                    }
-                },
-                "ocn":{
-                    "ALL":{
-                        "LWcld_alt":0.4975857071281952,
-                        "LWcld_amt":-0.1393601602606014,
-                        "LWcld_err":-0.06280877705900959,
-                        "LWcld_tau":0.007231371327716426,
-                        "LWcld_tot":0.3026481411363006,
-                        "SWcld_alt":-0.08402649998761523,
-                        "SWcld_amt":0.36427206680936397,
-                        "SWcld_err":0.00019021491566342252,
-                        "SWcld_tau":-0.054682131052422756,
-                        "SWcld_tot":0.2257536506849894
-                    },
-                    "HI680":{
-                        "LWcld_alt":0.2694606749583237,
-                        "LWcld_amt":0.04062891956538265,
-                        "LWcld_err":-0.0018534608552413538,
-                        "LWcld_tau":0.05850550195985516,
-                        "LWcld_tot":0.36674163562832013,
-                        "SWcld_alt":-0.02735268364929035,
-                        "SWcld_amt":-0.08106974647063953,
-                        "SWcld_err":-0.007104244293067469,
-                        "SWcld_tau":-0.10055703894180978,
-                        "SWcld_tot":-0.21608371335480714
-                    },
-                    "LO680":{
-                        "LWcld_alt":0.014336771176827032,
-                        "LWcld_amt":-0.08983439793962951,
-                        "LWcld_err":-0.0015303637907289438,
-                        "LWcld_tau":0.01293449606151187,
-                        "LWcld_tot":-0.06409349449201955,
-                        "SWcld_alt":-0.0069394199347602254,
-                        "SWcld_amt":0.549724747241309,
-                        "SWcld_err":0.0009239439803668828,
-                        "SWcld_tau":-0.10187190724711909,
-                        "SWcld_tot":0.44183736403979657
-                    }
-                },
-                "ocn_asc":{
-                    "ALL":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN
-                    },
-                    "HI680":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN
-                    },
-                    "LO680":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN
-                    }
-                },
-                "ocn_dsc":{
-                    "ALL":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN
-                    },
-                    "HI680":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN
-                    },
-                    "LO680":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN
-                    }
-                }
-            },
-            "eq70":{
-                "all":{
-                    "ALL":{
-                        "LWcld_alt":0.6902830168970889,
-                        "LWcld_amt":-0.27422915474371795,
-                        "LWcld_err":-0.10651947568728948,
-                        "LWcld_tau":-0.017568047056834932,
-                        "LWcld_tot":0.29196633940924643,
-                        "SWcld_alt":-0.1077797511672876,
-                        "SWcld_amt":0.5861451652462742,
-                        "SWcld_err":0.004374250318418108,
-                        "SWcld_tau":-0.06360460564843046,
-                        "SWcld_tot":0.4191350587489742
-                    },
-                    "HI680":{
-                        "LWcld_alt":0.4107536869739261,
-                        "LWcld_amt":-0.04295900055329302,
-                        "LWcld_err":-0.030206207690358353,
-                        "LWcld_tau":0.031751031604629616,
-                        "LWcld_tot":0.36933951033490436,
-                        "SWcld_alt":-0.039875432234132334,
-                        "SWcld_amt":0.01998614924984554,
-                        "SWcld_err":-0.004930799276288742,
-                        "SWcld_tau":-0.06974117024129392,
-                        "SWcld_tot":-0.09456125250186947
-                    },
-                    "LO680":{
-                        "LWcld_alt":0.01653361924806196,
-                        "LWcld_amt":-0.1123750902758715,
-                        "LWcld_err":-0.002040843210126392,
-                        "LWcld_tau":0.02050887616862188,
-                        "LWcld_tot":-0.07737317092565793,
-                        "SWcld_alt":-0.008100681668794055,
-                        "SWcld_amt":0.6825850852456593,
-                        "SWcld_err":0.0012308750395732756,
-                        "SWcld_tau":-0.16201843889660913,
-                        "SWcld_tot":0.5136968397198293
-                    }
-                },
-                "lnd":{
-                    "ALL":{
-                        "LWcld_alt":0.16528807420968256,
-                        "LWcld_amt":-0.1327731232786748,
-                        "LWcld_err":-0.042351465267678134,
-                        "LWcld_tau":-0.03346507237992091,
-                        "LWcld_tot":-0.04330158671659127,
-                        "SWcld_alt":-0.019791609359909545,
-                        "SWcld_amt":0.21801695860116332,
-                        "SWcld_err":0.0038373733202206315,
-                        "SWcld_tau":0.04194614558841365,
-                        "SWcld_tot":0.244008868149888
-                    },
-                    "HI680":{
-                        "LWcld_alt":0.12934958579327221,
-                        "LWcld_amt":-0.10027681425351509,
-                        "LWcld_err":-0.029271332767804043,
-                        "LWcld_tau":-0.03296891350843678,
-                        "LWcld_tot":-0.0331674747364837,
-                        "SWcld_alt":-0.011578534356446275,
-                        "SWcld_amt":0.1320848302478565,
-                        "SWcld_err":0.002331340275034572,
-                        "SWcld_tau":0.04773255601478712,
-                        "SWcld_tot":0.17057019218123193
-                    },
-                    "LO680":{
-                        "LWcld_alt":0.0015948568737678814,
-                        "LWcld_amt":-0.016459166171843783,
-                        "LWcld_err":-0.00013068389586443386,
-                        "LWcld_tau":0.004860614070176625,
-                        "LWcld_tot":-0.010134111980107587,
-                        "SWcld_alt":-0.0008118652405172504,
-                        "SWcld_amt":0.10242770190737183,
-                        "SWcld_err":4.120922658621424e-05,
-                        "SWcld_tau":-0.028217841455799166,
-                        "SWcld_tot":0.07343920443764164
-                    }
-                },
-                "ocn":{
-                    "ALL":{
-                        "LWcld_alt":0.5249949426874062,
-                        "LWcld_amt":-0.14145603146504318,
-                        "LWcld_err":-0.06416801041961133,
-                        "LWcld_tau":0.015897025323085984,
-                        "LWcld_tot":0.3352679261258377,
-                        "SWcld_alt":-0.08798814180737806,
-                        "SWcld_amt":0.368128206645111,
-                        "SWcld_err":0.0005368769981974776,
-                        "SWcld_tau":-0.10555075123684411,
-                        "SWcld_tot":0.17512619059908632
-                    },
-                    "HI680":{
-                        "LWcld_alt":0.2814041011806539,
-                        "LWcld_amt":0.05731781370022208,
-                        "LWcld_err":-0.000934874922554309,
-                        "LWcld_tau":0.0647199451130664,
-                        "LWcld_tot":0.40250698507138805,
-                        "SWcld_alt":-0.028296897877686064,
-                        "SWcld_amt":-0.11209868099801101,
-                        "SWcld_err":-0.007262139551323314,
-                        "SWcld_tau":-0.11747372625608103,
-                        "SWcld_tot":-0.26513144468310146
-                    },
-                    "LO680":{
-                        "LWcld_alt":0.014938762374294081,
-                        "LWcld_amt":-0.09591592410402773,
-                        "LWcld_err":-0.001910159314261958,
-                        "LWcld_tau":0.01564826209844526,
-                        "LWcld_tot":-0.06723905894555034,
-                        "SWcld_alt":-0.007288816428276804,
-                        "SWcld_amt":0.5801573833382875,
-                        "SWcld_err":0.0011896658129870615,
-                        "SWcld_tau":-0.13380059744080997,
-                        "SWcld_tot":0.44025763528218764
-                    }
-                },
-                "ocn_asc":{
-                    "ALL":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN
-                    },
-                    "HI680":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN
-                    },
-                    "LO680":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN
-                    }
-                },
-                "ocn_dsc":{
-                    "ALL":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN
-                    },
-                    "HI680":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN
-                    },
-                    "LO680":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN
-                    }
-                }
-            },
-            "eq80":{
-                "all":{
-                    "ALL":{
-                        "LWcld_alt":0.7010955400205224,
-                        "LWcld_amt":-0.273858078371155,
-                        "LWcld_err":-0.10746373593872861,
-                        "LWcld_tau":-0.0038657453830886162,
-                        "LWcld_tot":0.31590798032755013,
-                        "SWcld_alt":-0.10985402582517965,
-                        "SWcld_amt":0.5865292694302967,
-                        "SWcld_err":0.004383720009826058,
-                        "SWcld_tau":-0.07841219749746171,
-                        "SWcld_tot":0.4026467661174813
-                    },
-                    "HI680":{
-                        "LWcld_alt":0.4144715798565579,
-                        "LWcld_amt":-0.03537032104515476,
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-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN
-                    },
-                    "LO680":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN
-                    }
-                },
-                "ocn_dsc":{
-                    "ALL":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN
-                    },
-                    "HI680":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN
-                    },
-                    "LO680":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN
-                    }
-                }
-            },
-            "eq10":{
-                "all":{
-                    "ALL":{
-                        "LWcld_alt":0.1428522977238966,
-                        "LWcld_amt":-0.010308889407096197,
-                        "LWcld_err":0.04762317014240375,
-                        "LWcld_tau":-0.010634064785838235,
-                        "LWcld_tot":0.16953251367336594,
-                        "SWcld_alt":-0.024122375301070403,
-                        "SWcld_amt":0.015641858051410728,
-                        "SWcld_err":-0.010379470116758884,
-                        "SWcld_tau":0.034391174984305835,
-                        "SWcld_tot":0.015531187617887273
-                    },
-                    "HI680":{
-                        "LWcld_alt":0.07294275160945284,
-                        "LWcld_amt":0.037327883614200814,
-                        "LWcld_err":0.03967166661663535,
-                        "LWcld_tau":0.025647336173536817,
-                        "LWcld_tot":0.1755896380138258,
-                        "SWcld_alt":-0.007795029331174679,
-                        "SWcld_amt":-0.038303929836347894,
-                        "SWcld_err":-0.006065968221413263,
-                        "SWcld_tau":-0.002030395997340584,
-                        "SWcld_tot":-0.05419532338627642
-                    },
-                    "LO680":{
-                        "LWcld_alt":0.0038433058614647047,
-                        "LWcld_amt":-0.009599295000652654,
-                        "LWcld_err":8.435436762334385e-05,
-                        "LWcld_tau":-0.0003854895688953015,
-                        "LWcld_tot":-0.006057124340459908,
-                        "SWcld_alt":-0.0016811494700774204,
-                        "SWcld_amt":0.07143430381703343,
-                        "SWcld_err":-0.00013969473562565313,
-                        "SWcld_tau":0.00011305139283332563,
-                        "SWcld_tot":0.0697265110041637
-                    }
-                },
-                "lnd":{
-                    "ALL":{
-                        "LWcld_alt":0.02711972843955621,
-                        "LWcld_amt":-0.014743796389380765,
-                        "LWcld_err":0.006318774232286176,
-                        "LWcld_tau":-0.004842544928527596,
-                        "LWcld_tot":0.013852161353934028,
-                        "SWcld_alt":-0.0037832944387624565,
-                        "SWcld_amt":0.017516071969110245,
-                        "SWcld_err":-0.0012995199566668508,
-                        "SWcld_tau":0.010095561713265595,
-                        "SWcld_tot":0.022528819286946538
-                    },
-                    "HI680":{
-                        "LWcld_alt":0.018121953682164994,
-                        "LWcld_amt":-0.0076246274828589535,
-                        "LWcld_err":0.006174029354649572,
-                        "LWcld_tau":-0.0019536458793248434,
-                        "LWcld_tot":0.014717709674630769,
-                        "SWcld_alt":-0.0017273057276304372,
-                        "SWcld_amt":0.008125456207091682,
-                        "SWcld_err":-0.0009197373353432935,
-                        "SWcld_tau":0.007468192465134647,
-                        "SWcld_tot":0.0129466056092526
-                    },
-                    "LO680":{
-                        "LWcld_alt":0.0008134680116741526,
-                        "LWcld_amt":-0.0017205700376723791,
-                        "LWcld_err":7.963268022323312e-06,
-                        "LWcld_tau":3.359043727915937e-05,
-                        "LWcld_tot":-0.0008655483206967436,
-                        "SWcld_alt":-0.00038672084855357155,
-                        "SWcld_amt":0.01151402867940138,
-                        "SWcld_err":9.044383498337966e-07,
-                        "SWcld_tau":-0.001545998591503708,
-                        "SWcld_tot":0.009582213677693937
-                    }
-                },
-                "ocn":{
-                    "ALL":{
-                        "LWcld_alt":0.11573256928434039,
-                        "LWcld_amt":0.00443490698228457,
-                        "LWcld_err":0.04130439591011758,
-                        "LWcld_tau":-0.005791519857310637,
-                        "LWcld_tot":0.1556803523194319,
-                        "SWcld_alt":-0.02033908086230795,
-                        "SWcld_amt":-0.0018742139176995186,
-                        "SWcld_err":-0.009079950160092031,
-                        "SWcld_tau":0.024295613271040236,
-                        "SWcld_tot":-0.0069976316690592635
-                    },
-                    "HI680":{
-                        "LWcld_alt":0.05482079792728784,
-                        "LWcld_amt":0.04495251109705978,
-                        "LWcld_err":0.03349763726198577,
-                        "LWcld_tau":0.027600982052861656,
-                        "LWcld_tot":0.16087192833919509,
-                        "SWcld_alt":-0.0060677236035442395,
-                        "SWcld_amt":-0.046429386043439574,
-                        "SWcld_err":-0.005146230886069969,
-                        "SWcld_tau":-0.009498588462475232,
-                        "SWcld_tot":-0.06714192899552902
-                    },
-                    "LO680":{
-                        "LWcld_alt":0.0030298378497905514,
-                        "LWcld_amt":-0.007878724962980277,
-                        "LWcld_err":7.639109960102054e-05,
-                        "LWcld_tau":-0.0004190800061744608,
-                        "LWcld_tot":-0.005191576019763165,
-                        "SWcld_alt":-0.001294428621523849,
-                        "SWcld_amt":0.05992027513763206,
-                        "SWcld_err":-0.00014059917397548692,
-                        "SWcld_tau":0.0016590499843370337,
-                        "SWcld_tot":0.06014429732646975
-                    }
-                },
-                "ocn_asc":{
-                    "ALL":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN
-                    },
-                    "HI680":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN
-                    },
-                    "LO680":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN
-                    }
-                },
-                "ocn_dsc":{
-                    "ALL":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN
-                    },
-                    "HI680":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN
-                    },
-                    "LO680":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN
-                    }
-                }
-            },
-            "eq20":{
-                "all":{
-                    "ALL":{
-                        "LWcld_alt":0.23274238644872922,
-                        "LWcld_amt":-0.00812691940622028,
-                        "LWcld_err":0.07968494401797853,
-                        "LWcld_tau":-0.020637511523385704,
-                        "LWcld_tot":0.2836628995371017,
-                        "SWcld_alt":-0.03904550068282665,
-                        "SWcld_amt":0.010597445147547587,
-                        "SWcld_err":-0.017479067327167194,
-                        "SWcld_tau":0.051061016415691135,
-                        "SWcld_tot":0.005133893553244876
-                    },
-                    "HI680":{
-                        "LWcld_alt":0.11125885807802575,
-                        "LWcld_amt":0.07337325771472057,
-                        "LWcld_err":0.06426561640680258,
-                        "LWcld_tau":0.04462940947578579,
-                        "LWcld_tot":0.2935271416753346,
-                        "SWcld_alt":-0.01142805378438235,
-                        "SWcld_amt":-0.07493910587564549,
-                        "SWcld_err":-0.009495634636567387,
-                        "SWcld_tau":-0.01344800094869267,
-                        "SWcld_tot":-0.1093107952452879
-                    },
-                    "LO680":{
-                        "LWcld_alt":0.00717490831621435,
-                        "LWcld_amt":-0.016711701145645724,
-                        "LWcld_err":0.0001132716501390741,
-                        "LWcld_tau":-0.000440721017100184,
-                        "LWcld_tot":-0.009864242138232919,
-                        "SWcld_alt":-0.0031127960385482355,
-                        "SWcld_amt":0.12083603430417639,
-                        "SWcld_err":-0.00019255358699047447,
-                        "SWcld_tau":-0.0030859955495205287,
-                        "SWcld_tot":0.11444468912911716
-                    }
-                },
-                "lnd":{
-                    "ALL":{
-                        "LWcld_alt":0.045079260736180266,
-                        "LWcld_amt":-0.026943124312951646,
-                        "LWcld_err":0.009789687827296367,
-                        "LWcld_tau":-0.015676421108566502,
-                        "LWcld_tot":0.01224940314195849,
-                        "SWcld_alt":-0.006091597618584078,
-                        "SWcld_amt":0.03076063739975735,
-                        "SWcld_err":-0.002016347276813013,
-                        "SWcld_tau":0.02261875334421729,
-                        "SWcld_tot":0.04527144584857754
-                    },
-                    "HI680":{
-                        "LWcld_alt":0.029057902154767095,
-                        "LWcld_amt":-0.014432089199280974,
-                        "LWcld_err":0.009575397360309415,
-                        "LWcld_tau":-0.010120758588207848,
-                        "LWcld_tot":0.01408045172758769,
-                        "SWcld_alt":-0.0025941686872255243,
-                        "SWcld_amt":0.015116255954232665,
-                        "SWcld_err":-0.0013882022430137225,
-                        "SWcld_tau":0.01738090343231652,
-                        "SWcld_tot":0.028514788456309934
-                    },
-                    "LO680":{
-                        "LWcld_alt":0.0012427981857381992,
-                        "LWcld_amt":-0.003171667893460096,
-                        "LWcld_err":7.813377688185033e-06,
-                        "LWcld_tau":9.000768624494251e-05,
-                        "LWcld_tot":-0.0018310485856292025,
-                        "SWcld_alt":-0.0005860600145540485,
-                        "SWcld_amt":0.020252968053903208,
-                        "SWcld_err":7.717573270562284e-07,
-                        "SWcld_tau":-0.00291102207382422,
-                        "SWcld_tot":0.016756657722851995
-                    }
-                },
-                "ocn":{
-                    "ALL":{
-                        "LWcld_alt":0.18766312571254895,
-                        "LWcld_amt":0.01881620490673137,
-                        "LWcld_err":0.06989525619068217,
-                        "LWcld_tau":-0.004961090414819201,
-                        "LWcld_tot":0.2714134963951433,
-                        "SWcld_alt":-0.032953903064242576,
-                        "SWcld_amt":-0.02016319225220976,
-                        "SWcld_err":-0.01546272005035418,
-                        "SWcld_tau":0.02844226307147385,
-                        "SWcld_tot":-0.040137552295332675
-                    },
-                    "HI680":{
-                        "LWcld_alt":0.08220095592325864,
-                        "LWcld_amt":0.08780534691400155,
-                        "LWcld_err":0.05469021904649315,
-                        "LWcld_tau":0.05475016806399363,
-                        "LWcld_tot":0.279446689947747,
-                        "SWcld_alt":-0.008833885097156824,
-                        "SWcld_amt":-0.09005536182987815,
-                        "SWcld_err":-0.008107432393553665,
-                        "SWcld_tau":-0.030828904381009197,
-                        "SWcld_tot":-0.13782558370159784
-                    },
-                    "LO680":{
-                        "LWcld_alt":0.005932110130476151,
-                        "LWcld_amt":-0.013540033252185626,
-                        "LWcld_err":0.00010545827245088907,
-                        "LWcld_tau":-0.0005307287033451266,
-                        "LWcld_tot":-0.008033193552603714,
-                        "SWcld_alt":-0.002526736023994187,
-                        "SWcld_amt":0.10058306625027319,
-                        "SWcld_err":-0.00019332534431753067,
-                        "SWcld_tau":-0.00017497347569630787,
-                        "SWcld_tot":0.09768803140626516
-                    }
-                },
-                "ocn_asc":{
-                    "ALL":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN
-                    },
-                    "HI680":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN
-                    },
-                    "LO680":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN
-                    }
-                },
-                "ocn_dsc":{
-                    "ALL":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN
-                    },
-                    "HI680":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN
-                    },
-                    "LO680":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN
-                    }
-                }
-            },
-            "eq30":{
-                "all":{
-                    "ALL":{
-                        "LWcld_alt":0.31840120840452585,
-                        "LWcld_amt":-0.024523675113554518,
-                        "LWcld_err":0.09276255648123273,
-                        "LWcld_tau":-0.033198491563373164,
-                        "LWcld_tot":0.3534415982088308,
-                        "SWcld_alt":-0.05181351165760012,
-                        "SWcld_amt":0.030354223825196147,
-                        "SWcld_err":-0.020732174318396128,
-                        "SWcld_tau":0.05896450808649407,
-                        "SWcld_tot":0.016773045935693967
-                    },
-                    "HI680":{
-                        "LWcld_alt":0.15468889284768825,
-                        "LWcld_amt":0.0869366219056983,
-                        "LWcld_err":0.07805176922683779,
-                        "LWcld_tau":0.04927876603632155,
-                        "LWcld_tot":0.3689560500165459,
-                        "SWcld_alt":-0.015346131782117542,
-                        "SWcld_amt":-0.08738481308399626,
-                        "SWcld_err":-0.011450347021460168,
-                        "SWcld_tau":-0.020880158439608745,
-                        "SWcld_tot":-0.13506145032718272
-                    },
-                    "LO680":{
-                        "LWcld_alt":0.009298278510589942,
-                        "LWcld_amt":-0.02474849730918907,
-                        "LWcld_err":0.00019247381107332772,
-                        "LWcld_tau":-0.0002567068783488227,
-                        "LWcld_tot":-0.015514451807715055,
-                        "SWcld_alt":-0.003950361167028371,
-                        "SWcld_amt":0.16531458363431648,
-                        "SWcld_err":-0.0002772526626878006,
-                        "SWcld_tau":-0.009252473211139237,
-                        "SWcld_tot":0.15183449659346107
-                    }
-                },
-                "lnd":{
-                    "ALL":{
-                        "LWcld_alt":0.06883010427980636,
-                        "LWcld_amt":-0.04589013382213697,
-                        "LWcld_err":0.01134124231413259,
-                        "LWcld_tau":-0.026645956295041493,
-                        "LWcld_tot":0.007635256476760492,
-                        "SWcld_alt":-0.00851323064044105,
-                        "SWcld_amt":0.053075900460113484,
-                        "SWcld_err":-0.002405933216993642,
-                        "SWcld_tau":0.03306488591226659,
-                        "SWcld_tot":0.0752216225149454
-                    },
-                    "HI680":{
-                        "LWcld_alt":0.04742798883939328,
-                        "LWcld_amt":-0.028326499113051438,
-                        "LWcld_err":0.012042208350468486,
-                        "LWcld_tau":-0.02013346758147298,
-                        "LWcld_tot":0.011010230495337364,
-                        "SWcld_alt":-0.004004202912729785,
-                        "SWcld_amt":0.029717513865398407,
-                        "SWcld_err":-0.0017573605006882618,
-                        "SWcld_tau":0.02668260222327894,
-                        "SWcld_tot":0.05063855267525929
-                    },
-                    "LO680":{
-                        "LWcld_alt":0.0015113750037395674,
-                        "LWcld_amt":-0.005031085263504683,
-                        "LWcld_err":4.071273405015063e-06,
-                        "LWcld_tau":0.0001406649096236661,
-                        "LWcld_tot":-0.0033749740185768672,
-                        "SWcld_alt":-0.0007059144898144361,
-                        "SWcld_amt":0.029084354515015145,
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-                        "SWcld_tot":0.0917147739052946
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-            "eq40":{
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-                        "LWcld_tau":0.05484502361939069,
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-                        "SWcld_tot":0.18581096622970372
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-                        "SWcld_err":8.506314591459097e-06,
-                        "SWcld_tau":-0.004945748308396581,
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-                        "SWcld_amt":0.16540690787617682,
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-                        "SWcld_tot":0.15618668720037296
-                    }
-                },
-                "ocn_asc":{
-                    "ALL":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN
-                    },
-                    "HI680":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN
-                    },
-                    "LO680":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN
-                    }
-                },
-                "ocn_dsc":{
-                    "ALL":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN
-                    },
-                    "HI680":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN
-                    },
-                    "LO680":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN
-                    }
-                }
-            },
-            "eq50":{
-                "all":{
-                    "ALL":{
-                        "LWcld_alt":0.45601640788746073,
-                        "LWcld_amt":-0.09083960788567774,
-                        "LWcld_err":0.1039840435781325,
-                        "LWcld_tau":-0.025750945957367533,
-                        "LWcld_tot":0.443409897622548,
-                        "SWcld_alt":-0.06761745482176854,
-                        "SWcld_amt":0.13876264269118144,
-                        "SWcld_err":-0.022675411496340198,
-                        "SWcld_tau":0.04180226827018967,
-                        "SWcld_tot":0.09027204464326234
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-                        "LWcld_amt":0.04149333296365981,
-                        "LWcld_err":0.09485901458258177,
-                        "LWcld_tau":0.0625659168132122,
-                        "LWcld_tot":0.46762111609396056,
-                        "SWcld_alt":-0.026670125381914506,
-                        "SWcld_amt":-0.022700997161852088,
-                        "SWcld_err":-0.014138335999605045,
-                        "SWcld_tau":-0.03968865191613021,
-                        "SWcld_tot":-0.10319811045950189
-                    },
-                    "LO680":{
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-                        "LWcld_err":0.0002779346971204789,
-                        "LWcld_tau":0.000462720354587592,
-                        "LWcld_tot":-0.024211218471412704,
-                        "SWcld_alt":-0.003980467166970929,
-                        "SWcld_amt":0.2123362149123913,
-                        "SWcld_err":-0.0003159200991975077,
-                        "SWcld_tau":-0.01456967221286111,
-                        "SWcld_tot":0.19347015543336174
-                    }
-                },
-                "lnd":{
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-                        "LWcld_alt":0.10456475013115125,
-                        "LWcld_amt":-0.07166178395624546,
-                        "LWcld_err":0.011285441737527322,
-                        "LWcld_tau":-0.021529998219923494,
-                        "LWcld_tot":0.022658409692509613,
-                        "SWcld_alt":-0.01216880127629238,
-                        "SWcld_amt":0.08478034025663501,
-                        "SWcld_err":-0.0026401119632595304,
-                        "SWcld_tau":0.026668875073791246,
-                        "SWcld_tot":0.09664030209087436
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-                        "LWcld_err":0.013957060316694081,
-                        "LWcld_tau":-0.015650926308311542,
-                        "LWcld_tot":0.027790835617995096,
-                        "SWcld_alt":-0.0068950884721171,
-                        "SWcld_amt":0.05329861773932095,
-                        "SWcld_err":-0.0022758909025048495,
-                        "SWcld_tau":0.02277314012120575,
-                        "SWcld_tot":0.06690077848590477
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-                    "LO680":{
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-                        "LWcld_err":-1.1456011849498652e-05,
-                        "LWcld_tau":0.0007576063413180928,
-                        "LWcld_tot":-0.005132425925485475,
-                        "SWcld_alt":-0.000697336300055254,
-                        "SWcld_amt":0.03775232070137312,
-                        "SWcld_err":8.898394877705355e-06,
-                        "SWcld_tau":-0.0073243588606284335,
-                        "SWcld_tot":0.02973952393556713
-                    }
-                },
-                "ocn":{
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-                        "LWcld_amt":-0.019177823929432288,
-                        "LWcld_err":0.09269860184060517,
-                        "LWcld_tau":-0.0042209477374440365,
-                        "LWcld_tot":0.4207514879300384,
-                        "SWcld_alt":-0.05544865354547616,
-                        "SWcld_amt":0.05398230243454639,
-                        "SWcld_err":-0.020035299533080666,
-                        "SWcld_tau":0.015133393196398422,
-                        "SWcld_tot":-0.006368257447612013
-                    },
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-                        "LWcld_amt":0.09003322566143902,
-                        "LWcld_err":0.08090195426588767,
-                        "LWcld_tau":0.07821684312152376,
-                        "LWcld_tot":0.4398302804759655,
-                        "SWcld_alt":-0.01977503690979741,
-                        "SWcld_amt":-0.07599961490117305,
-                        "SWcld_err":-0.011862445097100195,
-                        "SWcld_tau":-0.06246179203733596,
-                        "SWcld_tot":-0.17009888894540662
-                    },
-                    "LO680":{
-                        "LWcld_alt":0.00761241410450035,
-                        "LWcld_amt":-0.026685711372667054,
-                        "LWcld_err":0.00028939070896997755,
-                        "LWcld_tau":-0.0002948859867305008,
-                        "LWcld_tot":-0.019078792545927228,
-                        "SWcld_alt":-0.003283130866915674,
-                        "SWcld_amt":0.17458389421101816,
-                        "SWcld_err":-0.0003248184940752131,
-                        "SWcld_tau":-0.007245313352232677,
-                        "SWcld_tot":0.16373063149779457
-                    }
-                },
-                "ocn_asc":{
-                    "ALL":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN
-                    },
-                    "HI680":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN
-                    },
-                    "LO680":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN
-                    }
-                },
-                "ocn_dsc":{
-                    "ALL":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN
-                    },
-                    "HI680":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN
-                    },
-                    "LO680":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN
-                    }
-                }
-            },
-            "eq60":{
-                "all":{
-                    "ALL":{
-                        "LWcld_alt":0.4915518746756578,
-                        "LWcld_amt":-0.08961036821122265,
-                        "LWcld_err":0.10737406264718148,
-                        "LWcld_tau":-0.0025912106033805222,
-                        "LWcld_tot":0.5067243585082362,
-                        "SWcld_alt":-0.07004198050337469,
-                        "SWcld_amt":0.1370934775017846,
-                        "SWcld_err":-0.022568445714517726,
-                        "SWcld_tau":-0.04288713725489685,
-                        "SWcld_tot":0.0015959140289953724
-                    },
-                    "HI680":{
-                        "LWcld_alt":0.29978997567244486,
-                        "LWcld_amt":0.04977424224822396,
-                        "LWcld_err":0.09880234035583137,
-                        "LWcld_tau":0.08390530232248405,
-                        "LWcld_tot":0.5322718605989843,
-                        "SWcld_alt":-0.029664216987547395,
-                        "SWcld_amt":-0.028960667921889182,
-                        "SWcld_err":-0.01466921713921608,
-                        "SWcld_tau":-0.09965352877394339,
-                        "SWcld_tot":-0.17294763082259604
-                    },
-                    "LO680":{
-                        "LWcld_alt":0.006919714290772661,
-                        "LWcld_amt":-0.03587168897111754,
-                        "LWcld_err":0.00021894167394946582,
-                        "LWcld_tau":0.0031855308574713294,
-                        "LWcld_tot":-0.025547502090748176,
-                        "SWcld_alt":-0.0028566033321922707,
-                        "SWcld_amt":0.21517085502604716,
-                        "SWcld_err":-0.000276381094130132,
-                        "SWcld_tau":-0.03749432541753581,
-                        "SWcld_tot":0.17454354518218895
-                    }
-                },
-                "lnd":{
-                    "ALL":{
-                        "LWcld_alt":0.1146994198883264,
-                        "LWcld_amt":-0.07151666304117188,
-                        "LWcld_err":0.010995579483695478,
-                        "LWcld_tau":-0.013537939747561132,
-                        "LWcld_tot":0.040640396583288864,
-                        "SWcld_alt":-0.012744907446391446,
-                        "SWcld_amt":0.08556860833068097,
-                        "SWcld_err":-0.0025687739632273333,
-                        "SWcld_tau":0.013646403298648099,
-                        "SWcld_tot":0.08390133021971029
-                    },
-                    "HI680":{
-                        "LWcld_alt":0.0881414260401764,
-                        "LWcld_amt":-0.04841510240160565,
-                        "LWcld_err":0.014625041765773492,
-                        "LWcld_tau":-0.009313760568571675,
-                        "LWcld_tot":0.045037604835772564,
-                        "SWcld_alt":-0.007881401281485687,
-                        "SWcld_amt":0.05642631909466278,
-                        "SWcld_err":-0.0024483943174123586,
-                        "SWcld_tau":0.013575081869682322,
-                        "SWcld_tot":0.05967160536544706
-                    },
-                    "LO680":{
-                        "LWcld_alt":0.0012319869773594202,
-                        "LWcld_amt":-0.007233132582313243,
-                        "LWcld_err":-2.2547339347336895e-05,
-                        "LWcld_tau":0.001626484633641545,
-                        "LWcld_tot":-0.004397208252483708,
-                        "SWcld_alt":-0.0005991901488276984,
-                        "SWcld_amt":0.035162481602590286,
-                        "SWcld_err":7.944812828943468e-06,
-                        "SWcld_tau":-0.010341511081730757,
-                        "SWcld_tot":0.02422972518486077
-                    }
-                },
-                "ocn":{
-                    "ALL":{
-                        "LWcld_alt":0.3768524547873315,
-                        "LWcld_amt":-0.018093705170050767,
-                        "LWcld_err":0.09637848316348602,
-                        "LWcld_tau":0.010946729144180609,
-                        "LWcld_tot":0.4660839619249472,
-                        "SWcld_alt":-0.057297073056983235,
-                        "SWcld_amt":0.05152486917110367,
-                        "SWcld_err":-0.019999671751290388,
-                        "SWcld_tau":-0.05653354055354495,
-                        "SWcld_tot":-0.08230541619071492
-                    },
-                    "HI680":{
-                        "LWcld_alt":0.2116485496322685,
-                        "LWcld_amt":0.0981893446498296,
-                        "LWcld_err":0.08417729859005789,
-                        "LWcld_tau":0.09321906289105573,
-                        "LWcld_tot":0.48723425576321167,
-                        "SWcld_alt":-0.021782815706061706,
-                        "SWcld_amt":-0.08538698701655195,
-                        "SWcld_err":-0.012220822821803723,
-                        "SWcld_tau":-0.1132286106436257,
-                        "SWcld_tot":-0.23261923618804306
-                    },
-                    "LO680":{
-                        "LWcld_alt":0.005687727313413241,
-                        "LWcld_amt":-0.0286385563888043,
-                        "LWcld_err":0.00024148901329680276,
-                        "LWcld_tau":0.001559046223829785,
-                        "LWcld_tot":-0.021150293838264472,
-                        "SWcld_alt":-0.002257413183364572,
-                        "SWcld_amt":0.18000837342345688,
-                        "SWcld_err":-0.00028432590695907554,
-                        "SWcld_tau":-0.027152814335805058,
-                        "SWcld_tot":0.15031381999732815
-                    }
-                },
-                "ocn_asc":{
-                    "ALL":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN
-                    },
-                    "HI680":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN
-                    },
-                    "LO680":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN
-                    }
-                },
-                "ocn_dsc":{
-                    "ALL":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN
-                    },
-                    "HI680":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN
-                    },
-                    "LO680":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN
-                    }
-                }
-            },
-            "eq70":{
-                "all":{
-                    "ALL":{
-                        "LWcld_alt":0.5167020711343934,
-                        "LWcld_amt":-0.08595226254893944,
-                        "LWcld_err":0.10861974073497345,
-                        "LWcld_tau":0.020656934805850468,
-                        "LWcld_tot":0.5600264841262778,
-                        "SWcld_alt":-0.0722412265826393,
-                        "SWcld_amt":0.13168449767551463,
-                        "SWcld_err":-0.022525573494164532,
-                        "SWcld_tau":-0.09848367380224471,
-                        "SWcld_tot":-0.06156597620353396
-                    },
-                    "HI680":{
-                        "LWcld_alt":0.3166732039432951,
-                        "LWcld_amt":0.06246446131154856,
-                        "LWcld_err":0.1019609470324047,
-                        "LWcld_tau":0.1034568611348972,
-                        "LWcld_tot":0.5845554734221456,
-                        "SWcld_alt":-0.030964306528489704,
-                        "SWcld_amt":-0.04140701488438967,
-                        "SWcld_err":-0.01499470349899988,
-                        "SWcld_tau":-0.13600143702008985,
-                        "SWcld_tot":-0.22336746193196913
-                    },
-                    "LO680":{
-                        "LWcld_alt":0.007179281767205327,
-                        "LWcld_amt":-0.03754073974317828,
-                        "LWcld_err":0.00015464258923452394,
-                        "LWcld_tau":0.0056778260326946035,
-                        "LWcld_tot":-0.024528989295867918,
-                        "SWcld_alt":-0.0028462804420257086,
-                        "SWcld_amt":0.2201052456243727,
-                        "SWcld_err":-0.0002498154770577777,
-                        "SWcld_tau":-0.055207663646256545,
-                        "SWcld_tot":0.16180148605903266
-                    }
-                },
-                "lnd":{
-                    "ALL":{
-                        "LWcld_alt":0.12297126382690719,
-                        "LWcld_amt":-0.06966730085222635,
-                        "LWcld_err":0.011004920294889345,
-                        "LWcld_tau":-0.003365929980839137,
-                        "LWcld_tot":0.06094295328873104,
-                        "SWcld_alt":-0.013214019638920322,
-                        "SWcld_amt":0.08412267004454854,
-                        "SWcld_err":-0.0025314770474718835,
-                        "SWcld_tau":0.0017493014865598002,
-                        "SWcld_tot":0.07012647484471617
-                    },
-                    "HI680":{
-                        "LWcld_alt":0.09559458479570525,
-                        "LWcld_amt":-0.04587476373843108,
-                        "LWcld_err":0.015236691785257274,
-                        "LWcld_tau":-0.00036029124071111006,
-                        "LWcld_tot":0.06459622160182034,
-                        "SWcld_alt":-0.008472620269569048,
-                        "SWcld_amt":0.0561722814858634,
-                        "SWcld_err":-0.002563310085923133,
-                        "SWcld_tau":0.005131421144801315,
-                        "SWcld_tot":0.05026777227517254
-                    },
-                    "LO680":{
-                        "LWcld_alt":0.0012191975156807685,
-                        "LWcld_amt":-0.007234360199288752,
-                        "LWcld_err":-1.9689637273460196e-05,
-                        "LWcld_tau":0.002381583949616234,
-                        "LWcld_tot":-0.0036532683130893035,
-                        "SWcld_alt":-0.0005631101676369209,
-                        "SWcld_amt":0.0337354573250094,
-                        "SWcld_err":4.7293809253612605e-06,
-                        "SWcld_tau":-0.013318373638156687,
-                        "SWcld_tot":0.01985870290014115
-                    }
-                },
-                "ocn":{
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-                        "LWcld_alt":0.39373080730748605,
-                        "LWcld_amt":-0.016284961696713094,
-                        "LWcld_err":0.09761482044008413,
-                        "LWcld_tau":0.024022864786689607,
-                        "LWcld_tot":0.4990835308375467,
-                        "SWcld_alt":-0.059027206943719,
-                        "SWcld_amt":0.04756182763096604,
-                        "SWcld_err":-0.01999409644669264,
-                        "SWcld_tau":-0.10023297528880452,
-                        "SWcld_tot":-0.13169245104825011
-                    },
-                    "HI680":{
-                        "LWcld_alt":0.2210786191475899,
-                        "LWcld_amt":0.10833922504997962,
-                        "LWcld_err":0.0867242552471474,
-                        "LWcld_tau":0.10381715237560829,
-                        "LWcld_tot":0.5199592518203252,
-                        "SWcld_alt":-0.022491686258920656,
-                        "SWcld_amt":-0.09757929637025305,
-                        "SWcld_err":-0.012431393413076745,
-                        "SWcld_tau":-0.14113285816489116,
-                        "SWcld_tot":-0.27363523420714164
-                    },
-                    "LO680":{
-                        "LWcld_alt":0.005960084251524559,
-                        "LWcld_amt":-0.030306379543889525,
-                        "LWcld_err":0.00017433222650798416,
-                        "LWcld_tau":0.0032962420830783697,
-                        "LWcld_tot":-0.020875720982778612,
-                        "SWcld_alt":-0.002283170274388788,
-                        "SWcld_amt":0.18636978829936326,
-                        "SWcld_err":-0.0002545448579831389,
-                        "SWcld_tau":-0.04188929000809985,
-                        "SWcld_tot":0.1419427831588915
-                    }
-                },
-                "ocn_asc":{
-                    "ALL":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN
-                    },
-                    "HI680":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN
-                    },
-                    "LO680":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN
-                    }
-                },
-                "ocn_dsc":{
-                    "ALL":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN
-                    },
-                    "HI680":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN
-                    },
-                    "LO680":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN
-                    }
-                }
-            },
-            "eq80":{
-                "all":{
-                    "ALL":{
-                        "LWcld_alt":0.5231952722174593,
-                        "LWcld_amt":-0.08332955958832662,
-                        "LWcld_err":0.10888036818627712,
-                        "LWcld_tau":0.03470319538879627,
-                        "LWcld_tot":0.5834492762042061,
-                        "SWcld_alt":-0.07292441166837489,
-                        "SWcld_amt":0.12597617185648255,
-                        "SWcld_err":-0.02257244735568156,
-                        "SWcld_tau":-0.1144949897457946,
-                        "SWcld_tot":-0.08401567691336853
-                    },
-                    "HI680":{
-                        "LWcld_alt":0.3201364303267363,
-                        "LWcld_amt":0.06770554031125516,
-                        "LWcld_err":0.10265261454421354,
-                        "LWcld_tau":0.11615168806388354,
-                        "LWcld_tot":0.6066462732460886,
-                        "SWcld_alt":-0.03120004461372163,
-                        "SWcld_amt":-0.047083196234671366,
-                        "SWcld_err":-0.01509300163628844,
-                        "SWcld_tau":-0.1457201974171057,
-                        "SWcld_tot":-0.23909643990178717
-                    },
-                    "LO680":{
-                        "LWcld_alt":0.0076309022262873545,
-                        "LWcld_amt":-0.03779546592462465,
-                        "LWcld_err":0.00018613284674452108,
-                        "LWcld_tau":0.006781363188965263,
-                        "LWcld_tot":-0.02319699704188247,
-                        "SWcld_alt":-0.002954890025782385,
-                        "SWcld_amt":0.21955444326692306,
-                        "SWcld_err":-0.0002561268436739663,
-                        "SWcld_tau":-0.06126265521573612,
-                        "SWcld_tot":0.15508077118173058
-                    }
-                },
-                "lnd":{
-                    "ALL":{
-                        "LWcld_alt":0.12503091866256524,
-                        "LWcld_amt":-0.06907766721771612,
-                        "LWcld_err":0.010657566009060038,
-                        "LWcld_tau":0.004489757280352312,
-                        "LWcld_tot":0.07110057473426147,
-                        "SWcld_alt":-0.013355822228312593,
-                        "SWcld_amt":0.08270117948145236,
-                        "SWcld_err":-0.00253402840993194,
-                        "SWcld_tau":-0.0021373641964716372,
-                        "SWcld_tot":0.06467396464673618
-                    },
-                    "HI680":{
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-                        "LWcld_err":0.01496524986505591,
-                        "LWcld_tau":0.007192190426992475,
-                        "LWcld_tot":0.07446638033373516,
-                        "SWcld_alt":-0.00861060172999136,
-                        "SWcld_amt":0.05547918261377349,
-                        "SWcld_err":-0.002576625535045512,
-                        "SWcld_tau":0.0022609487164750816,
-                        "SWcld_tot":0.0465529040652117
-                    },
-                    "LO680":{
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-                        "LWcld_amt":-0.007248951617928876,
-                        "LWcld_err":-1.0590473157094231e-05,
-                        "LWcld_tau":0.0026047968621584323,
-                        "LWcld_tot":-0.0033658055994736816,
-                        "SWcld_alt":-0.0005742081183680121,
-                        "SWcld_amt":0.03294203717588117,
-                        "SWcld_err":2.067599920058404e-06,
-                        "SWcld_tau":-0.01424882788259677,
-                        "SWcld_tot":0.018121068774836446
-                    }
-                },
-                "ocn":{
-                    "ALL":{
-                        "LWcld_alt":0.398164353554894,
-                        "LWcld_amt":-0.01425189237061053,
-                        "LWcld_err":0.09822280217721709,
-                        "LWcld_tau":0.03021343810844396,
-                        "LWcld_tot":0.5123487014699446,
-                        "SWcld_alt":-0.0595685894400623,
-                        "SWcld_amt":0.04327499237503018,
-                        "SWcld_err":-0.020038418945749616,
-                        "SWcld_tau":-0.11235762554932298,
-                        "SWcld_tot":-0.14868964156010475
-                    },
-                    "HI680":{
-                        "LWcld_alt":0.22278078907918694,
-                        "LWcld_amt":0.11275224151711777,
-                        "LWcld_err":0.08768736467915762,
-                        "LWcld_tau":0.1089594976368911,
-                        "LWcld_tot":0.5321798929123533,
-                        "SWcld_alt":-0.02258944288373026,
-                        "SWcld_amt":-0.10256237884844488,
-                        "SWcld_err":-0.012516376101242926,
-                        "SWcld_tau":-0.1479811461335808,
-                        "SWcld_tot":-0.2856493439669988
-                    },
-                    "LO680":{
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-                        "LWcld_amt":NaN,
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-                        "LWcld_tau":NaN,
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-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
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-                        "LWcld_amt":NaN,
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-                        "LWcld_tau":NaN,
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-                        "SWcld_alt":NaN,
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-                        "SWcld_tau":NaN,
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-                        "LWcld_tau":NaN,
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-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
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-                        "LWcld_amt":NaN,
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-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "SWcld_alt":NaN,
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-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN
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-                        "SWcld_tot":0.07761626074226606
-                    },
-                    "HI680":{
-                        "LWcld_alt":0.0191216424546108,
-                        "LWcld_amt":-0.044627175915849214,
-                        "LWcld_err":0.004569198087203384,
-                        "LWcld_tau":0.0032323101478759064,
-                        "LWcld_tot":-0.01770402522615912,
-                        "SWcld_alt":-0.0013686182380444444,
-                        "SWcld_amt":0.04871164219591526,
-                        "SWcld_err":-0.0005833339934656325,
-                        "SWcld_tau":0.007207137046425353,
-                        "SWcld_tot":0.05396682701083053
-                    },
-                    "LO680":{
-                        "LWcld_alt":0.0009198980678661804,
-                        "LWcld_amt":-0.002732560229012162,
-                        "LWcld_err":-1.786633504795856e-05,
-                        "LWcld_tau":-0.00021444238324397878,
-                        "LWcld_tot":-0.002044970879437919,
-                        "SWcld_alt":-0.00047638374949422047,
-                        "SWcld_amt":0.021445497666716384,
-                        "SWcld_err":1.1915338968359128e-05,
-                        "SWcld_tau":0.0026684044752450157,
-                        "SWcld_tot":0.023649433731435538
-                    }
-                },
-                "ocn":{
-                    "ALL":{
-                        "LWcld_alt":0.11557741393187752,
-                        "LWcld_amt":-0.04731678175653369,
-                        "LWcld_err":0.02949896825855991,
-                        "LWcld_tau":0.05366354281713951,
-                        "LWcld_tot":0.15142314325104325,
-                        "SWcld_alt":-0.02023599127049121,
-                        "SWcld_amt":0.09022828013092159,
-                        "SWcld_err":-0.006093916243102589,
-                        "SWcld_tau":-0.0794352813913464,
-                        "SWcld_tot":-0.015536908774018624
-                    },
-                    "HI680":{
-                        "LWcld_alt":0.048849867727438184,
-                        "LWcld_amt":0.010806379115361973,
-                        "LWcld_err":0.01863166201428351,
-                        "LWcld_tau":0.08173818739716998,
-                        "LWcld_tot":0.16002609625425365,
-                        "SWcld_alt":-0.003737157702625589,
-                        "SWcld_amt":-0.008954635937081576,
-                        "SWcld_err":-0.0016536850518716723,
-                        "SWcld_tau":-0.11389984619809175,
-                        "SWcld_tot":-0.1282453248896706
-                    },
-                    "LO680":{
-                        "LWcld_alt":0.004231207118306171,
-                        "LWcld_amt":-0.012156986801659957,
-                        "LWcld_err":0.0001245647505737523,
-                        "LWcld_tau":-0.0008017380704303932,
-                        "LWcld_tot":-0.008602953003210427,
-                        "SWcld_alt":-0.002048564194742526,
-                        "SWcld_amt":0.11151816732907127,
-                        "SWcld_err":-0.0002005053529554008,
-                        "SWcld_tau":0.003439318334278617,
-                        "SWcld_tot":0.11270841611565197
-                    }
-                },
-                "ocn_asc":{
-                    "ALL":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN
-                    },
-                    "HI680":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN
-                    },
-                    "LO680":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN
-                    }
-                },
-                "ocn_dsc":{
-                    "ALL":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN
-                    },
-                    "HI680":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN
-                    },
-                    "LO680":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN
-                    }
-                }
-            },
-            "eq20":{
-                "all":{
-                    "ALL":{
-                        "LWcld_alt":0.2675843973563922,
-                        "LWcld_amt":-0.1619231117888488,
-                        "LWcld_err":0.05878664817716461,
-                        "LWcld_tau":0.07791793081995498,
-                        "LWcld_tot":0.242365864564663,
-                        "SWcld_alt":-0.0457765162835135,
-                        "SWcld_amt":0.24217655788796566,
-                        "SWcld_err":-0.012775842731233153,
-                        "SWcld_tau":-0.09495158745958915,
-                        "SWcld_tot":0.08867261141362986
-                    },
-                    "HI680":{
-                        "LWcld_alt":0.11498275818038978,
-                        "LWcld_amt":-0.02848627747379597,
-                        "LWcld_err":0.03690873312608249,
-                        "LWcld_tau":0.13626854628764332,
-                        "LWcld_tot":0.2596737601203196,
-                        "SWcld_alt":-0.008608444387328431,
-                        "SWcld_amt":0.035694062473461886,
-                        "SWcld_err":-0.00355556854710149,
-                        "SWcld_tau":-0.17352719134876113,
-                        "SWcld_tot":-0.14999714180972912
-                    },
-                    "LO680":{
-                        "LWcld_alt":0.011513684907837898,
-                        "LWcld_amt":-0.02738715127063784,
-                        "LWcld_err":0.00018759278868127295,
-                        "LWcld_tau":-0.001622032913105739,
-                        "LWcld_tot":-0.017307895555656656,
-                        "SWcld_alt":-0.005445517010952281,
-                        "SWcld_amt":0.23634246941849388,
-                        "SWcld_err":-0.0002758829720872508,
-                        "SWcld_tau":0.008048733976437407,
-                        "SWcld_tot":0.2386698034118917
-                    }
-                },
-                "lnd":{
-                    "ALL":{
-                        "LWcld_alt":0.05184159666799767,
-                        "LWcld_amt":-0.09714646872360028,
-                        "LWcld_err":0.008917710848702604,
-                        "LWcld_tau":-0.0036720233067829965,
-                        "LWcld_tot":-0.04005918451368302,
-                        "SWcld_alt":-0.006716007374442111,
-                        "SWcld_amt":0.11619503710766087,
-                        "SWcld_err":-0.0018242837978874281,
-                        "SWcld_tau":0.027092947576459747,
-                        "SWcld_tot":0.13474769351179108
-                    },
-                    "HI680":{
-                        "LWcld_alt":0.032606401253604804,
-                        "LWcld_amt":-0.07839781652693091,
-                        "LWcld_err":0.007216533257283361,
-                        "LWcld_tau":0.0029514576574021984,
-                        "LWcld_tot":-0.035623424358640556,
-                        "SWcld_alt":-0.002249579726778294,
-                        "SWcld_amt":0.07956278772991067,
-                        "SWcld_err":-0.0008659655783032042,
-                        "SWcld_tau":0.01469069627995401,
-                        "SWcld_tot":0.09113793870478319
-                    },
-                    "LO680":{
-                        "LWcld_alt":0.0013666806450517953,
-                        "LWcld_amt":-0.005474976373098556,
-                        "LWcld_err":-3.224525455815117e-05,
-                        "LWcld_tau":-0.00029523010400531207,
-                        "LWcld_tot":-0.004435760155042475,
-                        "SWcld_alt":-0.0006980373025801696,
-                        "SWcld_amt":0.04061580437721953,
-                        "SWcld_err":2.375738632678194e-05,
-                        "SWcld_tau":0.003668280534574448,
-                        "SWcld_tot":0.04360980499554059
-                    }
-                },
-                "ocn":{
-                    "ALL":{
-                        "LWcld_alt":0.21574280068839458,
-                        "LWcld_amt":-0.0647766430652485,
-                        "LWcld_err":0.049868937328462,
-                        "LWcld_tau":0.08158995412673796,
-                        "LWcld_tot":0.28242504907834604,
-                        "SWcld_alt":-0.03906050890907139,
-                        "SWcld_amt":0.1259815207803048,
-                        "SWcld_err":-0.010951558933345725,
-                        "SWcld_tau":-0.12204453503604891,
-                        "SWcld_tot":-0.04607508209816121
-                    },
-                    "HI680":{
-                        "LWcld_alt":0.08237635692678498,
-                        "LWcld_amt":0.049911539053134946,
-                        "LWcld_err":0.02969219986879913,
-                        "LWcld_tau":0.13331708863024114,
-                        "LWcld_tot":0.29529718447896014,
-                        "SWcld_alt":-0.006358864660550137,
-                        "SWcld_amt":-0.04386872525644878,
-                        "SWcld_err":-0.002689602968798286,
-                        "SWcld_tau":-0.18821788762871508,
-                        "SWcld_tot":-0.24113508051451235
-                    },
-                    "LO680":{
-                        "LWcld_alt":0.010147004262786103,
-                        "LWcld_amt":-0.021912174897539282,
-                        "LWcld_err":0.00021983804323942417,
-                        "LWcld_tau":-0.0013268028091004268,
-                        "LWcld_tot":-0.012872135400614182,
-                        "SWcld_alt":-0.0047474797083721125,
-                        "SWcld_amt":0.19572666504127434,
-                        "SWcld_err":-0.0002996403584140327,
-                        "SWcld_tau":0.00438045344186296,
-                        "SWcld_tot":0.1950599984163511
-                    }
-                },
-                "ocn_asc":{
-                    "ALL":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN
-                    },
-                    "HI680":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN
-                    },
-                    "LO680":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN
-                    }
-                },
-                "ocn_dsc":{
-                    "ALL":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN
-                    },
-                    "HI680":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN
-                    },
-                    "LO680":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN
-                    }
-                }
-            },
-            "eq30":{
-                "all":{
-                    "ALL":{
-                        "LWcld_alt":0.376718164544019,
-                        "LWcld_amt":-0.22027520956927016,
-                        "LWcld_err":0.07210821059994046,
-                        "LWcld_tau":0.08177311161285312,
-                        "LWcld_tot":0.3103242771875424,
-                        "SWcld_alt":-0.06561457407789949,
-                        "SWcld_amt":0.34213593169314294,
-                        "SWcld_err":-0.016157204045896652,
-                        "SWcld_tau":-0.10141345015835768,
-                        "SWcld_tot":0.15895070341098919
-                    },
-                    "HI680":{
-                        "LWcld_alt":0.15273510553094377,
-                        "LWcld_amt":-0.017102227612149207,
-                        "LWcld_err":0.044123578352955516,
-                        "LWcld_tau":0.16071198904518977,
-                        "LWcld_tot":0.3404684453169398,
-                        "SWcld_alt":-0.011693002303016812,
-                        "SWcld_amt":0.02911157240488089,
-                        "SWcld_err":-0.004117189804626174,
-                        "SWcld_tau":-0.21097984133361106,
-                        "SWcld_tot":-0.19767846103637313
-                    },
-                    "LO680":{
-                        "LWcld_alt":0.015264043164707539,
-                        "LWcld_amt":-0.04365007581638012,
-                        "LWcld_err":0.0003384360451644797,
-                        "LWcld_tau":-0.002096640917590167,
-                        "LWcld_tot":-0.030144168129397454,
-                        "SWcld_alt":-0.007044414111110003,
-                        "SWcld_amt":0.35775255853740284,
-                        "SWcld_err":-0.0004662651472673925,
-                        "SWcld_tau":0.0063875387103802415,
-                        "SWcld_tot":0.3566294179894056
-                    }
-                },
-                "lnd":{
-                    "ALL":{
-                        "LWcld_alt":0.06675027011932501,
-                        "LWcld_amt":-0.11895083711345439,
-                        "LWcld_err":0.010497885974776315,
-                        "LWcld_tau":-0.0040373752682771715,
-                        "LWcld_tot":-0.045740056287630226,
-                        "SWcld_alt":-0.00879274101569587,
-                        "SWcld_amt":0.14532404991366224,
-                        "SWcld_err":-0.0021338289585030247,
-                        "SWcld_tau":0.03200804826198882,
-                        "SWcld_tot":0.16640552820145219
-                    },
-                    "HI680":{
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-                        "LWcld_amt":-0.09327717071686653,
-                        "LWcld_err":0.00869983179489098,
-                        "LWcld_tau":0.003729340544748381,
-                        "LWcld_tot":-0.03931767517869631,
-                        "SWcld_alt":-0.002922111884375213,
-                        "SWcld_amt":0.09497496096184493,
-                        "SWcld_err":-0.0010003746612764562,
-                        "SWcld_tau":0.01734555803969876,
-                        "SWcld_tot":0.10839803245589202
-                    },
-                    "LO680":{
-                        "LWcld_alt":0.0016859642694901575,
-                        "LWcld_amt":-0.007731781033911697,
-                        "LWcld_err":-3.575113656097043e-05,
-                        "LWcld_tau":-0.00034088260265221983,
-                        "LWcld_tot":-0.006422381108933913,
-                        "SWcld_alt":-0.0008572526272206472,
-                        "SWcld_amt":0.05462530856193492,
-                        "SWcld_err":2.628350947983037e-05,
-                        "SWcld_tau":0.004213409843409417,
-                        "SWcld_tot":0.05800774928760352
-                    }
-                },
-                "ocn":{
-                    "ALL":{
-                        "LWcld_alt":0.3099678944246939,
-                        "LWcld_amt":-0.10132437245581576,
-                        "LWcld_err":0.06161032462516416,
-                        "LWcld_tau":0.0858104868811303,
-                        "LWcld_tot":0.3560643334751726,
-                        "SWcld_alt":-0.0568218330622036,
-                        "SWcld_amt":0.1968118817794807,
-                        "SWcld_err":-0.014023375087393625,
-                        "SWcld_tau":-0.13342149842034648,
-                        "SWcld_tot":-0.007454824790463041
-                    },
-                    "HI680":{
-                        "LWcld_alt":0.11120478233241288,
-                        "LWcld_amt":0.07617494310471734,
-                        "LWcld_err":0.03542374655806454,
-                        "LWcld_tau":0.1569826485004414,
-                        "LWcld_tot":0.37978612049563615,
-                        "SWcld_alt":-0.008770890418641599,
-                        "SWcld_amt":-0.06586338855696404,
-                        "SWcld_err":-0.0031168151433497177,
-                        "SWcld_tau":-0.22832539937330973,
-                        "SWcld_tot":-0.3060764934922651
-                    },
-                    "LO680":{
-                        "LWcld_alt":0.013578078895217385,
-                        "LWcld_amt":-0.035918294782468434,
-                        "LWcld_err":0.00037418718172545017,
-                        "LWcld_tau":-0.0017557583149379469,
-                        "LWcld_tot":-0.023721787020463545,
-                        "SWcld_alt":-0.006187161483889355,
-                        "SWcld_amt":0.3031272499754679,
-                        "SWcld_err":-0.0004925486567472228,
-                        "SWcld_tau":0.002174128866970823,
-                        "SWcld_tot":0.2986216687018021
-                    }
-                },
-                "ocn_asc":{
-                    "ALL":{
-                        "LWcld_alt":0.16188017402771424,
-                        "LWcld_amt":0.001319047410227506,
-                        "LWcld_err":0.048510313567216544,
-                        "LWcld_tau":0.08288842885518184,
-                        "LWcld_tot":0.29459796386034015,
-                        "SWcld_alt":-0.025000566257813384,
-                        "SWcld_amt":-0.0030827314908861633,
-                        "SWcld_err":-0.008684064395211858,
-                        "SWcld_tau":-0.15218085613992033,
-                        "SWcld_tot":-0.1889482182838317
-                    },
-                    "HI680":{
-                        "LWcld_alt":0.07428472973425758,
-                        "LWcld_amt":0.08402881072534252,
-                        "LWcld_err":0.03057399360136234,
-                        "LWcld_tau":0.11019984318044522,
-                        "LWcld_tot":0.29908737724140766,
-                        "SWcld_alt":-0.004492510001134354,
-                        "SWcld_amt":-0.07659931101148336,
-                        "SWcld_err":-0.001995780993484499,
-                        "SWcld_tau":-0.16867353648448738,
-                        "SWcld_tot":-0.25176113849058956
-                    },
-                    "LO680":{
-                        "LWcld_alt":0.003581914426575051,
-                        "LWcld_amt":-0.008208255445246976,
-                        "LWcld_err":0.0002502328312631734,
-                        "LWcld_tau":-0.00011330519365875425,
-                        "LWcld_tot":-0.004489413381067507,
-                        "SWcld_alt":-0.0016720874943745693,
-                        "SWcld_amt":0.07488237400617473,
-                        "SWcld_err":-0.00024393880511289573,
-                        "SWcld_tau":-0.010153427499929391,
-                        "SWcld_tot":0.06281292020675787
-                    }
-                },
-                "ocn_dsc":{
-                    "ALL":{
-                        "LWcld_alt":0.06489595367853708,
-                        "LWcld_amt":-0.024284220377419594,
-                        "LWcld_err":0.026547490232417082,
-                        "LWcld_tau":0.0029737472636955586,
-                        "LWcld_tot":0.07013297079723013,
-                        "SWcld_alt":-0.013647563856467057,
-                        "SWcld_amt":0.17732189266711362,
-                        "SWcld_err":-0.005685515484642076,
-                        "SWcld_tau":0.0014221990561020595,
-                        "SWcld_tot":0.15941101238210661
-                    },
-                    "HI680":{
-                        "LWcld_alt":0.008476436480005083,
-                        "LWcld_amt":0.04095077872430248,
-                        "LWcld_err":0.013519782617381385,
-                        "LWcld_tau":0.026613706777613846,
-                        "LWcld_tot":0.08956070459930278,
-                        "SWcld_alt":0.000643284396739674,
-                        "SWcld_amt":-0.03493465225618463,
-                        "SWcld_err":-0.0009634962665561066,
-                        "SWcld_tau":-0.02778364790803799,
-                        "SWcld_tot":-0.06303851203403904
-                    },
-                    "LO680":{
-                        "LWcld_alt":0.011590585271883336,
-                        "LWcld_amt":-0.030451479449216706,
-                        "LWcld_err":0.00019652494539081525,
-                        "LWcld_tau":-0.0007633645701301241,
-                        "LWcld_tot":-0.01942773380207267,
-                        "SWcld_alt":-0.00513751178641868,
-                        "SWcld_amt":0.22356649412188986,
-                        "SWcld_err":6.873853968266946e-05,
-                        "SWcld_tau":0.003951803540991813,
-                        "SWcld_tot":0.2224495244161456
-                    }
-                }
-            },
-            "eq40":{
-                "all":{
-                    "ALL":{
-                        "LWcld_alt":0.45782373928839587,
-                        "LWcld_amt":-0.2934569043309695,
-                        "LWcld_err":0.07814683559321438,
-                        "LWcld_tau":0.08363292429721982,
-                        "LWcld_tot":0.3261465948478607,
-                        "SWcld_alt":-0.07913223799920473,
-                        "SWcld_amt":0.4683327834744748,
-                        "SWcld_err":-0.01770556367250299,
-                        "SWcld_tau":-0.0942489259099858,
-                        "SWcld_tot":0.27724605589278123
-                    },
-                    "HI680":{
-                        "LWcld_alt":0.19079056374709566,
-                        "LWcld_amt":-0.04190510271527224,
-                        "LWcld_err":0.04861519864667418,
-                        "LWcld_tau":0.1710265939254283,
-                        "LWcld_tot":0.3685272536039259,
-                        "SWcld_alt":-0.014629955095499353,
-                        "SWcld_amt":0.055762481297764115,
-                        "SWcld_err":-0.004362787140861515,
-                        "SWcld_tau":-0.21908838366039896,
-                        "SWcld_tot":-0.18231864459899572
-                    },
-                    "LO680":{
-                        "LWcld_alt":0.017748791577906372,
-                        "LWcld_amt":-0.0582742363817623,
-                        "LWcld_err":0.000566173013395959,
-                        "LWcld_tau":-0.0024214648020700305,
-                        "LWcld_tot":-0.04238065875606522,
-                        "SWcld_alt":-0.008087998554861458,
-                        "SWcld_amt":0.45926152397457026,
-                        "SWcld_err":-0.0006695643019472858,
-                        "SWcld_tau":0.00906102030022418,
-                        "SWcld_tot":0.45956498141798563
-                    }
-                },
-                "lnd":{
-                    "ALL":{
-                        "LWcld_alt":0.07543556194787633,
-                        "LWcld_amt":-0.13855815579563627,
-                        "LWcld_err":0.011719361834411081,
-                        "LWcld_tau":0.00047550222243306924,
-                        "LWcld_tot":-0.0509277297909158,
-                        "SWcld_alt":-0.009824981910443748,
-                        "SWcld_amt":0.1680866629485942,
-                        "SWcld_err":-0.00235618424130654,
-                        "SWcld_tau":0.027763100700811005,
-                        "SWcld_tot":0.18366859749765488
-                    },
-                    "HI680":{
-                        "LWcld_alt":0.048063763318235496,
-                        "LWcld_amt":-0.1098298040659047,
-                        "LWcld_err":0.00974523289246728,
-                        "LWcld_tau":0.00893739504726421,
-                        "LWcld_tot":-0.043083412807937704,
-                        "SWcld_alt":-0.0034729057834072875,
-                        "SWcld_amt":0.11012788156115733,
-                        "SWcld_err":-0.0011102857334247692,
-                        "SWcld_tau":0.012089136808553324,
-                        "SWcld_tot":0.1176338268528786
-                    },
-                    "LO680":{
-                        "LWcld_alt":0.0017283051714124936,
-                        "LWcld_amt":-0.009255849591465573,
-                        "LWcld_err":-3.337650989233975e-05,
-                        "LWcld_tau":-0.0002834738894974668,
-                        "LWcld_tot":-0.007844316982978099,
-                        "SWcld_alt":-0.0008863460875804079,
-                        "SWcld_amt":0.06273641658109345,
-                        "SWcld_err":2.3945624361923547e-05,
-                        "SWcld_tau":0.004161035453110019,
-                        "SWcld_tot":0.06603505157098498
-                    }
-                },
-                "ocn":{
-                    "ALL":{
-                        "LWcld_alt":0.38238817734051955,
-                        "LWcld_amt":-0.15489874853533317,
-                        "LWcld_err":0.0664274737588033,
-                        "LWcld_tau":0.08315742207478674,
-                        "LWcld_tot":0.37707432463877644,
-                        "SWcld_alt":-0.06930725608876097,
-                        "SWcld_amt":0.3002461205258806,
-                        "SWcld_err":-0.01534937943119645,
-                        "SWcld_tau":-0.12201202661079681,
-                        "SWcld_tot":0.09357745839512632
-                    },
-                    "HI680":{
-                        "LWcld_alt":0.14272680042886018,
-                        "LWcld_amt":0.06792470135063246,
-                        "LWcld_err":0.0388699657542069,
-                        "LWcld_tau":0.1620891988781641,
-                        "LWcld_tot":0.41161066641186367,
-                        "SWcld_alt":-0.011157049312092064,
-                        "SWcld_amt":-0.054365400263393225,
-                        "SWcld_err":-0.003252501407436746,
-                        "SWcld_tau":-0.23117752046895232,
-                        "SWcld_tot":-0.29995247145187437
-                    },
-                    "LO680":{
-                        "LWcld_alt":0.016020486406493884,
-                        "LWcld_amt":-0.049018386790296735,
-                        "LWcld_err":0.0005995495232882989,
-                        "LWcld_tau":-0.0021379909125725637,
-                        "LWcld_tot":-0.03453634177308712,
-                        "SWcld_alt":-0.00720165246728105,
-                        "SWcld_amt":0.3965251073934768,
-                        "SWcld_err":-0.0006935099263092093,
-                        "SWcld_tau":0.004899984847114163,
-                        "SWcld_tot":0.3935299298470007
-                    }
-                },
-                "ocn_asc":{
-                    "ALL":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN
-                    },
-                    "HI680":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN
-                    },
-                    "LO680":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN
-                    }
-                },
-                "ocn_dsc":{
-                    "ALL":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN
-                    },
-                    "HI680":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN
-                    },
-                    "LO680":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN
-                    }
-                }
-            },
-            "eq50":{
-                "all":{
-                    "ALL":{
-                        "LWcld_alt":0.5207962502646046,
-                        "LWcld_amt":-0.318391332968857,
-                        "LWcld_err":0.07994877327878751,
-                        "LWcld_tau":0.09550288948609187,
-                        "LWcld_tot":0.377856580060627,
-                        "SWcld_alt":-0.08784786446051628,
-                        "SWcld_amt":0.5272654044470709,
-                        "SWcld_err":-0.018009835905089967,
-                        "SWcld_tau":-0.09899012623819277,
-                        "SWcld_tot":0.3224175778432719
-                    },
-                    "HI680":{
-                        "LWcld_alt":0.23344407485381521,
-                        "LWcld_amt":-0.04528188678208737,
-                        "LWcld_err":0.05161466818527901,
-                        "LWcld_tau":0.18314015081830218,
-                        "LWcld_tot":0.422917007075309,
-                        "SWcld_alt":-0.017571662454648692,
-                        "SWcld_amt":0.059052160582790024,
-                        "SWcld_err":-0.00459833149501195,
-                        "SWcld_tau":-0.222628644842132,
-                        "SWcld_tot":-0.1857464782090026
-                    },
-                    "LO680":{
-                        "LWcld_alt":0.019787689563964952,
-                        "LWcld_amt":-0.0644988553657856,
-                        "LWcld_err":0.0008506762438379212,
-                        "LWcld_tau":-0.0012000152931640987,
-                        "LWcld_tot":-0.04506042701468204,
-                        "SWcld_alt":-0.009016488582260892,
-                        "SWcld_amt":0.5122673486989088,
-                        "SWcld_err":-0.0007669312573960696,
-                        "SWcld_tau":0.005680408119231484,
-                        "SWcld_tot":0.5081643369784833
-                    }
-                },
-                "lnd":{
-                    "ALL":{
-                        "LWcld_alt":0.08860312111735408,
-                        "LWcld_amt":-0.14662421273073464,
-                        "LWcld_err":0.011436199403256748,
-                        "LWcld_tau":0.0068878976009391244,
-                        "LWcld_tot":-0.039696994609184695,
-                        "SWcld_alt":-0.01124640109575969,
-                        "SWcld_amt":0.18609504299302285,
-                        "SWcld_err":-0.0023745295905100547,
-                        "SWcld_tau":0.023456794993793162,
-                        "SWcld_tot":0.19593090730054621
-                    },
-                    "HI680":{
-                        "LWcld_alt":0.059299421896745995,
-                        "LWcld_amt":-0.11563578129667611,
-                        "LWcld_err":0.010370121271464318,
-                        "LWcld_tau":0.014687401968310998,
-                        "LWcld_tot":-0.0312788361601548,
-                        "SWcld_alt":-0.004279254728102144,
-                        "SWcld_amt":0.11881019703120693,
-                        "SWcld_err":-0.001192302575041345,
-                        "SWcld_tau":0.008421353196697803,
-                        "SWcld_tot":0.12175999292476124
-                    },
-                    "LO680":{
-                        "LWcld_alt":0.0015413915989079218,
-                        "LWcld_amt":-0.010178098001856073,
-                        "LWcld_err":-4.6541871852469025e-05,
-                        "LWcld_tau":0.00026501198930594473,
-                        "LWcld_tot":-0.008418158449029888,
-                        "SWcld_alt":-0.0008256870749187728,
-                        "SWcld_amt":0.07256172750189761,
-                        "SWcld_err":2.5486184915683914e-05,
-                        "SWcld_tau":0.0024096686900991772,
-                        "SWcld_tot":0.0741711953019937
-                    }
-                },
-                "ocn":{
-                    "ALL":{
-                        "LWcld_alt":0.43219312914725055,
-                        "LWcld_amt":-0.17176712023812235,
-                        "LWcld_err":0.06851257387553074,
-                        "LWcld_tau":0.08861499188515276,
-                        "LWcld_tot":0.41755357466981163,
-                        "SWcld_alt":-0.07660146336475658,
-                        "SWcld_amt":0.34117036145404817,
-                        "SWcld_err":-0.01563530631457991,
-                        "SWcld_tau":-0.12244692123198593,
-                        "SWcld_tot":0.12648667054272572
-                    },
-                    "HI680":{
-                        "LWcld_alt":0.17414465295706918,
-                        "LWcld_amt":0.07035389451458873,
-                        "LWcld_err":0.0412445469138147,
-                        "LWcld_tau":0.1684527488499912,
-                        "LWcld_tot":0.45419584323546386,
-                        "SWcld_alt":-0.013292407726546546,
-                        "SWcld_amt":-0.059758036448416894,
-                        "SWcld_err":-0.0034060289199706064,
-                        "SWcld_tau":-0.23104999803882986,
-                        "SWcld_tot":-0.30750647113376384
-                    },
-                    "LO680":{
-                        "LWcld_alt":0.018246297965057032,
-                        "LWcld_amt":-0.05432075736392952,
-                        "LWcld_err":0.00089721811569039,
-                        "LWcld_tau":-0.0014650272824700437,
-                        "LWcld_tot":-0.03664226856565215,
-                        "SWcld_alt":-0.008190801507342119,
-                        "SWcld_amt":0.4397056211970112,
-                        "SWcld_err":-0.0007924174423117534,
-                        "SWcld_tau":0.003270739429132306,
-                        "SWcld_tot":0.4339931416764895
-                    }
-                },
-                "ocn_asc":{
-                    "ALL":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN
-                    },
-                    "HI680":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN
-                    },
-                    "LO680":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN
-                    }
-                },
-                "ocn_dsc":{
-                    "ALL":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN
-                    },
-                    "HI680":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN
-                    },
-                    "LO680":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN
-                    }
-                }
-            },
-            "eq60":{
-                "all":{
-                    "ALL":{
-                        "LWcld_alt":0.5743814195315222,
-                        "LWcld_amt":-0.31143875018911377,
-                        "LWcld_err":0.08225839877120593,
-                        "LWcld_tau":0.12251944557851518,
-                        "LWcld_tot":0.4677205136921296,
-                        "SWcld_alt":-0.09491889509891697,
-                        "SWcld_amt":0.5218962115255102,
-                        "SWcld_err":-0.01798363871133764,
-                        "SWcld_tau":-0.16175504119580597,
-                        "SWcld_tot":0.24723863651944963
-                    },
-                    "HI680":{
-                        "LWcld_alt":0.2710936597698807,
-                        "LWcld_amt":-0.02145114957093809,
-                        "LWcld_err":0.05493868920408596,
-                        "LWcld_tau":0.2067796462426796,
-                        "LWcld_tot":0.5113608456457083,
-                        "SWcld_alt":-0.020325374715075735,
-                        "SWcld_amt":0.021470410115391067,
-                        "SWcld_err":-0.004867388773091012,
-                        "SWcld_tau":-0.25601753590014525,
-                        "SWcld_tot":-0.25973988927292097
-                    },
-                    "LO680":{
-                        "LWcld_alt":0.01982880194737877,
-                        "LWcld_amt":-0.06686974856657103,
-                        "LWcld_err":0.0011936555856679406,
-                        "LWcld_tau":0.0022068812434808756,
-                        "LWcld_tot":-0.04364033195357866,
-                        "SWcld_alt":-0.009209132588304495,
-                        "SWcld_amt":0.5381543324864461,
-                        "SWcld_err":-0.0009427473027770844,
-                        "SWcld_tau":-0.021023645876785207,
-                        "SWcld_tot":0.5069788067185793
-                    }
-                },
-                "lnd":{
-                    "ALL":{
-                        "LWcld_alt":0.09869039338145687,
-                        "LWcld_amt":-0.14086313537291767,
-                        "LWcld_err":0.011999492332506615,
-                        "LWcld_tau":0.01619501695655031,
-                        "LWcld_tot":-0.01397823270240386,
-                        "SWcld_alt":-0.011945204761333789,
-                        "SWcld_amt":0.1813345155030671,
-                        "SWcld_err":-0.0024089876560237805,
-                        "SWcld_tau":0.013826994029040205,
-                        "SWcld_tot":0.18080731711474973
-                    },
-                    "HI680":{
-                        "LWcld_alt":0.0692002388271118,
-                        "LWcld_amt":-0.11029632012948769,
-                        "LWcld_err":0.011669556993813496,
-                        "LWcld_tau":0.022810883645972394,
-                        "LWcld_tot":-0.0066156406625899845,
-                        "SWcld_alt":-0.005023810899206452,
-                        "SWcld_amt":0.11439475716714,
-                        "SWcld_err":-0.00131598436671593,
-                        "SWcld_tau":0.0012100204854998964,
-                        "SWcld_tot":0.1092649823867175
-                    },
-                    "LO680":{
-                        "LWcld_alt":0.0012646396665735014,
-                        "LWcld_amt":-0.009616824159920665,
-                        "LWcld_err":-6.57092351569711e-05,
-                        "LWcld_tau":0.0010552238522254715,
-                        "LWcld_tot":-0.0073625920398138756,
-                        "SWcld_alt":-0.0006647142965830141,
-                        "SWcld_amt":0.07173030104012648,
-                        "SWcld_err":3.216243180886916e-05,
-                        "SWcld_tau":0.00044486647888857157,
-                        "SWcld_tot":0.0715426156542409
-                    }
-                },
-                "ocn":{
-                    "ALL":{
-                        "LWcld_alt":0.4756910261500653,
-                        "LWcld_amt":-0.17057561481619612,
-                        "LWcld_err":0.07025890643869931,
-                        "LWcld_tau":0.10632442862196485,
-                        "LWcld_tot":0.4816987463945334,
-                        "SWcld_alt":-0.08297369033758319,
-                        "SWcld_amt":0.34056169602244313,
-                        "SWcld_err":-0.015574651055313861,
-                        "SWcld_tau":-0.1755820352248462,
-                        "SWcld_tot":0.06643131940469989
-                    },
-                    "HI680":{
-                        "LWcld_alt":0.20189342094276894,
-                        "LWcld_amt":0.08884517055854961,
-                        "LWcld_err":0.043269132210272475,
-                        "LWcld_tau":0.18396876259670722,
-                        "LWcld_tot":0.5179764863082982,
-                        "SWcld_alt":-0.015301563815869284,
-                        "SWcld_amt":-0.09292434705174892,
-                        "SWcld_err":-0.003551404406375082,
-                        "SWcld_tau":-0.25722755638564515,
-                        "SWcld_tot":-0.3690048716596385
-                    },
-                    "LO680":{
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-                        "LWcld_amt":-0.05725292440665037,
-                        "LWcld_err":0.001259364820824912,
-                        "LWcld_tau":0.001151657391255404,
-                        "LWcld_tot":-0.03627773991376478,
-                        "SWcld_alt":-0.008544418291721484,
-                        "SWcld_amt":0.46642403144631955,
-                        "SWcld_err":-0.0009749097345859536,
-                        "SWcld_tau":-0.02146851235567378,
-                        "SWcld_tot":0.4354361910643383
-                    }
-                },
-                "ocn_asc":{
-                    "ALL":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN
-                    },
-                    "HI680":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN
-                    },
-                    "LO680":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN
-                    }
-                },
-                "ocn_dsc":{
-                    "ALL":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN
-                    },
-                    "HI680":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN
-                    },
-                    "LO680":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN
-                    }
-                }
-            },
-            "eq70":{
-                "all":{
-                    "ALL":{
-                        "LWcld_alt":0.6028380995859567,
-                        "LWcld_amt":-0.3027948220582969,
-                        "LWcld_err":0.08386944420981525,
-                        "LWcld_tau":0.14469591193969875,
-                        "LWcld_tot":0.5286086336771737,
-                        "SWcld_alt":-0.09868552417281483,
-                        "SWcld_amt":0.5069690964717165,
-                        "SWcld_err":-0.018003682991979315,
-                        "SWcld_tau":-0.2261674285787375,
-                        "SWcld_tot":0.16411246072818494
-                    },
-                    "HI680":{
-                        "LWcld_alt":0.2896702210093274,
-                        "LWcld_amt":-0.002424638435377655,
-                        "LWcld_err":0.057512748187536544,
-                        "LWcld_tau":0.2258504766866647,
-                        "LWcld_tot":0.5706088074481509,
-                        "SWcld_alt":-0.021724677537141215,
-                        "SWcld_amt":-0.005736376408394337,
-                        "SWcld_err":-0.005097323773013407,
-                        "SWcld_tau":-0.29152436964554224,
-                        "SWcld_tot":-0.3240827473640912
-                    },
-                    "LO680":{
-                        "LWcld_alt":0.019793212759316878,
-                        "LWcld_amt":-0.06777882466108734,
-                        "LWcld_err":0.0012956179967933263,
-                        "LWcld_tau":0.0046897422975351166,
-                        "LWcld_tot":-0.04200017377097721,
-                        "SWcld_alt":-0.009205348418368408,
-                        "SWcld_amt":0.5469428480332692,
-                        "SWcld_err":-0.0009777600503386443,
-                        "SWcld_tau":-0.04856425054607736,
-                        "SWcld_tot":0.48819548901848475
-                    }
-                },
-                "lnd":{
-                    "ALL":{
-                        "LWcld_alt":0.10804195099192036,
-                        "LWcld_amt":-0.13521208537530655,
-                        "LWcld_err":0.013129622435689667,
-                        "LWcld_tau":0.02388244472810265,
-                        "LWcld_tot":0.009841932780406149,
-                        "SWcld_alt":-0.012879851281521122,
-                        "SWcld_amt":0.1736687318432705,
-                        "SWcld_err":-0.002574971275537691,
-                        "SWcld_tau":0.0029118307804971125,
-                        "SWcld_tot":0.16112574006670882
-                    },
-                    "HI680":{
-                        "LWcld_alt":0.07629561485465497,
-                        "LWcld_amt":-0.10273231529932243,
-                        "LWcld_err":0.012856000774531466,
-                        "LWcld_tau":0.0302512672723849,
-                        "LWcld_tot":0.016670567602248915,
-                        "SWcld_alt":-0.005569265600602144,
-                        "SWcld_amt":0.10666961030614519,
-                        "SWcld_err":-0.0014303652257312007,
-                        "SWcld_tau":-0.007754809601442407,
-                        "SWcld_tot":0.09191516987836944
-                    },
-                    "LO680":{
-                        "LWcld_alt":0.0011706158695550656,
-                        "LWcld_amt":-0.009540867585175484,
-                        "LWcld_err":-6.920054565787289e-05,
-                        "LWcld_tau":0.0016107396029707447,
-                        "LWcld_tot":-0.006828634821842761,
-                        "SWcld_alt":-0.0005979880926624308,
-                        "SWcld_amt":0.07150948976491085,
-                        "SWcld_err":3.2415934293541994e-05,
-                        "SWcld_tau":-0.0017330664919938928,
-                        "SWcld_tot":0.06921085111454806
-                    }
-                },
-                "ocn":{
-                    "ALL":{
-                        "LWcld_alt":0.49479614859403626,
-                        "LWcld_amt":-0.16758273668299037,
-                        "LWcld_err":0.07073982177412559,
-                        "LWcld_tau":0.1208134672115961,
-                        "LWcld_tot":0.5187667008967676,
-                        "SWcld_alt":-0.08580567289129366,
-                        "SWcld_amt":0.33330036462844603,
-                        "SWcld_err":-0.015428711716441624,
-                        "SWcld_tau":-0.2290792593592346,
-                        "SWcld_tot":0.0029867206614761104
-                    },
-                    "HI680":{
-                        "LWcld_alt":0.2133746061546724,
-                        "LWcld_amt":0.10030767686394478,
-                        "LWcld_err":0.04465674741300508,
-                        "LWcld_tau":0.1955992094142798,
-                        "LWcld_tot":0.5539382398459021,
-                        "SWcld_alt":-0.01615541193653907,
-                        "SWcld_amt":-0.11240598671453954,
-                        "SWcld_err":-0.0036669585472822066,
-                        "SWcld_tau":-0.2837695600440998,
-                        "SWcld_tot":-0.4159979172424606
-                    },
-                    "LO680":{
-                        "LWcld_alt":0.01862259688976181,
-                        "LWcld_amt":-0.05823795707591184,
-                        "LWcld_err":0.0013648185424511988,
-                        "LWcld_tau":0.003079002694564372,
-                        "LWcld_tot":-0.035171538949134454,
-                        "SWcld_alt":-0.008607360325705976,
-                        "SWcld_amt":0.47543335826835836,
-                        "SWcld_err":-0.0010101759846321866,
-                        "SWcld_tau":-0.04683118405408348,
-                        "SWcld_tot":0.4189846379039368
-                    }
-                },
-                "ocn_asc":{
-                    "ALL":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN
-                    },
-                    "HI680":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN
-                    },
-                    "LO680":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN
-                    }
-                },
-                "ocn_dsc":{
-                    "ALL":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN
-                    },
-                    "HI680":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN
-                    },
-                    "LO680":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN
-                    }
-                }
-            },
-            "eq80":{
-                "all":{
-                    "ALL":{
-                        "LWcld_alt":0.6160875182148664,
-                        "LWcld_amt":-0.30026692555242146,
-                        "LWcld_err":0.08469839437713451,
-                        "LWcld_tau":0.15548148466113385,
-                        "LWcld_tot":0.5560004717007134,
-                        "SWcld_alt":-0.1006717304393772,
-                        "SWcld_amt":0.5048899849327207,
-                        "SWcld_err":-0.018101609742701762,
-                        "SWcld_tau":-0.24123071072892022,
-                        "SWcld_tot":0.14488593402172165
-                    },
-                    "HI680":{
-                        "LWcld_alt":0.2964924516252084,
-                        "LWcld_amt":0.006997499888865261,
-                        "LWcld_err":0.058639310599646326,
-                        "LWcld_tau":0.23555032278751128,
-                        "LWcld_tot":0.5976795849012312,
-                        "SWcld_alt":-0.022353608566350876,
-                        "SWcld_amt":-0.015361112656717272,
-                        "SWcld_err":-0.005199679701795254,
-                        "SWcld_tau":-0.30054280692209506,
-                        "SWcld_tot":-0.3434572078469585
-                    },
-                    "LO680":{
-                        "LWcld_alt":0.01987462501348614,
-                        "LWcld_amt":-0.0685603246499202,
-                        "LWcld_err":0.0013276573399366427,
-                        "LWcld_tau":0.005678836554786145,
-                        "LWcld_tot":-0.04167911320051782,
-                        "SWcld_alt":-0.009183847535569117,
-                        "SWcld_amt":0.552988619391369,
-                        "SWcld_err":-0.0009966810210122396,
-                        "SWcld_tau":-0.05446465964487218,
-                        "SWcld_tot":0.4883434311899153
-                    }
-                },
-                "lnd":{
-                    "ALL":{
-                        "LWcld_alt":0.11276117078443422,
-                        "LWcld_amt":-0.1339168497006281,
-                        "LWcld_err":0.013570052546933819,
-                        "LWcld_tau":0.029800858291618885,
-                        "LWcld_tot":0.022215231922358858,
-                        "SWcld_alt":-0.01341660863586086,
-                        "SWcld_amt":0.1730445162921055,
-                        "SWcld_err":-0.002616057953873617,
-                        "SWcld_tau":-0.0010046282460235646,
-                        "SWcld_tot":0.1560072214563474
-                    },
-                    "HI680":{
-                        "LWcld_alt":0.07951687644421514,
-                        "LWcld_amt":-0.09981056204903312,
-                        "LWcld_err":0.013386817171425584,
-                        "LWcld_tau":0.035873270646064805,
-                        "LWcld_tot":0.028966402212672396,
-                        "SWcld_alt":-0.005848260639224015,
-                        "SWcld_amt":0.10490523477462564,
-                        "SWcld_err":-0.001474397889939611,
-                        "SWcld_tau":-0.010889812360971932,
-                        "SWcld_tot":0.0866927638844901
-                    },
-                    "LO680":{
-                        "LWcld_alt":0.001184580824837294,
-                        "LWcld_amt":-0.009749153186938468,
-                        "LWcld_err":-6.55829160993608e-05,
-                        "LWcld_tau":0.001878892446693521,
-                        "LWcld_tot":-0.006751170290313542,
-                        "SWcld_alt":-0.0005944686488120955,
-                        "SWcld_amt":0.07243276185795906,
-                        "SWcld_err":2.995935907544603e-05,
-                        "SWcld_tau":-0.002553505675129826,
-                        "SWcld_tot":0.06931474689309257
-                    }
-                },
-                "ocn":{
-                    "ALL":{
-                        "LWcld_alt":0.5033263474304323,
-                        "LWcld_amt":-0.16635007585179337,
-                        "LWcld_err":0.07112834183020071,
-                        "LWcld_tau":0.12568062636951496,
-                        "LWcld_tot":0.5337852397783546,
-                        "SWcld_alt":-0.08725512180351631,
-                        "SWcld_amt":0.33184546864061526,
-                        "SWcld_err":-0.015485551788828144,
-                        "SWcld_tau":-0.24022608248289667,
-                        "SWcld_tot":-0.011121287434625815
-                    },
-                    "HI680":{
-                        "LWcld_alt":0.21697557518099328,
-                        "LWcld_amt":0.1068080619378984,
-                        "LWcld_err":0.04525249342822075,
-                        "LWcld_tau":0.1996770521414465,
-                        "LWcld_tot":0.5687131826885589,
-                        "SWcld_alt":-0.016505347927126858,
-                        "SWcld_amt":-0.12026634743134292,
-                        "SWcld_err":-0.003725281811855644,
-                        "SWcld_tau":-0.2896529945611232,
-                        "SWcld_tot":-0.43014997173144853
-                    },
-                    "LO680":{
-                        "LWcld_alt":0.018690044188648842,
-                        "LWcld_amt":-0.05881117146298175,
-                        "LWcld_err":0.0013932402560360036,
-                        "LWcld_tau":0.0037999441080926234,
-                        "LWcld_tot":-0.034927942910204283,
-                        "SWcld_alt":-0.008589378886757022,
-                        "SWcld_amt":0.48055585753340985,
-                        "SWcld_err":-0.0010266403800876856,
-                        "SWcld_tau":-0.05191115396974235,
-                        "SWcld_tot":0.4190286842968227
-                    }
-                },
-                "ocn_asc":{
-                    "ALL":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN
-                    },
-                    "HI680":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN
-                    },
-                    "LO680":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN
-                    }
-                },
-                "ocn_dsc":{
-                    "ALL":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN
-                    },
-                    "HI680":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN
-                    },
-                    "LO680":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN
-                    }
-                }
-            },
-            "eq90":{
-                "all":{
-                    "ALL":{
-                        "LWcld_alt":0.6197333079580979,
-                        "LWcld_amt":-0.3000478934019591,
-                        "LWcld_err":0.084790851074709,
-                        "LWcld_tau":0.15815526850154302,
-                        "LWcld_tot":0.5626315341323908,
-                        "SWcld_alt":-0.10121948028043459,
-                        "SWcld_amt":0.5052705495661258,
-                        "SWcld_err":-0.018126992350837964,
-                        "SWcld_tau":-0.24382170165529535,
-                        "SWcld_tot":0.14210237527955788
-                    },
-                    "HI680":{
-                        "LWcld_alt":0.298095942371184,
-                        "LWcld_amt":0.009625866083085181,
-                        "LWcld_err":0.05884454885841262,
-                        "LWcld_tau":0.23790761365571547,
-                        "LWcld_tot":0.6044739709683973,
-                        "SWcld_alt":-0.0225409512037269,
-                        "SWcld_amt":-0.01738380939965623,
-                        "SWcld_err":-0.0052218352476640405,
-                        "SWcld_tau":-0.3020215343543156,
-                        "SWcld_tot":-0.3471681302053628
-                    },
-                    "LO680":{
-                        "LWcld_alt":0.01982542331974992,
-                        "LWcld_amt":-0.06888889335683135,
-                        "LWcld_err":0.0013234519819440634,
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-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "SWcld_alt":NaN,
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-                        "SWcld_tau":NaN,
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-                        "LWcld_amt":NaN,
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-                        "LWcld_tau":NaN,
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-                        "SWcld_alt":NaN,
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-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
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-                        "LWcld_amt":NaN,
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-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN
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-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
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-                        "LWcld_amt":NaN,
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-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
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-                        "SWcld_tau":NaN,
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-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "SWcld_alt":NaN,
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-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN
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-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN
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-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN
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-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN
-                    }
-                },
-                "ocn_dsc":{
-                    "ALL":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN
-                    },
-                    "HI680":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN
-                    },
-                    "LO680":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN
-                    }
-                }
-            },
-            "eq20":{
-                "all":{
-                    "ALL":{
-                        "LWcld_alt":0.2982114154264682,
-                        "LWcld_amt":-0.07437055103757952,
-                        "LWcld_err":-0.0007771882166603955,
-                        "LWcld_tau":-0.03826999699994322,
-                        "LWcld_tot":0.18479367917228504,
-                        "SWcld_alt":-0.04662989396030095,
-                        "SWcld_amt":0.1435405205840171,
-                        "SWcld_err":-0.007353386302987594,
-                        "SWcld_tau":0.05993450371014971,
-                        "SWcld_tot":0.14949174403087825
-                    },
-                    "HI680":{
-                        "LWcld_alt":0.16484599978489006,
-                        "LWcld_amt":0.018640844714582147,
-                        "LWcld_err":0.010624294351876144,
-                        "LWcld_tau":0.01944564077711099,
-                        "LWcld_tot":0.21355677962845934,
-                        "SWcld_alt":-0.015462011017195021,
-                        "SWcld_amt":-0.02833447577720991,
-                        "SWcld_err":-0.004069357631961681,
-                        "SWcld_tau":-0.02797920758549277,
-                        "SWcld_tot":-0.07584505201185938
-                    },
-                    "LO680":{
-                        "LWcld_alt":0.006280910408780107,
-                        "LWcld_amt":-0.03471039216322007,
-                        "LWcld_err":-5.585140078009804e-05,
-                        "LWcld_tau":-0.0002777673009542631,
-                        "LWcld_tot":-0.028763100456174317,
-                        "SWcld_alt":-0.0030821165851388846,
-                        "SWcld_amt":0.2327400553387707,
-                        "SWcld_err":0.00013179051105818761,
-                        "SWcld_tau":-0.004452933221952331,
-                        "SWcld_tot":0.2253367960427377
-                    }
-                },
-                "lnd":{
-                    "ALL":{
-                        "LWcld_alt":0.05962803038136335,
-                        "LWcld_amt":-0.051354546426857474,
-                        "LWcld_err":-0.005238829631892293,
-                        "LWcld_tau":-0.0237102263515703,
-                        "LWcld_tot":-0.02067557202895674,
-                        "SWcld_alt":-0.006988709318028352,
-                        "SWcld_amt":0.07044514221630101,
-                        "SWcld_err":-0.0007380039785349841,
-                        "SWcld_tau":0.033927235230186224,
-                        "SWcld_tot":0.09664566414992388
-                    },
-                    "HI680":{
-                        "LWcld_alt":0.04233226915617794,
-                        "LWcld_amt":-0.038757196336472004,
-                        "LWcld_err":-0.004000126731327206,
-                        "LWcld_tau":-0.01631063806116893,
-                        "LWcld_tot":-0.01673569197279021,
-                        "SWcld_alt":-0.0030172719475702534,
-                        "SWcld_amt":0.04469511000542801,
-                        "SWcld_err":-0.00029714174272482045,
-                        "SWcld_tau":0.020187320537918028,
-                        "SWcld_tot":0.06156801685305097
-                    },
-                    "LO680":{
-                        "LWcld_alt":0.0011284069992517699,
-                        "LWcld_amt":-0.0049691362375192194,
-                        "LWcld_err":1.2016127537817089e-05,
-                        "LWcld_tau":-0.00011116694543689518,
-                        "LWcld_tot":-0.003939880056166528,
-                        "SWcld_alt":-0.0005852248901348591,
-                        "SWcld_amt":0.03240038532416894,
-                        "SWcld_err":1.9491046177475614e-05,
-                        "SWcld_tau":0.0032429958166613677,
-                        "SWcld_tot":0.035077647296872926
-                    }
-                },
-                "ocn":{
-                    "ALL":{
-                        "LWcld_alt":0.2385833850451048,
-                        "LWcld_amt":-0.023016004610722045,
-                        "LWcld_err":0.004461641415231897,
-                        "LWcld_tau":-0.01455977064837292,
-                        "LWcld_tot":0.2054692512012418,
-                        "SWcld_alt":-0.039641184642272585,
-                        "SWcld_amt":0.07309537836771611,
-                        "SWcld_err":-0.00661538232445261,
-                        "SWcld_tau":0.026007268479963475,
-                        "SWcld_tot":0.05284607988095439
-                    },
-                    "HI680":{
-                        "LWcld_alt":0.12251373062871214,
-                        "LWcld_amt":0.05739804105105415,
-                        "LWcld_err":0.014624421083203347,
-                        "LWcld_tau":0.03575627883827992,
-                        "LWcld_tot":0.23029247160124955,
-                        "SWcld_alt":-0.01244473906962477,
-                        "SWcld_amt":-0.07302958578263792,
-                        "SWcld_err":-0.0037722158892368606,
-                        "SWcld_tau":-0.0481665281234108,
-                        "SWcld_tot":-0.13741306886491036
-                    },
-                    "LO680":{
-                        "LWcld_alt":0.005152503409528337,
-                        "LWcld_amt":-0.029741255925700854,
-                        "LWcld_err":-6.786752831791512e-05,
-                        "LWcld_tau":-0.00016660035551736794,
-                        "LWcld_tot":-0.024823220400007794,
-                        "SWcld_alt":-0.002496891695004026,
-                        "SWcld_amt":0.2003396700146017,
-                        "SWcld_err":0.00011229946488071199,
-                        "SWcld_tau":-0.007695929038613697,
-                        "SWcld_tot":0.19025914874586472
-                    }
-                },
-                "ocn_asc":{
-                    "ALL":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN
-                    },
-                    "HI680":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN
-                    },
-                    "LO680":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN
-                    }
-                },
-                "ocn_dsc":{
-                    "ALL":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN
-                    },
-                    "HI680":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN
-                    },
-                    "LO680":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN
-                    }
-                }
-            },
-            "eq30":{
-                "all":{
-                    "ALL":{
-                        "LWcld_alt":0.41501824544424765,
-                        "LWcld_amt":-0.10199022961680164,
-                        "LWcld_err":-0.018456652274162006,
-                        "LWcld_tau":-0.05403100731704452,
-                        "LWcld_tot":0.24054035623623948,
-                        "SWcld_alt":-0.0639779218539069,
-                        "SWcld_amt":0.19030320597765105,
-                        "SWcld_err":-0.006850632884101045,
-                        "SWcld_tau":0.08655032741248013,
-                        "SWcld_tot":0.20602497865212321
-                    },
-                    "HI680":{
-                        "LWcld_alt":0.22624296540784672,
-                        "LWcld_amt":0.037323456387407784,
-                        "LWcld_err":0.004351016601864852,
-                        "LWcld_tau":0.020356888896231804,
-                        "LWcld_tot":0.28827432729335123,
-                        "SWcld_alt":-0.02046091074469177,
-                        "SWcld_amt":-0.06833610712859935,
-                        "SWcld_err":-0.004258979625813426,
-                        "SWcld_tau":-0.023903639792094224,
-                        "SWcld_tot":-0.11695963729119879
-                    },
-                    "LO680":{
-                        "LWcld_alt":0.007758191173538941,
-                        "LWcld_amt":-0.0553352500551074,
-                        "LWcld_err":-6.631153255259654e-05,
-                        "LWcld_tau":-9.06008086791405e-05,
-                        "LWcld_tot":-0.047733971057111746,
-                        "SWcld_alt":-0.0037309091874619432,
-                        "SWcld_amt":0.3378884753469084,
-                        "SWcld_err":0.0001607995251612572,
-                        "SWcld_tau":-0.0113337495599181,
-                        "SWcld_tot":0.32298461612468965
-                    }
-                },
-                "lnd":{
-                    "ALL":{
-                        "LWcld_alt":0.0853293510801895,
-                        "LWcld_amt":-0.06881290058395435,
-                        "LWcld_err":-0.00859635219660279,
-                        "LWcld_tau":-0.034525439454471105,
-                        "LWcld_tot":-0.02660534115483877,
-                        "SWcld_alt":-0.00939999232815632,
-                        "SWcld_amt":0.09668211961011318,
-                        "SWcld_err":-0.0007176435666404248,
-                        "SWcld_tau":0.05260421514276999,
-                        "SWcld_tot":0.13916869885808647
-                    },
-                    "HI680":{
-                        "LWcld_alt":0.061772457746522715,
-                        "LWcld_amt":-0.05071081926130394,
-                        "LWcld_err":-0.00616329458709972,
-                        "LWcld_tau":-0.025279873267718647,
-                        "LWcld_tot":-0.020381529369599603,
-                        "SWcld_alt":-0.004242951470626613,
-                        "SWcld_amt":0.060240761194353835,
-                        "SWcld_err":-0.00023919602533300165,
-                        "SWcld_tau":0.034753837465895866,
-                        "SWcld_tot":0.09051245116429009
-                    },
-                    "LO680":{
-                        "LWcld_alt":0.0013854212287660464,
-                        "LWcld_amt":-0.007496093382616133,
-                        "LWcld_err":9.620381739560123e-06,
-                        "LWcld_tau":-0.00012276017881709129,
-                        "LWcld_tot":-0.006223811785239169,
-                        "SWcld_alt":-0.0007084712780037497,
-                        "SWcld_amt":0.045358445200709994,
-                        "SWcld_err":2.9065318226413357e-05,
-                        "SWcld_tau":0.003977208634231322,
-                        "SWcld_tot":0.04865624787516398
-                    }
-                },
-                "ocn":{
-                    "ALL":{
-                        "LWcld_alt":0.3296888943640581,
-                        "LWcld_amt":-0.03317732903284726,
-                        "LWcld_err":-0.009860300077559217,
-                        "LWcld_tau":-0.019505567862573406,
-                        "LWcld_tot":0.26714569739107824,
-                        "SWcld_alt":-0.05457792952575058,
-                        "SWcld_amt":0.09362108636753785,
-                        "SWcld_err":-0.00613298931746062,
-                        "SWcld_tau":0.03394611226971014,
-                        "SWcld_tot":0.06685627979403679
-                    },
-                    "HI680":{
-                        "LWcld_alt":0.164470507661324,
-                        "LWcld_amt":0.08803427564871175,
-                        "LWcld_err":0.010514311188964573,
-                        "LWcld_tau":0.045636762163950455,
-                        "LWcld_tot":0.3086558566629508,
-                        "SWcld_alt":-0.016217959274065157,
-                        "SWcld_amt":-0.12857686832295323,
-                        "SWcld_err":-0.004019783600480424,
-                        "SWcld_tau":-0.05865747725799009,
-                        "SWcld_tot":-0.20747208845548884
-                    },
-                    "LO680":{
-                        "LWcld_alt":0.0063727699447728966,
-                        "LWcld_amt":-0.04783915667249126,
-                        "LWcld_err":-7.593191429215665e-05,
-                        "LWcld_tau":3.2159370137950775e-05,
-                        "LWcld_tot":-0.041510159271872564,
-                        "SWcld_alt":-0.0030224379094581934,
-                        "SWcld_amt":0.29253003014619844,
-                        "SWcld_err":0.00013173420693484385,
-                        "SWcld_tau":-0.015310958194149423,
-                        "SWcld_tot":0.2743283682495257
-                    }
-                },
-                "ocn_asc":{
-                    "ALL":{
-                        "LWcld_alt":0.16507891199004412,
-                        "LWcld_amt":0.04986217608697799,
-                        "LWcld_err":0.025026550295773557,
-                        "LWcld_tau":0.014923750472641203,
-                        "LWcld_tot":0.2548913888454369,
-                        "SWcld_alt":-0.02346996058211834,
-                        "SWcld_amt":-0.07906592894150635,
-                        "SWcld_err":-0.008820825944944701,
-                        "SWcld_tau":-0.050876889147878246,
-                        "SWcld_tot":-0.16223360461644767
-                    },
-                    "HI680":{
-                        "LWcld_alt":0.10087260148570855,
-                        "LWcld_amt":0.09785841389982664,
-                        "LWcld_err":0.018321156481224243,
-                        "LWcld_tau":0.04511858542965347,
-                        "LWcld_tot":0.26217075729641287,
-                        "SWcld_alt":-0.008456753115847005,
-                        "SWcld_amt":-0.13042248597964326,
-                        "SWcld_err":-0.0029260303233754593,
-                        "SWcld_tau":-0.07890142079007267,
-                        "SWcld_tot":-0.22070669020893843
-                    },
-                    "LO680":{
-                        "LWcld_alt":0.0029373809942873715,
-                        "LWcld_amt":-0.010472964290605652,
-                        "LWcld_err":-3.0153580510741478e-05,
-                        "LWcld_tau":0.0002863684258529872,
-                        "LWcld_tot":-0.007279368450976036,
-                        "SWcld_alt":-0.001439655476090952,
-                        "SWcld_amt":0.07360802338195822,
-                        "SWcld_err":4.772941752508297e-05,
-                        "SWcld_tau":-0.013743011730901603,
-                        "SWcld_tot":0.05847308559249076
-                    }
-                },
-                "ocn_dsc":{
-                    "ALL":{
-                        "LWcld_alt":0.0943880448799637,
-                        "LWcld_amt":-0.03980070831304095,
-                        "LWcld_err":-0.015352695706116241,
-                        "LWcld_tau":-0.021599979471866013,
-                        "LWcld_tot":0.01763466138894051,
-                        "SWcld_alt":-0.01484583995316293,
-                        "SWcld_amt":0.1585174878390506,
-                        "SWcld_err":-0.0005200438582601946,
-                        "SWcld_tau":0.05862173091884442,
-                        "SWcld_tot":0.20177333494647187
-                    },
-                    "HI680":{
-                        "LWcld_alt":0.02541381777526072,
-                        "LWcld_amt":0.023877758555385268,
-                        "LWcld_err":-0.006538485990040293,
-                        "LWcld_tau":0.009847277862699703,
-                        "LWcld_tot":0.05260036820330539,
-                        "SWcld_alt":0.00010699802935833977,
-                        "SWcld_amt":-0.011471991089520827,
-                        "SWcld_err":0.0013062815362774093,
-                        "SWcld_tau":0.006623725284331809,
-                        "SWcld_tot":-0.003434986239553277
-                    },
-                    "LO680":{
-                        "LWcld_alt":0.004897046661371231,
-                        "LWcld_amt":-0.03927715626241644,
-                        "LWcld_err":-0.00012484836461904461,
-                        "LWcld_tau":-0.0004607488487006152,
-                        "LWcld_tot":-0.03496570681436488,
-                        "SWcld_alt":-0.0021980011199875566,
-                        "SWcld_amt":0.20178356071214087,
-                        "SWcld_err":0.00022358895147069446,
-                        "SWcld_tau":0.005399172642401155,
-                        "SWcld_tot":0.20520832118602517
-                    }
-                }
-            },
-            "eq40":{
-                "all":{
-                    "ALL":{
-                        "LWcld_alt":0.5141143341519625,
-                        "LWcld_amt":-0.16948079771116353,
-                        "LWcld_err":-0.029705759244519764,
-                        "LWcld_tau":-0.06046959509002939,
-                        "LWcld_tot":0.2544581821062499,
-                        "SWcld_alt":-0.07841848492659502,
-                        "SWcld_amt":0.3075021335233041,
-                        "SWcld_err":-0.006924276679077519,
-                        "SWcld_tau":0.09196237300506888,
-                        "SWcld_tot":0.31412174492270045
-                    },
-                    "HI680":{
-                        "LWcld_alt":0.27657140355852966,
-                        "LWcld_amt":0.02756582963956579,
-                        "LWcld_err":0.0018244648605118081,
-                        "LWcld_tau":0.02237874431072161,
-                        "LWcld_tot":0.3283404423693288,
-                        "SWcld_alt":-0.02314638375115977,
-                        "SWcld_amt":-0.07186178350991028,
-                        "SWcld_err":-0.004263514167196428,
-                        "SWcld_tau":-0.01963862517221621,
-                        "SWcld_tot":-0.1189103066004827
-                    },
-                    "LO680":{
-                        "LWcld_alt":0.008324369744198224,
-                        "LWcld_amt":-0.0836583029038405,
-                        "LWcld_err":3.854131283016651e-05,
-                        "LWcld_tau":0.0014131260301247356,
-                        "LWcld_tot":-0.07388226026307897,
-                        "SWcld_alt":-0.004047133744505317,
-                        "SWcld_amt":0.4662018825644356,
-                        "SWcld_err":6.921108205182726e-05,
-                        "SWcld_tau":-0.029191902769267645,
-                        "SWcld_tot":0.4330320571327144
-                    }
-                },
-                "lnd":{
-                    "ALL":{
-                        "LWcld_alt":0.1101700560965354,
-                        "LWcld_amt":-0.09215858848882214,
-                        "LWcld_err":-0.012057893457740776,
-                        "LWcld_tau":-0.040436286369871774,
-                        "LWcld_tot":-0.0344827122198993,
-                        "SWcld_alt":-0.012148349404242533,
-                        "SWcld_amt":0.12999381230435872,
-                        "SWcld_err":-0.0005802319106888455,
-                        "SWcld_tau":0.06515052639759675,
-                        "SWcld_tot":0.1824157573870241
-                    },
-                    "HI680":{
-                        "LWcld_alt":0.08049305195267861,
-                        "LWcld_amt":-0.06672451865214193,
-                        "LWcld_err":-0.007995811498174165,
-                        "LWcld_tau":-0.03006770450071748,
-                        "LWcld_tot":-0.024294982698354946,
-                        "SWcld_alt":-0.005847145168437502,
-                        "SWcld_amt":0.07903111772979453,
-                        "SWcld_err":-0.00017997392325370693,
-                        "SWcld_tau":0.04436387997512418,
-                        "SWcld_tot":0.11736787861322749
-                    },
-                    "LO680":{
-                        "LWcld_alt":0.0013642507568846747,
-                        "LWcld_amt":-0.011578617688751252,
-                        "LWcld_err":1.0187209460180323e-05,
-                        "LWcld_tau":1.6444647253623037e-05,
-                        "LWcld_tot":-0.010187729521544354,
-                        "SWcld_alt":-0.000713291753252705,
-                        "SWcld_amt":0.062102364962905617,
-                        "SWcld_err":2.8378172144627737e-05,
-                        "SWcld_tau":0.003630433001530387,
-                        "SWcld_tot":0.06504788438332794
-                    }
-                },
-                "ocn":{
-                    "ALL":{
-                        "LWcld_alt":0.4039442780554272,
-                        "LWcld_amt":-0.0773222092223414,
-                        "LWcld_err":-0.017647865786778984,
-                        "LWcld_tau":-0.020033308720157624,
-                        "LWcld_tot":0.28894089432614917,
-                        "SWcld_alt":-0.06627013552235249,
-                        "SWcld_amt":0.17750832121894536,
-                        "SWcld_err":-0.006344044768388672,
-                        "SWcld_tau":0.026811846607472156,
-                        "SWcld_tot":0.13170598753567633
-                    },
-                    "HI680":{
-                        "LWcld_alt":0.19607835160585105,
-                        "LWcld_amt":0.0942903482917077,
-                        "LWcld_err":0.009820276358685973,
-                        "LWcld_tau":0.05244644881143909,
-                        "LWcld_tot":0.35263542506768375,
-                        "SWcld_alt":-0.01729923858272227,
-                        "SWcld_amt":-0.15089290123970478,
-                        "SWcld_err":-0.004083540243942721,
-                        "SWcld_tau":-0.0640025051473404,
-                        "SWcld_tot":-0.23627818521371022
-                    },
-                    "LO680":{
-                        "LWcld_alt":0.006960118987313548,
-                        "LWcld_amt":-0.07207968521508924,
-                        "LWcld_err":2.8354103369986185e-05,
-                        "LWcld_tau":0.0013966813828711127,
-                        "LWcld_tot":-0.0636945307415346,
-                        "SWcld_alt":-0.003333841991252613,
-                        "SWcld_amt":0.4040995176015299,
-                        "SWcld_err":4.083290990719951e-05,
-                        "SWcld_tau":-0.032822335770798024,
-                        "SWcld_tot":0.3679841727493865
-                    }
-                },
-                "ocn_asc":{
-                    "ALL":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN
-                    },
-                    "HI680":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN
-                    },
-                    "LO680":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN
-                    }
-                },
-                "ocn_dsc":{
-                    "ALL":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN
-                    },
-                    "HI680":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN
-                    },
-                    "LO680":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN
-                    }
-                }
-            },
-            "eq50":{
-                "all":{
-                    "ALL":{
-                        "LWcld_alt":0.597727953760406,
-                        "LWcld_amt":-0.2100406563278686,
-                        "LWcld_err":-0.039917763230594434,
-                        "LWcld_tau":-0.05753745112502218,
-                        "LWcld_tot":0.2902320830769208,
-                        "SWcld_alt":-0.09119826951984349,
-                        "SWcld_amt":0.3970399731107066,
-                        "SWcld_err":-0.006253039103777423,
-                        "SWcld_tau":0.07752958800743125,
-                        "SWcld_tot":0.37711825249451675
-                    },
-                    "HI680":{
-                        "LWcld_alt":0.3209142956057043,
-                        "LWcld_amt":0.039520054551015406,
-                        "LWcld_err":-0.0015211096440253084,
-                        "LWcld_tau":0.022897971350739555,
-                        "LWcld_tot":0.38181121186343386,
-                        "SWcld_alt":-0.026154461003672424,
-                        "SWcld_amt":-0.0983446127022839,
-                        "SWcld_err":-0.0037836807777098433,
-                        "SWcld_tau":-0.006541550662694711,
-                        "SWcld_tot":-0.13482430514636087
-                    },
-                    "LO680":{
-                        "LWcld_alt":0.010367487161979926,
-                        "LWcld_amt":-0.10658668381428388,
-                        "LWcld_err":4.1974589444419715e-05,
-                        "LWcld_tau":0.004598087722737986,
-                        "LWcld_tot":-0.0915791287865131,
-                        "SWcld_alt":-0.004958854528934809,
-                        "SWcld_amt":0.5761767308783149,
-                        "SWcld_err":6.455355020495216e-05,
-                        "SWcld_tau":-0.059339866649176114,
-                        "SWcld_tot":0.511942563250409
-                    }
-                },
-                "lnd":{
-                    "ALL":{
-                        "LWcld_alt":0.13165756090016487,
-                        "LWcld_amt":-0.10556497745054645,
-                        "LWcld_err":-0.015643048050449578,
-                        "LWcld_tau":-0.042702311444252815,
-                        "LWcld_tot":-0.03225277604508395,
-                        "SWcld_alt":-0.01476283500257377,
-                        "SWcld_amt":0.1555244555952983,
-                        "SWcld_err":-0.00018037490534173647,
-                        "SWcld_tau":0.07168661489198525,
-                        "SWcld_tot":0.21226786057936806
-                    },
-                    "HI680":{
-                        "LWcld_alt":0.09616465627975679,
-                        "LWcld_amt":-0.07334846773982447,
-                        "LWcld_err":-0.009517772751233961,
-                        "LWcld_tau":-0.032595755808594966,
-                        "LWcld_tot":-0.019297340019896612,
-                        "SWcld_alt":-0.007380694511307964,
-                        "SWcld_amt":0.09020496119097582,
-                        "SWcld_err":-5.4427126783446066e-05,
-                        "SWcld_tau":0.05106334725131606,
-                        "SWcld_tot":0.13383318680420045
-                    },
-                    "LO680":{
-                        "LWcld_alt":0.0014272846896563642,
-                        "LWcld_amt":-0.014894086185269315,
-                        "LWcld_err":-1.58805420249494e-05,
-                        "LWcld_tau":0.0005272404588421376,
-                        "LWcld_tot":-0.012955436025187344,
-                        "SWcld_alt":-0.0007313469643218156,
-                        "SWcld_amt":0.07772930332441717,
-                        "SWcld_err":3.3001195066068005e-05,
-                        "SWcld_tau":0.001403721829537496,
-                        "SWcld_tot":0.0784346793846989
-                    }
-                },
-                "ocn":{
-                    "ALL":{
-                        "LWcld_alt":0.46607039286024116,
-                        "LWcld_amt":-0.10447567887732213,
-                        "LWcld_err":-0.024274715180144856,
-                        "LWcld_tau":-0.014835139680769369,
-                        "LWcld_tot":0.32248485912200475,
-                        "SWcld_alt":-0.07643543451726971,
-                        "SWcld_amt":0.2415155175154082,
-                        "SWcld_err":-0.0060726641984356865,
-                        "SWcld_tau":0.005842973115445984,
-                        "SWcld_tot":0.16485039191514878
-                    },
-                    "HI680":{
-                        "LWcld_alt":0.2247496393259475,
-                        "LWcld_amt":0.11286852229083988,
-                        "LWcld_err":0.007996663107208653,
-                        "LWcld_tau":0.05549372715933452,
-                        "LWcld_tot":0.40110855188333044,
-                        "SWcld_alt":-0.018773766492364456,
-                        "SWcld_amt":-0.18854957389325974,
-                        "SWcld_err":-0.003729253650926396,
-                        "SWcld_tau":-0.057604897914010765,
-                        "SWcld_tot":-0.2686574919505613
-                    },
-                    "LO680":{
-                        "LWcld_alt":0.008940202472323565,
-                        "LWcld_amt":-0.09169259762901456,
-                        "LWcld_err":5.785513146936911e-05,
-                        "LWcld_tau":0.0040708472638958495,
-                        "LWcld_tot":-0.07862369276132576,
-                        "SWcld_alt":-0.004227507564612994,
-                        "SWcld_amt":0.49844742755389776,
-                        "SWcld_err":3.155235513888416e-05,
-                        "SWcld_tau":-0.060743588478713605,
-                        "SWcld_tot":0.4335078838657101
-                    }
-                },
-                "ocn_asc":{
-                    "ALL":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN
-                    },
-                    "HI680":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN
-                    },
-                    "LO680":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN
-                    }
-                },
-                "ocn_dsc":{
-                    "ALL":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN
-                    },
-                    "HI680":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN
-                    },
-                    "LO680":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN
-                    }
-                }
-            },
-            "eq60":{
-                "all":{
-                    "ALL":{
-                        "LWcld_alt":0.6689716449321774,
-                        "LWcld_amt":-0.22155217234812427,
-                        "LWcld_err":-0.04480424645549308,
-                        "LWcld_tau":-0.046987771033272126,
-                        "LWcld_tot":0.355627455095288,
-                        "SWcld_alt":-0.10166037069872103,
-                        "SWcld_amt":0.42649121054607736,
-                        "SWcld_err":-0.005673316185722391,
-                        "SWcld_tau":0.026812068606296893,
-                        "SWcld_tot":0.34596959226793084
-                    },
-                    "HI680":{
-                        "LWcld_alt":0.36039192849938445,
-                        "LWcld_amt":0.07000466394727528,
-                        "LWcld_err":-0.0013689922622235182,
-                        "LWcld_tau":0.027617312995492523,
-                        "LWcld_tot":0.4566449131799288,
-                        "SWcld_alt":-0.02941121223712483,
-                        "SWcld_amt":-0.1491613657102287,
-                        "SWcld_err":-0.003719424362847524,
-                        "SWcld_tau":-0.008363706342721143,
-                        "SWcld_tot":-0.19065570865292222
-                    },
-                    "LO680":{
-                        "LWcld_alt":0.012903252099575513,
-                        "LWcld_amt":-0.12228410060353735,
-                        "LWcld_err":-0.00014269679832746405,
-                        "LWcld_tau":0.008506081664040028,
-                        "LWcld_tot":-0.10101745808464085,
-                        "SWcld_alt":-0.00626434393624879,
-                        "SWcld_amt":0.6458616281126209,
-                        "SWcld_err":0.0001403472425791947,
-                        "SWcld_tau":-0.10311232488856698,
-                        "SWcld_tot":0.5366253065303844
-                    }
-                },
-                "lnd":{
-                    "ALL":{
-                        "LWcld_alt":0.15055278584505713,
-                        "LWcld_amt":-0.10897490975578596,
-                        "LWcld_err":-0.017187884086465374,
-                        "LWcld_tau":-0.0401242273133022,
-                        "LWcld_tot":-0.015734235310496423,
-                        "SWcld_alt":-0.01709302114476498,
-                        "SWcld_amt":0.16656531099605035,
-                        "SWcld_err":0.00011343765657134393,
-                        "SWcld_tau":0.06743630665717056,
-                        "SWcld_tot":0.2170220341650273
-                    },
-                    "HI680":{
-                        "LWcld_alt":0.10965173951123226,
-                        "LWcld_amt":-0.07048650875578473,
-                        "LWcld_err":-0.009694749968436556,
-                        "LWcld_tau":-0.03041126611620765,
-                        "LWcld_tot":-0.00094078532919667,
-                        "SWcld_alt":-0.008710278092809864,
-                        "SWcld_amt":0.0888503163810726,
-                        "SWcld_err":2.2741450492458243e-05,
-                        "SWcld_tau":0.05000851806417472,
-                        "SWcld_tot":0.13017129780292994
-                    },
-                    "LO680":{
-                        "LWcld_alt":0.0014925110468838787,
-                        "LWcld_amt":-0.017478303614111085,
-                        "LWcld_err":-4.983003576154037e-05,
-                        "LWcld_tau":0.0012421670680805791,
-                        "LWcld_tot":-0.014793449981299752,
-                        "SWcld_alt":-0.0007263435700869065,
-                        "SWcld_amt":0.09052782696487909,
-                        "SWcld_err":3.402323509257272e-05,
-                        "SWcld_tau":-0.0029847646582560807,
-                        "SWcld_tot":0.08685074197162868
-                    }
-                },
-                "ocn":{
-                    "ALL":{
-                        "LWcld_alt":0.5184188590871203,
-                        "LWcld_amt":-0.1125772625923383,
-                        "LWcld_err":-0.027616362369027706,
-                        "LWcld_tau":-0.006863543719969921,
-                        "LWcld_tot":0.3713616904057844,
-                        "SWcld_alt":-0.08456734955395602,
-                        "SWcld_amt":0.25992589955002704,
-                        "SWcld_err":-0.005786753842293734,
-                        "SWcld_tau":-0.040624238050873666,
-                        "SWcld_tot":0.1289475581029036
-                    },
-                    "HI680":{
-                        "LWcld_alt":0.25074018898815226,
-                        "LWcld_amt":0.14049117270306002,
-                        "LWcld_err":0.00832575770621304,
-                        "LWcld_tau":0.058028579111700174,
-                        "LWcld_tot":0.4575856985091254,
-                        "SWcld_alt":-0.020700934144314967,
-                        "SWcld_amt":-0.2380116820913013,
-                        "SWcld_err":-0.003742165813339982,
-                        "SWcld_tau":-0.05837222440689587,
-                        "SWcld_tot":-0.3208270064558521
-                    },
-                    "LO680":{
-                        "LWcld_alt":0.011410741052691636,
-                        "LWcld_amt":-0.10480579698942626,
-                        "LWcld_err":-9.286676256592364e-05,
-                        "LWcld_tau":0.007263914595959447,
-                        "LWcld_tot":-0.08622400810334109,
-                        "SWcld_alt":-0.0055380003661618825,
-                        "SWcld_amt":0.5553338011477418,
-                        "SWcld_err":0.00010632400748662194,
-                        "SWcld_tau":-0.10012756023031089,
-                        "SWcld_tot":0.44977456455875564
-                    }
-                },
-                "ocn_asc":{
-                    "ALL":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN
-                    },
-                    "HI680":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN
-                    },
-                    "LO680":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN
-                    }
-                },
-                "ocn_dsc":{
-                    "ALL":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN
-                    },
-                    "HI680":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN
-                    },
-                    "LO680":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN
-                    }
-                }
-            },
-            "eq70":{
-                "all":{
-                    "ALL":{
-                        "LWcld_alt":0.71882642162645,
-                        "LWcld_amt":-0.22426339453814056,
-                        "LWcld_err":-0.044388275515115844,
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-                        "SWcld_alt":NaN,
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-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
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-                        "LWcld_amt":NaN,
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-                        "LWcld_tau":NaN,
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-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN
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-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN
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-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "SWcld_alt":NaN,
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-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN
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-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN
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-            "eq80":{
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-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "SWcld_alt":NaN,
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-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN
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-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN
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-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "SWcld_alt":NaN,
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-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN
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-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "SWcld_alt":NaN,
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-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
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-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "SWcld_alt":NaN,
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-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN
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-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "SWcld_alt":NaN,
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-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN
-                    }
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-                        "SWcld_tot":0.45683018790284385
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-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN
-                    },
-                    "HI680":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN
-                    },
-                    "LO680":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN
-                    }
-                },
-                "ocn_dsc":{
-                    "ALL":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN
-                    },
-                    "HI680":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN
-                    },
-                    "LO680":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN
-                    }
-                }
-            }
-        }
-    },
-    "GFDL-CM4":{
-        "r1i1p1f1":{
-            "Arctic":{
-                "all":{
-                    "ALL":{
-                        "LWcld_alt":0.006676728084196049,
-                        "LWcld_amt":0.002103905255783731,
-                        "LWcld_err":0.0012007852268920381,
-                        "LWcld_tau":0.008951370845159947,
-                        "LWcld_tot":0.018932789412031765,
-                        "SWcld_alt":-0.0015932486178732554,
-                        "SWcld_amt":-0.003734889127363727,
-                        "SWcld_err":-0.00021113233689811235,
-                        "SWcld_tau":-0.013246447494686989,
-                        "SWcld_tot":-0.018785717576822084
-                    },
-                    "HI680":{
-                        "LWcld_alt":0.0018899048283239784,
-                        "LWcld_amt":0.006539446013101064,
-                        "LWcld_err":0.0008854577205130015,
-                        "LWcld_tau":0.00880564798637113,
-                        "LWcld_tot":0.018120456548309172,
-                        "SWcld_alt":-0.00010209065720708566,
-                        "SWcld_amt":-0.013358149028889195,
-                        "SWcld_err":-0.00011066351207103315,
-                        "SWcld_tau":-0.00838056006003731,
-                        "SWcld_tot":-0.021951463258204625
-                    },
-                    "LO680":{
-                        "LWcld_alt":0.0005862236668125492,
-                        "LWcld_amt":-0.0007904338006561796,
-                        "LWcld_err":9.28607702310093e-05,
-                        "LWcld_tau":0.0009236775545062567,
-                        "LWcld_tot":0.0008123328637225903,
-                        "SWcld_alt":-0.0002935988739342588,
-                        "SWcld_amt":0.008061969558182879,
-                        "SWcld_err":-2.002911362221532e-05,
-                        "SWcld_tau":-0.00458259434770296,
-                        "SWcld_tot":0.003165747222923444
-                    }
-                },
-                "lnd":{
-                    "ALL":{
-                        "LWcld_alt":0.0011362380705512304,
-                        "LWcld_amt":0.0007887781361357589,
-                        "LWcld_err":0.0002886746436412647,
-                        "LWcld_tau":0.003199792143267705,
-                        "LWcld_tot":0.00541348299359596,
-                        "SWcld_alt":-0.00022761161657850837,
-                        "SWcld_amt":-0.0011010085606911726,
-                        "SWcld_err":-5.664937065143081e-05,
-                        "SWcld_tau":-0.003345941673083595,
-                        "SWcld_tot":-0.0047312112210047075
-                    },
-                    "HI680":{
-                        "LWcld_alt":0.0006130703230711917,
-                        "LWcld_amt":0.0011812538858031669,
-                        "LWcld_err":0.0002446582667987333,
-                        "LWcld_tau":0.003080940763832385,
-                        "LWcld_tot":0.005119923239505477,
-                        "SWcld_alt":-5.0697743562310484e-05,
-                        "SWcld_amt":-0.0018649188001932861,
-                        "SWcld_err":-2.5999308372562616e-05,
-                        "SWcld_tau":-0.002386251145568065,
-                        "SWcld_tot":-0.004327866997696225
-                    },
-                    "LO680":{
-                        "LWcld_alt":5.913210219800514e-05,
-                        "LWcld_amt":-3.4305211373293296e-05,
-                        "LWcld_err":1.407792401697454e-05,
-                        "LWcld_tau":0.0002546498416172086,
-                        "LWcld_tot":0.0002935597540904822,
-                        "SWcld_alt":-3.0901566664502276e-05,
-                        "SWcld_amt":0.0006042174740066511,
-                        "SWcld_err":-5.798953873812073e-06,
-                        "SWcld_tau":-0.0009708594950958304,
-                        "SWcld_tot":-0.00040334254162749367
-                    }
-                },
-                "ocn":{
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-                        "LWcld_alt":0.006147465294026277,
-                        "LWcld_amt":0.0015063912338101294,
-                        "LWcld_err":0.0010212728766045955,
-                        "LWcld_tau":0.0065653396878158745,
-                        "LWcld_tot":0.015240469092256875,
-                        "SWcld_alt":-0.0015104777847377703,
-                        "SWcld_amt":-0.002973415941887438,
-                        "SWcld_err":-0.0001736768150556009,
-                        "SWcld_tau":-0.011104728321120394,
-                        "SWcld_tot":-0.015762298862801203
-                    },
-                    "HI680":{
-                        "LWcld_alt":0.0014486440351004213,
-                        "LWcld_amt":0.005952687219397995,
-                        "LWcld_err":0.0007212956101245408,
-                        "LWcld_tau":0.006525220675845211,
-                        "LWcld_tot":0.014647847540468172,
-                        "SWcld_alt":-6.0673882481782955e-05,
-                        "SWcld_amt":-0.01270760741314038,
-                        "SWcld_err":-9.47245229776554e-05,
-                        "SWcld_tau":-0.006756178010836274,
-                        "SWcld_tot":-0.01961918382943609
-                    },
-                    "LO680":{
-                        "LWcld_alt":0.0005803846252338665,
-                        "LWcld_amt":-0.0008279862075243574,
-                        "LWcld_err":8.72247344168538e-05,
-                        "LWcld_tau":0.0007529983996623443,
-                        "LWcld_tot":0.0005926215517887072,
-                        "SWcld_alt":-0.0002893881139910528,
-                        "SWcld_amt":0.008190658407647398,
-                        "SWcld_err":-1.605098825951373e-05,
-                        "SWcld_tau":-0.0040283343387619444,
-                        "SWcld_tot":0.003856884966634888
-                    }
-                },
-                "ocn_asc":{
-                    "ALL":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN
-                    },
-                    "HI680":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN
-                    },
-                    "LO680":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN
-                    }
-                },
-                "ocn_dsc":{
-                    "ALL":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN
-                    },
-                    "HI680":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN
-                    },
-                    "LO680":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN
-                    }
-                }
-            },
-            "eq10":{
-                "all":{
-                    "ALL":{
-                        "LWcld_alt":0.09327933914433954,
-                        "LWcld_amt":-0.0650350930322889,
-                        "LWcld_err":0.03600019314528009,
-                        "LWcld_tau":0.03275655376968748,
-                        "LWcld_tot":0.09700099302701824,
-                        "SWcld_alt":-0.01559003469437491,
-                        "SWcld_amt":0.08933971646159694,
-                        "SWcld_err":-0.006363661283726796,
-                        "SWcld_tau":-0.07793400320507814,
-                        "SWcld_tot":-0.010547982721582893
-                    },
-                    "HI680":{
-                        "LWcld_alt":0.038838676229096515,
-                        "LWcld_amt":-0.027630626601239616,
-                        "LWcld_err":0.025188299449758006,
-                        "LWcld_tau":0.06768863225393379,
-                        "LWcld_tot":0.10408498133154871,
-                        "SWcld_alt":-0.0027584313955638985,
-                        "SWcld_amt":0.033340210826911514,
-                        "SWcld_err":-0.0025282016180908262,
-                        "SWcld_tau":-0.12131069337019929,
-                        "SWcld_tot":-0.0932571155569425
-                    },
-                    "LO680":{
-                        "LWcld_alt":0.0022864787216531983,
-                        "LWcld_amt":-0.009121052810738906,
-                        "LWcld_err":5.851990875653726e-05,
-                        "LWcld_tau":-0.0003079341242013067,
-                        "LWcld_tot":-0.0070839883045304775,
-                        "SWcld_alt":-0.0011959471498466255,
-                        "SWcld_amt":0.08059361853768907,
-                        "SWcld_err":-0.00011284112887694109,
-                        "SWcld_tau":0.003424302576394093,
-                        "SWcld_tot":0.08270913283535959
-                    }
-                },
-                "lnd":{
-                    "ALL":{
-                        "LWcld_alt":0.013647093563663781,
-                        "LWcld_amt":-0.0462572363660237,
-                        "LWcld_err":0.001131339550485223,
-                        "LWcld_tau":-0.006662980513487856,
-                        "LWcld_tot":-0.03814178376536255,
-                        "SWcld_alt":-0.0009246327046597003,
-                        "SWcld_amt":0.060619683344561,
-                        "SWcld_err":-3.55162147083709e-05,
-                        "SWcld_tau":-0.00035531150326724496,
-                        "SWcld_tot":0.05930422292192567
-                    },
-                    "HI680":{
-                        "LWcld_alt":0.009844168118874334,
-                        "LWcld_amt":-0.04315205023997939,
-                        "LWcld_err":0.001101813528060698,
-                        "LWcld_tau":-0.0049126299899999466,
-                        "LWcld_tot":-0.037118698583044306,
-                        "SWcld_alt":-0.00012112244684475248,
-                        "SWcld_amt":0.05106908947538888,
-                        "SWcld_err":4.8438218640108554e-05,
-                        "SWcld_tau":-0.002223089095983366,
-                        "SWcld_tot":0.048773316151200875
-                    },
-                    "LO680":{
-                        "LWcld_alt":0.0005266946948479923,
-                        "LWcld_amt":-0.0015355164484357013,
-                        "LWcld_err":-1.1544214193355596e-05,
-                        "LWcld_tau":-2.719214537179402e-06,
-                        "LWcld_tot":-0.001023085182318244,
-                        "SWcld_alt":-0.00029217731839384196,
-                        "SWcld_amt":0.011512648359134348,
-                        "SWcld_err":1.380199460055708e-05,
-                        "SWcld_tau":-0.0007033662646162498,
-                        "SWcld_tot":0.010530906770724812
-                    }
-                },
-                "ocn":{
-                    "ALL":{
-                        "LWcld_alt":0.07963224558067576,
-                        "LWcld_amt":-0.01877785666626519,
-                        "LWcld_err":0.03486885359479487,
-                        "LWcld_tau":0.03941953428317534,
-                        "LWcld_tot":0.13514277679238076,
-                        "SWcld_alt":-0.014665401989715205,
-                        "SWcld_amt":0.028720033117035956,
-                        "SWcld_err":-0.006328145069018427,
-                        "SWcld_tau":-0.07757869170181089,
-                        "SWcld_tot":-0.06985220564350857
-                    },
-                    "HI680":{
-                        "LWcld_alt":0.028994508110222177,
-                        "LWcld_amt":0.015521423638739783,
-                        "LWcld_err":0.02408648592169731,
-                        "LWcld_tau":0.07260126224393373,
-                        "LWcld_tot":0.141203679914593,
-                        "SWcld_alt":-0.002637308948719146,
-                        "SWcld_amt":-0.017728878648477355,
-                        "SWcld_err":-0.0025766398367309353,
-                        "SWcld_tau":-0.11908760427421594,
-                        "SWcld_tot":-0.14203043170814336
-                    },
-                    "LO680":{
-                        "LWcld_alt":0.0017597840268052058,
-                        "LWcld_amt":-0.007585536362303205,
-                        "LWcld_err":7.006412294989286e-05,
-                        "LWcld_tau":-0.0003052149096641273,
-                        "LWcld_tot":-0.006060903122212233,
-                        "SWcld_alt":-0.0009037698314527834,
-                        "SWcld_amt":0.06908097017855473,
-                        "SWcld_err":-0.00012664312347749815,
-                        "SWcld_tau":0.004127668841010343,
-                        "SWcld_tot":0.0721782260646348
-                    }
-                },
-                "ocn_asc":{
-                    "ALL":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN
-                    },
-                    "HI680":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN
-                    },
-                    "LO680":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN
-                    }
-                },
-                "ocn_dsc":{
-                    "ALL":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN
-                    },
-                    "HI680":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN
-                    },
-                    "LO680":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN
-                    }
-                }
-            },
-            "eq20":{
-                "all":{
-                    "ALL":{
-                        "LWcld_alt":0.1646921606453276,
-                        "LWcld_amt":-0.11981192936639938,
-                        "LWcld_err":0.06686489151825183,
-                        "LWcld_tau":0.08058062788323035,
-                        "LWcld_tot":0.19232575068041044,
-                        "SWcld_alt":-0.029193603376058582,
-                        "SWcld_amt":0.1613901049044297,
-                        "SWcld_err":-0.01171707258003802,
-                        "SWcld_tau":-0.16435650064280902,
-                        "SWcld_tot":-0.04387707169447596
-                    },
-                    "HI680":{
-                        "LWcld_alt":0.051633869100696894,
-                        "LWcld_amt":-0.03706549490353355,
-                        "LWcld_err":0.04488867996336383,
-                        "LWcld_tau":0.1525331806793744,
-                        "LWcld_tot":0.21199023483990162,
-                        "SWcld_alt":-0.0033561712070878913,
-                        "SWcld_amt":0.03587765610725601,
-                        "SWcld_err":-0.004434116900470175,
-                        "SWcld_tau":-0.25857947537843595,
-                        "SWcld_tot":-0.23049210737873804
-                    },
-                    "LO680":{
-                        "LWcld_alt":0.003937416806173163,
-                        "LWcld_amt":-0.02287192119953949,
-                        "LWcld_err":5.333405047149255e-05,
-                        "LWcld_tau":-0.0007833138165963437,
-                        "LWcld_tot":-0.019664484159491177,
-                        "SWcld_alt":-0.0019967139866237343,
-                        "SWcld_amt":0.17631385892661722,
-                        "SWcld_err":-0.0001094431363922344,
-                        "SWcld_tau":0.012407333880660829,
-                        "SWcld_tot":0.18661503568426208
-                    }
-                },
-                "lnd":{
-                    "ALL":{
-                        "LWcld_alt":0.026325622250376976,
-                        "LWcld_amt":-0.08143222123933166,
-                        "LWcld_err":0.003970100744073639,
-                        "LWcld_tau":-0.0029468830026627493,
-                        "LWcld_tot":-0.0540833812475438,
-                        "SWcld_alt":-0.002484888643115192,
-                        "SWcld_amt":0.10414504766278261,
-                        "SWcld_err":-0.00047073176106819783,
-                        "SWcld_tau":-0.0028667818122808,
-                        "SWcld_tot":0.09832264544631841
-                    },
-                    "HI680":{
-                        "LWcld_alt":0.01578972508920246,
-                        "LWcld_amt":-0.07227760024717941,
-                        "LWcld_err":0.003261360639188924,
-                        "LWcld_tau":0.0019634070010470686,
-                        "LWcld_tot":-0.05126310751774097,
-                        "SWcld_alt":-0.0004007413107087661,
-                        "SWcld_amt":0.08333674263408615,
-                        "SWcld_err":-0.00011715797936168638,
-                        "SWcld_tau":-0.0078878823114799,
-                        "SWcld_tot":0.0749309610325358
-                    },
-                    "LO680":{
-                        "LWcld_alt":0.0008082624674606436,
-                        "LWcld_amt":-0.00361012061845672,
-                        "LWcld_err":-1.904390135636018e-05,
-                        "LWcld_tau":6.283225496083801e-07,
-                        "LWcld_tot":-0.002820273729802829,
-                        "SWcld_alt":-0.00043616340478432107,
-                        "SWcld_amt":0.024944395119291723,
-                        "SWcld_err":2.7031975841510533e-05,
-                        "SWcld_tau":-0.001143579276566298,
-                        "SWcld_tot":0.023391684413782615
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-                "ocn":{
-                    "ALL":{
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-                        "LWcld_amt":-0.038379708127067695,
-                        "LWcld_err":0.06289479077417819,
-                        "LWcld_tau":0.0835275108858931,
-                        "LWcld_tot":0.24640913192795422,
-                        "SWcld_alt":-0.02670871473294339,
-                        "SWcld_amt":0.05724505724164708,
-                        "SWcld_err":-0.011246340818969823,
-                        "SWcld_tau":-0.16148971883052823,
-                        "SWcld_tot":-0.14219971714079438
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-                    "HI680":{
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-                        "LWcld_amt":0.03521210534364587,
-                        "LWcld_err":0.04162731932417491,
-                        "LWcld_tau":0.15056977367832736,
-                        "LWcld_tot":0.26325334235764253,
-                        "SWcld_alt":-0.002955429896379125,
-                        "SWcld_amt":-0.047459086526830147,
-                        "SWcld_err":-0.00431695892110849,
-                        "SWcld_tau":-0.25069159306695604,
-                        "SWcld_tot":-0.3054230684112738
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-                    "LO680":{
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-                        "LWcld_amt":-0.019261800581082768,
-                        "LWcld_err":7.237795182785273e-05,
-                        "LWcld_tau":-0.000783942139145952,
-                        "LWcld_tot":-0.01684421042968835,
-                        "SWcld_alt":-0.0015605505818394134,
-                        "SWcld_amt":0.1513694638073255,
-                        "SWcld_err":-0.00013647511223374495,
-                        "SWcld_tau":0.013550913157227127,
-                        "SWcld_tot":0.16322335127047946
-                    }
-                },
-                "ocn_asc":{
-                    "ALL":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN
-                    },
-                    "HI680":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN
-                    },
-                    "LO680":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN
-                    }
-                },
-                "ocn_dsc":{
-                    "ALL":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN
-                    },
-                    "HI680":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN
-                    },
-                    "LO680":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN
-                    }
-                }
-            },
-            "eq30":{
-                "all":{
-                    "ALL":{
-                        "LWcld_alt":0.2152838041951176,
-                        "LWcld_amt":-0.1610589268580338,
-                        "LWcld_err":0.07975716612807542,
-                        "LWcld_tau":0.09946558483565016,
-                        "LWcld_tot":0.23344762830080934,
-                        "SWcld_alt":-0.03965695694636125,
-                        "SWcld_amt":0.21908970304615838,
-                        "SWcld_err":-0.013880775316067928,
-                        "SWcld_tau":-0.21908763588147517,
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-                        "LWcld_err":0.05459558063172099,
-                        "LWcld_tau":0.19492765856615543,
-                        "LWcld_tot":0.26687666550670225,
-                        "SWcld_alt":-0.0011926308924924051,
-                        "SWcld_amt":0.012538901287508382,
-                        "SWcld_err":-0.004697166302224584,
-                        "SWcld_tau":-0.3411542376029473,
-                        "SWcld_tot":-0.33450513351015587
-                    },
-                    "LO680":{
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-                        "LWcld_amt":-0.03880980697731724,
-                        "LWcld_err":0.00016829333305721334,
-                        "LWcld_tau":-0.0008397028523269598,
-                        "LWcld_tot":-0.03342903720589293,
-                        "SWcld_alt":-0.0029087028894399483,
-                        "SWcld_amt":0.2736589765469356,
-                        "SWcld_err":-0.00020797743965187323,
-                        "SWcld_tau":0.010427223498667391,
-                        "SWcld_tot":0.2809695197165112
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-                "lnd":{
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-                        "LWcld_amt":-0.10429776902604569,
-                        "LWcld_err":0.0054184090110507835,
-                        "LWcld_tau":-5.6705490902357027e-05,
-                        "LWcld_tot":-0.06549243898312687,
-                        "SWcld_alt":-0.003187190236660841,
-                        "SWcld_amt":0.13756143683766878,
-                        "SWcld_err":-0.0006718086966751258,
-                        "SWcld_tau":-0.007045423872817575,
-                        "SWcld_tot":0.12665701403151525
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-                        "LWcld_err":0.004632831254589273,
-                        "LWcld_tau":0.00608044038783851,
-                        "LWcld_tot":-0.06105720179412192,
-                        "SWcld_alt":-0.0005143924306070107,
-                        "SWcld_amt":0.10705924547627087,
-                        "SWcld_err":-0.00021324012723569413,
-                        "SWcld_tau":-0.013807569475808253,
-                        "SWcld_tot":0.0925240434426199
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-                    "LO680":{
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-                        "LWcld_err":-2.3390732282544516e-05,
-                        "LWcld_tau":-2.909958642863681e-06,
-                        "LWcld_tot":-0.004435237189004944,
-                        "SWcld_alt":-0.0005871426514893284,
-                        "SWcld_amt":0.035633402149409694,
-                        "SWcld_err":3.726805448933021e-05,
-                        "SWcld_tau":-0.0009505056594131402,
-                        "SWcld_tot":0.03413302189299655
-                    }
-                },
-                "ocn":{
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-                        "LWcld_amt":-0.056761157831988145,
-                        "LWcld_err":0.07433875711702465,
-                        "LWcld_tau":0.09952229032655252,
-                        "LWcld_tot":0.29894006728393624,
-                        "SWcld_alt":-0.03646976670970042,
-                        "SWcld_amt":0.08152826620848959,
-                        "SWcld_err":-0.013208966619392798,
-                        "SWcld_tau":-0.21204221200865758,
-                        "SWcld_tot":-0.1801926791292612
-                    },
-                    "HI680":{
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-                        "LWcld_amt":0.06358834790403364,
-                        "LWcld_err":0.04996274937713172,
-                        "LWcld_tau":0.18884721817831696,
-                        "LWcld_tot":0.3279338673008242,
-                        "SWcld_alt":-0.0006782384618853944,
-                        "SWcld_amt":-0.09452034418876248,
-                        "SWcld_err":-0.00448392617498889,
-                        "SWcld_tau":-0.3273466681271391,
-                        "SWcld_tot":-0.42702917695277587
-                    },
-                    "LO680":{
-                        "LWcld_alt":0.004939775808247374,
-                        "LWcld_amt":-0.03328846699679102,
-                        "LWcld_err":0.00019168406533975785,
-                        "LWcld_tau":-0.0008367928936840958,
-                        "LWcld_tot":-0.028993800016887988,
-                        "SWcld_alt":-0.00232156023795062,
-                        "SWcld_amt":0.23802557439752592,
-                        "SWcld_err":-0.00024524549414120343,
-                        "SWcld_tau":0.011377729158080531,
-                        "SWcld_tot":0.24683649782351466
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-                },
-                "ocn_asc":{
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-                        "LWcld_alt":0.05926732130341572,
-                        "LWcld_amt":0.008305089794138208,
-                        "LWcld_err":0.04505627228253808,
-                        "LWcld_tau":0.09136111457240564,
-                        "LWcld_tot":0.20398979795249764,
-                        "SWcld_alt":-0.008397610531053777,
-                        "SWcld_amt":-0.023260220569505743,
-                        "SWcld_err":-0.006586158414860402,
-                        "SWcld_tau":-0.22588972860723566,
-                        "SWcld_tot":-0.26413371812265557
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-                    "HI680":{
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-                        "LWcld_amt":0.03700304505969986,
-                        "LWcld_err":0.029905356537851698,
-                        "LWcld_tau":0.11417888580791036,
-                        "LWcld_tot":0.20553152618948778,
-                        "SWcld_alt":-0.0006528715438309244,
-                        "SWcld_amt":-0.043928374919534545,
-                        "SWcld_err":-0.0017650794304620888,
-                        "SWcld_tau":-0.24047800489475088,
-                        "SWcld_tot":-0.2868243307885784
-                    },
-                    "LO680":{
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-                        "LWcld_amt":-0.0033592630281485507,
-                        "LWcld_err":3.8211613891500036e-05,
-                        "LWcld_tau":0.00014073065161065602,
-                        "LWcld_tot":-0.0015417282369901263,
-                        "SWcld_alt":-0.0008399756469660673,
-                        "SWcld_amt":0.030532795280808215,
-                        "SWcld_err":-9.24322111533308e-05,
-                        "SWcld_tau":-0.006909774756765988,
-                        "SWcld_tot":0.022690612665922833
-                    }
-                },
-                "ocn_dsc":{
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-                        "LWcld_alt":0.037400141711200666,
-                        "LWcld_amt":0.0008092903900221871,
-                        "LWcld_err":0.03777916784455769,
-                        "LWcld_tau":0.02485725216786548,
-                        "LWcld_tot":0.10084585211364604,
-                        "SWcld_alt":-0.008677756398539384,
-                        "SWcld_amt":0.09570534895617476,
-                        "SWcld_err":-0.007360721530022891,
-                        "SWcld_tau":-0.02493546908071713,
-                        "SWcld_tot":0.05473140194689535
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-                    "HI680":{
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-                        "LWcld_amt":0.0670434914156232,
-                        "LWcld_err":0.0040012375873981435,
-                        "LWcld_tau":0.08015413748330119,
-                        "LWcld_tot":0.12877116761987892,
-                        "SWcld_alt":0.004631499999225272,
-                        "SWcld_amt":-0.0671637174388862,
-                        "SWcld_err":0.0024699851509051465,
-                        "SWcld_tau":-0.09170250856521443,
-                        "SWcld_tot":-0.1517647408539702
-                    },
-                    "LO680":{
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-                        "LWcld_amt":-0.03383656198652868,
-                        "LWcld_err":6.229529508672274e-05,
-                        "LWcld_tau":-0.0006364105775099153,
-                        "LWcld_tot":-0.027925315506232867,
-                        "SWcld_alt":-0.003028403872404609,
-                        "SWcld_amt":0.19329533815696862,
-                        "SWcld_err":0.00010988315732962375,
-                        "SWcld_tau":0.016119325358971967,
-                        "SWcld_tot":0.20649614280086556
-                    }
-                }
-            },
-            "eq40":{
-                "all":{
-                    "ALL":{
-                        "LWcld_alt":0.25009705516234454,
-                        "LWcld_amt":-0.21693522470505058,
-                        "LWcld_err":0.0929777933613918,
-                        "LWcld_tau":0.11312289706454033,
-                        "LWcld_tot":0.239262520883226,
-                        "SWcld_alt":-0.04560606305156247,
-                        "SWcld_amt":0.30962981503353254,
-                        "SWcld_err":-0.016163925786447483,
-                        "SWcld_tau":-0.25953305319863545,
-                        "SWcld_tot":-0.01167322700311283
-                    },
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-                        "LWcld_amt":-0.06271935247616416,
-                        "LWcld_err":0.06241969843448731,
-                        "LWcld_tau":0.21971596882881714,
-                        "LWcld_tot":0.27750236339351775,
-                        "SWcld_alt":-0.0015991162516603094,
-                        "SWcld_amt":0.055532395909379174,
-                        "SWcld_err":-0.005060840033754683,
-                        "SWcld_tau":-0.38869183504756905,
-                        "SWcld_tot":-0.33981939542360484
-                    },
-                    "LO680":{
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-                        "LWcld_amt":-0.04734521711491709,
-                        "LWcld_err":0.000364286862314313,
-                        "LWcld_tau":-0.0007525211216128554,
-                        "LWcld_tot":-0.03823984251029169,
-                        "SWcld_alt":-0.004363300473305196,
-                        "SWcld_amt":0.32466680291570105,
-                        "SWcld_err":-0.00035108442977273296,
-                        "SWcld_tau":0.00819383813023289,
-                        "SWcld_tot":0.32814625614285603
-                    }
-                },
-                "lnd":{
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-                        "LWcld_alt":0.0441869878301818,
-                        "LWcld_amt":-0.12627169742046496,
-                        "LWcld_err":0.006520003595980273,
-                        "LWcld_tau":0.0013842283328068274,
-                        "LWcld_tot":-0.07418047766149603,
-                        "SWcld_alt":-0.00433278187883781,
-                        "SWcld_amt":0.16398775409010763,
-                        "SWcld_err":-0.0008987952048603341,
-                        "SWcld_tau":-0.010246193438408087,
-                        "SWcld_tot":0.1485099835680014
-                    },
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-                        "LWcld_amt":-0.11033629020639052,
-                        "LWcld_err":0.005733438884246438,
-                        "LWcld_tau":0.008359351688532891,
-                        "LWcld_tot":-0.06823246318285495,
-                        "SWcld_alt":-0.0012100662940075925,
-                        "SWcld_amt":0.12639802742601622,
-                        "SWcld_err":-0.0003642394632722592,
-                        "SWcld_tau":-0.018307901235906653,
-                        "SWcld_tot":0.10651582043282974
-                    },
-                    "LO680":{
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-                        "LWcld_amt":-0.007159879256852588,
-                        "LWcld_err":-1.800877401042636e-05,
-                        "LWcld_tau":3.3623726303276405e-05,
-                        "LWcld_tot":-0.005948014478641079,
-                        "SWcld_alt":-0.0006299683021864603,
-                        "SWcld_amt":0.04352049945130018,
-                        "SWcld_err":3.419904657786481e-05,
-                        "SWcld_tau":-0.0009304793381559442,
-                        "SWcld_tot":0.041994250857535644
-                    }
-                },
-                "ocn":{
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-                        "LWcld_amt":-0.09066352728458564,
-                        "LWcld_err":0.08645778976541152,
-                        "LWcld_tau":0.1117386687317335,
-                        "LWcld_tot":0.3134429985447221,
-                        "SWcld_alt":-0.041273281172724656,
-                        "SWcld_amt":0.1456420609434249,
-                        "SWcld_err":-0.015265130581587147,
-                        "SWcld_tau":-0.24928685976022735,
-                        "SWcld_tot":-0.16018321057111423
-                    },
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-                        "LWcld_amt":0.04761693773022635,
-                        "LWcld_err":0.056686259550240876,
-                        "LWcld_tau":0.21135661714028417,
-                        "LWcld_tot":0.3457348265763727,
-                        "SWcld_alt":-0.00038904995765271676,
-                        "SWcld_amt":-0.07086563151663706,
-                        "SWcld_err":-0.004696600570482424,
-                        "SWcld_tau":-0.3703839338116624,
-                        "SWcld_tot":-0.44633521585643465
-                    },
-                    "LO680":{
-                        "LWcld_alt":0.008297359038005285,
-                        "LWcld_amt":-0.040185337858064496,
-                        "LWcld_err":0.00038229563632473937,
-                        "LWcld_tau":-0.0007861448479161317,
-                        "LWcld_tot":-0.0322918280316506,
-                        "SWcld_alt":-0.0037333321711187357,
-                        "SWcld_amt":0.28114630346440084,
-                        "SWcld_err":-0.0003852834763505978,
-                        "SWcld_tau":0.009124317468388835,
-                        "SWcld_tot":0.28615200528532037
-                    }
-                },
-                "ocn_asc":{
-                    "ALL":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN
-                    },
-                    "HI680":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN
-                    },
-                    "LO680":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN
-                    }
-                },
-                "ocn_dsc":{
-                    "ALL":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN
-                    },
-                    "HI680":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN
-                    },
-                    "LO680":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN
-                    }
-                }
-            },
-            "eq50":{
-                "all":{
-                    "ALL":{
-                        "LWcld_alt":0.2922774971334411,
-                        "LWcld_amt":-0.2535443044271794,
-                        "LWcld_err":0.09961237202289153,
-                        "LWcld_tau":0.12868508595809452,
-                        "LWcld_tot":0.26703065068724774,
-                        "SWcld_alt":-0.050496679615559266,
-                        "SWcld_amt":0.3872471572847181,
-                        "SWcld_err":-0.01731850916467039,
-                        "SWcld_tau":-0.29535797530600344,
-                        "SWcld_tot":0.02407399319848504
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-                    "HI680":{
-                        "LWcld_alt":0.0862413672875797,
-                        "LWcld_amt":-0.0830499893405217,
-                        "LWcld_err":0.06626789045604614,
-                        "LWcld_tau":0.24061205701906208,
-                        "LWcld_tot":0.31007132542216626,
-                        "SWcld_alt":-0.003821736037217923,
-                        "SWcld_amt":0.09056864619725331,
-                        "SWcld_err":-0.005437247252138045,
-                        "SWcld_tau":-0.4264471970448293,
-                        "SWcld_tot":-0.3451375341369319
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-                    "LO680":{
-                        "LWcld_alt":0.011358763598024865,
-                        "LWcld_amt":-0.05513643731557111,
-                        "LWcld_err":0.0005938200516375059,
-                        "LWcld_tau":0.00014316661977360244,
-                        "LWcld_tot":-0.043040674734918495,
-                        "SWcld_alt":-0.005261844788700461,
-                        "SWcld_amt":0.36736175703109214,
-                        "SWcld_err":-0.0004473312563604802,
-                        "SWcld_tau":0.007559034071749743,
-                        "SWcld_tot":0.36921161505778094
-                    }
-                },
-                "lnd":{
-                    "ALL":{
-                        "LWcld_alt":0.05553933265922342,
-                        "LWcld_amt":-0.13812837376714512,
-                        "LWcld_err":0.006648724145619446,
-                        "LWcld_tau":0.006265085356935789,
-                        "LWcld_tot":-0.06967523160536647,
-                        "SWcld_alt":-0.005491619984026056,
-                        "SWcld_amt":0.18646880135913177,
-                        "SWcld_err":-0.0009814776685526038,
-                        "SWcld_tau":-0.019277692234591415,
-                        "SWcld_tot":0.16071801147196169
-                    },
-                    "HI680":{
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-                        "LWcld_amt":-0.11886285416603415,
-                        "LWcld_err":0.0062500336896495505,
-                        "LWcld_tau":0.013445924464028121,
-                        "LWcld_tot":-0.06269059247668765,
-                        "SWcld_alt":-0.001952314432666524,
-                        "SWcld_amt":0.1407235147654359,
-                        "SWcld_err":-0.0004812685256581006,
-                        "SWcld_tau":-0.026462572248531418,
-                        "SWcld_tot":0.1118273595585799
-                    },
-                    "LO680":{
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-                        "LWcld_amt":-0.008675737940679806,
-                        "LWcld_err":4.138653353535311e-06,
-                        "LWcld_tau":0.0005064751217318332,
-                        "LWcld_tot":-0.006984639128678816,
-                        "SWcld_alt":-0.0006214938121709752,
-                        "SWcld_amt":0.05208460371507812,
-                        "SWcld_err":2.345293853917119e-05,
-                        "SWcld_tau":-0.002595823205700515,
-                        "SWcld_tot":0.0488907396357458
-                    }
-                },
-                "ocn":{
-                    "ALL":{
-                        "LWcld_alt":0.23673816447421767,
-                        "LWcld_amt":-0.11541593066003426,
-                        "LWcld_err":0.09296364787727208,
-                        "LWcld_tau":0.12242000060115871,
-                        "LWcld_tot":0.3367058822926141,
-                        "SWcld_alt":-0.04500505963153322,
-                        "SWcld_amt":0.20077835592558638,
-                        "SWcld_err":-0.016337031496117784,
-                        "SWcld_tau":-0.276080283071412,
-                        "SWcld_tot":-0.1366440182734767
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-                    "HI680":{
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-                        "LWcld_amt":0.035812864825512436,
-                        "LWcld_err":0.060017856766396586,
-                        "LWcld_tau":0.22716613255503396,
-                        "LWcld_tot":0.3727619178988539,
-                        "SWcld_alt":-0.0018694216045513983,
-                        "SWcld_amt":-0.0501548685681826,
-                        "SWcld_err":-0.0049559787264799446,
-                        "SWcld_tau":-0.39998462479629787,
-                        "SWcld_tot":-0.4569648936955118
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-                    "LO680":{
-                        "LWcld_alt":0.01017829087232588,
-                        "LWcld_amt":-0.0464606993748913,
-                        "LWcld_err":0.0005896813982839707,
-                        "LWcld_tau":-0.0003633085019582308,
-                        "LWcld_tot":-0.03605603560623968,
-                        "SWcld_alt":-0.004640350976529486,
-                        "SWcld_amt":0.31527715331601397,
-                        "SWcld_err":-0.0004707841948996514,
-                        "SWcld_tau":0.010154857277450259,
-                        "SWcld_tot":0.32032087542203513
-                    }
-                },
-                "ocn_asc":{
-                    "ALL":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN
-                    },
-                    "HI680":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN
-                    },
-                    "LO680":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN
-                    }
-                },
-                "ocn_dsc":{
-                    "ALL":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN
-                    },
-                    "HI680":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN
-                    },
-                    "LO680":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN
-                    }
-                }
-            },
-            "eq60":{
-                "all":{
-                    "ALL":{
-                        "LWcld_alt":0.3454883539020049,
-                        "LWcld_amt":-0.26515195682524745,
-                        "LWcld_err":0.10434240598892208,
-                        "LWcld_tau":0.15130352803905028,
-                        "LWcld_tot":0.33598233110472986,
-                        "SWcld_alt":-0.057519709254815976,
-                        "SWcld_amt":0.41706944291047315,
-                        "SWcld_err":-0.01784340673854121,
-                        "SWcld_tau":-0.3786165241481852,
-                        "SWcld_tot":-0.03691019723106919
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-                        "LWcld_amt":-0.06710827576520194,
-                        "LWcld_err":0.06951993761151905,
-                        "LWcld_tau":0.26673275810208114,
-                        "LWcld_tot":0.3840896912276693,
-                        "SWcld_alt":-0.006142338331005771,
-                        "SWcld_amt":0.06409725262916229,
-                        "SWcld_err":-0.005826896436012564,
-                        "SWcld_tau":-0.48252854977729087,
-                        "SWcld_tot":-0.4304005319151469
-                    },
-                    "LO680":{
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-                        "LWcld_amt":-0.0643412236221342,
-                        "LWcld_err":0.0009189486511108303,
-                        "LWcld_tau":0.0030168428727547977,
-                        "LWcld_tot":-0.048107360122939556,
-                        "SWcld_alt":-0.005698229946984085,
-                        "SWcld_amt":0.4208408171107057,
-                        "SWcld_err":-0.000497479111553358,
-                        "SWcld_tau":-0.02115468564572662,
-                        "SWcld_tot":0.39349042240644166
-                    }
-                },
-                "lnd":{
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-                        "LWcld_alt":0.06587639301499781,
-                        "LWcld_amt":-0.139723025879714,
-                        "LWcld_err":0.006890030183759146,
-                        "LWcld_tau":0.01508267657129927,
-                        "LWcld_tot":-0.05187392610965774,
-                        "SWcld_alt":-0.006580056916528048,
-                        "SWcld_amt":0.1926068953413981,
-                        "SWcld_err":-0.0010856936652346527,
-                        "SWcld_tau":-0.03286509548831896,
-                        "SWcld_tot":0.1520760492713165
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-                        "LWcld_amt":-0.1181013293918584,
-                        "LWcld_err":0.006645065802283963,
-                        "LWcld_tau":0.021919825257635518,
-                        "LWcld_tot":-0.04522891269699075,
-                        "SWcld_alt":-0.0026788931043521154,
-                        "SWcld_amt":0.14188210622130196,
-                        "SWcld_err":-0.0005957418612337674,
-                        "SWcld_tau":-0.03718681458867861,
-                        "SWcld_tot":0.10142065666703744
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-                    "LO680":{
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-                        "LWcld_amt":-0.009334874156260187,
-                        "LWcld_err":4.6826061730229756e-05,
-                        "LWcld_tau":0.001633213192172501,
-                        "LWcld_tot":-0.006645013412666981,
-                        "SWcld_alt":-0.0005441494744232793,
-                        "SWcld_amt":0.0572274033734646,
-                        "SWcld_err":2.556374755490626e-06,
-                        "SWcld_tau":-0.006030329947153821,
-                        "SWcld_tot":0.05065548032664299
-                    }
-                },
-                "ocn":{
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-                        "LWcld_alt":0.27961196088700707,
-                        "LWcld_amt":-0.12542893094553345,
-                        "LWcld_err":0.09745237580516293,
-                        "LWcld_tau":0.136220851467751,
-                        "LWcld_tot":0.38785625721438755,
-                        "SWcld_alt":-0.050939652338287934,
-                        "SWcld_amt":0.2244625475690751,
-                        "SWcld_err":-0.01675771307330656,
-                        "SWcld_tau":-0.34575142865986624,
-                        "SWcld_tot":-0.18898624650238563
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-                        "LWcld_amt":0.05099305362665644,
-                        "LWcld_err":0.06287487180923507,
-                        "LWcld_tau":0.2448129328444456,
-                        "LWcld_tot":0.4293186039246601,
-                        "SWcld_alt":-0.0034634452266536554,
-                        "SWcld_amt":-0.07778485359213966,
-                        "SWcld_err":-0.005231154574778798,
-                        "SWcld_tau":-0.4453417351886122,
-                        "SWcld_tot":-0.5318211885821843
-                    },
-                    "LO680":{
-                        "LWcld_alt":0.011288250485638538,
-                        "LWcld_amt":-0.055006349465873995,
-                        "LWcld_err":0.0008721225893806008,
-                        "LWcld_tau":0.0013836296805822968,
-                        "LWcld_tot":-0.04146234671027257,
-                        "SWcld_alt":-0.005154080472560806,
-                        "SWcld_amt":0.36361341373724115,
-                        "SWcld_err":-0.0005000354863088486,
-                        "SWcld_tau":-0.015124355698572803,
-                        "SWcld_tot":0.3428349420797987
-                    }
-                },
-                "ocn_asc":{
-                    "ALL":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN
-                    },
-                    "HI680":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN
-                    },
-                    "LO680":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN
-                    }
-                },
-                "ocn_dsc":{
-                    "ALL":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN
-                    },
-                    "HI680":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN
-                    },
-                    "LO680":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN
-                    }
-                }
-            },
-            "eq70":{
-                "all":{
-                    "ALL":{
-                        "LWcld_alt":0.37751867867096284,
-                        "LWcld_amt":-0.26522918000014956,
-                        "LWcld_err":0.10633657294270632,
-                        "LWcld_tau":0.17423767939209278,
-                        "LWcld_tot":0.3928637510056123,
-                        "SWcld_alt":-0.0619025157944165,
-                        "SWcld_amt":0.4219097404146455,
-                        "SWcld_err":-0.017949544779646453,
-                        "SWcld_tau":-0.4419235432705859,
-                        "SWcld_tot":-0.0998658634300033
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-                        "LWcld_amt":-0.05279300946583639,
-                        "LWcld_err":0.07170828976865708,
-                        "LWcld_tau":0.28945148406672044,
-                        "LWcld_tot":0.4415754435606562,
-                        "SWcld_alt":-0.0074668865379393,
-                        "SWcld_amt":0.03712920431664955,
-                        "SWcld_err":-0.006050196936221331,
-                        "SWcld_tau":-0.5198231134307585,
-                        "SWcld_tot":-0.4962109925882697
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-                    "LO680":{
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-                        "LWcld_amt":-0.0684612804119542,
-                        "LWcld_err":0.0011270553130798098,
-                        "LWcld_tau":0.005581873889385572,
-                        "LWcld_tot":-0.04871169255504382,
-                        "SWcld_alt":-0.006013501500483041,
-                        "SWcld_amt":0.45020850154297154,
-                        "SWcld_err":-0.0005319602945278078,
-                        "SWcld_tau":-0.047317822867330335,
-                        "SWcld_tot":0.39634521688063035
-                    }
-                },
-                "lnd":{
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-                        "LWcld_err":0.007033760433586701,
-                        "LWcld_tau":0.026584385893369006,
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-                        "SWcld_alt":-0.0077214730874100345,
-                        "SWcld_amt":0.19348612651535912,
-                        "SWcld_err":-0.001147374892294408,
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-                        "SWcld_tot":0.13807629760030102
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-                        "LWcld_err":0.007226461845588609,
-                        "LWcld_tau":0.032633347486909804,
-                        "LWcld_tot":-0.02384491447701563,
-                        "SWcld_alt":-0.003258368534461945,
-                        "SWcld_amt":0.137263654042836,
-                        "SWcld_err":-0.0006794290582239861,
-                        "SWcld_tau":-0.047421933699199316,
-                        "SWcld_tot":0.08590392275095073
-                    },
-                    "LO680":{
-                        "LWcld_alt":0.000944894088631631,
-                        "LWcld_amt":-0.009961667208234153,
-                        "LWcld_err":8.909392709032558e-05,
-                        "LWcld_tau":0.0027091895870477926,
-                        "LWcld_tot":-0.006218477294247765,
-                        "SWcld_alt":-0.0005019392291986216,
-                        "SWcld_amt":0.06257628407803223,
-                        "SWcld_err":-2.109527574102915e-05,
-                        "SWcld_tau":-0.009880787001378349,
-                        "SWcld_tot":0.052172462571714234
-                    }
-                },
-                "ocn":{
-                    "ALL":{
-                        "LWcld_alt":0.30260214900678134,
-                        "LWcld_amt":-0.126631112237749,
-                        "LWcld_err":0.09930281250911961,
-                        "LWcld_tau":0.14765329349872378,
-                        "LWcld_tot":0.4229271427768757,
-                        "SWcld_alt":-0.05418104270700647,
-                        "SWcld_amt":0.22842361389928637,
-                        "SWcld_err":-0.016802169887352048,
-                        "SWcld_tau":-0.3953825623352322,
-                        "SWcld_tot":-0.2379421610303043
-                    },
-                    "HI680":{
-                        "LWcld_alt":0.08202520387387169,
-                        "LWcld_amt":0.06209518966092095,
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-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
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-                        "LWcld_tau":NaN,
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-                        "SWcld_err":NaN,
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-                        "LWcld_amt":NaN,
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-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
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-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "SWcld_alt":NaN,
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-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
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-                        "LWcld_amt":NaN,
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-                        "LWcld_tau":NaN,
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-                        "SWcld_err":NaN,
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-                        "LWcld_amt":NaN,
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-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "SWcld_alt":NaN,
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-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
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-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "SWcld_alt":NaN,
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-                        "SWcld_tau":NaN,
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-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN
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-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "SWcld_alt":NaN,
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-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN
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-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "SWcld_alt":NaN,
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-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
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-                        "LWcld_amt":NaN,
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-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "SWcld_alt":NaN,
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-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
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-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "SWcld_alt":NaN,
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-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN
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-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "SWcld_alt":NaN,
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-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN
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-                        "LWcld_amt":NaN,
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-                        "LWcld_tau":NaN,
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-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
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-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "SWcld_alt":NaN,
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-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN
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-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
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-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN
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-                        "LWcld_amt":NaN,
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-                        "LWcld_tau":NaN,
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-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN
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-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN
-                    }
-                }
-            }
-        }
-    },
-    "HadGEM3-GC31-LL":{
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-                        "LWcld_err":-1.9934378985886106e-05,
-                        "LWcld_tau":0.0022270303958901046,
-                        "LWcld_tot":0.003971328141150575,
-                        "SWcld_alt":-0.0004713731837133514,
-                        "SWcld_amt":0.003376327710151958,
-                        "SWcld_err":5.6037563513256456e-05,
-                        "SWcld_tau":0.00034003383393757975,
-                        "SWcld_tot":0.003301025923889442
-                    },
-                    "HI680":{
-                        "LWcld_alt":0.0011796900885208438,
-                        "LWcld_amt":0.0007179179774341983,
-                        "LWcld_err":0.0002904155761362909,
-                        "LWcld_tau":0.0022291747688662844,
-                        "LWcld_tot":0.0044171984109576175,
-                        "SWcld_alt":-0.00014919924353210188,
-                        "SWcld_amt":-0.0006237839460673064,
-                        "SWcld_err":-1.855531067478677e-05,
-                        "SWcld_tau":-9.155264742150036e-05,
-                        "SWcld_tot":-0.0008830911476956954
-                    },
-                    "LO680":{
-                        "LWcld_alt":3.649808787340356e-05,
-                        "LWcld_amt":-0.0005981487616041891,
-                        "LWcld_err":-9.688893019070722e-06,
-                        "LWcld_tau":0.00012546822936646007,
-                        "LWcld_tot":-0.0004458702698070426,
-                        "SWcld_alt":-4.46768255114881e-06,
-                        "SWcld_amt":0.004306893596948815,
-                        "SWcld_err":6.883312679661901e-06,
-                        "SWcld_tau":-0.00012519209910521215,
-                        "SWcld_tot":0.004184117127972115
-                    }
-                },
-                "ocn":{
-                    "ALL":{
-                        "LWcld_alt":0.008437708727619174,
-                        "LWcld_amt":-0.00040260756214687194,
-                        "LWcld_err":0.0006908685052674237,
-                        "LWcld_tau":0.003374634760387599,
-                        "LWcld_tot":0.012100604431127322,
-                        "SWcld_alt":-0.0012596027620072063,
-                        "SWcld_amt":0.0021967733941452385,
-                        "SWcld_err":-7.318901064526962e-08,
-                        "SWcld_tau":-0.000998285997047584,
-                        "SWcld_tot":-6.118855392019705e-05
-                    },
-                    "HI680":{
-                        "LWcld_alt":0.0029083607101205652,
-                        "LWcld_amt":0.005552113872504121,
-                        "LWcld_err":0.0013164797918649796,
-                        "LWcld_tau":0.0033239117221165616,
-                        "LWcld_tot":0.013100866096606227,
-                        "SWcld_alt":-0.00030785454709210646,
-                        "SWcld_amt":-0.0054335981096405575,
-                        "SWcld_err":-0.00011888071973343039,
-                        "SWcld_tau":-0.0015468129275206294,
-                        "SWcld_tot":-0.0074071463039867246
-                    },
-                    "LO680":{
-                        "LWcld_alt":0.00029607415439805295,
-                        "LWcld_amt":-0.0016412113883864253,
-                        "LWcld_err":-0.00010175819499036477,
-                        "LWcld_tau":0.00044663376349983426,
-                        "LWcld_tot":-0.0010002616654789032,
-                        "SWcld_alt":-6.20288595248336e-05,
-                        "SWcld_amt":0.007331162505673589,
-                        "SWcld_err":7.942118882234155e-05,
-                        "SWcld_tau":-2.5970849045706338e-06,
-                        "SWcld_tot":0.007345957750066528
-                    }
-                },
-                "ocn_asc":{
-                    "ALL":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN
-                    },
-                    "HI680":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN
-                    },
-                    "LO680":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN
-                    }
-                },
-                "ocn_dsc":{
-                    "ALL":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN
-                    },
-                    "HI680":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN
-                    },
-                    "LO680":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN
-                    }
-                }
-            },
-            "eq10":{
-                "all":{
-                    "ALL":{
-                        "LWcld_alt":0.026944858859628537,
-                        "LWcld_amt":-0.17423022028477017,
-                        "LWcld_err":-0.01541428769537255,
-                        "LWcld_tau":-0.07216780749521333,
-                        "LWcld_tot":-0.23486745661572753,
-                        "SWcld_alt":0.002568818134869111,
-                        "SWcld_amt":0.244717589891554,
-                        "SWcld_err":0.005397727300289962,
-                        "SWcld_tau":0.09669744771143002,
-                        "SWcld_tot":0.34938158303814304
-                    },
-                    "HI680":{
-                        "LWcld_alt":0.05111024829272933,
-                        "LWcld_amt":-0.1906163345782392,
-                        "LWcld_err":0.011392749919200883,
-                        "LWcld_tau":-0.10568977917489675,
-                        "LWcld_tot":-0.23380311554120578,
-                        "SWcld_alt":-0.002715418796870521,
-                        "SWcld_amt":0.1918214923271081,
-                        "SWcld_err":-0.00035458585945696575,
-                        "SWcld_tau":0.11795275935821194,
-                        "SWcld_tot":0.3067042470289925
-                    },
-                    "LO680":{
-                        "LWcld_alt":0.004755431996822052,
-                        "LWcld_amt":-0.005271395837590706,
-                        "LWcld_err":0.0001636046175060899,
-                        "LWcld_tau":-0.0007119818512592085,
-                        "LWcld_tot":-0.0010643410745217732,
-                        "SWcld_alt":-0.0023830205943405004,
-                        "SWcld_amt":0.03378186198566707,
-                        "SWcld_err":-0.00016865463965537445,
-                        "SWcld_tau":0.011447149257479345,
-                        "SWcld_tot":0.04267733600915055
-                    }
-                },
-                "lnd":{
-                    "ALL":{
-                        "LWcld_alt":0.014518130899486078,
-                        "LWcld_amt":-0.059866989050431925,
-                        "LWcld_err":-0.0059831080976559215,
-                        "LWcld_tau":-0.03197034718773895,
-                        "LWcld_tot":-0.08330231343634074,
-                        "SWcld_alt":-0.0007098994864763358,
-                        "SWcld_amt":0.06987845408909453,
-                        "SWcld_err":0.001403249484924221,
-                        "SWcld_tau":0.03611466475795834,
-                        "SWcld_tot":0.10668646884550076
-                    },
-                    "HI680":{
-                        "LWcld_alt":0.014690976121875608,
-                        "LWcld_amt":-0.05926324386041046,
-                        "LWcld_err":-0.00036748274666891515,
-                        "LWcld_tau":-0.037803493744990264,
-                        "LWcld_tot":-0.08274324423019402,
-                        "SWcld_alt":-0.0007729722401732029,
-                        "SWcld_amt":0.0581151537855111,
-                        "SWcld_err":0.00018638282128208598,
-                        "SWcld_tau":0.03987784412602163,
-                        "SWcld_tot":0.09740640849264161
-                    },
-                    "LO680":{
-                        "LWcld_alt":0.001114538428267282,
-                        "LWcld_amt":-0.001804312342742244,
-                        "LWcld_err":2.5104555408898024e-05,
-                        "LWcld_tau":0.00010560015291935817,
-                        "LWcld_tot":-0.0005590692061467055,
-                        "SWcld_alt":-0.0005447620084975796,
-                        "SWcld_amt":0.011360966245986696,
-                        "SWcld_err":-1.544795745035184e-05,
-                        "SWcld_tau":-0.0015206959271796454,
-                        "SWcld_tot":0.009280060352859122
-                    }
-                },
-                "ocn":{
-                    "ALL":{
-                        "LWcld_alt":0.012426727960142462,
-                        "LWcld_amt":-0.11436323123433824,
-                        "LWcld_err":-0.009431179597716628,
-                        "LWcld_tau":-0.04019746030747438,
-                        "LWcld_tot":-0.15156514317938677,
-                        "SWcld_alt":0.0032787176213454473,
-                        "SWcld_amt":0.17483913580245947,
-                        "SWcld_err":0.003994477815365741,
-                        "SWcld_tau":0.06058278295347169,
-                        "SWcld_tot":0.24269511419264234
-                    },
-                    "HI680":{
-                        "LWcld_alt":0.03641927217085371,
-                        "LWcld_amt":-0.13135309071782872,
-                        "LWcld_err":0.011760232665869797,
-                        "LWcld_tau":-0.06788628542990649,
-                        "LWcld_tot":-0.1510598713110117,
-                        "SWcld_alt":-0.001942446556697318,
-                        "SWcld_amt":0.13370633854159703,
-                        "SWcld_err":-0.0005409686807390518,
-                        "SWcld_tau":0.07807491523219029,
-                        "SWcld_tot":0.20929783853635095
-                    },
-                    "LO680":{
-                        "LWcld_alt":0.003640893568554769,
-                        "LWcld_amt":-0.0034670834948484623,
-                        "LWcld_err":0.0001385000620971919,
-                        "LWcld_tau":-0.0008175820041785666,
-                        "LWcld_tot":-0.0005052718683750676,
-                        "SWcld_alt":-0.0018382585858429208,
-                        "SWcld_amt":0.02242089573968038,
-                        "SWcld_err":-0.0001532066822050226,
-                        "SWcld_tau":0.012967845184658993,
-                        "SWcld_tot":0.03339727565629142
-                    }
-                },
-                "ocn_asc":{
-                    "ALL":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN
-                    },
-                    "HI680":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN
-                    },
-                    "LO680":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN
-                    }
-                },
-                "ocn_dsc":{
-                    "ALL":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN
-                    },
-                    "HI680":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN
-                    },
-                    "LO680":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN
-                    }
-                }
-            },
-            "eq20":{
-                "all":{
-                    "ALL":{
-                        "LWcld_alt":0.03845007230978246,
-                        "LWcld_amt":-0.2966938812784781,
-                        "LWcld_err":-0.014645205097731066,
-                        "LWcld_tau":-0.08916826285682354,
-                        "LWcld_tot":-0.36205727692325024,
-                        "SWcld_alt":0.0048656268007763025,
-                        "SWcld_amt":0.42374004442536733,
-                        "SWcld_err":0.0066723291863593145,
-                        "SWcld_tau":0.13215338381680442,
-                        "SWcld_tot":0.5674313842293074
-                    },
-                    "HI680":{
-                        "LWcld_alt":0.0820941133962217,
-                        "LWcld_amt":-0.32353278459952794,
-                        "LWcld_err":0.021519195659058035,
-                        "LWcld_tau":-0.13899266122635556,
-                        "LWcld_tot":-0.3589121367706037,
-                        "SWcld_alt":-0.004463541245057725,
-                        "SWcld_amt":0.33022619101719725,
-                        "SWcld_err":-0.001072615714775542,
-                        "SWcld_tau":0.16468887514668495,
-                        "SWcld_tot":0.4893789092040488
-                    },
-                    "LO680":{
-                        "LWcld_alt":0.007591108568114365,
-                        "LWcld_amt":-0.009482755207287382,
-                        "LWcld_err":0.00014261779860337375,
-                        "LWcld_tau":-0.0013961281134335392,
-                        "LWcld_tot":-0.0031451401526465836,
-                        "SWcld_alt":-0.0037580219209170703,
-                        "SWcld_amt":0.05867001445538361,
-                        "SWcld_err":-0.00011648334824045788,
-                        "SWcld_tau":0.023257012010328332,
-                        "SWcld_tot":0.07805252119655441
-                    }
-                },
-                "lnd":{
-                    "ALL":{
-                        "LWcld_alt":0.023638947107894274,
-                        "LWcld_amt":-0.10629981752514395,
-                        "LWcld_err":-0.007148722584588069,
-                        "LWcld_tau":-0.04317121814492753,
-                        "LWcld_tot":-0.13298081114676527,
-                        "SWcld_alt":-0.0013546444524159737,
-                        "SWcld_amt":0.12264421365970762,
-                        "SWcld_err":0.001743000186314931,
-                        "SWcld_tau":0.052671657403034665,
-                        "SWcld_tot":0.17570422679664122
-                    },
-                    "HI680":{
-                        "LWcld_alt":0.02222730058112041,
-                        "LWcld_amt":-0.10349375842199564,
-                        "LWcld_err":0.00024312990272545146,
-                        "LWcld_tau":-0.050146366185044366,
-                        "LWcld_tot":-0.13116969412319415,
-                        "SWcld_alt":-0.0011222203057640653,
-                        "SWcld_amt":0.10168836581003578,
-                        "SWcld_err":0.0002179986447432437,
-                        "SWcld_tau":0.05656347337897821,
-                        "SWcld_tot":0.1573476175279932
-                    },
-                    "LO680":{
-                        "LWcld_alt":0.0015729405127814042,
-                        "LWcld_amt":-0.0035484550842024786,
-                        "LWcld_err":1.4510002436562552e-05,
-                        "LWcld_tau":0.0001498707440567785,
-                        "LWcld_tot":-0.001811117023571134,
-                        "SWcld_alt":-0.0007606440978669139,
-                        "SWcld_amt":0.02095991529741495,
-                        "SWcld_err":-3.899032693262313e-06,
-                        "SWcld_tau":-0.0018387167269107725,
-                        "SWcld_tot":0.018356655439944004
-                    }
-                },
-                "ocn":{
-                    "ALL":{
-                        "LWcld_alt":0.014811125201888181,
-                        "LWcld_amt":-0.19039406375333415,
-                        "LWcld_err":-0.0074964825131429965,
-                        "LWcld_tau":-0.045997044711896,
-                        "LWcld_tot":-0.229076465776485,
-                        "SWcld_alt":0.0062202712531922765,
-                        "SWcld_amt":0.3010958307656597,
-                        "SWcld_err":0.004929329000044383,
-                        "SWcld_tau":0.07948172641376976,
-                        "SWcld_tot":0.3917271574326662
-                    },
-                    "HI680":{
-                        "LWcld_alt":0.05986681281510131,
-                        "LWcld_amt":-0.22003902617753224,
-                        "LWcld_err":0.02127606575633258,
-                        "LWcld_tau":-0.08884629504131115,
-                        "LWcld_tot":-0.2277424426474096,
-                        "SWcld_alt":-0.00334132093929366,
-                        "SWcld_amt":0.22853782520716145,
-                        "SWcld_err":-0.0012906143595187859,
-                        "SWcld_tau":0.10812540176770673,
-                        "SWcld_tot":0.3320312916760557
-                    },
-                    "LO680":{
-                        "LWcld_alt":0.006018168055332961,
-                        "LWcld_amt":-0.005934300123084903,
-                        "LWcld_err":0.0001281077961668112,
-                        "LWcld_tau":-0.0015459988574903175,
-                        "LWcld_tot":-0.0013340231290754493,
-                        "SWcld_alt":-0.0029973778230501565,
-                        "SWcld_amt":0.03771009915796865,
-                        "SWcld_err":-0.00011258431554719557,
-                        "SWcld_tau":0.0250957287372391,
-                        "SWcld_tot":0.05969586575661041
-                    }
-                },
-                "ocn_asc":{
-                    "ALL":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN
-                    },
-                    "HI680":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN
-                    },
-                    "LO680":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN
-                    }
-                },
-                "ocn_dsc":{
-                    "ALL":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN
-                    },
-                    "HI680":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN
-                    },
-                    "LO680":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN
-                    }
-                }
-            },
-            "eq30":{
-                "all":{
-                    "ALL":{
-                        "LWcld_alt":0.053345520907161235,
-                        "LWcld_amt":-0.400296273743701,
-                        "LWcld_err":-0.010173242597193273,
-                        "LWcld_tau":-0.09351170657621359,
-                        "LWcld_tot":-0.4506357020099467,
-                        "SWcld_alt":0.005860427880286612,
-                        "SWcld_amt":0.6059118195975852,
-                        "SWcld_err":0.0062421955494704464,
-                        "SWcld_tau":0.15329790514994984,
-                        "SWcld_tot":0.7713123481772919
-                    },
-                    "HI680":{
-                        "LWcld_alt":0.11227935753194825,
-                        "LWcld_amt":-0.4332737781320473,
-                        "LWcld_err":0.027353914702278895,
-                        "LWcld_tau":-0.14417555792096806,
-                        "LWcld_tot":-0.4378160638187883,
-                        "SWcld_alt":-0.007337811486997386,
-                        "SWcld_amt":0.46493113702429595,
-                        "SWcld_err":-0.0019264731917240513,
-                        "SWcld_tau":0.17511688173809978,
-                        "SWcld_tot":0.6307837340836743
-                    },
-                    "LO680":{
-                        "LWcld_alt":0.007188634908245621,
-                        "LWcld_amt":-0.017112588224538077,
-                        "LWcld_err":-5.2731730815289036e-05,
-                        "LWcld_tau":-0.0028430105517376542,
-                        "LWcld_tot":-0.012819638191158424,
-                        "SWcld_alt":-0.0036480564715701794,
-                        "SWcld_amt":0.10235665545360895,
-                        "SWcld_err":4.796711325092206e-05,
-                        "SWcld_tau":0.0417722124352661,
-                        "SWcld_tot":0.1405287785305558
-                    }
-                },
-                "lnd":{
-                    "ALL":{
-                        "LWcld_alt":0.03402640593196133,
-                        "LWcld_amt":-0.14611207858729985,
-                        "LWcld_err":-0.007566127658798546,
-                        "LWcld_tau":-0.0484773188160032,
-                        "LWcld_tot":-0.16812911913014025,
-                        "SWcld_alt":-0.0022772916411197305,
-                        "SWcld_amt":0.17326097495949447,
-                        "SWcld_err":0.0018229612851221706,
-                        "SWcld_tau":0.06398058575143345,
-                        "SWcld_tot":0.23678723035493032
-                    },
-                    "HI680":{
-                        "LWcld_alt":0.03170555736911728,
-                        "LWcld_amt":-0.14109270267807006,
-                        "LWcld_err":0.0005226736047342136,
-                        "LWcld_tau":-0.055596089876130926,
-                        "LWcld_tot":-0.1644605615803495,
-                        "SWcld_alt":-0.0018468096560858014,
-                        "SWcld_amt":0.14254667021787334,
-                        "SWcld_err":0.0001800168306078764,
-                        "SWcld_tau":0.06631634781223684,
-                        "SWcld_tot":0.20719622520463232
-                    },
-                    "LO680":{
-                        "LWcld_alt":0.0018534781973072536,
-                        "LWcld_amt":-0.005547008302905135,
-                        "LWcld_err":3.827680167181595e-06,
-                        "LWcld_tau":2.1087467952968824e-05,
-                        "LWcld_tot":-0.0036685575497907553,
-                        "SWcld_alt":-0.0008884416952481838,
-                        "SWcld_amt":0.03101075006141189,
-                        "SWcld_err":9.108148670213706e-06,
-                        "SWcld_tau":-0.0005402469275977689,
-                        "SWcld_tot":0.029591169587236146
-                    }
-                },
-                "ocn":{
-                    "ALL":{
-                        "LWcld_alt":0.019319114975199895,
-                        "LWcld_amt":-0.2541841951564012,
-                        "LWcld_err":-0.0026071149383947267,
-                        "LWcld_tau":-0.04503438776021038,
-                        "LWcld_tot":-0.2825065828798065,
-                        "SWcld_alt":0.008137719521406342,
-                        "SWcld_amt":0.4326508446380906,
-                        "SWcld_err":0.004419234264348274,
-                        "SWcld_tau":0.08931731939851638,
-                        "SWcld_tot":0.5345251178223617
-                    },
-                    "HI680":{
-                        "LWcld_alt":0.080573800162831,
-                        "LWcld_amt":-0.2921810754539773,
-                        "LWcld_err":0.026831241097544683,
-                        "LWcld_tau":-0.08857946804483714,
-                        "LWcld_tot":-0.2733555022384388,
-                        "SWcld_alt":-0.005491001830911584,
-                        "SWcld_amt":0.3223844668064226,
-                        "SWcld_err":-0.0021064900223319275,
-                        "SWcld_tau":0.10880053392586293,
-                        "SWcld_tot":0.42358750887904195
-                    },
-                    "LO680":{
-                        "LWcld_alt":0.005335156710938368,
-                        "LWcld_amt":-0.011565579921632943,
-                        "LWcld_err":-5.655941098247062e-05,
-                        "LWcld_tau":-0.002864098019690623,
-                        "LWcld_tot":-0.00915108064136767,
-                        "SWcld_alt":-0.002759614776321995,
-                        "SWcld_amt":0.07134590539219705,
-                        "SWcld_err":3.885896458070835e-05,
-                        "SWcld_tau":0.04231245936286387,
-                        "SWcld_tot":0.11093760894331962
-                    }
-                },
-                "ocn_asc":{
-                    "ALL":{
-                        "LWcld_alt":0.022438954169885394,
-                        "LWcld_amt":-0.15250149846000124,
-                        "LWcld_err":0.0009262070575595847,
-                        "LWcld_tau":-0.02946271579640465,
-                        "LWcld_tot":-0.15859905302896096,
-                        "SWcld_alt":0.0036104835786565943,
-                        "SWcld_amt":0.19876274061604712,
-                        "SWcld_err":0.002842120713877157,
-                        "SWcld_tau":0.04001044167056739,
-                        "SWcld_tot":0.24522578657914829
-                    },
-                    "HI680":{
-                        "LWcld_alt":0.050129152592941456,
-                        "LWcld_amt":-0.17948209389325012,
-                        "LWcld_err":0.016662968680859577,
-                        "LWcld_tau":-0.04983168272436847,
-                        "LWcld_tot":-0.1625216553438176,
-                        "SWcld_alt":-0.002671581625239888,
-                        "SWcld_amt":0.20209201563633172,
-                        "SWcld_err":-0.000885273289797993,
-                        "SWcld_tau":0.0566837276175573,
-                        "SWcld_tot":0.25521888833885115
-                    },
-                    "LO680":{
-                        "LWcld_alt":0.002573474304901883,
-                        "LWcld_amt":0.0017873700234340981,
-                        "LWcld_err":-7.822697492602272e-05,
-                        "LWcld_tau":-0.00036001503855331217,
-                        "LWcld_tot":0.003922602314856646,
-                        "SWcld_alt":-0.0012570255078956894,
-                        "SWcld_amt":-0.015048176434348089,
-                        "SWcld_err":6.221447383352866e-05,
-                        "SWcld_tau":0.006249885708707354,
-                        "SWcld_tot":-0.009993101759702893
-                    }
-                },
-                "ocn_dsc":{
-                    "ALL":{
-                        "LWcld_alt":-0.061346333843104915,
-                        "LWcld_amt":-0.08617817855245068,
-                        "LWcld_err":0.0076525430063637,
-                        "LWcld_tau":0.019284092943300444,
-                        "LWcld_tot":-0.12058787644589146,
-                        "SWcld_alt":0.014761459222887732,
-                        "SWcld_amt":0.2724272198617598,
-                        "SWcld_err":0.0007119432249505954,
-                        "SWcld_tau":-0.014381661122521755,
-                        "SWcld_tot":0.2735189611870763
-                    },
-                    "HI680":{
-                        "LWcld_alt":0.007953690429151781,
-                        "LWcld_amt":-0.10305312902782982,
-                        "LWcld_err":0.005593204933458316,
-                        "LWcld_tau":-0.018204272905446302,
-                        "LWcld_tot":-0.10771050657066604,
-                        "SWcld_alt":0.0005622923333460766,
-                        "SWcld_amt":0.1322498732004053,
-                        "SWcld_err":0.0005903336478817847,
-                        "SWcld_tau":0.02969804292233713,
-                        "SWcld_tot":0.16310054210397026
-                    },
-                    "LO680":{
-                        "LWcld_alt":0.0037112365411335712,
-                        "LWcld_amt":-0.014323056222938243,
-                        "LWcld_err":-0.00032286456093837004,
-                        "LWcld_tau":-0.0019426856324823566,
-                        "LWcld_tot":-0.012877369875225397,
-                        "SWcld_alt":-0.001772130788336339,
-                        "SWcld_amt":0.07950865250169716,
-                        "SWcld_err":0.00037214071451413495,
-                        "SWcld_tau":0.032309756655231045,
-                        "SWcld_tot":0.11041841908310601
-                    }
-                }
-            },
-            "eq40":{
-                "all":{
-                    "ALL":{
-                        "LWcld_alt":0.08118023722390692,
-                        "LWcld_amt":-0.525038328121892,
-                        "LWcld_err":-0.004691822762913557,
-                        "LWcld_tau":-0.10497341538521388,
-                        "LWcld_tot":-0.5535233290461126,
-                        "SWcld_alt":0.005499189899295965,
-                        "SWcld_amt":0.8404358413655397,
-                        "SWcld_err":0.004567412416914291,
-                        "SWcld_tau":0.1972491577194294,
-                        "SWcld_tot":1.0477516014011794
-                    },
-                    "HI680":{
-                        "LWcld_alt":0.1513433021256291,
-                        "LWcld_amt":-0.5540824427579125,
-                        "LWcld_err":0.03102317724802416,
-                        "LWcld_tau":-0.149777728539358,
-                        "LWcld_tot":-0.5214936919236173,
-                        "SWcld_alt":-0.011059585982400087,
-                        "SWcld_amt":0.6288027487000748,
-                        "SWcld_err":-0.0026610573392481136,
-                        "SWcld_tau":0.19064161925797843,
-                        "SWcld_tot":0.8057237246364051
-                    },
-                    "LO680":{
-                        "LWcld_alt":0.0038282712954726826,
-                        "LWcld_amt":-0.030073140980213988,
-                        "LWcld_err":-7.293656807496521e-05,
-                        "LWcld_tau":-0.0057118902923703205,
-                        "LWcld_tot":-0.03202963712249524,
-                        "SWcld_alt":-0.002499129176849091,
-                        "SWcld_amt":0.1717828463471719,
-                        "SWcld_err":7.47442797290157e-05,
-                        "SWcld_tau":0.07266958525901217,
-                        "SWcld_tot":0.24202804670906397
-                    }
-                },
-                "lnd":{
-                    "ALL":{
-                        "LWcld_alt":0.04525235989510196,
-                        "LWcld_amt":-0.18379289097115548,
-                        "LWcld_err":-0.00738904262880419,
-                        "LWcld_tau":-0.051834274239830114,
-                        "LWcld_tot":-0.19776384794468785,
-                        "SWcld_alt":-0.0034859314267725004,
-                        "SWcld_amt":0.22068474744012403,
-                        "SWcld_err":0.0016707891226407487,
-                        "SWcld_tau":0.07255094966969779,
-                        "SWcld_tot":0.29142055480569007
-                    },
-                    "HI680":{
-                        "LWcld_alt":0.04233532389044695,
-                        "LWcld_amt":-0.17647878827648214,
-                        "LWcld_err":0.0007591736477708396,
-                        "LWcld_tau":-0.058346226193803495,
-                        "LWcld_tot":-0.19173051693206786,
-                        "SWcld_alt":-0.0029534688111539265,
-                        "SWcld_amt":0.18124097857223598,
-                        "SWcld_err":6.421674986185323e-05,
-                        "SWcld_tau":0.07308710921632201,
-                        "SWcld_tot":0.25143883572726594
-                    },
-                    "LO680":{
-                        "LWcld_alt":0.0017972060679100701,
-                        "LWcld_amt":-0.007704555239002791,
-                        "LWcld_err":9.119231548082598e-06,
-                        "LWcld_tau":-0.00013516049576673504,
-                        "LWcld_tot":-0.006033331012620026,
-                        "SWcld_alt":-0.0008865475270745947,
-                        "SWcld_amt":0.04007640973073738,
-                        "SWcld_err":1.0356979336625942e-05,
-                        "SWcld_tau":0.0007816698397143351,
-                        "SWcld_tot":0.03998188902271375
-                    }
-                },
-                "ocn":{
-                    "ALL":{
-                        "LWcld_alt":0.03592787732880497,
-                        "LWcld_amt":-0.3412454371507365,
-                        "LWcld_err":0.002697219865890633,
-                        "LWcld_tau":-0.05313914114538378,
-                        "LWcld_tot":-0.3557594811014247,
-                        "SWcld_alt":0.008985121326068463,
-                        "SWcld_amt":0.6197510939254157,
-                        "SWcld_err":0.0028966232942735427,
-                        "SWcld_tau":0.12469820804973164,
-                        "SWcld_tot":0.7563310465954894
-                    },
-                    "HI680":{
-                        "LWcld_alt":0.10900797823518216,
-                        "LWcld_amt":-0.37760365448143046,
-                        "LWcld_err":0.03026400360025332,
-                        "LWcld_tau":-0.09143150234555453,
-                        "LWcld_tot":-0.32976317499154945,
-                        "SWcld_alt":-0.008106117171246159,
-                        "SWcld_amt":0.4475617701278389,
-                        "SWcld_err":-0.002725274089109967,
-                        "SWcld_tau":0.11755451004165642,
-                        "SWcld_tot":0.5542848889091391
-                    },
-                    "LO680":{
-                        "LWcld_alt":0.0020310652275626127,
-                        "LWcld_amt":-0.0223685857412112,
-                        "LWcld_err":-8.205579962304782e-05,
-                        "LWcld_tau":-0.005576729796603587,
-                        "LWcld_tot":-0.025996306109875217,
-                        "SWcld_alt":-0.0016125816497744962,
-                        "SWcld_amt":0.13170643661643452,
-                        "SWcld_err":6.438730039238976e-05,
-                        "SWcld_tau":0.07188791541929782,
-                        "SWcld_tot":0.20204615768635023
-                    }
-                },
-                "ocn_asc":{
-                    "ALL":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN
-                    },
-                    "HI680":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN
-                    },
-                    "LO680":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN
-                    }
-                },
-                "ocn_dsc":{
-                    "ALL":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN
-                    },
-                    "HI680":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN
-                    },
-                    "LO680":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN
-                    }
-                }
-            },
-            "eq50":{
-                "all":{
-                    "ALL":{
-                        "LWcld_alt":0.13058253175720988,
-                        "LWcld_amt":-0.6039270094408461,
-                        "LWcld_err":-0.009265875329183609,
-                        "LWcld_tau":-0.11372631551867857,
-                        "LWcld_tot":-0.5963366685314982,
-                        "SWcld_alt":0.0015900380807497142,
-                        "SWcld_amt":1.0108365291024632,
-                        "SWcld_err":0.0046829650181254385,
-                        "SWcld_tau":0.23460661643754976,
-                        "SWcld_tot":1.2517161486388881
-                    },
-                    "HI680":{
-                        "LWcld_alt":0.19575019040677963,
-                        "LWcld_amt":-0.6145939679943702,
-                        "LWcld_err":0.03043964562135372,
-                        "LWcld_tau":-0.15863755061614052,
-                        "LWcld_tot":-0.5470416825823774,
-                        "SWcld_alt":-0.015024480970583301,
-                        "SWcld_amt":0.7268204379776901,
-                        "SWcld_err":-0.0027968763937981905,
-                        "SWcld_tau":0.21904930113107687,
-                        "SWcld_tot":0.9280483817443854
-                    },
-                    "LO680":{
-                        "LWcld_alt":0.0011539856939892243,
-                        "LWcld_amt":-0.04442450079643509,
-                        "LWcld_err":5.970309331292499e-05,
-                        "LWcld_tau":-0.006084233362679426,
-                        "LWcld_tot":-0.04929498594912102,
-                        "SWcld_alt":-0.0016739362603017541,
-                        "SWcld_amt":0.24503759920519538,
-                        "SWcld_err":6.689881657773621e-05,
-                        "SWcld_tau":0.08023737507732116,
-                        "SWcld_tot":0.3236679368387925
-                    }
-                },
-                "lnd":{
-                    "ALL":{
-                        "LWcld_alt":0.05944333966362093,
-                        "LWcld_amt":-0.20950328185150005,
-                        "LWcld_err":-0.009528160633556193,
-                        "LWcld_tau":-0.05056982850987404,
-                        "LWcld_tot":-0.21015793133130933,
-                        "SWcld_alt":-0.005330975263335901,
-                        "SWcld_amt":0.2647641042519102,
-                        "SWcld_err":0.0017551704102657616,
-                        "SWcld_tau":0.07952357114878861,
-                        "SWcld_tot":0.3407118705476287
-                    },
-                    "HI680":{
-                        "LWcld_alt":0.05404321535701797,
-                        "LWcld_amt":-0.19789334493622426,
-                        "LWcld_err":-7.096462165668897e-05,
-                        "LWcld_tau":-0.057770162828555995,
-                        "LWcld_tot":-0.20169125702941904,
-                        "SWcld_alt":-0.004273191784125059,
-                        "SWcld_amt":0.2110112694124142,
-                        "SWcld_err":1.2799245094842119e-05,
-                        "SWcld_tau":0.07943565763575279,
-                        "SWcld_tot":0.2861865345091368
-                    },
-                    "LO680":{
-                        "LWcld_alt":0.0016860491113153146,
-                        "LWcld_amt":-0.010692203409196325,
-                        "LWcld_err":2.5570560399387283e-05,
-                        "LWcld_tau":0.0005138500128999622,
-                        "LWcld_tot":-0.008466674301890314,
-                        "SWcld_alt":-0.000849382384284137,
-                        "SWcld_amt":0.05563240831051438,
-                        "SWcld_err":8.167391431737988e-06,
-                        "SWcld_tau":-0.0002656873348803957,
-                        "SWcld_tot":0.0545255059827816
-                    }
-                },
-                "ocn":{
-                    "ALL":{
-                        "LWcld_alt":0.07113919209358892,
-                        "LWcld_amt":-0.3944237275893459,
-                        "LWcld_err":0.0002622853043725828,
-                        "LWcld_tau":-0.06315648700880452,
-                        "LWcld_tot":-0.38617873720018886,
-                        "SWcld_alt":0.006921013344085615,
-                        "SWcld_amt":0.7460724248505531,
-                        "SWcld_err":0.002927794607859677,
-                        "SWcld_tau":0.15508304528876118,
-                        "SWcld_tot":0.9110042780912595
-                    },
-                    "HI680":{
-                        "LWcld_alt":0.14170697504976162,
-                        "LWcld_amt":-0.4167006230581458,
-                        "LWcld_err":0.03051061024301041,
-                        "LWcld_tau":-0.10086738778758449,
-                        "LWcld_tot":-0.3453504255529582,
-                        "SWcld_alt":-0.010751289186458242,
-                        "SWcld_amt":0.5158091685652758,
-                        "SWcld_err":-0.002809675638893032,
-                        "SWcld_tau":0.13961364349532404,
-                        "SWcld_tot":0.6418618472352486
-                    },
-                    "LO680":{
-                        "LWcld_alt":-0.0005320634173260903,
-                        "LWcld_amt":-0.03373229738723877,
-                        "LWcld_err":3.413253291353769e-05,
-                        "LWcld_tau":-0.006598083375579388,
-                        "LWcld_tot":-0.04082831164723071,
-                        "SWcld_alt":-0.0008245538760176171,
-                        "SWcld_amt":0.189405190894681,
-                        "SWcld_err":5.873142514599823e-05,
-                        "SWcld_tau":0.08050306241220156,
-                        "SWcld_tot":0.2691424308560109
-                    }
-                },
-                "ocn_asc":{
-                    "ALL":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN
-                    },
-                    "HI680":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN
-                    },
-                    "LO680":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN
-                    }
-                },
-                "ocn_dsc":{
-                    "ALL":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN
-                    },
-                    "HI680":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN
-                    },
-                    "LO680":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN
-                    }
-                }
-            },
-            "eq60":{
-                "all":{
-                    "ALL":{
-                        "LWcld_alt":0.18957706586501696,
-                        "LWcld_amt":-0.6333080708246392,
-                        "LWcld_err":-0.011319977765085469,
-                        "LWcld_tau":-0.10485891985970337,
-                        "LWcld_tot":-0.5599099025844112,
-                        "SWcld_alt":-0.0051393917503254755,
-                        "SWcld_amt":1.081577639195905,
-                        "SWcld_err":0.005178703212310963,
-                        "SWcld_tau":0.19541626278427637,
-                        "SWcld_tot":1.277033213442167
-                    },
-                    "HI680":{
-                        "LWcld_alt":0.23772564558269535,
-                        "LWcld_amt":-0.6180332983852915,
-                        "LWcld_err":0.03286262827102584,
-                        "LWcld_tau":-0.1525075197184431,
-                        "LWcld_tot":-0.49995254425001345,
-                        "SWcld_alt":-0.0187336089091415,
-                        "SWcld_amt":0.7373295388361267,
-                        "SWcld_err":-0.0031288358841252595,
-                        "SWcld_tau":0.2012937037478393,
-                        "SWcld_tot":0.9167607977906993
-                    },
-                    "LO680":{
-                        "LWcld_alt":-0.00010156602864725712,
-                        "LWcld_amt":-0.05622453461287965,
-                        "LWcld_err":-6.464588672026883e-05,
-                        "LWcld_tau":-0.003566671228841896,
-                        "LWcld_tot":-0.05995735833439771,
-                        "SWcld_alt":-0.001367759705862626,
-                        "SWcld_amt":0.3045794441951879,
-                        "SWcld_err":0.00020222638309846895,
-                        "SWcld_tau":0.05685867472333364,
-                        "SWcld_tot":0.3602725855957573
-                    }
-                },
-                "lnd":{
-                    "ALL":{
-                        "LWcld_alt":0.07386790233199224,
-                        "LWcld_amt":-0.2226216551947878,
-                        "LWcld_err":-0.010956236199316733,
-                        "LWcld_tau":-0.044762114707933724,
-                        "LWcld_tot":-0.20447210377004602,
-                        "SWcld_alt":-0.007305643776715461,
-                        "SWcld_amt":0.294671688738411,
-                        "SWcld_err":0.0018706214629822873,
-                        "SWcld_tau":0.0832250266972634,
-                        "SWcld_tot":0.3724616931219413
-                    },
-                    "HI680":{
-                        "LWcld_alt":0.06539623844250757,
-                        "LWcld_amt":-0.2068441395920898,
-                        "LWcld_err":0.00028424237048030456,
-                        "LWcld_tau":-0.05324438616665495,
-                        "LWcld_tot":-0.1944080449457569,
-                        "SWcld_alt":-0.005539925429590313,
-                        "SWcld_amt":0.22594592437223013,
-                        "SWcld_err":-0.00010433569969708504,
-                        "SWcld_tau":0.08304109658394453,
-                        "SWcld_tot":0.30334275982688724
-                    },
-                    "LO680":{
-                        "LWcld_alt":0.0014452824665175076,
-                        "LWcld_amt":-0.013235773054302421,
-                        "LWcld_err":3.5366349805084184e-05,
-                        "LWcld_tau":0.00169100599099937,
-                        "LWcld_tot":-0.010064058824289112,
-                        "SWcld_alt":-0.0007485612748887822,
-                        "SWcld_amt":0.07209111998407541,
-                        "SWcld_err":6.591314828375478e-06,
-                        "SWcld_tau":-0.002230046784671386,
-                        "SWcld_tot":0.06911910323934362
-                    }
-                },
-                "ocn":{
-                    "ALL":{
-                        "LWcld_alt":0.11570916353302473,
-                        "LWcld_amt":-0.41068641562985153,
-                        "LWcld_err":-0.0003637415657687345,
-                        "LWcld_tau":-0.06009680515176965,
-                        "LWcld_tot":-0.35543779881436516,
-                        "SWcld_alt":0.0021662520263899844,
-                        "SWcld_amt":0.786905950457494,
-                        "SWcld_err":0.0033080817493286747,
-                        "SWcld_tau":0.11219123608701298,
-                        "SWcld_tot":0.9045715203202257
-                    },
-                    "HI680":{
-                        "LWcld_alt":0.17232940714018777,
-                        "LWcld_amt":-0.4111891587932017,
-                        "LWcld_err":0.03257838590054553,
-                        "LWcld_tau":-0.09926313355178813,
-                        "LWcld_tot":-0.3055444993042566,
-                        "SWcld_alt":-0.013193683479551188,
-                        "SWcld_amt":0.5113836144638967,
-                        "SWcld_err":-0.003024500184428174,
-                        "SWcld_tau":0.11825260716389475,
-                        "SWcld_tot":0.613418037963812
-                    },
-                    "LO680":{
-                        "LWcld_alt":-0.001546848495164765,
-                        "LWcld_amt":-0.04298876155857722,
-                        "LWcld_err":-0.00010001223652535302,
-                        "LWcld_tau":-0.0052576772198412655,
-                        "LWcld_tot":-0.0498932995101086,
-                        "SWcld_alt":-0.0006191984309738439,
-                        "SWcld_amt":0.2324883242111124,
-                        "SWcld_err":0.00019563506827009348,
-                        "SWcld_tau":0.05908872150800502,
-                        "SWcld_tot":0.2911534823564137
-                    }
-                },
-                "ocn_asc":{
-                    "ALL":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN
-                    },
-                    "HI680":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN
-                    },
-                    "LO680":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN
-                    }
-                },
-                "ocn_dsc":{
-                    "ALL":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN
-                    },
-                    "HI680":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN
-                    },
-                    "LO680":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN
-                    }
-                }
-            },
-            "eq70":{
-                "all":{
-                    "ALL":{
-                        "LWcld_alt":0.2270153274100527,
-                        "LWcld_amt":-0.6422184828195028,
-                        "LWcld_err":-0.0080488641696996,
-                        "LWcld_tau":-0.08942512636108647,
-                        "LWcld_tot":-0.5126771459402362,
-                        "SWcld_alt":-0.009049966669322016,
-                        "SWcld_amt":1.1044762721238939,
-                        "SWcld_err":0.004874440892958113,
-                        "SWcld_tau":0.15781043356813215,
-                        "SWcld_tot":1.2581111799156621
-                    },
-                    "HI680":{
-                        "LWcld_alt":0.2627392532908915,
-                        "LWcld_amt":-0.6116860837762546,
-                        "LWcld_err":0.03789123886001669,
-                        "LWcld_tau":-0.13754614598471473,
-                        "LWcld_tot":-0.4486017376100612,
-                        "SWcld_alt":-0.0207253609610345,
-                        "SWcld_amt":0.7335594780399609,
-                        "SWcld_err":-0.0035975336751070328,
-                        "SWcld_tau":0.17647954203048746,
-                        "SWcld_tot":0.8857161254343069
-                    },
-                    "LO680":{
-                        "LWcld_alt":-0.0005631201287295767,
-                        "LWcld_amt":-0.06151815685381253,
-                        "LWcld_err":-0.00018866399500778788,
-                        "LWcld_tau":-0.00180552683149339,
-                        "LWcld_tot":-0.06407540833017498,
-                        "SWcld_alt":-0.0012223815039860075,
-                        "SWcld_amt":0.33195729325852497,
-                        "SWcld_err":0.0002756894628234374,
-                        "SWcld_tau":0.04138462320828234,
-                        "SWcld_tot":0.37239522442564477
-                    }
-                },
-                "lnd":{
-                    "ALL":{
-                        "LWcld_alt":0.08776247894772983,
-                        "LWcld_amt":-0.2291293764148239,
-                        "LWcld_err":-0.01093325173059242,
-                        "LWcld_tau":-0.03689076655141856,
-                        "LWcld_tot":-0.18919091574910504,
-                        "SWcld_alt":-0.009364903870046064,
-                        "SWcld_amt":0.3131838963157388,
-                        "SWcld_err":0.0019437251742142646,
-                        "SWcld_tau":0.08393835475418769,
-                        "SWcld_tot":0.38970107237409474
-                    },
-                    "HI680":{
-                        "LWcld_alt":0.07540215438414903,
-                        "LWcld_amt":-0.20840953181605673,
-                        "LWcld_err":0.0017986311163031394,
-                        "LWcld_tau":-0.04597056463298998,
-                        "LWcld_tot":-0.17717931094859454,
-                        "SWcld_alt":-0.006559861786394305,
-                        "SWcld_amt":0.22890179780134193,
-                        "SWcld_err":-0.0002580428619544639,
-                        "SWcld_tau":0.08318411081477776,
-                        "SWcld_tot":0.3052680039677709
-                    },
-                    "LO680":{
-                        "LWcld_alt":0.001288963305993693,
-                        "LWcld_amt":-0.015750826338876738,
-                        "LWcld_err":2.340968643353381e-05,
-                        "LWcld_tau":0.0024267890670706927,
-                        "LWcld_tot":-0.012011604800510514,
-                        "SWcld_alt":-0.0006791128905576737,
-                        "SWcld_amt":0.08886874441024055,
-                        "SWcld_err":1.003312316115285e-05,
-                        "SWcld_tau":-0.003766426292230607,
-                        "SWcld_tot":0.08443323835061341
-                    }
-                },
-                "ocn":{
-                    "ALL":{
-                        "LWcld_alt":0.1392528484623229,
-                        "LWcld_amt":-0.4130891064046789,
-                        "LWcld_err":0.0028843875608928204,
-                        "LWcld_tau":-0.052534359809667926,
-                        "LWcld_tot":-0.3234862301911311,
-                        "SWcld_alt":0.0003149372007240473,
-                        "SWcld_amt":0.791292375808155,
-                        "SWcld_err":0.002930715718743848,
-                        "SWcld_tau":0.07387207881394446,
-                        "SWcld_tot":0.8684101075415672
-                    },
-                    "HI680":{
-                        "LWcld_alt":0.1873370989067424,
-                        "LWcld_amt":-0.4032765519601978,
-                        "LWcld_err":0.036092607743713555,
-                        "LWcld_tau":-0.09157558135172476,
-                        "LWcld_tot":-0.2714224266614666,
-                        "SWcld_alt":-0.014165499174640194,
-                        "SWcld_amt":0.504657680238619,
-                        "SWcld_err":-0.0033394908131525697,
-                        "SWcld_tau":0.09329543121570971,
-                        "SWcld_tot":0.5804481214665359
-                    },
-                    "LO680":{
-                        "LWcld_alt":-0.0018520834347232698,
-                        "LWcld_amt":-0.04576733051493578,
-                        "LWcld_err":-0.0002120736814413217,
-                        "LWcld_tau":-0.004232315898564082,
-                        "LWcld_tot":-0.052063803529664464,
-                        "SWcld_alt":-0.0005432686134283338,
-                        "SWcld_amt":0.24308854884828443,
-                        "SWcld_err":0.0002656563396622846,
-                        "SWcld_tau":0.045151049500512956,
-                        "SWcld_tot":0.2879619860750313
-                    }
-                },
-                "ocn_asc":{
-                    "ALL":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN
-                    },
-                    "HI680":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN
-                    },
-                    "LO680":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN
-                    }
-                },
-                "ocn_dsc":{
-                    "ALL":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN
-                    },
-                    "HI680":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN
-                    },
-                    "LO680":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN
-                    }
-                }
-            },
-            "eq80":{
-                "all":{
-                    "ALL":{
-                        "LWcld_alt":0.2390146163936442,
-                        "LWcld_amt":-0.6433986742511097,
-                        "LWcld_err":-0.006793518692435767,
-                        "LWcld_tau":-0.0797536353331581,
-                        "LWcld_tot":-0.4909312118830595,
-                        "SWcld_alt":-0.010555941115369526,
-                        "SWcld_amt":1.1091174450963297,
-                        "SWcld_err":0.004793837956896845,
-                        "SWcld_tau":0.15376660376103138,
-                        "SWcld_tot":1.2571219456988885
-                    },
-                    "HI680":{
-                        "LWcld_alt":0.26922778351564286,
-                        "LWcld_amt":-0.6069310103040888,
-                        "LWcld_err":0.03964758216748874,
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-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN
-                    },
-                    "LO680":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN
-                    }
-                },
-                "ocn_dsc":{
-                    "ALL":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN
-                    },
-                    "HI680":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN
-                    },
-                    "LO680":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN
-                    }
-                }
-            },
-            "eq10":{
-                "all":{
-                    "ALL":{
-                        "LWcld_alt":0.18004237028793238,
-                        "LWcld_amt":-0.13471903833785334,
-                        "LWcld_err":0.024366414066854702,
-                        "LWcld_tau":0.015449280138216262,
-                        "LWcld_tot":0.08513902615514997,
-                        "SWcld_alt":-0.018267923955747018,
-                        "SWcld_amt":0.23715957671646168,
-                        "SWcld_err":-0.0021461695089354123,
-                        "SWcld_tau":0.0014801672555922764,
-                        "SWcld_tot":0.2182256505073715
-                    },
-                    "HI680":{
-                        "LWcld_alt":0.16423307092968478,
-                        "LWcld_amt":-0.10812609164234684,
-                        "LWcld_err":0.02725965731515706,
-                        "LWcld_tau":0.017516817368727965,
-                        "LWcld_tot":0.100883453971223,
-                        "SWcld_alt":-0.014711032395894238,
-                        "SWcld_amt":0.14874967343944798,
-                        "SWcld_err":-0.0026230507627794906,
-                        "SWcld_tau":-0.009298572802584329,
-                        "SWcld_tot":0.12211701747818993
-                    },
-                    "LO680":{
-                        "LWcld_alt":-0.002490817862298511,
-                        "LWcld_amt":-0.011900075128551145,
-                        "LWcld_err":0.00042941502351906473,
-                        "LWcld_tau":-0.0017829498487424286,
-                        "LWcld_tot":-0.01574442781607302,
-                        "SWcld_alt":0.0011467849565942468,
-                        "SWcld_amt":0.07550146105797038,
-                        "SWcld_err":-0.0005379786741433817,
-                        "SWcld_tau":0.019998365688760334,
-                        "SWcld_tot":0.0961086330291816
-                    }
-                },
-                "lnd":{
-                    "ALL":{
-                        "LWcld_alt":0.045129324613148426,
-                        "LWcld_amt":-0.030864078612172333,
-                        "LWcld_err":0.0032276025614578828,
-                        "LWcld_tau":0.007978699241301663,
-                        "LWcld_tot":0.02547154780373564,
-                        "SWcld_alt":-0.006088726923487927,
-                        "SWcld_amt":0.05242412662682383,
-                        "SWcld_err":-0.00023689998197870844,
-                        "SWcld_tau":-0.025338562751340336,
-                        "SWcld_tot":0.02075993697001685
-                    },
-                    "HI680":{
-                        "LWcld_alt":0.03301009800544888,
-                        "LWcld_amt":-0.017111333844944683,
-                        "LWcld_err":0.005526836946581398,
-                        "LWcld_tau":0.005597297206689167,
-                        "LWcld_tot":0.02702289831377476,
-                        "SWcld_alt":-0.0029099408126643084,
-                        "SWcld_amt":0.029611353767872786,
-                        "SWcld_err":-0.0011673030028203483,
-                        "SWcld_tau":-0.01512239342234343,
-                        "SWcld_tot":0.010411716530044704
-                    },
-                    "LO680":{
-                        "LWcld_alt":0.0003917717860745628,
-                        "LWcld_amt":-0.002117114215488166,
-                        "LWcld_err":0.00012079867984926059,
-                        "LWcld_tau":5.3193239525221947e-05,
-                        "LWcld_tot":-0.0015513505100391211,
-                        "SWcld_alt":-0.00018140282137044174,
-                        "SWcld_amt":0.014681269969178812,
-                        "SWcld_err":-0.00010737965245129605,
-                        "SWcld_tau":-0.0040442670553849204,
-                        "SWcld_tot":0.010348220439972152
-                    }
-                },
-                "ocn":{
-                    "ALL":{
-                        "LWcld_alt":0.13491304567478393,
-                        "LWcld_amt":-0.10385495972568104,
-                        "LWcld_err":0.021138811505396815,
-                        "LWcld_tau":0.007470580896914599,
-                        "LWcld_tot":0.05966747835141434,
-                        "SWcld_alt":-0.012179197032259094,
-                        "SWcld_amt":0.1847354500896378,
-                        "SWcld_err":-0.0019092695269567039,
-                        "SWcld_tau":0.026818730006932617,
-                        "SWcld_tot":0.19746571353735468
-                    },
-                    "HI680":{
-                        "LWcld_alt":0.13122297292423593,
-                        "LWcld_amt":-0.09101475779740216,
-                        "LWcld_err":0.02173282036857566,
-                        "LWcld_tau":0.011919520162038797,
-                        "LWcld_tot":0.07386055565744824,
-                        "SWcld_alt":-0.011801091583229931,
-                        "SWcld_amt":0.11913831967157519,
-                        "SWcld_err":-0.0014557477599591426,
-                        "SWcld_tau":0.0058238206197590995,
-                        "SWcld_tot":0.11170530094814524
-                    },
-                    "LO680":{
-                        "LWcld_alt":-0.0028825896483730736,
-                        "LWcld_amt":-0.009782960913062978,
-                        "LWcld_err":0.0003086163436698042,
-                        "LWcld_tau":-0.001836143088267651,
-                        "LWcld_tot":-0.0141930773060339,
-                        "SWcld_alt":0.0013281877779646888,
-                        "SWcld_amt":0.06082019108879158,
-                        "SWcld_err":-0.0004305990216920855,
-                        "SWcld_tau":0.024042632744145257,
-                        "SWcld_tot":0.08576041258920945
-                    }
-                },
-                "ocn_asc":{
-                    "ALL":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN
-                    },
-                    "HI680":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN
-                    },
-                    "LO680":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN
-                    }
-                },
-                "ocn_dsc":{
-                    "ALL":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN
-                    },
-                    "HI680":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN
-                    },
-                    "LO680":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN
-                    }
-                }
-            },
-            "eq20":{
-                "all":{
-                    "ALL":{
-                        "LWcld_alt":0.26378026041591296,
-                        "LWcld_amt":-0.22890065742967117,
-                        "LWcld_err":0.03829560532836012,
-                        "LWcld_tau":0.027563565242193864,
-                        "LWcld_tot":0.10073877355679578,
-                        "SWcld_alt":-0.025808182632316476,
-                        "SWcld_amt":0.44010912180776113,
-                        "SWcld_err":-0.0040207328126139336,
-                        "SWcld_tau":-0.00011394281053012506,
-                        "SWcld_tot":0.4101662635523007
-                    },
-                    "HI680":{
-                        "LWcld_alt":0.24796436822542822,
-                        "LWcld_amt":-0.1907236182948235,
-                        "LWcld_err":0.03986157824003151,
-                        "LWcld_tau":0.03364352785345432,
-                        "LWcld_tot":0.13074585602409056,
-                        "SWcld_alt":-0.023106043594106895,
-                        "SWcld_amt":0.29034582235668466,
-                        "SWcld_err":-0.003647025164743961,
-                        "SWcld_tau":-0.02953118879399294,
-                        "SWcld_tot":0.23406156480384088
-                    },
-                    "LO680":{
-                        "LWcld_alt":-0.00551676051707621,
-                        "LWcld_amt":-0.022079226871655856,
-                        "LWcld_err":0.0006131154147384984,
-                        "LWcld_tau":-0.0030242932044716522,
-                        "LWcld_tot":-0.030007082467294773,
-                        "SWcld_alt":0.0025321047250509154,
-                        "SWcld_amt":0.13488053835936806,
-                        "SWcld_err":-0.0008273392800991556,
-                        "SWcld_tau":0.03951960812067524,
-                        "SWcld_tot":0.17610491192499503
-                    }
-                },
-                "lnd":{
-                    "ALL":{
-                        "LWcld_alt":0.06427127189101206,
-                        "LWcld_amt":-0.06084123806179465,
-                        "LWcld_err":0.005506921931183277,
-                        "LWcld_tau":0.011237676628009568,
-                        "LWcld_tot":0.020174632388410264,
-                        "SWcld_alt":-0.008390847712105815,
-                        "SWcld_amt":0.09712372041552704,
-                        "SWcld_err":-0.0004598091166904395,
-                        "SWcld_tau":-0.0372680105952013,
-                        "SWcld_tot":0.05100505299152947
-                    },
-                    "HI680":{
-                        "LWcld_alt":0.04900504818335762,
-                        "LWcld_amt":-0.04323036291525321,
-                        "LWcld_err":0.008462461149796948,
-                        "LWcld_tau":0.008283508578269318,
-                        "LWcld_tot":0.022520654996170678,
-                        "SWcld_alt":-0.00441298262101054,
-                        "SWcld_amt":0.06621515089355057,
-                        "SWcld_err":-0.0016338118116872077,
-                        "SWcld_tau":-0.024428276922448932,
-                        "SWcld_tot":0.0357400795384039
-                    },
-                    "LO680":{
-                        "LWcld_alt":0.0005209762138876648,
-                        "LWcld_amt":-0.003088709468809438,
-                        "LWcld_err":0.00013849920953859884,
-                        "LWcld_tau":8.312872645230941e-05,
-                        "LWcld_tot":-0.0023460226077604185,
-                        "SWcld_alt":-0.00023835029119685362,
-                        "SWcld_amt":0.020946201399956565,
-                        "SWcld_err":-0.00012877415822522175,
-                        "SWcld_tau":-0.0053138903208736175,
-                        "SWcld_tot":0.01526518662966087
-                    }
-                },
-                "ocn":{
-                    "ALL":{
-                        "LWcld_alt":0.19950898852490095,
-                        "LWcld_amt":-0.16805941936787652,
-                        "LWcld_err":0.03278868339717684,
-                        "LWcld_tau":0.016325888614184297,
-                        "LWcld_tot":0.08056414116838553,
-                        "SWcld_alt":-0.01741733492021066,
-                        "SWcld_amt":0.34298540139223416,
-                        "SWcld_err":-0.0035609236959234943,
-                        "SWcld_tau":0.03715406778467118,
-                        "SWcld_tot":0.35916121056077116
-                    },
-                    "HI680":{
-                        "LWcld_alt":0.19895932004207062,
-                        "LWcld_amt":-0.14749325537957028,
-                        "LWcld_err":0.03139911709023456,
-                        "LWcld_tau":0.025360019275185,
-                        "LWcld_tot":0.1082252010279199,
-                        "SWcld_alt":-0.018693060973096354,
-                        "SWcld_amt":0.22413067146313412,
-                        "SWcld_err":-0.0020132133530567525,
-                        "SWcld_tau":-0.005102911871544001,
-                        "SWcld_tot":0.198321485265437
-                    },
-                    "LO680":{
-                        "LWcld_alt":-0.006037736730963875,
-                        "LWcld_amt":-0.018990517402846414,
-                        "LWcld_err":0.00047461620519989974,
-                        "LWcld_tau":-0.003107421930923962,
-                        "LWcld_tot":-0.02766105985953435,
-                        "SWcld_alt":0.002770455016247769,
-                        "SWcld_amt":0.11393433695941148,
-                        "SWcld_err":-0.000698565121873934,
-                        "SWcld_tau":0.04483349844154885,
-                        "SWcld_tot":0.16083972529533416
-                    }
-                },
-                "ocn_asc":{
-                    "ALL":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN
-                    },
-                    "HI680":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN
-                    },
-                    "LO680":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN
-                    }
-                },
-                "ocn_dsc":{
-                    "ALL":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN
-                    },
-                    "HI680":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN
-                    },
-                    "LO680":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN
-                    }
-                }
-            },
-            "eq30":{
-                "all":{
-                    "ALL":{
-                        "LWcld_alt":0.31377637807767217,
-                        "LWcld_amt":-0.3413730655852348,
-                        "LWcld_err":0.04256479059056397,
-                        "LWcld_tau":0.03236605359116327,
-                        "LWcld_tot":0.04733415667416463,
-                        "SWcld_alt":-0.03372912473004936,
-                        "SWcld_amt":0.6838515836633247,
-                        "SWcld_err":-0.004458464942750565,
-                        "SWcld_tau":-0.01689070361628946,
-                        "SWcld_tot":0.6287732903742352
-                    },
-                    "HI680":{
-                        "LWcld_alt":0.2834136207849803,
-                        "LWcld_amt":-0.26564929329649356,
-                        "LWcld_err":0.041782985391265716,
-                        "LWcld_tau":0.04308788727759341,
-                        "LWcld_tot":0.10263520015734585,
-                        "SWcld_alt":-0.028090995023899043,
-                        "SWcld_amt":0.41835095622279556,
-                        "SWcld_err":-0.003164807416073128,
-                        "SWcld_tau":-0.060976467219240243,
-                        "SWcld_tot":0.32611868656358317
-                    },
-                    "LO680":{
-                        "LWcld_alt":-0.00889732830212194,
-                        "LWcld_amt":-0.04317307639984999,
-                        "LWcld_err":0.0007939184624194416,
-                        "LWcld_tau":-0.004024835921742642,
-                        "LWcld_tot":-0.05530104348318126,
-                        "SWcld_alt":0.003972861689092827,
-                        "SWcld_amt":0.24812135905505464,
-                        "SWcld_err":-0.0010175852646577208,
-                        "SWcld_tau":0.051578877028051924,
-                        "SWcld_tot":0.30265551250754175
-                    }
-                },
-                "lnd":{
-                    "ALL":{
-                        "LWcld_alt":0.07597868697804054,
-                        "LWcld_amt":-0.0957541453397478,
-                        "LWcld_err":0.006047424069317694,
-                        "LWcld_tau":0.012360202036558516,
-                        "LWcld_tot":-0.0013678322558310443,
-                        "SWcld_alt":-0.009591905641743578,
-                        "SWcld_amt":0.15217599764874368,
-                        "SWcld_err":-0.00043983175407486566,
-                        "SWcld_tau":-0.04275772564087645,
-                        "SWcld_tot":0.09938653461204876
-                    },
-                    "HI680":{
-                        "LWcld_alt":0.05896528460538592,
-                        "LWcld_amt":-0.07495290394619347,
-                        "LWcld_err":0.009034335925520345,
-                        "LWcld_tau":0.009472892631194857,
-                        "LWcld_tot":0.002519609215907653,
-                        "SWcld_alt":-0.005204265249915786,
-                        "SWcld_amt":0.11273947426718017,
-                        "SWcld_err":-0.0016318487818567772,
-                        "SWcld_tau":-0.029887894916023953,
-                        "SWcld_tot":0.07601546531938366
-                    },
-                    "LO680":{
-                        "LWcld_alt":0.0005876030648395205,
-                        "LWcld_amt":-0.0046686066488963365,
-                        "LWcld_err":0.00014449472824838876,
-                        "LWcld_tau":4.8808810226883325e-05,
-                        "LWcld_tot":-0.003887441471738698,
-                        "SWcld_alt":-0.0002672227952383121,
-                        "SWcld_amt":0.02892848790664172,
-                        "SWcld_err":-0.00013042245240009527,
-                        "SWcld_tau":-0.00515891808612772,
-                        "SWcld_tot":0.023371924572875583
-                    }
-                },
-                "ocn":{
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-                        "LWcld_err":0.0425911303263378,
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-                        "SWcld_tot":0.02882334845567681
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-                        "LWcld_tau":0.00012617385832450266,
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-                        "SWcld_tau":0.049751824850916955,
-                        "SWcld_tot":0.24770821914330618
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-                }
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-            "eq40":{
-                "all":{
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-                        "LWcld_amt":-0.45880470844661847,
-                        "LWcld_err":0.04554582845090208,
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-                        "LWcld_err":0.04272273253966192,
-                        "LWcld_tau":0.053891164138470976,
-                        "LWcld_tot":0.07743210789498642,
-                        "SWcld_alt":-0.02548988718364258,
-                        "SWcld_amt":0.4625818867793374,
-                        "SWcld_err":-0.0020689549080323694,
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-                        "SWcld_tot":0.35603392176336174
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-                        "LWcld_tau":-0.0045998005435213775,
-                        "LWcld_tot":-0.09363140039087649,
-                        "SWcld_alt":0.00483901053019676,
-                        "SWcld_amt":0.42853979307057677,
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-                        "SWcld_tau":0.05638226565604005,
-                        "SWcld_tot":0.4885588461099464
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-                "lnd":{
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-                        "LWcld_err":0.006602534240575295,
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-                        "LWcld_err":0.009493741319593503,
-                        "LWcld_tau":0.015387582601928506,
-                        "LWcld_tot":-0.010663488512245481,
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-                        "SWcld_amt":0.15004992609114984,
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-                        "LWcld_tau":0.00013521007464567216,
-                        "LWcld_tot":-0.006431187012453245,
-                        "SWcld_alt":-0.0002577775091932694,
-                        "SWcld_amt":0.039489765128850406,
-                        "SWcld_err":-0.00013427856365681072,
-                        "SWcld_tau":-0.00537454341776731,
-                        "SWcld_tot":0.033723165638233014
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-                "ocn":{
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-                        "LWcld_err":0.03322899122006842,
-                        "LWcld_tau":0.038503581536542465,
-                        "LWcld_tot":0.08809559640723191,
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-                        "SWcld_amt":0.3125319606881875,
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-                        "SWcld_tot":0.25904875134432587
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-                        "LWcld_err":0.0008631976841254335,
-                        "LWcld_tau":-0.00473501061816705,
-                        "LWcld_tot":-0.08720021337842322,
-                        "SWcld_alt":0.005096788039390028,
-                        "SWcld_amt":0.38905002794172644,
-                        "SWcld_err":-0.0010679445832103927,
-                        "SWcld_tau":0.061756809073807366,
-                        "SWcld_tot":0.45483568047171336
-                    }
-                },
-                "ocn_asc":{
-                    "ALL":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN
-                    },
-                    "HI680":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN
-                    },
-                    "LO680":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN
-                    }
-                },
-                "ocn_dsc":{
-                    "ALL":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN
-                    },
-                    "HI680":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN
-                    },
-                    "LO680":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN
-                    }
-                }
-            },
-            "eq50":{
-                "all":{
-                    "ALL":{
-                        "LWcld_alt":0.406060746835391,
-                        "LWcld_amt":-0.5272004477968132,
-                        "LWcld_err":0.04644421725519247,
-                        "LWcld_tau":0.048859058215942754,
-                        "LWcld_tot":-0.025836425490286963,
-                        "SWcld_alt":-0.04636705202856898,
-                        "SWcld_amt":1.078435953799018,
-                        "SWcld_err":-0.004166303548442045,
-                        "SWcld_tau":-0.059381687771157717,
-                        "SWcld_tot":0.9685209104508492
-                    },
-                    "HI680":{
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-                        "LWcld_amt":-0.32328416264694,
-                        "LWcld_err":0.04248741394363076,
-                        "LWcld_tau":0.06333030849769865,
-                        "LWcld_tot":0.09305197786576681,
-                        "SWcld_alt":-0.02495108722680083,
-                        "SWcld_amt":0.4852306089177425,
-                        "SWcld_err":-0.001102036486246083,
-                        "SWcld_tau":-0.09835295376484414,
-                        "SWcld_tot":0.36082453143985144
-                    },
-                    "LO680":{
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-                        "LWcld_amt":-0.1042036968366478,
-                        "LWcld_err":0.0011979394636322026,
-                        "LWcld_tau":-0.0036392829610576286,
-                        "LWcld_tot":-0.11888840335605382,
-                        "SWcld_alt":0.0051996303826740324,
-                        "SWcld_amt":0.5473613433231842,
-                        "SWcld_err":-0.0012893160488745413,
-                        "SWcld_tau":0.05642593987830354,
-                        "SWcld_tot":0.6076975975352872
-                    }
-                },
-                "lnd":{
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-                        "LWcld_alt":0.09663049105729109,
-                        "LWcld_amt":-0.14693038027587538,
-                        "LWcld_err":0.006412095268529369,
-                        "LWcld_tau":0.02592325780104866,
-                        "LWcld_tot":-0.017964536149006255,
-                        "SWcld_alt":-0.01208064451070841,
-                        "SWcld_amt":0.2444769491273238,
-                        "SWcld_err":-4.153917977690538e-06,
-                        "SWcld_tau":-0.08093078676423009,
-                        "SWcld_tot":0.1514613639344076
-                    },
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-                        "LWcld_amt":-0.11423765686661054,
-                        "LWcld_err":0.009520692173123818,
-                        "LWcld_tau":0.021361397953341178,
-                        "LWcld_tot":-0.009434781140695165,
-                        "SWcld_alt":-0.0064878654687201736,
-                        "SWcld_amt":0.1774232609608578,
-                        "SWcld_err":-0.001348591235959453,
-                        "SWcld_tau":-0.062321325268118344,
-                        "SWcld_tot":0.10726547898805987
-                    },
-                    "LO680":{
-                        "LWcld_alt":0.000724069213319245,
-                        "LWcld_amt":-0.010717723115983709,
-                        "LWcld_err":0.00017565645313164825,
-                        "LWcld_tau":0.0012879089427255766,
-                        "LWcld_tot":-0.00852975500831109,
-                        "SWcld_alt":-0.0002901828963461132,
-                        "SWcld_amt":0.053911029072480125,
-                        "SWcld_err":-0.00014189403029392074,
-                        "SWcld_tau":-0.00928190209188196,
-                        "SWcld_tot":0.044197050053958124
-                    }
-                },
-                "ocn":{
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-                        "LWcld_alt":0.3094302557780999,
-                        "LWcld_amt":-0.3802700675209378,
-                        "LWcld_err":0.040032121986663095,
-                        "LWcld_tau":0.022935800414894097,
-                        "LWcld_tot":-0.00787188934128071,
-                        "SWcld_alt":-0.03428640751786056,
-                        "SWcld_amt":0.8339590046716941,
-                        "SWcld_err":-0.004162149630464356,
-                        "SWcld_tau":0.02154909899307236,
-                        "SWcld_tot":0.8170595465164415
-                    },
-                    "HI680":{
-                        "LWcld_alt":0.23659763247192692,
-                        "LWcld_amt":-0.20904650578032938,
-                        "LWcld_err":0.03296672177050695,
-                        "LWcld_tau":0.041968910544357495,
-                        "LWcld_tot":0.102486759006462,
-                        "SWcld_alt":-0.018463221758080656,
-                        "SWcld_amt":0.30780734795688464,
-                        "SWcld_err":0.0002465547497133701,
-                        "SWcld_tau":-0.03603162849672579,
-                        "SWcld_tot":0.2535590524517916
-                    },
-                    "LO680":{
-                        "LWcld_alt":-0.012967785838067005,
-                        "LWcld_amt":-0.09348597372066408,
-                        "LWcld_err":0.0010222830105005543,
-                        "LWcld_tau":-0.0049271919037832046,
-                        "LWcld_tot":-0.11035864834774271,
-                        "SWcld_alt":0.005489813279020146,
-                        "SWcld_amt":0.493450314250704,
-                        "SWcld_err":-0.0011474220185806205,
-                        "SWcld_tau":0.06570784197018549,
-                        "SWcld_tot":0.5635005474813292
-                    }
-                },
-                "ocn_asc":{
-                    "ALL":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN
-                    },
-                    "HI680":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN
-                    },
-                    "LO680":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN
-                    }
-                },
-                "ocn_dsc":{
-                    "ALL":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN
-                    },
-                    "HI680":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN
-                    },
-                    "LO680":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN
-                    }
-                }
-            },
-            "eq60":{
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-                    "ALL":{
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-                        "LWcld_amt":-0.5571870113074863,
-                        "LWcld_err":0.047161210929761936,
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-                        "LWcld_err":0.042203228534128695,
-                        "LWcld_tau":0.07916037707147512,
-                        "LWcld_tot":0.15888967540501076,
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-                        "SWcld_amt":0.466923726808722,
-                        "SWcld_err":-0.0007694154594471786,
-                        "SWcld_tau":-0.16591246492344125,
-                        "SWcld_tot":0.2732430819976401
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-                        "LWcld_amt":-0.12381142524288528,
-                        "LWcld_err":0.00128370354142105,
-                        "LWcld_tau":-0.0005137382895157909,
-                        "LWcld_tot":-0.13646118411235691,
-                        "SWcld_alt":0.005755953145527231,
-                        "SWcld_amt":0.6245364219000306,
-                        "SWcld_err":-0.0012696426367124764,
-                        "SWcld_tau":0.029137428409749005,
-                        "SWcld_tot":0.6581601608185944
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-                "lnd":{
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-                        "LWcld_amt":-0.1579153851564776,
-                        "LWcld_err":0.006465636165125234,
-                        "LWcld_tau":0.03650901792462801,
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-                        "SWcld_amt":0.27212439332645505,
-                        "SWcld_err":0.00021001265443953705,
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-                        "SWcld_tot":0.054718863463844934
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-                        "LWcld_err":0.04069557476463669,
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-                        "LWcld_err":0.03266333747965292,
-                        "LWcld_tau":0.04921195145507414,
-                        "LWcld_tot":0.1551464604711433,
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-                        "SWcld_amt":0.2800423697616649,
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-                        "LWcld_err":0.0010797702031655554,
-                        "LWcld_tau":-0.0035125758445189326,
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-                        "SWcld_tot":0.6034412973547494
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-                },
-                "ocn_asc":{
-                    "ALL":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN
-                    },
-                    "HI680":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN
-                    },
-                    "LO680":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN
-                    }
-                },
-                "ocn_dsc":{
-                    "ALL":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN
-                    },
-                    "HI680":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN
-                    },
-                    "LO680":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN
-                    }
-                }
-            },
-            "eq70":{
-                "all":{
-                    "ALL":{
-                        "LWcld_alt":0.4978476270268308,
-                        "LWcld_amt":-0.5671848000594746,
-                        "LWcld_err":0.05012671490331105,
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-                        "SWcld_amt":1.1685938723873128,
-                        "SWcld_err":-0.002873663086269113,
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-                        "SWcld_tot":0.8821280658405678
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-                        "LWcld_err":0.04402271653246689,
-                        "LWcld_tau":0.09715849742645137,
-                        "LWcld_tot":0.21003437189529747,
-                        "SWcld_alt":-0.028289516748696997,
-                        "SWcld_amt":0.45546918442470147,
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-                        "SWcld_tau":-0.21371111209439012,
-                        "SWcld_tot":0.21269337099474447
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-                        "LWcld_err":0.0011550013240834193,
-                        "LWcld_tau":0.0017138064999015492,
-                        "LWcld_tot":-0.14117699291643715,
-                        "SWcld_alt":0.005919281879330056,
-                        "SWcld_amt":0.6513889263494069,
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-                        "SWcld_tau":0.013332342954535776,
-                        "SWcld_tot":0.6694359157536082
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-                        "LWcld_err":0.010333316429209155,
-                        "LWcld_tau":0.04075928266102391,
-                        "LWcld_tot":0.02172269130532097,
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-                        "SWcld_amt":0.18866347406186337,
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-                        "LWcld_tau":0.004042255315092158,
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-                        "SWcld_tau":-0.018400686435772896,
-                        "SWcld_tot":0.06229033572336129
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-                        "LWcld_err":0.04244406171992205,
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-                        "LWcld_tau":0.05639921476542745,
-                        "LWcld_tot":0.18831168058997647,
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-                        "LWcld_tot":-0.13031461360664112,
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-                        "SWcld_amt":0.5701656695015452,
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-                        "SWcld_tau":0.03173302939030866,
-                        "SWcld_tot":0.6071455800302469
-                    }
-                },
-                "ocn_asc":{
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-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN
-                    },
-                    "HI680":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN
-                    },
-                    "LO680":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN
-                    }
-                },
-                "ocn_dsc":{
-                    "ALL":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN
-                    },
-                    "HI680":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN
-                    },
-                    "LO680":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN
-                    }
-                }
-            },
-            "eq80":{
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-                    "ALL":{
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-                        "LWcld_err":0.051613123790748554,
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-                        "SWcld_tot":0.864271453439969
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-                        "SWcld_tau":0.009710288870792257,
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-                        "SWcld_tau":-0.0190648060981829,
-                        "SWcld_tot":0.06291841290016449
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-                },
-                "ocn":{
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-                        "LWcld_amt":-0.40398081241640177,
-                        "LWcld_err":0.04351226087367437,
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-                        "LWcld_tot":0.0703235154657056,
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-                        "SWcld_amt":0.8800329397273264,
-                        "SWcld_err":-0.0031385012631201907,
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-                        "LWcld_err":0.03435819519708302,
-                        "LWcld_tau":0.05997023194951895,
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-                        "SWcld_alt":-0.020819289285007248,
-                        "SWcld_amt":0.2594270082848424,
-                        "SWcld_err":0.0004248382118108595,
-                        "SWcld_tau":-0.1273217411395865,
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-                        "SWcld_alt":0.006263814080954022,
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-                        "SWcld_tau":0.028775094968975155,
-                        "SWcld_tot":0.6079917583343845
-                    }
-                },
-                "ocn_asc":{
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-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN
-                    },
-                    "HI680":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN
-                    },
-                    "LO680":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN
-                    }
-                },
-                "ocn_dsc":{
-                    "ALL":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN
-                    },
-                    "HI680":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN
-                    },
-                    "LO680":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN
-                    }
-                }
-            },
-            "eq90":{
-                "all":{
-                    "ALL":{
-                        "LWcld_alt":0.5084348085081204,
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-                        "LWcld_err":0.05184568893849822,
-                        "LWcld_tau":0.10396229914884147,
-                        "LWcld_tot":0.09640835412181319,
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-                        "SWcld_amt":1.1674872896056787,
-                        "SWcld_err":-0.002902417900361391,
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-                        "LWcld_amt":-0.2880971413677567,
-                        "LWcld_err":0.04514836029093874,
-                        "LWcld_tau":0.11113229304671735,
-                        "LWcld_tot":0.23808409740039735,
-                        "SWcld_alt":-0.02863169795361854,
-                        "SWcld_amt":0.44475446784076555,
-                        "SWcld_err":-0.0008381255669868186,
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-                        "SWcld_tot":0.18862536966653076
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-                        "LWcld_err":0.0010738096442934272,
-                        "LWcld_tau":0.002816918403871965,
-                        "LWcld_tot":-0.14167574327858415,
-                        "SWcld_alt":0.005832209462747737,
-                        "SWcld_amt":0.6575184926020722,
-                        "SWcld_err":-0.0011819440282386236,
-                        "SWcld_tau":0.008432220145121051,
-                        "SWcld_tot":0.6706009781817023
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-                "lnd":{
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-                        "LWcld_err":0.008151593771732286,
-                        "LWcld_tau":0.058113075090315804,
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-                        "SWcld_amt":0.28801414908872075,
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-                        "SWcld_tot":0.06292679563351943
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-                        "LWcld_err":0.04369409516676593,
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-                        "SWcld_tot":0.7153004785811113
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-                        "SWcld_amt":0.2572059871362408,
-                        "SWcld_err":0.00041577205071671985,
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-                        "LWcld_err":0.0008945293302183645,
-                        "LWcld_tau":-0.0014652797235368104,
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-                        "SWcld_amt":0.5749298753483096,
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-                        "SWcld_tau":0.02754124942569582,
-                        "SWcld_tot":0.607674182548183
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-                "ocn_asc":{
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-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN
-                    },
-                    "HI680":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN
-                    },
-                    "LO680":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN
-                    }
-                },
-                "ocn_dsc":{
-                    "ALL":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN
-                    },
-                    "HI680":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN
-                    },
-                    "LO680":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN
-                    }
-                }
-            }
-        }
-    },
-    "MIROC6":{
-        "r1i1p1f1":{
-            "Arctic":{
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-                        "LWcld_amt":0.009098277085355834,
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-                        "SWcld_err":1.0720827854063817e-05,
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-                        "LWcld_err":5.3762890181843844e-05,
-                        "LWcld_tau":0.0004254538877747033,
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-                        "SWcld_alt":-0.0005940020254014517,
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-                        "SWcld_err":9.047505465430594e-06,
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-                        "SWcld_tot":0.0019437620171892658
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-                        "SWcld_err":4.2638678657999875e-05,
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-                        "LWcld_tau":0.001151991315700412,
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-                        "SWcld_alt":7.219875321357699e-05,
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-                        "SWcld_err":1.700817280790813e-05,
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-                        "LWcld_amt":0.00021001751025149487,
-                        "LWcld_err":1.2631440995702129e-05,
-                        "LWcld_tau":0.0001174164699500593,
-                        "LWcld_tot":0.0005188132060153028,
-                        "SWcld_alt":-8.984131675820987e-05,
-                        "SWcld_amt":0.00030740404886757475,
-                        "SWcld_err":-1.2224811880651293e-06,
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-                        "SWcld_tot":-0.00043419786394327254
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-                },
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-                        "LWcld_amt":0.006686504529982041,
-                        "LWcld_err":-0.00028091615012506926,
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-                        "SWcld_err":0.0001021530426239319,
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-                        "LWcld_amt":0.012564379539406628,
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-                        "LWcld_tau":0.0022451153008960795,
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-                        "SWcld_alt":0.00046464010082144684,
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-                        "SWcld_err":-5.312724239838512e-06,
-                        "SWcld_tau":-0.002190222989564406,
-                        "SWcld_tot":-0.014850554650784848
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-                        "LWcld_err":4.601898465721841e-05,
-                        "LWcld_tau":0.00034671504398598065,
-                        "LWcld_tot":0.0014717085489785976,
-                        "SWcld_alt":-0.0005581608927706465,
-                        "SWcld_amt":0.004723988511225782,
-                        "SWcld_err":1.109248715035305e-05,
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-                        "SWcld_tot":0.0025546655190588356
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-                },
-                "ocn_asc":{
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-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN
-                    },
-                    "HI680":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN
-                    },
-                    "LO680":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN
-                    }
-                },
-                "ocn_dsc":{
-                    "ALL":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN
-                    },
-                    "HI680":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN
-                    },
-                    "LO680":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN
-                    }
-                }
-            },
-            "eq10":{
-                "all":{
-                    "ALL":{
-                        "LWcld_alt":0.0021587666674808682,
-                        "LWcld_amt":-0.10042910181818958,
-                        "LWcld_err":-0.015691426303468918,
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-                        "SWcld_amt":0.19935278708641227,
-                        "SWcld_err":0.004005464555476694,
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-                        "SWcld_tot":0.19695351940684577
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-                        "LWcld_err":-0.010273824019277768,
-                        "LWcld_tau":-0.029214547595711234,
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-                        "SWcld_amt":0.20467849995923057,
-                        "SWcld_err":0.0007694818999791509,
-                        "SWcld_tau":0.02361770663626363,
-                        "SWcld_tot":0.22299942941119436
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-                        "LWcld_err":0.00018337189724587793,
-                        "LWcld_tau":0.00033485947300236434,
-                        "LWcld_tot":0.010007371285513583,
-                        "SWcld_alt":-0.005706872936225376,
-                        "SWcld_amt":0.00855155490033833,
-                        "SWcld_err":-0.0006184904906466089,
-                        "SWcld_tau":-0.028272101477814917,
-                        "SWcld_tot":-0.026045910004348565
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-                },
-                "lnd":{
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-                        "LWcld_err":-0.006744651749300185,
-                        "LWcld_tau":-0.00721839706102394,
-                        "LWcld_tot":-0.061783960659847766,
-                        "SWcld_alt":0.00016589244950322452,
-                        "SWcld_amt":0.08133742625120947,
-                        "SWcld_err":0.0010596554742983962,
-                        "SWcld_tau":0.004604427541520296,
-                        "SWcld_tot":0.08716740171653138
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-                        "LWcld_amt":-0.0428766914504883,
-                        "LWcld_err":-0.0051945876716522526,
-                        "LWcld_tau":-0.00915992707443689,
-                        "LWcld_tot":-0.06306803750019459,
-                        "SWcld_alt":0.0009854717636817265,
-                        "SWcld_amt":0.07072139116615257,
-                        "SWcld_err":0.0005569025092434296,
-                        "SWcld_tau":0.008053366237527644,
-                        "SWcld_tot":0.08031713167660538
-                    },
-                    "LO680":{
-                        "LWcld_alt":0.002583661502411134,
-                        "LWcld_amt":-0.0013656009929321397,
-                        "LWcld_err":2.2360524242900134e-05,
-                        "LWcld_tau":4.365580662494462e-05,
-                        "LWcld_tot":0.001284076840346839,
-                        "SWcld_alt":-0.0014628144411079156,
-                        "SWcld_amt":0.011728229098166995,
-                        "SWcld_err":-1.8323092007450867e-05,
-                        "SWcld_tau":-0.0033968215251256252,
-                        "SWcld_tot":0.006850270039926003
-                    }
-                },
-                "ocn":{
-                    "ALL":{
-                        "LWcld_alt":0.00746423664522181,
-                        "LWcld_amt":-0.05791365994640687,
-                        "LWcld_err":-0.008946774554168733,
-                        "LWcld_tau":-0.0113805784213937,
-                        "LWcld_tot":-0.0707767762767475,
-                        "SWcld_alt":-0.0045815526002389835,
-                        "SWcld_amt":0.11801536083520281,
-                        "SWcld_err":0.0029458090811782986,
-                        "SWcld_tau":-0.006593499625827749,
-                        "SWcld_tot":0.10978611769031438
-                    },
-                    "HI680":{
-                        "LWcld_alt":0.027043054499418604,
-                        "LWcld_amt":-0.08140926835243298,
-                        "LWcld_err":-0.005079236347625514,
-                        "LWcld_tau":-0.02005462052127435,
-                        "LWcld_tot":-0.07950007072191424,
-                        "SWcld_alt":-0.007051730847960726,
-                        "SWcld_amt":0.13395710879307796,
-                        "SWcld_err":0.00021257939073572143,
-                        "SWcld_tau":0.015564340398735985,
-                        "SWcld_tot":0.14268229773458893
-                    },
-                    "LO680":{
-                        "LWcld_alt":0.008338240458258503,
-                        "LWcld_amt":-6.716105247215745e-05,
-                        "LWcld_err":0.00016101137300297778,
-                        "LWcld_tau":0.0002912036663774197,
-                        "LWcld_tot":0.008723294445166743,
-                        "SWcld_alt":-0.004244058495117459,
-                        "SWcld_amt":-0.003176674197828664,
-                        "SWcld_err":-0.0006001673986391581,
-                        "SWcld_tau":-0.02487527995268929,
-                        "SWcld_tot":-0.03289618004427457
-                    }
-                },
-                "ocn_asc":{
-                    "ALL":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN
-                    },
-                    "HI680":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN
-                    },
-                    "LO680":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN
-                    }
-                },
-                "ocn_dsc":{
-                    "ALL":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN
-                    },
-                    "HI680":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN
-                    },
-                    "LO680":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN
-                    }
-                }
-            },
-            "eq20":{
-                "all":{
-                    "ALL":{
-                        "LWcld_alt":0.018941701120734997,
-                        "LWcld_amt":-0.1736251747761445,
-                        "LWcld_err":-0.017676739438386702,
-                        "LWcld_tau":-0.01724156924904829,
-                        "LWcld_tot":-0.1896017823428445,
-                        "SWcld_alt":-0.011613979280336173,
-                        "SWcld_amt":0.3232032915738558,
-                        "SWcld_err":0.005912829462049192,
-                        "SWcld_tau":-0.053198778372139786,
-                        "SWcld_tot":0.26430336338342914
-                    },
-                    "HI680":{
-                        "LWcld_alt":0.033523056200022036,
-                        "LWcld_amt":-0.19694375658088384,
-                        "LWcld_err":-0.010664473101487689,
-                        "LWcld_tau":-0.029906539888143343,
-                        "LWcld_tot":-0.20399171337049282,
-                        "SWcld_alt":-0.009265758989263616,
-                        "SWcld_amt":0.30230443223229847,
-                        "SWcld_err":0.0010701978068779156,
-                        "SWcld_tau":-0.014287724095568192,
-                        "SWcld_tot":0.27982114695434457
-                    },
-                    "LO680":{
-                        "LWcld_alt":0.018105957518637087,
-                        "LWcld_amt":-0.004412856874834062,
-                        "LWcld_err":0.0002576237578722189,
-                        "LWcld_tau":0.000438980081451172,
-                        "LWcld_tot":0.014389931027648364,
-                        "SWcld_alt":-0.00936209924204588,
-                        "SWcld_amt":0.03491681139559226,
-                        "SWcld_err":-0.0008705998758954733,
-                        "SWcld_tau":-0.04020086141458585,
-                        "SWcld_tot":-0.01551674913693494
-                    }
-                },
-                "lnd":{
-                    "ALL":{
-                        "LWcld_alt":-0.004764350049046737,
-                        "LWcld_amt":-0.079914571077578,
-                        "LWcld_err":-0.009303193036071024,
-                        "LWcld_tau":-0.007190352767268385,
-                        "LWcld_tot":-0.10117246692996414,
-                        "SWcld_alt":-0.0005541478271550533,
-                        "SWcld_amt":0.14154670034257041,
-                        "SWcld_err":0.001316294012649763,
-                        "SWcld_tau":-0.00041109830919736426,
-                        "SWcld_tot":0.1418977482188678
-                    },
-                    "HI680":{
-                        "LWcld_alt":-0.009100319362983337,
-                        "LWcld_amt":-0.07604831921626869,
-                        "LWcld_err":-0.007238076606341264,
-                        "LWcld_tau":-0.009015165865503319,
-                        "LWcld_tot":-0.1014018810510966,
-                        "SWcld_alt":0.0013593027943192611,
-                        "SWcld_amt":0.11711739157022906,
-                        "SWcld_err":0.0007198106365392345,
-                        "SWcld_tau":0.0032570912501507497,
-                        "SWcld_tot":0.1224535962512383
-                    },
-                    "LO680":{
-                        "LWcld_alt":0.0034777772226930273,
-                        "LWcld_amt":-0.0033336863402589987,
-                        "LWcld_err":1.959159852081654e-05,
-                        "LWcld_tau":6.550872946394823e-05,
-                        "LWcld_tot":0.00022941412113245871,
-                        "SWcld_alt":-0.001971380494850385,
-                        "SWcld_amt":0.02585070266790934,
-                        "SWcld_err":2.027467261079709e-06,
-                        "SWcld_tau":-0.004436179301375382,
-                        "SWcld_tot":0.019445170338944644
-                    }
-                },
-                "ocn":{
-                    "ALL":{
-                        "LWcld_alt":0.023706051169781726,
-                        "LWcld_amt":-0.0937106036985665,
-                        "LWcld_err":-0.008373546402315676,
-                        "LWcld_tau":-0.010051216481779904,
-                        "LWcld_tot":-0.08842931541288035,
-                        "SWcld_alt":-0.011059831453181122,
-                        "SWcld_amt":0.1816565912312854,
-                        "SWcld_err":0.004596535449399429,
-                        "SWcld_tau":-0.052787680062942426,
-                        "SWcld_tot":0.12240561516456132
-                    },
-                    "HI680":{
-                        "LWcld_alt":0.04262337556300536,
-                        "LWcld_amt":-0.12089543736461517,
-                        "LWcld_err":-0.0034263964951464256,
-                        "LWcld_tau":-0.020891374022640023,
-                        "LWcld_tot":-0.10258983231939624,
-                        "SWcld_alt":-0.010625061783582879,
-                        "SWcld_amt":0.1851870406620694,
-                        "SWcld_err":0.0003503871703386811,
-                        "SWcld_tau":-0.01754481534571894,
-                        "SWcld_tot":0.15736755070310626
-                    },
-                    "LO680":{
-                        "LWcld_alt":0.014628180295944064,
-                        "LWcld_amt":-0.001079170534575063,
-                        "LWcld_err":0.00023803215935140233,
-                        "LWcld_tau":0.0003734713519872237,
-                        "LWcld_tot":0.014160516906515902,
-                        "SWcld_alt":-0.007390718747195495,
-                        "SWcld_amt":0.009066108727682926,
-                        "SWcld_err":-0.0008726273431565529,
-                        "SWcld_tau":-0.03576468211321046,
-                        "SWcld_tot":-0.03496191947587959
-                    }
-                },
-                "ocn_asc":{
-                    "ALL":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN
-                    },
-                    "HI680":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN
-                    },
-                    "LO680":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN
-                    }
-                },
-                "ocn_dsc":{
-                    "ALL":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN
-                    },
-                    "HI680":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN
-                    },
-                    "LO680":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN
-                    }
-                }
-            },
-            "eq30":{
-                "all":{
-                    "ALL":{
-                        "LWcld_alt":0.06651643746211003,
-                        "LWcld_amt":-0.19813008636992716,
-                        "LWcld_err":-0.016710891706609787,
-                        "LWcld_tau":-0.01588991532974082,
-                        "LWcld_tot":-0.16421445594416778,
-                        "SWcld_alt":-0.022758607168520253,
-                        "SWcld_amt":0.3760864177377277,
-                        "SWcld_err":0.007167953725894413,
-                        "SWcld_tau":-0.08766754436742956,
-                        "SWcld_tot":0.27282821992767237
-                    },
-                    "HI680":{
-                        "LWcld_alt":0.04170161291129334,
-                        "LWcld_amt":-0.18392445819592673,
-                        "LWcld_err":-0.007656646794413836,
-                        "LWcld_tau":-0.022834360245853863,
-                        "LWcld_tot":-0.17271385232490108,
-                        "SWcld_alt":-0.010058071511718522,
-                        "SWcld_amt":0.2844053154839432,
-                        "SWcld_err":0.0015293897798613993,
-                        "SWcld_tau":-0.05394368606629888,
-                        "SWcld_tot":0.22193294768578722
-                    },
-                    "LO680":{
-                        "LWcld_alt":0.02173754348280449,
-                        "LWcld_amt":-0.013885484974802515,
-                        "LWcld_err":0.00030300631575965836,
-                        "LWcld_tau":0.0003434207991863303,
-                        "LWcld_tot":0.008499396380733312,
-                        "SWcld_alt":-0.011120321157367422,
-                        "SWcld_amt":0.1037326040487405,
-                        "SWcld_err":-0.000945868303866634,
-                        "SWcld_tau":-0.04076684775950142,
-                        "SWcld_tot":0.05089956682800503
-                    }
-                },
-                "lnd":{
-                    "ALL":{
-                        "LWcld_alt":0.0025471457307876754,
-                        "LWcld_amt":-0.100918148917912,
-                        "LWcld_err":-0.010340030403205601,
-                        "LWcld_tau":-0.007592453369139578,
-                        "LWcld_tot":-0.11630348695946952,
-                        "SWcld_alt":-0.0016951816743152762,
-                        "SWcld_amt":0.1862019459546428,
-                        "SWcld_err":0.0014195759752712296,
-                        "SWcld_tau":-0.000129468790098161,
-                        "SWcld_tot":0.18579687146550058
-                    },
-                    "HI680":{
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-                        "LWcld_amt":-0.09194721710086419,
-                        "LWcld_err":-0.007818177902358637,
-                        "LWcld_tau":-0.009189937350329426,
-                        "LWcld_tot":-0.11465553202285715,
-                        "SWcld_alt":0.0011361885219907926,
-                        "SWcld_amt":0.1467899212936596,
-                        "SWcld_err":0.0007924275491785279,
-                        "SWcld_tau":0.0031489407347411592,
-                        "SWcld_tot":0.15186747809957005
-                    },
-                    "LO680":{
-                        "LWcld_alt":0.003891243578445759,
-                        "LWcld_amt":-0.005628967577041057,
-                        "LWcld_err":1.4491101983518702e-05,
-                        "LWcld_tau":7.44291858589108e-05,
-                        "LWcld_tot":-0.0016479549366123693,
-                        "SWcld_alt":-0.002201697130045179,
-                        "SWcld_amt":0.04070037599480179,
-                        "SWcld_err":2.091322330642634e-05,
-                        "SWcld_tau":-0.004586227942507088,
-                        "SWcld_tot":0.03393336414555595
-                    }
-                },
-                "ocn":{
-                    "ALL":{
-                        "LWcld_alt":0.06396929173132233,
-                        "LWcld_amt":-0.09721193745201517,
-                        "LWcld_err":-0.006370861303404185,
-                        "LWcld_tau":-0.008297461960601242,
-                        "LWcld_tot":-0.047910968984698245,
-                        "SWcld_alt":-0.02106342549420498,
-                        "SWcld_amt":0.1898844717830849,
-                        "SWcld_err":0.005748377750623183,
-                        "SWcld_tau":-0.08753807557733138,
-                        "SWcld_tot":0.08703134846217174
-                    },
-                    "HI680":{
-                        "LWcld_alt":0.047401812580598245,
-                        "LWcld_amt":-0.09197724109506251,
-                        "LWcld_err":0.00016153110794480007,
-                        "LWcld_tau":-0.013644422895524436,
-                        "LWcld_tot":-0.05805832030204394,
-                        "SWcld_alt":-0.011194260033709316,
-                        "SWcld_amt":0.13761539419028365,
-                        "SWcld_err":0.0007369622306828712,
-                        "SWcld_tau":-0.05709262680104005,
-                        "SWcld_tot":0.07006546958621716
-                    },
-                    "LO680":{
-                        "LWcld_alt":0.017846299904358733,
-                        "LWcld_amt":-0.008256517397761455,
-                        "LWcld_err":0.0002885152137761397,
-                        "LWcld_tau":0.00026899161332741945,
-                        "LWcld_tot":0.01014735131734568,
-                        "SWcld_alt":-0.008918624027322243,
-                        "SWcld_amt":0.0630322280539387,
-                        "SWcld_err":-0.0009667815271730604,
-                        "SWcld_tau":-0.03618061981699433,
-                        "SWcld_tot":0.016966202682449073
-                    }
-                },
-                "ocn_asc":{
-                    "ALL":{
-                        "LWcld_alt":-0.0034863430155899883,
-                        "LWcld_amt":-0.014724235437504674,
-                        "LWcld_err":-0.0059724863225712535,
-                        "LWcld_tau":0.00014738104469901365,
-                        "LWcld_tot":-0.0240356837309669,
-                        "SWcld_alt":-0.004155842145215716,
-                        "SWcld_amt":0.025448551821763393,
-                        "SWcld_err":0.00448001099779229,
-                        "SWcld_tau":-0.07705511345879319,
-                        "SWcld_tot":-0.05128239278445324
-                    },
-                    "HI680":{
-                        "LWcld_alt":0.020562293946678186,
-                        "LWcld_amt":-0.0493030229657097,
-                        "LWcld_err":-0.0019428303093096488,
-                        "LWcld_tau":-0.00830788961523247,
-                        "LWcld_tot":-0.03899144894357364,
-                        "SWcld_alt":-0.006605636318940847,
-                        "SWcld_amt":0.07797815068467781,
-                        "SWcld_err":0.0008669127684140573,
-                        "SWcld_tau":-0.044207432838775006,
-                        "SWcld_tot":0.028031994295376016
-                    },
-                    "LO680":{
-                        "LWcld_alt":0.010528523163811865,
-                        "LWcld_amt":0.0037668430392416474,
-                        "LWcld_err":0.0001609748797819193,
-                        "LWcld_tau":0.0004994241297713145,
-                        "LWcld_tot":0.014955765212606748,
-                        "SWcld_alt":-0.005233419745854895,
-                        "SWcld_amt":-0.04026663975743713,
-                        "SWcld_err":-0.0006459523363514002,
-                        "SWcld_tau":-0.03316837524018581,
-                        "SWcld_tot":-0.07931438707982924
-                    }
-                },
-                "ocn_dsc":{
-                    "ALL":{
-                        "LWcld_alt":-0.019217897737727863,
-                        "LWcld_amt":-0.012107618826150733,
-                        "LWcld_err":0.0037030899907331896,
-                        "LWcld_tau":0.0017908181880259728,
-                        "LWcld_tot":-0.025831608385119446,
-                        "SWcld_alt":0.0023304564559894435,
-                        "SWcld_amt":0.14691253494351791,
-                        "SWcld_err":0.0008543075393596995,
-                        "SWcld_tau":-0.030652266294452838,
-                        "SWcld_tot":0.11944503264441425
-                    },
-                    "HI680":{
-                        "LWcld_alt":0.0032005882349512445,
-                        "LWcld_amt":-0.017762902421759787,
-                        "LWcld_err":-0.00029620175569773015,
-                        "LWcld_tau":-0.005166906978749085,
-                        "LWcld_tot":-0.020025422921255354,
-                        "SWcld_alt":-0.0002225695264523374,
-                        "SWcld_amt":0.0417657731618661,
-                        "SWcld_err":0.0014278741731967746,
-                        "SWcld_tau":-0.005847847088389438,
-                        "SWcld_tot":0.03712323072022112
-                    },
-                    "LO680":{
-                        "LWcld_alt":0.012822279821946348,
-                        "LWcld_amt":-0.018862343549322095,
-                        "LWcld_err":7.200502014415655e-06,
-                        "LWcld_tau":0.00022667776149724734,
-                        "LWcld_tot":-0.00580618546386408,
-                        "SWcld_alt":-0.006126732147217994,
-                        "SWcld_amt":0.10415408916188233,
-                        "SWcld_err":0.0001183702751442524,
-                        "SWcld_tau":-0.01582392536561548,
-                        "SWcld_tot":0.08232180192419312
-                    }
-                }
-            },
-            "eq40":{
-                "all":{
-                    "ALL":{
-                        "LWcld_alt":0.07641042689432233,
-                        "LWcld_amt":-0.22377056108819224,
-                        "LWcld_err":-0.017573761207717472,
-                        "LWcld_tau":-0.014562779853603214,
-                        "LWcld_tot":-0.17949667525519056,
-                        "SWcld_alt":-0.0254946821930345,
-                        "SWcld_amt":0.43139192942181676,
-                        "SWcld_err":0.008165264803999036,
-                        "SWcld_tau":-0.11486465455673273,
-                        "SWcld_tot":0.29919785747604855
-                    },
-                    "HI680":{
-                        "LWcld_alt":0.04757710540303039,
-                        "LWcld_amt":-0.21029762890163398,
-                        "LWcld_err":-0.009760687257272282,
-                        "LWcld_tau":-0.019338944543748866,
-                        "LWcld_tot":-0.19182015529962473,
-                        "SWcld_alt":-0.010064600641190566,
-                        "SWcld_amt":0.3297479793608613,
-                        "SWcld_err":0.0025949049114332777,
-                        "SWcld_tau":-0.08362584327799726,
-                        "SWcld_tot":0.23865244035310684
-                    },
-                    "LO680":{
-                        "LWcld_alt":0.027013426072327724,
-                        "LWcld_amt":-0.015480821097108527,
-                        "LWcld_err":0.00040307445055682575,
-                        "LWcld_tau":0.00038671814916092003,
-                        "LWcld_tot":0.012323480044434149,
-                        "SWcld_alt":-0.013498405518758172,
-                        "SWcld_amt":0.11797402291027843,
-                        "SWcld_err":-0.001056735511781525,
-                        "SWcld_tau":-0.042868031347046066,
-                        "SWcld_tot":0.06055085053269268
-                    }
-                },
-                "lnd":{
-                    "ALL":{
-                        "LWcld_alt":0.01081434927626143,
-                        "LWcld_amt":-0.11197528836129132,
-                        "LWcld_err":-0.011847659409284672,
-                        "LWcld_tau":-0.008518276003009572,
-                        "LWcld_tot":-0.12152687449732413,
-                        "SWcld_alt":-0.0027974159113087487,
-                        "SWcld_amt":0.20768375636747927,
-                        "SWcld_err":0.0015404917969168094,
-                        "SWcld_tau":-0.0014253660454481877,
-                        "SWcld_tot":0.20500146620763918
-                    },
-                    "HI680":{
-                        "LWcld_alt":8.982855533837047e-05,
-                        "LWcld_amt":-0.09952655360523398,
-                        "LWcld_err":-0.008963593419755852,
-                        "LWcld_tau":-0.010190488223814043,
-                        "LWcld_tot":-0.11859080669346551,
-                        "SWcld_alt":0.0006045039174549508,
-                        "SWcld_amt":0.15937448278329486,
-                        "SWcld_err":0.0008834762300959628,
-                        "SWcld_tau":0.0020111343039385947,
-                        "SWcld_tot":0.16287359723478437
-                    },
-                    "LO680":{
-                        "LWcld_alt":0.004007278156383426,
-                        "LWcld_amt":-0.007087860595204763,
-                        "LWcld_err":1.2837776552577437e-05,
-                        "LWcld_tau":0.00013065637255777862,
-                        "LWcld_tot":-0.0029360678038586278,
-                        "SWcld_alt":-0.002272785586023702,
-                        "SWcld_amt":0.04943741263035298,
-                        "SWcld_err":2.5223882142396554e-05,
-                        "SWcld_tau":-0.0050568723503604225,
-                        "SWcld_tot":0.04213297857611125
-                    }
-                },
-                "ocn":{
-                    "ALL":{
-                        "LWcld_alt":0.0655960776180609,
-                        "LWcld_amt":-0.11179527272690089,
-                        "LWcld_err":-0.0057261017984328,
-                        "LWcld_tau":-0.006044503850593644,
-                        "LWcld_tot":-0.05796980075786645,
-                        "SWcld_alt":-0.02269726628172575,
-                        "SWcld_amt":0.22370817305433746,
-                        "SWcld_err":0.006624773007082225,
-                        "SWcld_tau":-0.11343928851128453,
-                        "SWcld_tot":0.0941963912684094
-                    },
-                    "HI680":{
-                        "LWcld_alt":0.04748727684769202,
-                        "LWcld_amt":-0.11077107529639997,
-                        "LWcld_err":-0.0007970938375164287,
-                        "LWcld_tau":-0.009148456319934826,
-                        "LWcld_tot":-0.07322934860615922,
-                        "SWcld_alt":-0.010669104558645517,
-                        "SWcld_amt":0.17037349657756648,
-                        "SWcld_err":0.001711428681337315,
-                        "SWcld_tau":-0.08563697758193585,
-                        "SWcld_tot":0.07577884311832246
-                    },
-                    "LO680":{
-                        "LWcld_alt":0.023006147915944297,
-                        "LWcld_amt":-0.008392960501903761,
-                        "LWcld_err":0.0003902366740042482,
-                        "LWcld_tau":0.0002560617766031414,
-                        "LWcld_tot":0.015259547848292773,
-                        "SWcld_alt":-0.011225619932734472,
-                        "SWcld_amt":0.06853661027992547,
-                        "SWcld_err":-0.0010819593939239217,
-                        "SWcld_tau":-0.03781115899668565,
-                        "SWcld_tot":0.018417871956581438
-                    }
-                },
-                "ocn_asc":{
-                    "ALL":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN
-                    },
-                    "HI680":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN
-                    },
-                    "LO680":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN
-                    }
-                },
-                "ocn_dsc":{
-                    "ALL":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN
-                    },
-                    "HI680":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN
-                    },
-                    "LO680":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN
-                    }
-                }
-            },
-            "eq50":{
-                "all":{
-                    "ALL":{
-                        "LWcld_alt":0.09555607203981363,
-                        "LWcld_amt":-0.2395194775869838,
-                        "LWcld_err":-0.022744516525538817,
-                        "LWcld_tau":-0.012915520729402307,
-                        "LWcld_tot":-0.17962344280211132,
-                        "SWcld_alt":-0.029235113956632825,
-                        "SWcld_amt":0.4774403317353066,
-                        "SWcld_err":0.009584902243318096,
-                        "SWcld_tau":-0.12606747236329996,
-                        "SWcld_tot":0.3317226476586919
-                    },
-                    "HI680":{
-                        "LWcld_alt":0.05964042662403187,
-                        "LWcld_amt":-0.22153472298072316,
-                        "LWcld_err":-0.013548376190486466,
-                        "LWcld_tau":-0.01953498377538063,
-                        "LWcld_tot":-0.19497765632255837,
-                        "SWcld_alt":-0.011155339938292099,
-                        "SWcld_amt":0.3560848654046271,
-                        "SWcld_err":0.003514333567278057,
-                        "SWcld_tau":-0.09160209736928344,
-                        "SWcld_tot":0.2568417616643296
-                    },
-                    "LO680":{
-                        "LWcld_alt":0.03155734903145256,
-                        "LWcld_amt":-0.017769419656976284,
-                        "LWcld_err":0.00044372106878906055,
-                        "LWcld_tau":0.0011214758204218208,
-                        "LWcld_tot":0.01535421352044703,
-                        "SWcld_alt":-0.015576566447200152,
-                        "SWcld_amt":0.13713954280203747,
-                        "SWcld_err":-0.0010571048158020508,
-                        "SWcld_tau":-0.04561952073165707,
-                        "SWcld_tot":0.07488635080737817
-                    }
-                },
-                "lnd":{
-                    "ALL":{
-                        "LWcld_alt":0.018808477538714646,
-                        "LWcld_amt":-0.11873294993030498,
-                        "LWcld_err":-0.01457355047667177,
-                        "LWcld_tau":-0.00911422226287304,
-                        "LWcld_tot":-0.12361224513113517,
-                        "SWcld_alt":-0.003922862300157313,
-                        "SWcld_amt":0.227140427910268,
-                        "SWcld_err":0.001872471586728439,
-                        "SWcld_tau":-0.001699574123232796,
-                        "SWcld_tot":0.22339046307360633
-                    },
-                    "HI680":{
-                        "LWcld_alt":0.004022172475356691,
-                        "LWcld_amt":-0.10142215436419205,
-                        "LWcld_err":-0.01078806848915923,
-                        "LWcld_tau":-0.011526195872611467,
-                        "LWcld_tot":-0.11971424625060605,
-                        "SWcld_alt":0.0002621814240946364,
-                        "SWcld_amt":0.16879810568871897,
-                        "SWcld_err":0.0010871469789946527,
-                        "SWcld_tau":0.003043897209470257,
-                        "SWcld_tot":0.17319133130127848
-                    },
-                    "LO680":{
-                        "LWcld_alt":0.004218940195691051,
-                        "LWcld_amt":-0.008485304486860537,
-                        "LWcld_err":1.2418587640115844e-05,
-                        "LWcld_tau":0.00035492154988524103,
-                        "LWcld_tot":-0.0038979988805291025,
-                        "SWcld_alt":-0.0023525503727029697,
-                        "SWcld_amt":0.05889076511782378,
-                        "SWcld_err":2.650352643457673e-05,
-                        "SWcld_tau":-0.006360445492706166,
-                        "SWcld_tot":0.050204272778849236
-                    }
-                },
-                "ocn":{
-                    "ALL":{
-                        "LWcld_alt":0.07674759450109896,
-                        "LWcld_amt":-0.12078652765667884,
-                        "LWcld_err":-0.008170966048867042,
-                        "LWcld_tau":-0.0038012984665292664,
-                        "LWcld_tot":-0.056011197670976176,
-                        "SWcld_alt":-0.02531225165647551,
-                        "SWcld_amt":0.2502999038250386,
-                        "SWcld_err":0.007712430656589657,
-                        "SWcld_tau":-0.12436789824006715,
-                        "SWcld_tot":0.10833218458508559
-                    },
-                    "HI680":{
-                        "LWcld_alt":0.05561825414867518,
-                        "LWcld_amt":-0.12011256861653109,
-                        "LWcld_err":-0.0027603077013272376,
-                        "LWcld_tau":-0.008008787902769167,
-                        "LWcld_tot":-0.07526341007195232,
-                        "SWcld_alt":-0.011417521362386737,
-                        "SWcld_amt":0.18728675971590814,
-                        "SWcld_err":0.002427186588283404,
-                        "SWcld_tau":-0.09464599457875371,
-                        "SWcld_tot":0.0836504303630511
-                    },
-                    "LO680":{
-                        "LWcld_alt":0.027338408835761507,
-                        "LWcld_amt":-0.009284115170115747,
-                        "LWcld_err":0.0004313024811489447,
-                        "LWcld_tau":0.0007665542705365798,
-                        "LWcld_tot":0.019252212400976135,
-                        "SWcld_alt":-0.013224016074497183,
-                        "SWcld_amt":0.07824877768421366,
-                        "SWcld_err":-0.0010836083422366275,
-                        "SWcld_tau":-0.03925907523895091,
-                        "SWcld_tot":0.024682078028528953
-                    }
-                },
-                "ocn_asc":{
-                    "ALL":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN
-                    },
-                    "HI680":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN
-                    },
-                    "LO680":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN
-                    }
-                },
-                "ocn_dsc":{
-                    "ALL":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN
-                    },
-                    "HI680":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN
-                    },
-                    "LO680":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN
-                    }
-                }
-            },
-            "eq60":{
-                "all":{
-                    "ALL":{
-                        "LWcld_alt":0.1261865056378272,
-                        "LWcld_amt":-0.2232481063104609,
-                        "LWcld_err":-0.026111311377900976,
-                        "LWcld_tau":-0.005896256237413057,
-                        "LWcld_tot":-0.12906916828794776,
-                        "SWcld_alt":-0.03619868424674171,
-                        "SWcld_amt":0.4612826130256507,
-                        "SWcld_err":0.010777204778935524,
-                        "SWcld_tau":-0.17247551309475193,
-                        "SWcld_tot":0.2633856204630926
-                    },
-                    "HI680":{
-                        "LWcld_alt":0.06558787039378196,
-                        "LWcld_amt":-0.18162656014624087,
-                        "LWcld_err":-0.015492628929774252,
-                        "LWcld_tau":-0.014782192459553626,
-                        "LWcld_tot":-0.14631351114178678,
-                        "SWcld_alt":-0.01188238798649568,
-                        "SWcld_amt":0.3010829066146083,
-                        "SWcld_err":0.003815285354143865,
-                        "SWcld_tau":-0.11383190631227212,
-                        "SWcld_tot":0.1791838976699844
-                    },
-                    "LO680":{
-                        "LWcld_alt":0.034757529918802356,
-                        "LWcld_amt":-0.020735022079091215,
-                        "LWcld_err":0.0004903070213230301,
-                        "LWcld_tau":0.0027304407360450073,
-                        "LWcld_tot":0.017244342853839058,
-                        "SWcld_alt":-0.01743763655941669,
-                        "SWcld_amt":0.16659609752166832,
-                        "SWcld_err":-0.0011090805229003557,
-                        "SWcld_tau":-0.06384219283322719,
-                        "SWcld_tot":0.08420718760612411
-                    }
-                },
-                "lnd":{
-                    "ALL":{
-                        "LWcld_alt":0.02332153960749273,
-                        "LWcld_amt":-0.1124817860872898,
-                        "LWcld_err":-0.016762306814261704,
-                        "LWcld_tau":-0.007492814342571079,
-                        "LWcld_tot":-0.11341536763662985,
-                        "SWcld_alt":-0.004844427021246919,
-                        "SWcld_amt":0.22814650769878983,
-                        "SWcld_err":0.0022131082663220353,
-                        "SWcld_tau":-0.00475030136826127,
-                        "SWcld_tot":0.22076488757560364
-                    },
-                    "HI680":{
-                        "LWcld_alt":0.007102393242148306,
-                        "LWcld_amt":-0.09439810026102043,
-                        "LWcld_err":-0.012063483072157585,
-                        "LWcld_tau":-0.01099773696070479,
-                        "LWcld_tot":-0.1103569270517345,
-                        "SWcld_alt":-0.0001388726128745419,
-                        "SWcld_amt":0.16535114136560372,
-                        "SWcld_err":0.001237287242156971,
-                        "SWcld_tau":0.0024324611756520293,
-                        "SWcld_tot":0.1688820171705382
-                    },
-                    "LO680":{
-                        "LWcld_alt":0.0042884115248653576,
-                        "LWcld_amt":-0.008150292081924285,
-                        "LWcld_err":1.6753875861105235e-05,
-                        "LWcld_tau":0.0007856608231874301,
-                        "LWcld_tot":-0.0030584405848953673,
-                        "SWcld_alt":-0.002394088210206238,
-                        "SWcld_amt":0.06269076155231584,
-                        "SWcld_err":2.7171010915374156e-05,
-                        "SWcld_tau":-0.008435832941438091,
-                        "SWcld_tot":0.05188801141158688
-                    }
-                },
-                "ocn":{
-                    "ALL":{
-                        "LWcld_alt":0.10286496603033447,
-                        "LWcld_amt":-0.11076632022317111,
-                        "LWcld_err":-0.009349004563639273,
-                        "LWcld_tau":0.0015965581051580237,
-                        "LWcld_tot":-0.015653800651317887,
-                        "SWcld_alt":-0.031354257225494786,
-                        "SWcld_amt":0.23313610532686083,
-                        "SWcld_err":0.008564096512613488,
-                        "SWcld_tau":-0.16772521172649066,
-                        "SWcld_tot":0.0426207328874889
-                    },
-                    "HI680":{
-                        "LWcld_alt":0.05848547715163366,
-                        "LWcld_amt":-0.08722845988522045,
-                        "LWcld_err":-0.003429145857616668,
-                        "LWcld_tau":-0.003784455498848834,
-                        "LWcld_tot":-0.03595658409005231,
-                        "SWcld_alt":-0.011743515373621142,
-                        "SWcld_amt":0.13573176524900457,
-                        "SWcld_err":0.002577998111986894,
-                        "SWcld_tau":-0.11626436748792414,
-                        "SWcld_tot":0.010301880499446183
-                    },
-                    "LO680":{
-                        "LWcld_alt":0.030469118393937,
-                        "LWcld_amt":-0.012584729997166928,
-                        "LWcld_err":0.0004735531454619249,
-                        "LWcld_tau":0.0019447799128575773,
-                        "LWcld_tot":0.020302783438734425,
-                        "SWcld_alt":-0.01504354834921045,
-                        "SWcld_amt":0.10390533596935249,
-                        "SWcld_err":-0.0011362515338157297,
-                        "SWcld_tau":-0.055406359891789096,
-                        "SWcld_tot":0.03231917619453723
-                    }
-                },
-                "ocn_asc":{
-                    "ALL":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN
-                    },
-                    "HI680":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN
-                    },
-                    "LO680":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN
-                    }
-                },
-                "ocn_dsc":{
-                    "ALL":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN
-                    },
-                    "HI680":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN
-                    },
-                    "LO680":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN
-                    }
-                }
-            },
-            "eq70":{
-                "all":{
-                    "ALL":{
-                        "LWcld_alt":0.14099608286583518,
-                        "LWcld_amt":-0.1998114150253215,
-                        "LWcld_err":-0.028101729124907784,
-                        "LWcld_tau":0.0038792426593270792,
-                        "LWcld_tot":-0.08303781862506698,
-                        "SWcld_alt":-0.03972203959406338,
-                        "SWcld_amt":0.43611087432998286,
-                        "SWcld_err":0.011466636347021622,
-                        "SWcld_tau":-0.2154992966077395,
-                        "SWcld_tot":0.19235617447520156
-                    },
-                    "HI680":{
-                        "LWcld_alt":0.06553277799044807,
-                        "LWcld_amt":-0.14589026673062142,
-                        "LWcld_err":-0.016842867143048806,
-                        "LWcld_tau":-0.0065036924382631985,
-                        "LWcld_tot":-0.10370404832148533,
-                        "SWcld_alt":-0.011768001940964055,
-                        "SWcld_amt":0.2591347095918935,
-                        "SWcld_err":0.003973271130780698,
-                        "SWcld_tau":-0.13862037262610913,
-                        "SWcld_tot":0.11271960615560106
-                    },
-                    "LO680":{
-                        "LWcld_alt":0.037013343071736926,
-                        "LWcld_amt":-0.020967439509313828,
-                        "LWcld_err":0.0006367064174296276,
-                        "LWcld_tau":0.003982532459805744,
-                        "LWcld_tot":0.020666229696418348,
-                        "SWcld_alt":-0.018503697691370814,
-                        "SWcld_amt":0.17837813251536636,
-                        "SWcld_err":-0.001109314334429286,
-                        "SWcld_tau":-0.07912308735694985,
-                        "SWcld_tot":0.0796420331326164
-                    }
-                },
-                "lnd":{
-                    "ALL":{
-                        "LWcld_alt":0.025322818676658105,
-                        "LWcld_amt":-0.09929895405670731,
-                        "LWcld_err":-0.018200862867994157,
-                        "LWcld_tau":-0.003319500673749955,
-                        "LWcld_tot":-0.09549649892179334,
-                        "SWcld_alt":-0.005531887844336527,
-                        "SWcld_amt":0.22059080379277485,
-                        "SWcld_err":0.0024554135752958297,
-                        "SWcld_tau":-0.01293264783514101,
-                        "SWcld_tot":0.20458168168859323
-                    },
-                    "HI680":{
-                        "LWcld_alt":0.008392965685721777,
-                        "LWcld_amt":-0.0816801528035421,
-                        "LWcld_err":-0.012827186377203184,
-                        "LWcld_tau":-0.0077481092639063226,
-                        "LWcld_tot":-0.09386248275892982,
-                        "SWcld_alt":-0.00030427351312089547,
-                        "SWcld_amt":0.15523758096862103,
-                        "SWcld_err":0.0013280239079131613,
-                        "SWcld_tau":-0.003179908215968183,
-                        "SWcld_tot":0.15308142314744513
-                    },
-                    "LO680":{
-                        "LWcld_alt":0.004499874261629184,
-                        "LWcld_amt":-0.007445750656362042,
-                        "LWcld_err":4.4229916316819994e-05,
-                        "LWcld_tau":0.001266605042437503,
-                        "LWcld_tot":-0.0016340161628635127,
-                        "SWcld_alt":-0.002485879086472312,
-                        "SWcld_amt":0.0646533448788056,
-                        "SWcld_err":2.4806140724000435e-05,
-                        "SWcld_tau":-0.01068687238538787,
-                        "SWcld_tot":0.05150539954766944
-                    }
-                },
-                "ocn":{
-                    "ALL":{
-                        "LWcld_alt":0.1156732641891771,
-                        "LWcld_amt":-0.10051246096861415,
-                        "LWcld_err":-0.009900866256913622,
-                        "LWcld_tau":0.007198743333077034,
-                        "LWcld_tot":0.012458680296726338,
-                        "SWcld_alt":-0.03419015174972685,
-                        "SWcld_amt":0.21552007053720798,
-                        "SWcld_err":0.009011222771725792,
-                        "SWcld_tau":-0.20256664877259847,
-                        "SWcld_tot":-0.012225507213391598
-                    },
-                    "HI680":{
-                        "LWcld_alt":0.05713981230472629,
-                        "LWcld_amt":-0.0642101139270793,
-                        "LWcld_err":-0.004015680765845621,
-                        "LWcld_tau":0.001244416825643122,
-                        "LWcld_tot":-0.009841565562555518,
-                        "SWcld_alt":-0.011463728427843156,
-                        "SWcld_amt":0.10389712862327248,
-                        "SWcld_err":0.0026452472228675372,
-                        "SWcld_tau":-0.13544046441014093,
-                        "SWcld_tot":-0.04036181699184408
-                    },
-                    "LO680":{
-                        "LWcld_alt":0.03251346881010774,
-                        "LWcld_amt":-0.01352168885295178,
-                        "LWcld_err":0.0005924765011128076,
-                        "LWcld_tau":0.00271592741736824,
-                        "LWcld_tot":0.02230024585928186,
-                        "SWcld_alt":-0.0160178186048985,
-                        "SWcld_amt":0.11372478763656074,
-                        "SWcld_err":-0.0011341204751532865,
-                        "SWcld_tau":-0.06843621497156199,
-                        "SWcld_tot":0.028136633584946974
-                    }
-                },
-                "ocn_asc":{
-                    "ALL":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN
-                    },
-                    "HI680":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN
-                    },
-                    "LO680":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN
-                    }
-                },
-                "ocn_dsc":{
-                    "ALL":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN
-                    },
-                    "HI680":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN
-                    },
-                    "LO680":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN
-                    }
-                }
-            },
-            "eq80":{
-                "all":{
-                    "ALL":{
-                        "LWcld_alt":0.1455495101035791,
-                        "LWcld_amt":-0.1857102093647608,
-                        "LWcld_err":-0.028567034850480788,
-                        "LWcld_tau":0.009711666244383807,
-                        "LWcld_tot":-0.05901606786727865,
-                        "SWcld_alt":-0.04086470735591853,
-                        "SWcld_amt":0.4264600586226846,
-                        "SWcld_err":0.011594654940343325,
-                        "SWcld_tau":-0.2264780218803845,
-                        "SWcld_tot":0.17071198432672494
-                    },
-                    "HI680":{
-                        "LWcld_alt":0.06489689407845083,
-                        "LWcld_amt":-0.12868661811797116,
-                        "LWcld_err":-0.017003137078646677,
-                        "LWcld_tau":-0.0015498985204349816,
-                        "LWcld_tot":-0.08234275963860202,
-                        "SWcld_alt":-0.011434938582391516,
-                        "SWcld_amt":0.24524054560369726,
-                        "SWcld_err":0.004031119304149673,
-                        "SWcld_tau":-0.14506836616197044,
-                        "SWcld_tot":0.09276836016348496
-                    },
-                    "LO680":{
-                        "LWcld_alt":0.03843675269167826,
-                        "LWcld_amt":-0.020471520686036936,
-                        "LWcld_err":0.0007219972316152156,
-                        "LWcld_tau":0.004638372804186841,
-                        "LWcld_tot":0.023326691771323373,
-                        "SWcld_alt":-0.019067971767493596,
-                        "SWcld_amt":0.18074261128383676,
-                        "SWcld_err":-0.0011235200321065189,
-                        "SWcld_tau":-0.08260203030755961,
-                        "SWcld_tot":0.07794908917667703
-                    }
-                },
-                "lnd":{
-                    "ALL":{
-                        "LWcld_alt":0.026133482464202315,
-                        "LWcld_amt":-0.09175452500200099,
-                        "LWcld_err":-0.01842929076656152,
-                        "LWcld_tau":-0.0005942879416056523,
-                        "LWcld_tot":-0.08464462124596585,
-                        "SWcld_alt":-0.005608354081189521,
-                        "SWcld_amt":0.21788539507473081,
-                        "SWcld_err":0.002532401812162035,
-                        "SWcld_tau":-0.016147075923347038,
-                        "SWcld_tot":0.19866236688235625
-                    },
-                    "HI680":{
-                        "LWcld_alt":0.009315285801573786,
-                        "LWcld_amt":-0.07490120607266586,
-                        "LWcld_err":-0.012810198949083437,
-                        "LWcld_tau":-0.005464443331622656,
-                        "LWcld_tot":-0.08386056255179818,
-                        "SWcld_alt":-0.0002736988392859268,
-                        "SWcld_amt":0.15251228791619822,
-                        "SWcld_err":0.0013686911390301153,
-                        "SWcld_tau":-0.005507555468039219,
-                        "SWcld_tot":0.1480997247479032
-                    },
-                    "LO680":{
-                        "LWcld_alt":0.004669166726419898,
-                        "LWcld_amt":-0.00701995348264424,
-                        "LWcld_err":6.257269506482443e-05,
-                        "LWcld_tau":0.0015031276207566812,
-                        "LWcld_tot":-0.0007840586941676883,
-                        "SWcld_alt":-0.002560406872806999,
-                        "SWcld_amt":0.06461773715790489,
-                        "SWcld_err":2.1226288924557354e-05,
-                        "SWcld_tau":-0.011510773232626769,
-                        "SWcld_tot":0.05056778334139569
-                    }
-                },
-                "ocn":{
-                    "ALL":{
-                        "LWcld_alt":0.11941602763937681,
-                        "LWcld_amt":-0.09395568436275979,
-                        "LWcld_err":-0.01013774408391927,
-                        "LWcld_tau":0.01030595418598946,
-                        "LWcld_tot":0.02562855337868721,
-                        "SWcld_alt":-0.03525635327472901,
-                        "SWcld_amt":0.20857466354795381,
-                        "SWcld_err":0.00906225312818129,
-                        "SWcld_tau":-0.2103309459570374,
-                        "SWcld_tot":-0.027950382555631367
-                    },
-                    "HI680":{
-                        "LWcld_alt":0.05558160827687703,
-                        "LWcld_amt":-0.05378541204530531,
-                        "LWcld_err":-0.004192938129563243,
-                        "LWcld_tau":0.003914544811187675,
-                        "LWcld_tot":0.0015178029131961482,
-                        "SWcld_alt":-0.011161239743105587,
-                        "SWcld_amt":0.092728257687499,
-                        "SWcld_err":0.002662428165119558,
-                        "SWcld_tau":-0.1395608106939312,
-                        "SWcld_tot":-0.05533136458441824
-                    },
-                    "LO680":{
-                        "LWcld_alt":0.03376758596525836,
-                        "LWcld_amt":-0.013451567203392696,
-                        "LWcld_err":0.0006594245365503913,
-                        "LWcld_tau":0.00313524518343016,
-                        "LWcld_tot":0.02411075046549106,
-                        "SWcld_alt":-0.016507564894686595,
-                        "SWcld_amt":0.1161248741259319,
-                        "SWcld_err":-0.0011447463210310762,
-                        "SWcld_tau":-0.07109125707493286,
-                        "SWcld_tot":0.027381305835281377
-                    }
-                },
-                "ocn_asc":{
-                    "ALL":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN
-                    },
-                    "HI680":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN
-                    },
-                    "LO680":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN
-                    }
-                },
-                "ocn_dsc":{
-                    "ALL":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN
-                    },
-                    "HI680":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN
-                    },
-                    "LO680":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN
-                    }
-                }
-            },
-            "eq90":{
-                "all":{
-                    "ALL":{
-                        "LWcld_alt":0.14710408853799514,
-                        "LWcld_amt":-0.18178359123330048,
-                        "LWcld_err":-0.028735084241226987,
-                        "LWcld_tau":0.011063716142980467,
-                        "LWcld_tot":-0.052350870793551885,
-                        "SWcld_alt":-0.0412057884802839,
-                        "SWcld_amt":0.42424886045203913,
-                        "SWcld_err":0.01163424593906097,
-                        "SWcld_tau":-0.2281377174708709,
-                        "SWcld_tot":0.16653960043994528
-                    },
-                    "HI680":{
-                        "LWcld_alt":0.0642757458395984,
-                        "LWcld_amt":-0.12314694920342635,
-                        "LWcld_err":-0.017009060602458787,
-                        "LWcld_tau":-0.0005726155063218831,
-                        "LWcld_tot":-0.07645287947260863,
-                        "SWcld_alt":-0.011263142906574394,
-                        "SWcld_amt":0.24142431315990523,
-                        "SWcld_err":0.004031674519848815,
-                        "SWcld_tau":-0.1457250289826176,
-                        "SWcld_tot":0.08846781579056205
-                    },
-                    "LO680":{
-                        "LWcld_alt":0.038956584956577965,
-                        "LWcld_amt":-0.020444595229330047,
-                        "LWcld_err":0.0007402641342909668,
-                        "LWcld_tau":0.004848664780149004,
-                        "LWcld_tot":0.024102008679056746,
-                        "SWcld_alt":-0.019229965511007422,
-                        "SWcld_amt":0.18166078806127042,
-                        "SWcld_err":-0.0011210463514261814,
-                        "SWcld_tau":-0.083232526499487,
-                        "SWcld_tot":0.0780772496993498
-                    }
-                },
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-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN
-                    },
-                    "HI680":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN
-                    },
-                    "LO680":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN
-                    }
-                }
-            },
-            "eq20":{
-                "all":{
-                    "ALL":{
-                        "LWcld_alt":0.20319975348360284,
-                        "LWcld_amt":-0.05647229492067963,
-                        "LWcld_err":-0.019299085107128074,
-                        "LWcld_tau":-0.13825362812333494,
-                        "LWcld_tot":-0.010825254667539799,
-                        "SWcld_alt":-0.03256337007505306,
-                        "SWcld_amt":0.05674685544995936,
-                        "SWcld_err":0.00099773736753545,
-                        "SWcld_tau":0.13694891445358234,
-                        "SWcld_tot":0.1621301371960241
-                    },
-                    "HI680":{
-                        "LWcld_alt":0.11045296401462039,
-                        "LWcld_amt":0.0010541762785061012,
-                        "LWcld_err":0.004096707793804461,
-                        "LWcld_tau":-0.11860924479085848,
-                        "LWcld_tot":-0.003005396703927518,
-                        "SWcld_alt":-0.009608692930164744,
-                        "SWcld_amt":-0.02447749195502716,
-                        "SWcld_err":-0.0016083225281127488,
-                        "SWcld_tau":0.09829394604084625,
-                        "SWcld_tot":0.0625994386275416
-                    },
-                    "LO680":{
-                        "LWcld_alt":0.011419579538700828,
-                        "LWcld_amt":-0.019912837855772834,
-                        "LWcld_err":0.00010800831166049877,
-                        "LWcld_tau":0.000565377240943698,
-                        "LWcld_tot":-0.007819857963612283,
-                        "SWcld_alt":-0.0062297160373505295,
-                        "SWcld_amt":0.1297543569356877,
-                        "SWcld_err":-0.00010715392202739937,
-                        "SWcld_tau":-0.023886739748549,
-                        "SWcld_tot":0.09953074722776079
-                    }
-                },
-                "lnd":{
-                    "ALL":{
-                        "LWcld_alt":0.03371864563485811,
-                        "LWcld_amt":-0.0511088051018358,
-                        "LWcld_err":-0.006629991206559992,
-                        "LWcld_tau":-0.049753203036173275,
-                        "LWcld_tot":-0.07377335370971094,
-                        "SWcld_alt":-0.0027674611394803,
-                        "SWcld_amt":0.05346695942861831,
-                        "SWcld_err":0.0007387968608664378,
-                        "SWcld_tau":0.05462170525494916,
-                        "SWcld_tot":0.1060600004049536
-                    },
-                    "HI680":{
-                        "LWcld_alt":0.02904799200540043,
-                        "LWcld_amt":-0.04913282160667027,
-                        "LWcld_err":-0.002772293776609688,
-                        "LWcld_tau":-0.05105236587129198,
-                        "LWcld_tot":-0.07390948924917151,
-                        "SWcld_alt":-0.0017864468345124495,
-                        "SWcld_amt":0.047461073501361056,
-                        "SWcld_err":0.00017849662521452273,
-                        "SWcld_tau":0.054347575522561115,
-                        "SWcld_tot":0.10020069881462425
-                    },
-                    "LO680":{
-                        "LWcld_alt":0.0015351394110852427,
-                        "LWcld_amt":-0.0014528333752606068,
-                        "LWcld_err":9.22603417135685e-06,
-                        "LWcld_tau":4.458866860905574e-05,
-                        "LWcld_tot":0.00013613553946057705,
-                        "SWcld_alt":-0.0008596839183722294,
-                        "SWcld_amt":0.008670479465498006,
-                        "SWcld_err":-3.3231570517862985e-06,
-                        "SWcld_tau":-0.0019481221404663415,
-                        "SWcld_tot":0.00585935024960765
-                    }
-                },
-                "ocn":{
-                    "ALL":{
-                        "LWcld_alt":0.1694811078487447,
-                        "LWcld_amt":-0.005363489818843825,
-                        "LWcld_err":-0.012669093900568085,
-                        "LWcld_tau":-0.08850042508716166,
-                        "LWcld_tot":0.06294809904217115,
-                        "SWcld_alt":-0.029795908935572765,
-                        "SWcld_amt":0.0032798960213410583,
-                        "SWcld_err":0.00025894050666901223,
-                        "SWcld_tau":0.08232720919863322,
-                        "SWcld_tot":0.056070136791070514
-                    },
-                    "HI680":{
-                        "LWcld_alt":0.08140497200921995,
-                        "LWcld_amt":0.05018699788517638,
-                        "LWcld_err":0.0068690015704141495,
-                        "LWcld_tau":-0.06755687891956648,
-                        "LWcld_tot":0.070904092545244,
-                        "SWcld_alt":-0.007822246095652293,
-                        "SWcld_amt":-0.07193856545638821,
-                        "SWcld_err":-0.001786819153327271,
-                        "SWcld_tau":0.04394637051828515,
-                        "SWcld_tot":-0.03760126018708263
-                    },
-                    "LO680":{
-                        "LWcld_alt":0.009884440127615584,
-                        "LWcld_amt":-0.01846000448051223,
-                        "LWcld_err":9.878227748914191e-05,
-                        "LWcld_tau":0.0005207885723346424,
-                        "LWcld_tot":-0.007955993503072861,
-                        "SWcld_alt":-0.0053700321189783,
-                        "SWcld_amt":0.12108387747018974,
-                        "SWcld_err":-0.00010383076497561305,
-                        "SWcld_tau":-0.02193861760808266,
-                        "SWcld_tot":0.09367139697815315
-                    }
-                },
-                "ocn_asc":{
-                    "ALL":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN
-                    },
-                    "HI680":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN
-                    },
-                    "LO680":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN
-                    }
-                },
-                "ocn_dsc":{
-                    "ALL":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN
-                    },
-                    "HI680":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN
-                    },
-                    "LO680":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN
-                    }
-                }
-            },
-            "eq30":{
-                "all":{
-                    "ALL":{
-                        "LWcld_alt":0.26072566836540373,
-                        "LWcld_amt":-0.0935982233617274,
-                        "LWcld_err":-0.026289447602522383,
-                        "LWcld_tau":-0.14764476492481995,
-                        "LWcld_tot":-0.006806767523666102,
-                        "SWcld_alt":-0.04342596678739307,
-                        "SWcld_amt":0.129315388710882,
-                        "SWcld_err":0.001981155800989994,
-                        "SWcld_tau":0.11881303869208053,
-                        "SWcld_tot":0.20668361641655944
-                    },
-                    "HI680":{
-                        "LWcld_alt":0.12636915167634372,
-                        "LWcld_amt":0.007190078531855058,
-                        "LWcld_err":0.007864951036718587,
-                        "LWcld_tau":-0.12136276741223208,
-                        "LWcld_tot":0.020061413832685292,
-                        "SWcld_alt":-0.010185064349026227,
-                        "SWcld_amt":-0.04982272454917173,
-                        "SWcld_err":-0.001964744254266229,
-                        "SWcld_tau":0.07957175436058554,
-                        "SWcld_tot":0.01759922120812134
-                    },
-                    "LO680":{
-                        "LWcld_alt":0.013091964278229127,
-                        "LWcld_amt":-0.04117029749869173,
-                        "LWcld_err":0.00016774116336389588,
-                        "LWcld_tau":0.001042347179140515,
-                        "LWcld_tot":-0.026868181356351394,
-                        "SWcld_alt":-0.0070652082695411645,
-                        "SWcld_amt":0.24140904649955652,
-                        "SWcld_err":-0.0002397157469075104,
-                        "SWcld_tau":-0.045019484401432795,
-                        "SWcld_tot":0.18908463808167508
-                    }
-                },
-                "lnd":{
-                    "ALL":{
-                        "LWcld_alt":0.043484231200323624,
-                        "LWcld_amt":-0.06759384169411893,
-                        "LWcld_err":-0.007259077333338456,
-                        "LWcld_tau":-0.05801635742498948,
-                        "LWcld_tot":-0.08938504525212328,
-                        "SWcld_alt":-0.003710257443254391,
-                        "SWcld_amt":0.07515468399772665,
-                        "SWcld_err":0.000745645148095266,
-                        "SWcld_tau":0.06434483358795162,
-                        "SWcld_tot":0.13653490529051918
-                    },
-                    "HI680":{
-                        "LWcld_alt":0.03720857574198402,
-                        "LWcld_amt":-0.06379329714095178,
-                        "LWcld_err":-0.002293515660156605,
-                        "LWcld_tau":-0.05988935742004872,
-                        "LWcld_tot":-0.08876759447917308,
-                        "SWcld_alt":-0.0023290701593135493,
-                        "SWcld_amt":0.0634443307422029,
-                        "SWcld_err":9.711248476402313e-05,
-                        "SWcld_tau":0.06416452386604134,
-                        "SWcld_tot":0.12537689693369472
-                    },
-                    "LO680":{
-                        "LWcld_alt":0.0018757822898589178,
-                        "LWcld_amt":-0.002567144839039484,
-                        "LWcld_err":1.1411846366893872e-05,
-                        "LWcld_tau":6.243653332072684e-05,
-                        "LWcld_tot":-0.0006174507729501856,
-                        "SWcld_alt":-0.001036541167236218,
-                        "SWcld_amt":0.014697939801653266,
-                        "SWcld_err":-3.861126729006953e-06,
-                        "SWcld_tau":-0.002499286960787122,
-                        "SWcld_tot":0.011158250546900922
-                    }
-                },
-                "ocn":{
-                    "ALL":{
-                        "LWcld_alt":0.21724143716508004,
-                        "LWcld_amt":-0.026004381667608486,
-                        "LWcld_err":-0.019030370269183926,
-                        "LWcld_tau":-0.08962840749983046,
-                        "LWcld_tot":0.08257827772845716,
-                        "SWcld_alt":-0.039715709344138685,
-                        "SWcld_amt":0.054160704713155323,
-                        "SWcld_err":0.001235510652894728,
-                        "SWcld_tau":0.05446820510412889,
-                        "SWcld_tot":0.07014871112604026
-                    },
-                    "HI680":{
-                        "LWcld_alt":0.08916057593435972,
-                        "LWcld_amt":0.07098337567280684,
-                        "LWcld_err":0.010158466696875193,
-                        "LWcld_tau":-0.06147340999218336,
-                        "LWcld_tot":0.10882900831185836,
-                        "SWcld_alt":-0.007855994189712678,
-                        "SWcld_amt":-0.11326705529137465,
-                        "SWcld_err":-0.0020618567390302523,
-                        "SWcld_tau":0.015407230494544205,
-                        "SWcld_tot":-0.10777767572557338
-                    },
-                    "LO680":{
-                        "LWcld_alt":0.01121618198837021,
-                        "LWcld_amt":-0.038603152659652244,
-                        "LWcld_err":0.000156329316997002,
-                        "LWcld_tau":0.000979910645819788,
-                        "LWcld_tot":-0.026250730583401206,
-                        "SWcld_alt":-0.006028667102304946,
-                        "SWcld_amt":0.22671110669790326,
-                        "SWcld_err":-0.00023585462017850338,
-                        "SWcld_tau":-0.04252019744064567,
-                        "SWcld_tot":0.17792638753477416
-                    }
-                },
-                "ocn_asc":{
-                    "ALL":{
-                        "LWcld_alt":0.08335839439434169,
-                        "LWcld_amt":0.09059563695614131,
-                        "LWcld_err":0.008457443298949591,
-                        "LWcld_tau":-0.04286350129097399,
-                        "LWcld_tot":0.13954797335845856,
-                        "SWcld_alt":-0.011031071602653333,
-                        "SWcld_amt":-0.13426664322455942,
-                        "SWcld_err":-0.002575476562080964,
-                        "SWcld_tau":0.006339343658329578,
-                        "SWcld_tot":-0.14153384773096414
-                    },
-                    "HI680":{
-                        "LWcld_alt":0.05571951555843894,
-                        "LWcld_amt":0.10699874174146577,
-                        "LWcld_err":0.011797368322345875,
-                        "LWcld_tau":-0.04014710996430868,
-                        "LWcld_tot":0.13436851565794192,
-                        "SWcld_alt":-0.004090937240518114,
-                        "SWcld_amt":-0.12464617142649252,
-                        "SWcld_err":-0.0018847339160356297,
-                        "SWcld_tau":0.001203220353897586,
-                        "SWcld_tot":-0.12941862222914866
-                    },
-                    "LO680":{
-                        "LWcld_alt":0.004795270778335622,
-                        "LWcld_amt":0.00010927913367186899,
-                        "LWcld_err":2.4979996081492207e-05,
-                        "LWcld_tau":0.00024992779242770396,
-                        "LWcld_tot":0.0051794577005166865,
-                        "SWcld_alt":-0.0026033896932058927,
-                        "SWcld_amt":-0.0007184507001715604,
-                        "SWcld_err":-1.853351101699839e-06,
-                        "SWcld_tau":-0.00879153175733633,
-                        "SWcld_tot":-0.012115225501815484
-                    }
-                },
-                "ocn_dsc":{
-                    "ALL":{
-                        "LWcld_alt":0.03914764008357846,
-                        "LWcld_amt":-0.03624005407485167,
-                        "LWcld_err":-0.02431617860196074,
-                        "LWcld_tau":-0.026121408620534237,
-                        "LWcld_tot":-0.04753000121376819,
-                        "SWcld_alt":-0.007459967641356526,
-                        "SWcld_amt":0.15791356117267677,
-                        "SWcld_err":0.0038813704673822686,
-                        "SWcld_tau":0.018086756526690232,
-                        "SWcld_tot":0.17242172052539276
-                    },
-                    "HI680":{
-                        "LWcld_alt":-0.012234382810387669,
-                        "LWcld_amt":0.024614804322730745,
-                        "LWcld_err":-0.013766524478065966,
-                        "LWcld_tau":-0.01434633397124873,
-                        "LWcld_tot":-0.015732436936971617,
-                        "SWcld_alt":0.0042087805059668965,
-                        "SWcld_amt":-0.02127090065946165,
-                        "SWcld_err":0.004577034011574418,
-                        "SWcld_tau":0.011374443467989478,
-                        "SWcld_tot":-0.0011106426739308473
-                    },
-                    "LO680":{
-                        "LWcld_alt":0.005566870283097255,
-                        "LWcld_amt":-0.03822264397898887,
-                        "LWcld_err":0.00010677857670204881,
-                        "LWcld_tau":0.0007514308423929987,
-                        "LWcld_tot":-0.03179756427679656,
-                        "SWcld_alt":-0.002771339909789439,
-                        "SWcld_amt":0.2079906353918944,
-                        "SWcld_err":-0.0002577326289390002,
-                        "SWcld_tau":-0.031429199653842374,
-                        "SWcld_tot":0.17353236319932364
-                    }
-                }
-            },
-            "eq40":{
-                "all":{
-                    "ALL":{
-                        "LWcld_alt":0.3302300069660981,
-                        "LWcld_amt":-0.157666033967062,
-                        "LWcld_err":-0.03714681611978518,
-                        "LWcld_tau":-0.16157622195882623,
-                        "LWcld_tot":-0.02615906507957527,
-                        "SWcld_alt":-0.05527873593523525,
-                        "SWcld_amt":0.2380827039199059,
-                        "SWcld_err":0.003051377251128018,
-                        "SWcld_tau":0.1293332725779404,
-                        "SWcld_tot":0.31518861781373897
-                    },
-                    "HI680":{
-                        "LWcld_alt":0.15031868744661872,
-                        "LWcld_amt":-0.010729192655984718,
-                        "LWcld_err":0.007002791393111379,
-                        "LWcld_tau":-0.13009203191035887,
-                        "LWcld_tot":0.016500254273386458,
-                        "SWcld_alt":-0.010410770005207135,
-                        "SWcld_amt":-0.04661759928747975,
-                        "SWcld_err":-0.001415626008776364,
-                        "SWcld_tau":0.09395376865836617,
-                        "SWcld_tot":0.035509773356902904
-                    },
-                    "LO680":{
-                        "LWcld_alt":0.015987386670835307,
-                        "LWcld_amt":-0.06040606459621523,
-                        "LWcld_err":0.0002740398519274417,
-                        "LWcld_tau":0.0014852514699469058,
-                        "LWcld_tot":-0.042659319352961717,
-                        "SWcld_alt":-0.00821191888171888,
-                        "SWcld_amt":0.3505313368623793,
-                        "SWcld_err":-0.00035800425731261597,
-                        "SWcld_tau":-0.062282313678048346,
-                        "SWcld_tot":0.27967910004529944
-                    }
-                },
-                "lnd":{
-                    "ALL":{
-                        "LWcld_alt":0.05276404990301273,
-                        "LWcld_amt":-0.08783573065211674,
-                        "LWcld_err":-0.008530669009812543,
-                        "LWcld_tau":-0.06180062197506145,
-                        "LWcld_tot":-0.105402971733978,
-                        "SWcld_alt":-0.004561767135756076,
-                        "SWcld_amt":0.09741796176779437,
-                        "SWcld_err":0.0007643355807410027,
-                        "SWcld_tau":0.06964629200660861,
-                        "SWcld_tot":0.1632668222193879
-                    },
-                    "HI680":{
-                        "LWcld_alt":0.04501498560853687,
-                        "LWcld_amt":-0.08223060598924499,
-                        "LWcld_err":-0.002589631709303408,
-                        "LWcld_tau":-0.06387729246354389,
-                        "LWcld_tot":-0.10368254455355541,
-                        "SWcld_alt":-0.0029225991558728295,
-                        "SWcld_amt":0.08054491527306858,
-                        "SWcld_err":3.5176199540280536e-05,
-                        "SWcld_tau":0.06918107480891625,
-                        "SWcld_tot":0.14683856712565227
-                    },
-                    "LO680":{
-                        "LWcld_alt":0.00195216356296116,
-                        "LWcld_amt":-0.0037984118834212023,
-                        "LWcld_err":1.2889671392466936e-05,
-                        "LWcld_tau":0.00011286434316514135,
-                        "LWcld_tot":-0.0017204271804225964,
-                        "SWcld_alt":-0.0010796926910380874,
-                        "SWcld_amt":0.02056734266306485,
-                        "SWcld_err":-4.078406056320872e-06,
-                        "SWcld_tau":-0.00305506156693194,
-                        "SWcld_tot":0.016428509999038502
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-                },
-                "ocn":{
-                    "ALL":{
-                        "LWcld_alt":0.27746595706308547,
-                        "LWcld_amt":-0.06983030331494522,
-                        "LWcld_err":-0.028616147109972633,
-                        "LWcld_tau":-0.0997755999837648,
-                        "LWcld_tot":0.07924390665440273,
-                        "SWcld_alt":-0.050716968799479185,
-                        "SWcld_amt":0.14066474215211153,
-                        "SWcld_err":0.002287041670387015,
-                        "SWcld_tau":0.05968698057133178,
-                        "SWcld_tot":0.15192179559435112
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-                        "LWcld_amt":0.07150141333326028,
-                        "LWcld_err":0.009592423102414786,
-                        "LWcld_tau":-0.06621473944681501,
-                        "LWcld_tot":0.12018279882694187,
-                        "SWcld_alt":-0.007488170849334307,
-                        "SWcld_amt":-0.12716251456054833,
-                        "SWcld_err":-0.0014508022083166451,
-                        "SWcld_tau":0.02477269384944994,
-                        "SWcld_tot":-0.11132879376874937
-                    },
-                    "LO680":{
-                        "LWcld_alt":0.014035223107874147,
-                        "LWcld_amt":-0.056607652712794054,
-                        "LWcld_err":0.00026115018053497475,
-                        "LWcld_tau":0.0013723871267817642,
-                        "LWcld_tot":-0.040938892172539126,
-                        "SWcld_alt":-0.007132226190680793,
-                        "SWcld_amt":0.3299639941993145,
-                        "SWcld_err":-0.00035392585125629505,
-                        "SWcld_tau":-0.05922725211111641,
-                        "SWcld_tot":0.263250590046261
-                    }
-                },
-                "ocn_asc":{
-                    "ALL":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN
-                    },
-                    "HI680":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN
-                    },
-                    "LO680":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN
-                    }
-                },
-                "ocn_dsc":{
-                    "ALL":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN
-                    },
-                    "HI680":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN
-                    },
-                    "LO680":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN
-                    }
-                }
-            },
-            "eq50":{
-                "all":{
-                    "ALL":{
-                        "LWcld_alt":0.3748541719864564,
-                        "LWcld_amt":-0.17558150236775358,
-                        "LWcld_err":-0.040979960981353904,
-                        "LWcld_tau":-0.15895883664890212,
-                        "LWcld_tot":-0.0006661280115533574,
-                        "SWcld_alt":-0.061393531841082837,
-                        "SWcld_amt":0.2785778372743046,
-                        "SWcld_err":0.0031848110507757647,
-                        "SWcld_tau":0.12444144816327612,
-                        "SWcld_tot":0.34481056464727367
-                    },
-                    "HI680":{
-                        "LWcld_alt":0.18019806679989073,
-                        "LWcld_amt":-0.01846585833384544,
-                        "LWcld_err":0.0063448318485772445,
-                        "LWcld_tau":-0.12831918779721088,
-                        "LWcld_tot":0.039757852517411686,
-                        "SWcld_alt":-0.011503178840564987,
-                        "SWcld_amt":-0.043247598933148945,
-                        "SWcld_err":-0.0010217986926452875,
-                        "SWcld_tau":0.09801325511498782,
-                        "SWcld_tot":0.04224067864862858
-                    },
-                    "LO680":{
-                        "LWcld_alt":0.021747691197695002,
-                        "LWcld_amt":-0.06478654427140522,
-                        "LWcld_err":0.0004811007691990577,
-                        "LWcld_tau":0.002133704525002253,
-                        "LWcld_tot":-0.04042398052896503,
-                        "SWcld_alt":-0.010564758103283688,
-                        "SWcld_amt":0.38372680520880054,
-                        "SWcld_err":-0.000469378081228233,
-                        "SWcld_tau":-0.07012252743718013,
-                        "SWcld_tot":0.3025701415871084
-                    }
-                },
-                "lnd":{
-                    "ALL":{
-                        "LWcld_alt":0.062233350699375,
-                        "LWcld_amt":-0.09339041577854683,
-                        "LWcld_err":-0.010126325281755251,
-                        "LWcld_tau":-0.06116944090057234,
-                        "LWcld_tot":-0.1024528312614994,
-                        "SWcld_alt":-0.0052646755143611725,
-                        "SWcld_amt":0.10908574581091823,
-                        "SWcld_err":0.0009407229636598721,
-                        "SWcld_tau":0.0692157567087558,
-                        "SWcld_tot":0.17397754996897274
-                    },
-                    "HI680":{
-                        "LWcld_alt":0.0550168555530946,
-                        "LWcld_amt":-0.08897948231071096,
-                        "LWcld_err":-0.002995080950780401,
-                        "LWcld_tau":-0.0642283338467116,
-                        "LWcld_tot":-0.10118604155510835,
-                        "SWcld_alt":-0.0037041858644239127,
-                        "SWcld_amt":0.09118836636232935,
-                        "SWcld_err":-2.3319354090611983e-05,
-                        "SWcld_tau":0.07037759740910257,
-                        "SWcld_tot":0.15783845855291742
-                    },
-                    "LO680":{
-                        "LWcld_alt":0.002034253400276254,
-                        "LWcld_amt":-0.003614942359327765,
-                        "LWcld_err":1.9487586326648035e-05,
-                        "LWcld_tau":0.00029434454085397563,
-                        "LWcld_tot":-0.0012667897063910493,
-                        "SWcld_alt":-0.00112494519241807,
-                        "SWcld_amt":0.021418804529265326,
-                        "SWcld_err":-1.001849082092608e-05,
-                        "SWcld_tau":-0.0041444945246681,
-                        "SWcld_tot":0.016139346321358227
-                    }
-                },
-                "ocn":{
-                    "ALL":{
-                        "LWcld_alt":0.3126208212870813,
-                        "LWcld_amt":-0.08219108658920678,
-                        "LWcld_err":-0.030853635699598658,
-                        "LWcld_tau":-0.0977893957483298,
-                        "LWcld_tot":0.10178670324994606,
-                        "SWcld_alt":-0.056128856326721675,
-                        "SWcld_amt":0.1694920914633863,
-                        "SWcld_err":0.002244088087115893,
-                        "SWcld_tau":0.05522569145452033,
-                        "SWcld_tot":0.1708330146783009
-                    },
-                    "HI680":{
-                        "LWcld_alt":0.12518121124679618,
-                        "LWcld_amt":0.0705136239768655,
-                        "LWcld_err":0.009339912799357647,
-                        "LWcld_tau":-0.06409085395049928,
-                        "LWcld_tot":0.14094389407252003,
-                        "SWcld_alt":-0.0077989929761410745,
-                        "SWcld_amt":-0.1344359652954783,
-                        "SWcld_err":-0.0009984793385546754,
-                        "SWcld_tau":0.027635657705885233,
-                        "SWcld_tot":-0.11559777990428881
-                    },
-                    "LO680":{
-                        "LWcld_alt":0.019713437797418753,
-                        "LWcld_amt":-0.06117160191207746,
-                        "LWcld_err":0.0004616131828724096,
-                        "LWcld_tau":0.001839359984148277,
-                        "LWcld_tot":-0.039157190822573976,
-                        "SWcld_alt":-0.009439812910865616,
-                        "SWcld_amt":0.36230800067953517,
-                        "SWcld_err":-0.000459359590407307,
-                        "SWcld_tau":-0.06597803291251203,
-                        "SWcld_tot":0.28643079526575016
-                    }
-                },
-                "ocn_asc":{
-                    "ALL":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN
-                    },
-                    "HI680":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN
-                    },
-                    "LO680":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN
-                    }
-                },
-                "ocn_dsc":{
-                    "ALL":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN
-                    },
-                    "HI680":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN
-                    },
-                    "LO680":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN
-                    }
-                }
-            },
-            "eq60":{
-                "all":{
-                    "ALL":{
-                        "LWcld_alt":0.41856248825342957,
-                        "LWcld_amt":-0.1692001399391081,
-                        "LWcld_err":-0.03490559629407889,
-                        "LWcld_tau":-0.1444502705258694,
-                        "LWcld_tot":0.07000648149437318,
-                        "SWcld_alt":-0.06676547931701655,
-                        "SWcld_amt":0.27488646860012894,
-                        "SWcld_err":0.0027511626042894676,
-                        "SWcld_tau":0.06918860430419642,
-                        "SWcld_tot":0.2800607561915982
-                    },
-                    "HI680":{
-                        "LWcld_alt":0.21237298728351348,
-                        "LWcld_amt":-0.0033452646559925477,
-                        "LWcld_err":0.011803253042032167,
-                        "LWcld_tau":-0.11228934758160906,
-                        "LWcld_tot":0.108541628087944,
-                        "SWcld_alt":-0.014277022832453813,
-                        "SWcld_amt":-0.06403270743063817,
-                        "SWcld_err":-0.0015451174501997103,
-                        "SWcld_tau":0.06476403987906022,
-                        "SWcld_tot":-0.015090807834231487
-                    },
-                    "LO680":{
-                        "LWcld_alt":0.02390744364790619,
-                        "LWcld_amt":-0.06656711512773562,
-                        "LWcld_err":0.0007183959759136021,
-                        "LWcld_tau":0.0034060616598011345,
-                        "LWcld_tot":-0.038535146593570824,
-                        "SWcld_alt":-0.011529077831529122,
-                        "SWcld_amt":0.39714771768385543,
-                        "SWcld_err":-0.0005892269072924041,
-                        "SWcld_tau":-0.08987759333074082,
-                        "SWcld_tot":0.2951518196142931
-                    }
-                },
-                "lnd":{
-                    "ALL":{
-                        "LWcld_alt":0.07028018700480028,
-                        "LWcld_amt":-0.08765836073754939,
-                        "LWcld_err":-0.00882022574372387,
-                        "LWcld_tau":-0.05499129851505993,
-                        "LWcld_tot":-0.08118969799153289,
-                        "SWcld_alt":-0.005601821695672314,
-                        "SWcld_amt":0.10202356229566055,
-                        "SWcld_err":0.0009034216606543208,
-                        "SWcld_tau":0.061177847135051394,
-                        "SWcld_tot":0.15850300939569395
-                    },
-                    "HI680":{
-                        "LWcld_alt":0.06355553470357243,
-                        "LWcld_amt":-0.08506924111107844,
-                        "LWcld_err":-0.0017694482441592526,
-                        "LWcld_tau":-0.05800034427361609,
-                        "LWcld_tot":-0.08128349892528136,
-                        "SWcld_alt":-0.004374537475437924,
-                        "SWcld_amt":0.08903743844364391,
-                        "SWcld_err":-0.00017463565082440524,
-                        "SWcld_tau":0.06458840330888556,
-                        "SWcld_tot":0.14907666862626714
-                    },
-                    "LO680":{
-                        "LWcld_alt":0.001942436511419598,
-                        "LWcld_amt":-0.0025283058084453833,
-                        "LWcld_err":3.9128264133831596e-05,
-                        "LWcld_tau":0.0006404748411605708,
-                        "LWcld_tot":9.380093374845605e-05,
-                        "SWcld_alt":-0.0010978111953242073,
-                        "SWcld_amt":0.016235075853386904,
-                        "SWcld_err":-2.3032560250962345e-05,
-                        "SWcld_tau":-0.005687636423082047,
-                        "SWcld_tot":0.009426595674729686
-                    }
-                },
-                "ocn":{
-                    "ALL":{
-                        "LWcld_alt":0.3482823012486293,
-                        "LWcld_amt":-0.08154177920155871,
-                        "LWcld_err":-0.026085370550355022,
-                        "LWcld_tau":-0.08945897201080945,
-                        "LWcld_tot":0.15119617948590605,
-                        "SWcld_alt":-0.06116365762134426,
-                        "SWcld_amt":0.17286290630446832,
-                        "SWcld_err":0.0018477409436351469,
-                        "SWcld_tau":0.008010757169145021,
-                        "SWcld_tot":0.12155774679590427
-                    },
-                    "HI680":{
-                        "LWcld_alt":0.14881745257994103,
-                        "LWcld_amt":0.08172397645508588,
-                        "LWcld_err":0.013572701286191423,
-                        "LWcld_tau":-0.05428900330799297,
-                        "LWcld_tot":0.18982512701322532,
-                        "SWcld_alt":-0.009902485357015886,
-                        "SWcld_amt":-0.1530701458742821,
-                        "SWcld_err":-0.0013704817993753047,
-                        "SWcld_tau":0.0001756365701746689,
-                        "SWcld_tot":-0.16416747646049862
-                    },
-                    "LO680":{
-                        "LWcld_alt":0.021965007136486595,
-                        "LWcld_amt":-0.06403880931929025,
-                        "LWcld_err":0.0006792677117797704,
-                        "LWcld_tau":0.002765586818640564,
-                        "LWcld_tot":-0.038628947527319275,
-                        "SWcld_alt":-0.010431266636204915,
-                        "SWcld_amt":0.38091264183046847,
-                        "SWcld_err":-0.0005661943470414418,
-                        "SWcld_tau":-0.08418995690765878,
-                        "SWcld_tot":0.2857252239395634
-                    }
-                },
-                "ocn_asc":{
-                    "ALL":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN
-                    },
-                    "HI680":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN
-                    },
-                    "LO680":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN
-                    }
-                },
-                "ocn_dsc":{
-                    "ALL":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN
-                    },
-                    "HI680":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN
-                    },
-                    "LO680":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN
-                    }
-                }
-            },
-            "eq70":{
-                "all":{
-                    "ALL":{
-                        "LWcld_alt":0.44078284836293374,
-                        "LWcld_amt":-0.15758398870694498,
-                        "LWcld_err":-0.025986150040680778,
-                        "LWcld_tau":-0.12445373145448853,
-                        "LWcld_tot":0.13275897816081958,
-                        "SWcld_alt":-0.06886208998392566,
-                        "SWcld_amt":0.25926635449861873,
-                        "SWcld_err":0.002080050924636271,
-                        "SWcld_tau":0.023755712751158047,
-                        "SWcld_tot":0.2162400281904874
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-                        "SWcld_tau":NaN,
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-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
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-                        "LWcld_tau":NaN,
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-                        "SWcld_alt":NaN,
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-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
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-                        "LWcld_amt":NaN,
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-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
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-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN
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-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN
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-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN
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-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "SWcld_alt":NaN,
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-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN
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-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "SWcld_alt":NaN,
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-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN
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-                        "LWcld_amt":NaN,
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-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "SWcld_alt":NaN,
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-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN
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-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "SWcld_alt":NaN,
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-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN
-                    }
-                }
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-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN
-                    },
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-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN
-                    },
-                    "LO680":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN
-                    }
-                },
-                "ocn_dsc":{
-                    "ALL":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN
-                    },
-                    "HI680":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN
-                    },
-                    "LO680":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN
-                    }
-                }
-            }
-        }
-    },
-    "metadata":{
-        "author":"Mark D. Zelinka <zelinka1@llnl.gov>",
-        "date_modified":"2021-06-09"
-    }
-}
diff --git a/pcmdi_metrics/cloud_feedback/data/cmip6_amip-p4K_cld_obsc_fbks.json b/pcmdi_metrics/cloud_feedback/data/cmip6_amip-p4K_cld_obsc_fbks.json
deleted file mode 100644
index 74e4f3dc8..000000000
--- a/pcmdi_metrics/cloud_feedback/data/cmip6_amip-p4K_cld_obsc_fbks.json
+++ /dev/null
@@ -1,10243 +0,0 @@
-{
-    "BCC-CSM2-MR":{
-        "r1i1p1f1":{
-            "Arctic":{
-                "all":{
-                    "LO680":{
-                        "LWcld_alt":0.0017922354846158525,
-                        "LWcld_amt":0.0013171432412151444,
-                        "LWcld_err":3.8446845866028856e-05,
-                        "LWcld_tau":0.00040399915671014995,
-                        "LWcld_tot":0.0032558393343732448,
-                        "LWdobsc_cov_fbk":-4.023948242670165e-21,
-                        "LWdobsc_fbk":-0.0025655092729918215,
-                        "LWdunobsc_fbk":0.0035518247284071756,
-                        "SWcld_alt":-0.0006346719738797095,
-                        "SWcld_amt":-0.004681703229518845,
-                        "SWcld_err":2.1186580450673566e-05,
-                        "SWcld_tau":-0.004692627628867699,
-                        "SWcld_tot":-0.009987816251815577,
-                        "SWdobsc_cov_fbk":1.6346467149262885e-20,
-                        "SWdobsc_fbk":0.018249694277701626,
-                        "SWdunobsc_fbk":-0.010895799547435176
-                    }
-                },
-                "lnd":{
-                    "LO680":{
-                        "LWcld_alt":0.00033057153793945605,
-                        "LWcld_amt":0.00027706733731634416,
-                        "LWcld_err":8.437462929661965e-06,
-                        "LWcld_tau":0.0001063139031130695,
-                        "LWcld_tot":0.0007223902412985317,
-                        "LWdobsc_cov_fbk":-3.348398958123449e-22,
-                        "LWdobsc_fbk":-0.0004122647749272941,
-                        "LWdunobsc_fbk":0.0007223902412985317,
-                        "SWcld_alt":-0.00013134313463200776,
-                        "SWcld_amt":-0.00023417742476476418,
-                        "SWcld_err":1.3214770877246017e-06,
-                        "SWcld_tau":-0.001122770661972378,
-                        "SWcld_tot":-0.0014869697442814252,
-                        "SWdobsc_cov_fbk":4.917005667277504e-21,
-                        "SWdobsc_fbk":0.0021271330852106556,
-                        "SWdunobsc_fbk":-0.0014869697442814252
-                    }
-                },
-                "ocn":{
-                    "LO680":{
-                        "LWcld_alt":0.0014616639466763968,
-                        "LWcld_amt":0.0010400759038988,
-                        "LWcld_err":3.0009382936366895e-05,
-                        "LWcld_tau":0.0002976852535970804,
-                        "LWcld_tot":0.002829434487108644,
-                        "LWdobsc_cov_fbk":-3.68910834685782e-21,
-                        "LWdobsc_fbk":-0.0021532444980645275,
-                        "LWdunobsc_fbk":0.002829434487108644,
-                        "SWcld_alt":-0.0005610262914185843,
-                        "SWcld_amt":-0.004873135189255793,
-                        "SWcld_err":2.1791156131192026e-05,
-                        "SWcld_tau":-0.003996459478610565,
-                        "SWcld_tot":-0.009408829803153752,
-                        "SWdobsc_cov_fbk":1.142946148198538e-20,
-                        "SWdobsc_fbk":0.01612256119249097,
-                        "SWdunobsc_fbk":-0.009408829803153752
-                    }
-                },
-                "ocn_asc":{
-                    "LO680":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "LWdobsc_cov_fbk":NaN,
-                        "LWdobsc_fbk":NaN,
-                        "LWdunobsc_fbk":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN,
-                        "SWdobsc_cov_fbk":NaN,
-                        "SWdobsc_fbk":NaN,
-                        "SWdunobsc_fbk":NaN
-                    }
-                },
-                "ocn_dsc":{
-                    "LO680":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "LWdobsc_cov_fbk":NaN,
-                        "LWdobsc_fbk":NaN,
-                        "LWdunobsc_fbk":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN,
-                        "SWdobsc_cov_fbk":NaN,
-                        "SWdobsc_fbk":NaN,
-                        "SWdunobsc_fbk":NaN
-                    }
-                }
-            },
-            "eq10":{
-                "all":{
-                    "LO680":{
-                        "LWcld_alt":0.004766175635002841,
-                        "LWcld_amt":-0.007309156172748947,
-                        "LWcld_err":-5.786532120875532e-05,
-                        "LWcld_tau":-0.0004402714697669377,
-                        "LWcld_tot":-0.003041117328721798,
-                        "LWdobsc_cov_fbk":-3.7480889025741055e-21,
-                        "LWdobsc_fbk":0.003763638748150833,
-                        "LWdunobsc_fbk":-0.003041117328721798,
-                        "SWcld_alt":-0.0028677691901363433,
-                        "SWcld_amt":0.06760041500676474,
-                        "SWcld_err":-3.200747280107612e-05,
-                        "SWcld_tau":0.008552310521621304,
-                        "SWcld_tot":0.07325294886544861,
-                        "SWdobsc_cov_fbk":4.0042435298279303e-20,
-                        "SWdobsc_fbk":-0.030842271435314744,
-                        "SWdunobsc_fbk":0.07325294886544861
-                    }
-                },
-                "lnd":{
-                    "LO680":{
-                        "LWcld_alt":0.0012759962831896906,
-                        "LWcld_amt":-0.004059187148001966,
-                        "LWcld_err":1.2450221580713287e-05,
-                        "LWcld_tau":-1.2365336329568953e-05,
-                        "LWcld_tot":-0.002783105979561131,
-                        "LWdobsc_cov_fbk":-6.88403990941885e-21,
-                        "LWdobsc_fbk":0.0018747738756793603,
-                        "LWdunobsc_fbk":-0.002783105979561131,
-                        "SWcld_alt":-0.000798841767355102,
-                        "SWcld_amt":0.03269253889592701,
-                        "SWcld_err":-2.2963099525827257e-06,
-                        "SWcld_tau":0.00018281121352665638,
-                        "SWcld_tot":0.03207421203214599,
-                        "SWdobsc_cov_fbk":6.23519557111954e-20,
-                        "SWdobsc_fbk":-0.015556077832602275,
-                        "SWdunobsc_fbk":0.03207421203214599
-                    }
-                },
-                "ocn":{
-                    "LO680":{
-                        "LWcld_alt":0.003490179351813151,
-                        "LWcld_amt":-0.0032499690247469806,
-                        "LWcld_err":-7.03155427894686e-05,
-                        "LWcld_tau":-0.0004279061334373688,
-                        "LWcld_tot":-0.00025801134916066733,
-                        "LWdobsc_cov_fbk":3.1359510068447443e-21,
-                        "LWdobsc_fbk":0.0018888648724714732,
-                        "LWdunobsc_fbk":-0.0002580113491606672,
-                        "SWcld_alt":-0.0020689274227812414,
-                        "SWcld_amt":0.03490787611083771,
-                        "SWcld_err":-2.9711162848493402e-05,
-                        "SWcld_tau":0.008369499308094646,
-                        "SWcld_tot":0.04117873683330262,
-                        "SWdobsc_cov_fbk":-2.230952041291612e-20,
-                        "SWdobsc_fbk":-0.015286193602712468,
-                        "SWdunobsc_fbk":0.04117873683330262
-                    }
-                },
-                "ocn_asc":{
-                    "LO680":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "LWdobsc_cov_fbk":NaN,
-                        "LWdobsc_fbk":NaN,
-                        "LWdunobsc_fbk":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN,
-                        "SWdobsc_cov_fbk":NaN,
-                        "SWdobsc_fbk":NaN,
-                        "SWdunobsc_fbk":NaN
-                    }
-                },
-                "ocn_dsc":{
-                    "LO680":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "LWdobsc_cov_fbk":NaN,
-                        "LWdobsc_fbk":NaN,
-                        "LWdunobsc_fbk":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN,
-                        "SWdobsc_cov_fbk":NaN,
-                        "SWdobsc_fbk":NaN,
-                        "SWdunobsc_fbk":NaN
-                    }
-                }
-            },
-            "eq20":{
-                "all":{
-                    "LO680":{
-                        "LWcld_alt":0.009184951925721453,
-                        "LWcld_amt":-0.00909186656639025,
-                        "LWcld_err":-0.00021822506437056246,
-                        "LWcld_tau":-0.0007918014500751632,
-                        "LWcld_tot":-0.0009169411551145213,
-                        "LWdobsc_cov_fbk":-1.2797527940785136e-20,
-                        "LWdobsc_fbk":0.0058263991303809754,
-                        "LWdunobsc_fbk":-0.0009169411551145217,
-                        "SWcld_alt":-0.00527436053106042,
-                        "SWcld_amt":0.07978910667122033,
-                        "SWcld_err":3.688116129639025e-05,
-                        "SWcld_tau":0.013038737189929046,
-                        "SWcld_tot":0.08759036449138535,
-                        "SWdobsc_cov_fbk":8.766735977887795e-20,
-                        "SWdobsc_fbk":-0.04519724588973522,
-                        "SWdunobsc_fbk":0.08759036449138535
-                    }
-                },
-                "lnd":{
-                    "LO680":{
-                        "LWcld_alt":0.001726360795468693,
-                        "LWcld_amt":-0.006384938195390513,
-                        "LWcld_err":2.1861775712398974e-05,
-                        "LWcld_tau":-4.368562888563272e-05,
-                        "LWcld_tot":-0.004680401253095055,
-                        "LWdobsc_cov_fbk":-9.082804703415423e-21,
-                        "LWdobsc_fbk":0.002557295606731159,
-                        "LWdunobsc_fbk":-0.004680401253095055,
-                        "SWcld_alt":-0.0010574952995399366,
-                        "SWcld_amt":0.050243138962016015,
-                        "SWcld_err":-6.078271807391877e-06,
-                        "SWcld_tau":0.0006175007567514695,
-                        "SWcld_tot":0.04979706614742016,
-                        "SWdobsc_cov_fbk":8.10119163225955e-20,
-                        "SWdobsc_fbk":-0.020774600036794932,
-                        "SWdunobsc_fbk":0.04979706614742016
-                    }
-                },
-                "ocn":{
-                    "LO680":{
-                        "LWcld_alt":0.007458591130252761,
-                        "LWcld_amt":-0.0027069283709997365,
-                        "LWcld_err":-0.00024008684008296145,
-                        "LWcld_tau":-0.0007481158211895306,
-                        "LWcld_tot":0.003763460097980533,
-                        "LWdobsc_cov_fbk":-3.714723237369714e-21,
-                        "LWdobsc_fbk":0.0032691035236498173,
-                        "LWdunobsc_fbk":0.0037634600979805327,
-                        "SWcld_alt":-0.0042168652315204835,
-                        "SWcld_amt":0.029545967709204323,
-                        "SWcld_err":4.295943310378213e-05,
-                        "SWcld_tau":0.012421236433177578,
-                        "SWcld_tot":0.03779329834396519,
-                        "SWdobsc_cov_fbk":6.6554434562824505e-21,
-                        "SWdobsc_fbk":-0.02442264585294028,
-                        "SWdunobsc_fbk":0.03779329834396519
-                    }
-                },
-                "ocn_asc":{
-                    "LO680":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "LWdobsc_cov_fbk":NaN,
-                        "LWdobsc_fbk":NaN,
-                        "LWdunobsc_fbk":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN,
-                        "SWdobsc_cov_fbk":NaN,
-                        "SWdobsc_fbk":NaN,
-                        "SWdunobsc_fbk":NaN
-                    }
-                },
-                "ocn_dsc":{
-                    "LO680":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "LWdobsc_cov_fbk":NaN,
-                        "LWdobsc_fbk":NaN,
-                        "LWdunobsc_fbk":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN,
-                        "SWdobsc_cov_fbk":NaN,
-                        "SWdobsc_fbk":NaN,
-                        "SWdunobsc_fbk":NaN
-                    }
-                }
-            },
-            "eq30":{
-                "all":{
-                    "LO680":{
-                        "LWcld_alt":0.013518295504060964,
-                        "LWcld_amt":-0.010059051179815554,
-                        "LWcld_err":-0.0005923455779320672,
-                        "LWcld_tau":-0.0016565844417687986,
-                        "LWcld_tot":0.001210314304544544,
-                        "LWdobsc_cov_fbk":-2.1379997217688038e-20,
-                        "LWdobsc_fbk":0.005416714921169988,
-                        "LWdunobsc_fbk":0.0012103143045445435,
-                        "SWcld_alt":-0.007416728739813162,
-                        "SWcld_amt":0.08261123072999974,
-                        "SWcld_err":0.00017897643555832074,
-                        "SWcld_tau":0.018147450908163963,
-                        "SWcld_tot":0.09352092933390888,
-                        "SWdobsc_cov_fbk":1.6549920083366388e-19,
-                        "SWdobsc_fbk":-0.04189886319111277,
-                        "SWdunobsc_fbk":0.09352092933390886
-                    }
-                },
-                "lnd":{
-                    "LO680":{
-                        "LWcld_alt":0.0021941601120837105,
-                        "LWcld_amt":-0.009018137922576849,
-                        "LWcld_err":2.5176957655463587e-05,
-                        "LWcld_tau":-5.6812892041824526e-05,
-                        "LWcld_tot":-0.006855613744879504,
-                        "LWdobsc_cov_fbk":-1.6043838668990826e-20,
-                        "LWdobsc_fbk":0.0030978894701634424,
-                        "LWdunobsc_fbk":-0.006855613744879504,
-                        "SWcld_alt":-0.0013020312815215363,
-                        "SWcld_amt":0.06796854788464234,
-                        "SWcld_err":-2.5218862820028265e-06,
-                        "SWcld_tau":0.0006846967367334753,
-                        "SWcld_tot":0.06734869145357225,
-                        "SWdobsc_cov_fbk":1.3462005305153045e-19,
-                        "SWdobsc_fbk":-0.0245294020982542,
-                        "SWdunobsc_fbk":0.06734869145357225
-                    }
-                },
-                "ocn":{
-                    "LO680":{
-                        "LWcld_alt":0.011324135391977255,
-                        "LWcld_amt":-0.001040913257238703,
-                        "LWcld_err":-0.0006175225355875308,
-                        "LWcld_tau":-0.0015997715497269745,
-                        "LWcld_tot":0.008065928049424046,
-                        "LWdobsc_cov_fbk":-5.336158548697211e-21,
-                        "LWdobsc_fbk":0.002318825451006545,
-                        "LWdunobsc_fbk":0.008065928049424046,
-                        "SWcld_alt":-0.006114697458291626,
-                        "SWcld_amt":0.014642682845357415,
-                        "SWcld_err":0.00018149832184032361,
-                        "SWcld_tau":0.01746275417143049,
-                        "SWcld_tot":0.026172237880336607,
-                        "SWdobsc_cov_fbk":3.087914778213351e-20,
-                        "SWdobsc_fbk":-0.017369461092858574,
-                        "SWdunobsc_fbk":0.026172237880336607
-                    }
-                },
-                "ocn_asc":{
-                    "LO680":{
-                        "LWcld_alt":0.003125519843747635,
-                        "LWcld_amt":0.0034084762671087606,
-                        "LWcld_err":-9.91608357542044e-05,
-                        "LWcld_tau":-0.0002101973126211388,
-                        "LWcld_tot":0.006224637962481052,
-                        "LWdobsc_cov_fbk":-4.9243584714134596e-20,
-                        "LWdobsc_fbk":-4.6395920249720684e-05,
-                        "LWdunobsc_fbk":0.006224637962481052,
-                        "SWcld_alt":-0.001713619489926685,
-                        "SWcld_amt":-0.03565753487451715,
-                        "SWcld_err":-6.675471579170227e-05,
-                        "SWcld_tau":-0.003423591040433993,
-                        "SWcld_tot":-0.04086150012066953,
-                        "SWdobsc_cov_fbk":2.9459012259630684e-19,
-                        "SWdobsc_fbk":0.00048422630004293774,
-                        "SWdunobsc_fbk":-0.04086150012066953
-                    }
-                },
-                "ocn_dsc":{
-                    "LO680":{
-                        "LWcld_alt":0.010030626881412397,
-                        "LWcld_amt":-0.003477288932915988,
-                        "LWcld_err":-0.0005785810566682028,
-                        "LWcld_tau":-0.003219021474900946,
-                        "LWcld_tot":0.002755735416927262,
-                        "LWdobsc_cov_fbk":-5.581707162163222e-20,
-                        "LWdobsc_fbk":0.0002633521718935082,
-                        "LWdunobsc_fbk":0.002755735416927261,
-                        "SWcld_alt":-0.005303256439957211,
-                        "SWcld_amt":0.0029237773621457485,
-                        "SWcld_err":0.0002540180420989892,
-                        "SWcld_tau":0.03715592915192616,
-                        "SWcld_tot":0.03503046811621369,
-                        "SWdobsc_cov_fbk":4.523762919345695e-19,
-                        "SWdobsc_fbk":-0.0020961994106613218,
-                        "SWdunobsc_fbk":0.035030468116213694
-                    }
-                }
-            },
-            "eq40":{
-                "all":{
-                    "LO680":{
-                        "LWcld_alt":0.017493766988988104,
-                        "LWcld_amt":-0.015427879902121423,
-                        "LWcld_err":-0.0008147518761177313,
-                        "LWcld_tau":-0.0033353188151471085,
-                        "LWcld_tot":-0.002084183604398162,
-                        "LWdobsc_cov_fbk":-2.949423658842953e-20,
-                        "LWdobsc_fbk":0.006616770682237882,
-                        "LWdunobsc_fbk":-0.002084183604398162,
-                        "SWcld_alt":-0.009336307357698857,
-                        "SWcld_amt":0.11499844289661616,
-                        "SWcld_err":0.0002501864607131322,
-                        "SWcld_tau":0.03420126247719438,
-                        "SWcld_tot":0.1401135844768248,
-                        "SWdobsc_cov_fbk":2.5352903835599463e-19,
-                        "SWdobsc_fbk":-0.04781044332812708,
-                        "SWdunobsc_fbk":0.14011358447682482
-                    }
-                },
-                "lnd":{
-                    "LO680":{
-                        "LWcld_alt":0.0024234544633407362,
-                        "LWcld_amt":-0.011662340254511731,
-                        "LWcld_err":3.167538875933762e-05,
-                        "LWcld_tau":-4.402203725091809e-05,
-                        "LWcld_tot":-0.009251232439662575,
-                        "LWdobsc_cov_fbk":-1.6615602247985758e-20,
-                        "LWdobsc_fbk":0.0034760073171858446,
-                        "LWdunobsc_fbk":-0.009251232439662575,
-                        "SWcld_alt":-0.001403520569477472,
-                        "SWcld_amt":0.08195690194237255,
-                        "SWcld_err":-4.492848095275383e-06,
-                        "SWcld_tau":0.000374047645167767,
-                        "SWcld_tot":0.08092293616996758,
-                        "SWdobsc_cov_fbk":1.5478918000656533e-19,
-                        "SWdobsc_fbk":-0.026344948518248756,
-                        "SWdunobsc_fbk":0.08092293616996758
-                    }
-                },
-                "ocn":{
-                    "LO680":{
-                        "LWcld_alt":0.015070312525647363,
-                        "LWcld_amt":-0.003765539647609691,
-                        "LWcld_err":-0.0008464272648770689,
-                        "LWcld_tau":-0.0032912967778961906,
-                        "LWcld_tot":0.007167048835264415,
-                        "LWdobsc_cov_fbk":-1.2878634340443771e-20,
-                        "LWdobsc_fbk":0.0031407633650520368,
-                        "LWdunobsc_fbk":0.007167048835264415,
-                        "SWcld_alt":-0.007932786788221385,
-                        "SWcld_amt":0.033041540954243595,
-                        "SWcld_err":0.00025467930880840756,
-                        "SWcld_tau":0.03382721483202661,
-                        "SWcld_tot":0.05919064830685724,
-                        "SWdobsc_cov_fbk":9.873985834942924e-20,
-                        "SWdobsc_fbk":-0.021465494809878336,
-                        "SWdunobsc_fbk":0.05919064830685724
-                    }
-                },
-                "ocn_asc":{
-                    "LO680":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "LWdobsc_cov_fbk":NaN,
-                        "LWdobsc_fbk":NaN,
-                        "LWdunobsc_fbk":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN,
-                        "SWdobsc_cov_fbk":NaN,
-                        "SWdobsc_fbk":NaN,
-                        "SWdunobsc_fbk":NaN
-                    }
-                },
-                "ocn_dsc":{
-                    "LO680":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "LWdobsc_cov_fbk":NaN,
-                        "LWdobsc_fbk":NaN,
-                        "LWdunobsc_fbk":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN,
-                        "SWdobsc_cov_fbk":NaN,
-                        "SWdobsc_fbk":NaN,
-                        "SWdunobsc_fbk":NaN
-                    }
-                }
-            },
-            "eq50":{
-                "all":{
-                    "LO680":{
-                        "LWcld_alt":0.02148156498378993,
-                        "LWcld_amt":-0.02263938532463335,
-                        "LWcld_err":-0.0008015515651191781,
-                        "LWcld_tau":-0.004248510404582397,
-                        "LWcld_tot":-0.006207882310545002,
-                        "LWdobsc_cov_fbk":-3.1764138406356926e-20,
-                        "LWdobsc_fbk":0.007630768348540645,
-                        "LWdunobsc_fbk":-0.006207882310545001,
-                        "SWcld_alt":-0.011171933559667543,
-                        "SWcld_amt":0.16899102675444724,
-                        "SWcld_err":0.00023438119810763496,
-                        "SWcld_tau":0.04552732609468257,
-                        "SWcld_tot":0.2035808004875699,
-                        "SWdobsc_cov_fbk":2.4714866851049444e-19,
-                        "SWdobsc_fbk":-0.05801230136331561,
-                        "SWdunobsc_fbk":0.20358080048756996
-                    }
-                },
-                "lnd":{
-                    "LO680":{
-                        "LWcld_alt":0.0028551972566930963,
-                        "LWcld_amt":-0.013912731493679331,
-                        "LWcld_err":5.353991462359288e-05,
-                        "LWcld_tau":0.00040655360806126934,
-                        "LWcld_tot":-0.010597440714301373,
-                        "LWdobsc_cov_fbk":-2.2234029564244705e-20,
-                        "LWdobsc_fbk":0.0033742707336521017,
-                        "LWdunobsc_fbk":-0.01059744071430137,
-                        "SWcld_alt":-0.0015506535775607442,
-                        "SWcld_amt":0.09982864943873287,
-                        "SWcld_err":-5.7729419193343615e-06,
-                        "SWcld_tau":-0.0026039271636247416,
-                        "SWcld_tot":0.09566829575562803,
-                        "SWdobsc_cov_fbk":1.739201839088955e-19,
-                        "SWdobsc_fbk":-0.02721075076916123,
-                        "SWdunobsc_fbk":0.09566829575562803
-                    }
-                },
-                "ocn":{
-                    "LO680":{
-                        "LWcld_alt":0.01862636772709683,
-                        "LWcld_amt":-0.008726653830954023,
-                        "LWcld_err":-0.0008550914797427709,
-                        "LWcld_tau":-0.004655064012643667,
-                        "LWcld_tot":0.0043895584037563695,
-                        "LWdobsc_cov_fbk":-9.530108842112217e-21,
-                        "LWdobsc_fbk":0.004256497614888542,
-                        "LWdunobsc_fbk":0.0043895584037563695,
-                        "SWcld_alt":-0.0096212799821068,
-                        "SWcld_amt":0.06916237731571438,
-                        "SWcld_err":0.00024015414002696929,
-                        "SWcld_tau":0.04813125325830731,
-                        "SWcld_tot":0.10791250473194187,
-                        "SWdobsc_cov_fbk":7.322848460159892e-20,
-                        "SWdobsc_fbk":-0.030801550594154375,
-                        "SWdunobsc_fbk":0.10791250473194187
-                    }
-                },
-                "ocn_asc":{
-                    "LO680":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "LWdobsc_cov_fbk":NaN,
-                        "LWdobsc_fbk":NaN,
-                        "LWdunobsc_fbk":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN,
-                        "SWdobsc_cov_fbk":NaN,
-                        "SWdobsc_fbk":NaN,
-                        "SWdunobsc_fbk":NaN
-                    }
-                },
-                "ocn_dsc":{
-                    "LO680":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "LWdobsc_cov_fbk":NaN,
-                        "LWdobsc_fbk":NaN,
-                        "LWdunobsc_fbk":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN,
-                        "SWdobsc_cov_fbk":NaN,
-                        "SWdobsc_fbk":NaN,
-                        "SWdunobsc_fbk":NaN
-                    }
-                }
-            },
-            "eq60":{
-                "all":{
-                    "LO680":{
-                        "LWcld_alt":0.024896662661650623,
-                        "LWcld_amt":-0.02606306110159848,
-                        "LWcld_err":-0.0006265311464610726,
-                        "LWcld_tau":-0.003862700283457655,
-                        "LWcld_tot":-0.005655629869866583,
-                        "LWdobsc_cov_fbk":-3.479655218395196e-20,
-                        "LWdobsc_fbk":0.004230143203300456,
-                        "LWdunobsc_fbk":-0.005655629869866583,
-                        "SWcld_alt":-0.012837819053962307,
-                        "SWcld_amt":0.19530310833872508,
-                        "SWcld_err":0.0001600016736085389,
-                        "SWcld_tau":0.0387061608802144,
-                        "SWcld_tot":0.22133145183858574,
-                        "SWdobsc_cov_fbk":2.3345767900792954e-19,
-                        "SWdobsc_fbk":-0.03678567112355873,
-                        "SWdunobsc_fbk":0.22133145183858574
-                    }
-                },
-                "lnd":{
-                    "LO680":{
-                        "LWcld_alt":0.00324252453111119,
-                        "LWcld_amt":-0.014309945500883402,
-                        "LWcld_err":0.00010569021236486643,
-                        "LWcld_tau":0.0011172031228947304,
-                        "LWcld_tot":-0.009844527634512617,
-                        "LWdobsc_cov_fbk":-2.6819463635725862e-20,
-                        "LWdobsc_fbk":0.002753305608008972,
-                        "LWdunobsc_fbk":-0.009844527634512618,
-                        "SWcld_alt":-0.001728202453093103,
-                        "SWcld_amt":0.10918951937415211,
-                        "SWcld_err":-2.8439857254772556e-06,
-                        "SWcld_tau":-0.006951275061963892,
-                        "SWcld_tot":0.10050719787336965,
-                        "SWdobsc_cov_fbk":1.6841949348905736e-19,
-                        "SWdobsc_fbk":-0.025066419059916568,
-                        "SWdunobsc_fbk":0.10050719787336965
-                    }
-                },
-                "ocn":{
-                    "LO680":{
-                        "LWcld_alt":0.021654138130539433,
-                        "LWcld_amt":-0.011753115600715075,
-                        "LWcld_err":-0.000732221358825939,
-                        "LWcld_tau":-0.004979903406352386,
-                        "LWcld_tot":0.0041888977646460344,
-                        "LWdobsc_cov_fbk":-7.977088548226099e-21,
-                        "LWdobsc_fbk":0.0014768375952914834,
-                        "LWdunobsc_fbk":0.0041888977646460344,
-                        "SWcld_alt":-0.011109616600869204,
-                        "SWcld_amt":0.08611358896457298,
-                        "SWcld_err":0.00016284565933401613,
-                        "SWcld_tau":0.04565743594217828,
-                        "SWcld_tot":0.12082425396521607,
-                        "SWdobsc_cov_fbk":6.503818551887221e-20,
-                        "SWdobsc_fbk":-0.011719252063642153,
-                        "SWdunobsc_fbk":0.12082425396521608
-                    }
-                },
-                "ocn_asc":{
-                    "LO680":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "LWdobsc_cov_fbk":NaN,
-                        "LWdobsc_fbk":NaN,
-                        "LWdunobsc_fbk":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN,
-                        "SWdobsc_cov_fbk":NaN,
-                        "SWdobsc_fbk":NaN,
-                        "SWdunobsc_fbk":NaN
-                    }
-                },
-                "ocn_dsc":{
-                    "LO680":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "LWdobsc_cov_fbk":NaN,
-                        "LWdobsc_fbk":NaN,
-                        "LWdunobsc_fbk":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN,
-                        "SWdobsc_cov_fbk":NaN,
-                        "SWdobsc_fbk":NaN,
-                        "SWdunobsc_fbk":NaN
-                    }
-                }
-            },
-            "eq70":{
-                "all":{
-                    "LO680":{
-                        "LWcld_alt":0.027438548466458133,
-                        "LWcld_amt":-0.02555803663147403,
-                        "LWcld_err":-0.0005148483460570585,
-                        "LWcld_tau":-0.0031162051524756093,
-                        "LWcld_tot":-0.0017505416635485611,
-                        "LWdobsc_cov_fbk":-2.958897655459291e-20,
-                        "LWdobsc_fbk":0.0006815471603079875,
-                        "LWdunobsc_fbk":-0.0017505416635485611,
-                        "SWcld_alt":-0.014029884353019037,
-                        "SWcld_amt":0.19280023245027625,
-                        "SWcld_err":0.00020263877604275943,
-                        "SWcld_tau":0.026935091821015254,
-                        "SWcld_tot":0.20590807869431518,
-                        "SWdobsc_cov_fbk":2.1040849850859803e-19,
-                        "SWdobsc_fbk":-0.010649190956141133,
-                        "SWdunobsc_fbk":0.20590807869431518
-                    }
-                },
-                "lnd":{
-                    "LO680":{
-                        "LWcld_alt":0.004000709824916932,
-                        "LWcld_amt":-0.013929769231691964,
-                        "LWcld_err":0.00015173864969953145,
-                        "LWcld_tau":0.0016185923894287916,
-                        "LWcld_tot":-0.008158728367646707,
-                        "LWdobsc_cov_fbk":-2.6071201627326818e-20,
-                        "LWdobsc_fbk":0.0015212672892389674,
-                        "LWdunobsc_fbk":-0.008158728367646707,
-                        "SWcld_alt":-0.0020943850981606847,
-                        "SWcld_amt":0.1108037744227428,
-                        "SWcld_err":-3.1573642171953454e-06,
-                        "SWcld_tau":-0.011655165875996444,
-                        "SWcld_tot":0.09705106608436846,
-                        "SWdobsc_cov_fbk":1.7050319208940573e-19,
-                        "SWdobsc_fbk":-0.018509251850792666,
-                        "SWdunobsc_fbk":0.09705106608436846
-                    }
-                },
-                "ocn":{
-                    "LO680":{
-                        "LWcld_alt":0.0234378386415412,
-                        "LWcld_amt":-0.011628267399782065,
-                        "LWcld_err":-0.0006665869957565899,
-                        "LWcld_tau":-0.004734797541904401,
-                        "LWcld_tot":0.006408186704098145,
-                        "LWdobsc_cov_fbk":-3.5177749272660855e-21,
-                        "LWdobsc_fbk":-0.0008397201289309799,
-                        "LWdunobsc_fbk":0.006408186704098145,
-                        "SWcld_alt":-0.011935499254858352,
-                        "SWcld_amt":0.08199645802753346,
-                        "SWcld_err":0.00020579614025995473,
-                        "SWcld_tau":0.03859025769701171,
-                        "SWcld_tot":0.10885701260994674,
-                        "SWdobsc_cov_fbk":3.990530641919237e-20,
-                        "SWdobsc_fbk":0.007860060894651534,
-                        "SWdunobsc_fbk":0.10885701260994678
-                    }
-                },
-                "ocn_asc":{
-                    "LO680":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "LWdobsc_cov_fbk":NaN,
-                        "LWdobsc_fbk":NaN,
-                        "LWdunobsc_fbk":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN,
-                        "SWdobsc_cov_fbk":NaN,
-                        "SWdobsc_fbk":NaN,
-                        "SWdunobsc_fbk":NaN
-                    }
-                },
-                "ocn_dsc":{
-                    "LO680":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "LWdobsc_cov_fbk":NaN,
-                        "LWdobsc_fbk":NaN,
-                        "LWdunobsc_fbk":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN,
-                        "SWdobsc_cov_fbk":NaN,
-                        "SWdobsc_fbk":NaN,
-                        "SWdunobsc_fbk":NaN
-                    }
-                }
-            },
-            "eq80":{
-                "all":{
-                    "LO680":{
-                        "LWcld_alt":0.028885049992389708,
-                        "LWcld_amt":-0.024500672970219208,
-                        "LWcld_err":-0.00048063890874079743,
-                        "LWcld_tau":-0.0026283106316293717,
-                        "LWcld_tot":0.0012754274818003379,
-                        "LWdobsc_cov_fbk":-3.405397743220445e-20,
-                        "LWdobsc_fbk":-0.0013157475867755662,
-                        "LWdunobsc_fbk":0.0012754274818003377,
-                        "SWcld_alt":-0.014584773420568414,
-                        "SWcld_amt":0.18811797079906564,
-                        "SWcld_err":0.00021739432827778266,
-                        "SWcld_tau":0.021922834391232684,
-                        "SWcld_tot":0.19567342609800767,
-                        "SWdobsc_cov_fbk":2.2976199736999064e-19,
-                        "SWdobsc_fbk":0.0033812886622407233,
-                        "SWdunobsc_fbk":0.19567342609800767
-                    }
-                },
-                "lnd":{
-                    "LO680":{
-                        "LWcld_alt":0.004303116762686796,
-                        "LWcld_amt":-0.013695395120611567,
-                        "LWcld_err":0.00015840922705371161,
-                        "LWcld_tau":0.0018037006733015702,
-                        "LWcld_tot":-0.007430168457569489,
-                        "LWdobsc_cov_fbk":-2.7736472557787965e-20,
-                        "LWdobsc_fbk":0.0010749536018153263,
-                        "LWdunobsc_fbk":-0.00743016845756949,
-                        "SWcld_alt":-0.0022062078373120213,
-                        "SWcld_amt":0.11064629625521245,
-                        "SWcld_err":-1.5200836593408395e-06,
-                        "SWcld_tau":-0.012802392303087821,
-                        "SWcld_tot":0.09563617603115325,
-                        "SWdobsc_cov_fbk":1.760405203228221e-19,
-                        "SWdobsc_fbk":-0.0165950557603553,
-                        "SWdunobsc_fbk":0.09563617603115325
-                    }
-                },
-                "ocn":{
-                    "LO680":{
-                        "LWcld_alt":0.024581933229702913,
-                        "LWcld_amt":-0.010805277849607636,
-                        "LWcld_err":-0.000639048135794509,
-                        "LWcld_tau":-0.00443201130493094,
-                        "LWcld_tot":0.008705595939369828,
-                        "LWdobsc_cov_fbk":-6.317504874416481e-21,
-                        "LWdobsc_fbk":-0.0023907011885908923,
-                        "LWdunobsc_fbk":0.008705595939369828,
-                        "SWcld_alt":-0.01237856558325639,
-                        "SWcld_amt":0.07747167454385322,
-                        "SWcld_err":0.0002189144119371235,
-                        "SWcld_tau":0.03472522669432051,
-                        "SWcld_tot":0.10003725006685445,
-                        "SWdobsc_cov_fbk":5.372147704716852e-20,
-                        "SWdobsc_fbk":0.019976344422596028,
-                        "SWdunobsc_fbk":0.10003725006685445
-                    }
-                },
-                "ocn_asc":{
-                    "LO680":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "LWdobsc_cov_fbk":NaN,
-                        "LWdobsc_fbk":NaN,
-                        "LWdunobsc_fbk":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN,
-                        "SWdobsc_cov_fbk":NaN,
-                        "SWdobsc_fbk":NaN,
-                        "SWdunobsc_fbk":NaN
-                    }
-                },
-                "ocn_dsc":{
-                    "LO680":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "LWdobsc_cov_fbk":NaN,
-                        "LWdobsc_fbk":NaN,
-                        "LWdunobsc_fbk":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN,
-                        "SWdobsc_cov_fbk":NaN,
-                        "SWdobsc_fbk":NaN,
-                        "SWdunobsc_fbk":NaN
-                    }
-                }
-            },
-            "eq90":{
-                "all":{
-                    "LO680":{
-                        "LWcld_alt":0.029381822530722287,
-                        "LWcld_amt":-0.024113157133417315,
-                        "LWcld_err":-0.0004725435282154707,
-                        "LWcld_tau":-0.00248803231398034,
-                        "LWcld_tot":0.002308089555109164,
-                        "LWdobsc_cov_fbk":-3.4331399975162e-20,
-                        "LWdobsc_fbk":-0.002074747338776084,
-                        "LWdunobsc_fbk":0.002308089555109164,
-                        "SWcld_alt":-0.014734180368518443,
-                        "SWcld_amt":0.18684034652608428,
-                        "SWcld_err":0.00022629113477991706,
-                        "SWcld_tau":0.021024497777146864,
-                        "SWcld_tot":0.19335695506949258,
-                        "SWdobsc_cov_fbk":2.282157621553106e-19,
-                        "SWdobsc_fbk":0.007403160296229984,
-                        "SWdunobsc_fbk":0.1933569550694926
-                    }
-                },
-                "lnd":{
-                    "LO680":{
-                        "LWcld_alt":0.004348189861946446,
-                        "LWcld_amt":-0.013652584605596091,
-                        "LWcld_err":0.00015921381646393854,
-                        "LWcld_tau":0.0018597168792747537,
-                        "LWcld_tot":-0.007285464047910955,
-                        "LWdobsc_cov_fbk":-2.7462859237079386e-20,
-                        "LWdobsc_fbk":0.0009964294911489991,
-                        "LWdunobsc_fbk":-0.007285464047910955,
-                        "SWcld_alt":-0.00221603823419405,
-                        "SWcld_amt":0.11054689637510133,
-                        "SWcld_err":-1.3417673192389475e-06,
-                        "SWcld_tau":-0.012905918660020188,
-                        "SWcld_tot":0.09542359771356784,
-                        "SWdobsc_cov_fbk":1.7590860028506171e-19,
-                        "SWdobsc_fbk":-0.016452826549574827,
-                        "SWdunobsc_fbk":0.09542359771356784
-                    }
-                },
-                "ocn":{
-                    "LO680":{
-                        "LWcld_alt":0.02503363266877585,
-                        "LWcld_amt":-0.01046057252782122,
-                        "LWcld_err":-0.0006317573446794093,
-                        "LWcld_tau":-0.004347749193255093,
-                        "LWcld_tot":0.00959355360302012,
-                        "LWdobsc_cov_fbk":-6.868540738082614e-21,
-                        "LWdobsc_fbk":-0.003071176829925083,
-                        "LWdunobsc_fbk":0.00959355360302012,
-                        "SWcld_alt":-0.012518142134324393,
-                        "SWcld_amt":0.07629345015098295,
-                        "SWcld_err":0.00022763290209915597,
-                        "SWcld_tau":0.03393041643716706,
-                        "SWcld_tot":0.09793335735592477,
-                        "SWdobsc_cov_fbk":5.2307161870249035e-20,
-                        "SWdobsc_fbk":0.02385598684580481,
-                        "SWdunobsc_fbk":0.09793335735592477
-                    }
-                },
-                "ocn_asc":{
-                    "LO680":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "LWdobsc_cov_fbk":NaN,
-                        "LWdobsc_fbk":NaN,
-                        "LWdunobsc_fbk":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN,
-                        "SWdobsc_cov_fbk":NaN,
-                        "SWdobsc_fbk":NaN,
-                        "SWdunobsc_fbk":NaN
-                    }
-                },
-                "ocn_dsc":{
-                    "LO680":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "LWdobsc_cov_fbk":NaN,
-                        "LWdobsc_fbk":NaN,
-                        "LWdunobsc_fbk":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN,
-                        "SWdobsc_cov_fbk":NaN,
-                        "SWdobsc_fbk":NaN,
-                        "SWdunobsc_fbk":NaN
-                    }
-                }
-            }
-        }
-    },
-    "CESM2":{
-        "r1i1p1f1":{
-            "Arctic":{
-                "all":{
-                    "LO680":{
-                        "LWcld_alt":0.0010122329060984518,
-                        "LWcld_amt":-0.00010031490538311454,
-                        "LWcld_err":-0.00018145243918994483,
-                        "LWcld_tau":0.000877736341611621,
-                        "LWcld_tot":0.0014741850778755958,
-                        "LWdobsc_cov_fbk":9.74771680666185e-22,
-                        "LWdobsc_fbk":-0.002539463045793756,
-                        "LWdunobsc_fbk":0.0016082019031370132,
-                        "SWcld_alt":-0.00047662686099672586,
-                        "SWcld_amt":0.0012294131043502148,
-                        "SWcld_err":0.00012145576707848891,
-                        "SWcld_tau":-0.007344999417102695,
-                        "SWcld_tot":-0.006470757406670718,
-                        "SWdobsc_cov_fbk":-3.849689744887157e-21,
-                        "SWdobsc_fbk":0.0163444985598372,
-                        "SWdunobsc_fbk":-0.0070590080800044174
-                    }
-                },
-                "lnd":{
-                    "LO680":{
-                        "LWcld_alt":0.00012534928650071258,
-                        "LWcld_amt":-0.0001000921396410705,
-                        "LWcld_err":-1.9399507774735235e-05,
-                        "LWcld_tau":0.0002224028143221617,
-                        "LWcld_tot":0.00022826045340706852,
-                        "LWdobsc_cov_fbk":7.939018645063679e-22,
-                        "LWdobsc_fbk":-0.00036753527103281815,
-                        "LWdunobsc_fbk":0.00022826045340706852,
-                        "SWcld_alt":-5.273760516033453e-05,
-                        "SWcld_amt":0.0009574372790544361,
-                        "SWcld_err":1.300679598920448e-05,
-                        "SWcld_tau":-0.0009969457824989959,
-                        "SWcld_tot":-7.923931261568986e-05,
-                        "SWdobsc_cov_fbk":-6.539353180302701e-21,
-                        "SWdobsc_fbk":0.0018605952803067796,
-                        "SWdunobsc_fbk":-7.923931261568966e-05
-                    }
-                },
-                "ocn":{
-                    "LO680":{
-                        "LWcld_alt":0.0008868836195977391,
-                        "LWcld_amt":-2.2276574204405743e-07,
-                        "LWcld_err":-0.00016205293141520957,
-                        "LWcld_tau":0.0006553335272894594,
-                        "LWcld_tot":0.0013799414497299449,
-                        "LWdobsc_cov_fbk":1.808698161598168e-22,
-                        "LWdobsc_fbk":-0.002171927774760938,
-                        "LWdunobsc_fbk":0.0013799414497299449,
-                        "SWcld_alt":-0.00046721897047245726,
-                        "SWcld_amt":0.00038374065296397985,
-                        "SWcld_err":0.00011949040446005617,
-                        "SWcld_tau":-0.007015780854340306,
-                        "SWcld_tot":-0.00697976876738873,
-                        "SWdobsc_cov_fbk":2.689663435415548e-21,
-                        "SWdobsc_fbk":0.014483903279530414,
-                        "SWdunobsc_fbk":-0.00697976876738873
-                    }
-                },
-                "ocn_asc":{
-                    "LO680":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "LWdobsc_cov_fbk":NaN,
-                        "LWdobsc_fbk":NaN,
-                        "LWdunobsc_fbk":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN,
-                        "SWdobsc_cov_fbk":NaN,
-                        "SWdobsc_fbk":NaN,
-                        "SWdunobsc_fbk":NaN
-                    }
-                },
-                "ocn_dsc":{
-                    "LO680":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "LWdobsc_cov_fbk":NaN,
-                        "LWdobsc_fbk":NaN,
-                        "LWdunobsc_fbk":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN,
-                        "SWdobsc_cov_fbk":NaN,
-                        "SWdobsc_fbk":NaN,
-                        "SWdunobsc_fbk":NaN
-                    }
-                }
-            },
-            "eq10":{
-                "all":{
-                    "LO680":{
-                        "LWcld_alt":0.0029148060936505338,
-                        "LWcld_amt":-0.012705297162489066,
-                        "LWcld_err":5.053985020270653e-05,
-                        "LWcld_tau":-0.00024177350711724547,
-                        "LWcld_tot":-0.009981724725753068,
-                        "LWdobsc_cov_fbk":-8.537178482782106e-21,
-                        "LWdobsc_fbk":0.002010829501178771,
-                        "LWdunobsc_fbk":-0.009981724725753068,
-                        "SWcld_alt":-0.0015002769806686791,
-                        "SWcld_amt":0.0984706444949373,
-                        "SWcld_err":-2.1885923947572443e-06,
-                        "SWcld_tau":0.011481775651799475,
-                        "SWcld_tot":0.10844995457367333,
-                        "SWdobsc_cov_fbk":9.464021529467725e-20,
-                        "SWdobsc_fbk":-0.016114031649963282,
-                        "SWdunobsc_fbk":0.10844995457367333
-                    }
-                },
-                "lnd":{
-                    "LO680":{
-                        "LWcld_alt":0.0006623181835809967,
-                        "LWcld_amt":-0.004100841764062954,
-                        "LWcld_err":2.5561246697838588e-05,
-                        "LWcld_tau":3.173188127569045e-05,
-                        "LWcld_tot":-0.0033812304525084273,
-                        "LWdobsc_cov_fbk":-1.925609893772562e-21,
-                        "LWdobsc_fbk":0.001676866505575918,
-                        "LWdunobsc_fbk":-0.0033812304525084273,
-                        "SWcld_alt":-0.00037305361132445653,
-                        "SWcld_amt":0.030399723368139658,
-                        "SWcld_err":-1.7806286911802434e-05,
-                        "SWcld_tau":-0.001019427833152348,
-                        "SWcld_tot":0.02898943563675105,
-                        "SWdobsc_cov_fbk":1.7137249245298173e-20,
-                        "SWdobsc_fbk":-0.012687762609413289,
-                        "SWdunobsc_fbk":0.028989435636751052
-                    }
-                },
-                "ocn":{
-                    "LO680":{
-                        "LWcld_alt":0.0022524879100695367,
-                        "LWcld_amt":-0.008604455398426112,
-                        "LWcld_err":2.497860350486794e-05,
-                        "LWcld_tau":-0.00027350538839293593,
-                        "LWcld_tot":-0.006600494273244643,
-                        "LWdobsc_cov_fbk":-6.611568589009543e-21,
-                        "LWdobsc_fbk":0.0003339629956028525,
-                        "LWdunobsc_fbk":-0.006600494273244643,
-                        "SWcld_alt":-0.0011272233693442226,
-                        "SWcld_amt":0.06807092112679763,
-                        "SWcld_err":1.5617694517045186e-05,
-                        "SWcld_tau":0.012501203484951824,
-                        "SWcld_tot":0.07946051893692228,
-                        "SWdobsc_cov_fbk":7.750296604937911e-20,
-                        "SWdobsc_fbk":-0.0034262690405499918,
-                        "SWdunobsc_fbk":0.07946051893692228
-                    }
-                },
-                "ocn_asc":{
-                    "LO680":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "LWdobsc_cov_fbk":NaN,
-                        "LWdobsc_fbk":NaN,
-                        "LWdunobsc_fbk":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN,
-                        "SWdobsc_cov_fbk":NaN,
-                        "SWdobsc_fbk":NaN,
-                        "SWdunobsc_fbk":NaN
-                    }
-                },
-                "ocn_dsc":{
-                    "LO680":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "LWdobsc_cov_fbk":NaN,
-                        "LWdobsc_fbk":NaN,
-                        "LWdunobsc_fbk":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN,
-                        "SWdobsc_cov_fbk":NaN,
-                        "SWdobsc_fbk":NaN,
-                        "SWdunobsc_fbk":NaN
-                    }
-                }
-            },
-            "eq20":{
-                "all":{
-                    "LO680":{
-                        "LWcld_alt":0.006939808920181548,
-                        "LWcld_amt":-0.021505459831595047,
-                        "LWcld_err":2.953932036522844e-06,
-                        "LWcld_tau":-0.0005358506943592291,
-                        "LWcld_tot":-0.015098547673736203,
-                        "LWdobsc_cov_fbk":4.617104948915009e-22,
-                        "LWdobsc_fbk":0.0014619039220079959,
-                        "LWdunobsc_fbk":-0.015098547673736203,
-                        "SWcld_alt":-0.0033811895304682454,
-                        "SWcld_amt":0.16095374760271636,
-                        "SWcld_err":0.00010749749515253278,
-                        "SWcld_tau":0.02143812434782875,
-                        "SWcld_tot":0.1791181799152294,
-                        "SWdobsc_cov_fbk":1.8190603454022657e-20,
-                        "SWdobsc_fbk":-0.010265655108689462,
-                        "SWdunobsc_fbk":0.1791181799152294
-                    }
-                },
-                "lnd":{
-                    "LO680":{
-                        "LWcld_alt":0.0009458077740599797,
-                        "LWcld_amt":-0.005721172809131039,
-                        "LWcld_err":2.447474829680513e-05,
-                        "LWcld_tau":4.900383290920311e-05,
-                        "LWcld_tot":-0.004701886453865051,
-                        "LWdobsc_cov_fbk":-1.2577339283105715e-21,
-                        "LWdobsc_fbk":0.0018720418491711577,
-                        "LWdunobsc_fbk":-0.00470188645386505,
-                        "SWcld_alt":-0.0005191260718506143,
-                        "SWcld_amt":0.040816995524351675,
-                        "SWcld_err":-1.1987736009636008e-05,
-                        "SWcld_tau":-0.001390413493072658,
-                        "SWcld_tot":0.038895468223418765,
-                        "SWdobsc_cov_fbk":4.7534689549317676e-21,
-                        "SWdobsc_fbk":-0.014132866366940331,
-                        "SWdunobsc_fbk":0.038895468223418765
-                    }
-                },
-                "ocn":{
-                    "LO680":{
-                        "LWcld_alt":0.005994001146121568,
-                        "LWcld_amt":-0.015784287022464007,
-                        "LWcld_err":-2.152081626028229e-05,
-                        "LWcld_tau":-0.0005848545272684323,
-                        "LWcld_tot":-0.010396661219871152,
-                        "LWdobsc_cov_fbk":1.719444423202074e-21,
-                        "LWdobsc_fbk":-0.00041013792716316225,
-                        "LWdunobsc_fbk":-0.010396661219871149,
-                        "SWcld_alt":-0.002862063458617631,
-                        "SWcld_amt":0.1201367520783647,
-                        "SWcld_err":0.00011948523116216879,
-                        "SWcld_tau":0.02282853784090141,
-                        "SWcld_tot":0.14022271169181064,
-                        "SWdobsc_cov_fbk":1.3437134499090898e-20,
-                        "SWdobsc_fbk":0.003867211258250867,
-                        "SWdunobsc_fbk":0.14022271169181064
-                    }
-                },
-                "ocn_asc":{
-                    "LO680":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "LWdobsc_cov_fbk":NaN,
-                        "LWdobsc_fbk":NaN,
-                        "LWdunobsc_fbk":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN,
-                        "SWdobsc_cov_fbk":NaN,
-                        "SWdobsc_fbk":NaN,
-                        "SWdunobsc_fbk":NaN
-                    }
-                },
-                "ocn_dsc":{
-                    "LO680":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "LWdobsc_cov_fbk":NaN,
-                        "LWdobsc_fbk":NaN,
-                        "LWdunobsc_fbk":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN,
-                        "SWdobsc_cov_fbk":NaN,
-                        "SWdobsc_fbk":NaN,
-                        "SWdunobsc_fbk":NaN
-                    }
-                }
-            },
-            "eq30":{
-                "all":{
-                    "LO680":{
-                        "LWcld_alt":0.010282273408501017,
-                        "LWcld_amt":-0.03196904330642695,
-                        "LWcld_err":5.4933090101046535e-05,
-                        "LWcld_tau":-0.0005052418137515087,
-                        "LWcld_tot":-0.022137078621576392,
-                        "LWdobsc_cov_fbk":1.717904238673221e-20,
-                        "LWdobsc_fbk":-0.000248015239072153,
-                        "LWdunobsc_fbk":-0.022137078621576392,
-                        "SWcld_alt":-0.004833191381909663,
-                        "SWcld_amt":0.2275066972528775,
-                        "SWcld_err":0.00011592782919283495,
-                        "SWcld_tau":0.02831779237758823,
-                        "SWcld_tot":0.2511072260777489,
-                        "SWdobsc_cov_fbk":-8.626097441242795e-20,
-                        "SWdobsc_fbk":0.0009924582702920534,
-                        "SWdunobsc_fbk":0.2511072260777489
-                    }
-                },
-                "lnd":{
-                    "LO680":{
-                        "LWcld_alt":0.0012610349277496875,
-                        "LWcld_amt":-0.008171373429344505,
-                        "LWcld_err":1.96285082710669e-05,
-                        "LWcld_tau":0.0001330443702989317,
-                        "LWcld_tot":-0.00675766562302482,
-                        "LWdobsc_cov_fbk":1.6917821363845452e-21,
-                        "LWdobsc_fbk":0.002666797993005217,
-                        "LWdunobsc_fbk":-0.00675766562302482,
-                        "SWcld_alt":-0.000671488954313861,
-                        "SWcld_amt":0.05548811850212156,
-                        "SWcld_err":-3.263492675344407e-06,
-                        "SWcld_tau":-0.0017997904906352027,
-                        "SWcld_tot":0.05301357556449715,
-                        "SWdobsc_cov_fbk":-1.4917199095936532e-20,
-                        "SWdobsc_fbk":-0.019193715699032565,
-                        "SWdunobsc_fbk":0.05301357556449715
-                    }
-                },
-                "ocn":{
-                    "LO680":{
-                        "LWcld_alt":0.009021238480751329,
-                        "LWcld_amt":-0.023797669877082442,
-                        "LWcld_err":3.530458182997964e-05,
-                        "LWcld_tau":-0.0006382861840504403,
-                        "LWcld_tot":-0.015379412998551572,
-                        "LWdobsc_cov_fbk":1.5487260250347664e-20,
-                        "LWdobsc_fbk":-0.00291481323207737,
-                        "LWdunobsc_fbk":-0.015379412998551572,
-                        "SWcld_alt":-0.004161702427595801,
-                        "SWcld_amt":0.1720185787507559,
-                        "SWcld_err":0.00011919132186817938,
-                        "SWcld_tau":0.03011758286822343,
-                        "SWcld_tot":0.1980936505132517,
-                        "SWdobsc_cov_fbk":-7.134377531649139e-20,
-                        "SWdobsc_fbk":0.020186173969324612,
-                        "SWdunobsc_fbk":0.1980936505132517
-                    }
-                },
-                "ocn_asc":{
-                    "LO680":{
-                        "LWcld_alt":0.002670792484120046,
-                        "LWcld_amt":4.251773349047554e-06,
-                        "LWcld_err":2.5968282024161965e-05,
-                        "LWcld_tau":0.0002532694631434161,
-                        "LWcld_tot":0.0029542820026366725,
-                        "LWdobsc_cov_fbk":4.038252703978399e-20,
-                        "LWdobsc_fbk":-0.00010154788729408858,
-                        "LWdunobsc_fbk":0.0029542820026366707,
-                        "SWcld_alt":-0.0013184375319741821,
-                        "SWcld_amt":0.00017535832431455137,
-                        "SWcld_err":2.4084567745009743e-05,
-                        "SWcld_tau":-0.002056349795567296,
-                        "SWcld_tot":-0.0031753444354819184,
-                        "SWdobsc_cov_fbk":-3.83971665119543e-19,
-                        "SWdobsc_fbk":0.0007441521432759949,
-                        "SWdunobsc_fbk":-0.003175344435481908
-                    }
-                },
-                "ocn_dsc":{
-                    "LO680":{
-                        "LWcld_alt":0.006439324849798281,
-                        "LWcld_amt":-0.026865708485040968,
-                        "LWcld_err":-7.547189761920895e-05,
-                        "LWcld_tau":-0.0009822218058970701,
-                        "LWcld_tot":-0.021484077338758966,
-                        "LWdobsc_cov_fbk":-1.9053971271516227e-19,
-                        "LWdobsc_fbk":0.00016808217102694102,
-                        "LWdunobsc_fbk":-0.021484077338758966,
-                        "SWcld_alt":-0.003019139717940593,
-                        "SWcld_amt":0.17690977750415052,
-                        "SWcld_err":0.00028432949331345166,
-                        "SWcld_tau":0.03367059414190316,
-                        "SWcld_tot":0.20784556142142652,
-                        "SWdobsc_cov_fbk":1.0225847155544562e-18,
-                        "SWdobsc_fbk":-0.001213258793856184,
-                        "SWdunobsc_fbk":0.20784556142142652
-                    }
-                }
-            },
-            "eq40":{
-                "all":{
-                    "LO680":{
-                        "LWcld_alt":0.01243294286911182,
-                        "LWcld_amt":-0.06395122016055912,
-                        "LWcld_err":0.0001597874865443717,
-                        "LWcld_tau":0.0023371712704465834,
-                        "LWcld_tot":-0.04902131853445635,
-                        "LWdobsc_cov_fbk":3.734843096559143e-20,
-                        "LWdobsc_fbk":0.0004986298070109072,
-                        "LWdunobsc_fbk":-0.04902131853445635,
-                        "SWcld_alt":-0.005680766717126163,
-                        "SWcld_amt":0.3980392062516512,
-                        "SWcld_err":4.6226342633105035e-05,
-                        "SWcld_tau":0.009670248089517603,
-                        "SWcld_tot":0.4020749139666758,
-                        "SWdobsc_cov_fbk":-2.216556601155371e-19,
-                        "SWdobsc_fbk":0.0013647999949265952,
-                        "SWdunobsc_fbk":0.4020749139666758
-                    }
-                },
-                "lnd":{
-                    "LO680":{
-                        "LWcld_alt":0.0014362911061109606,
-                        "LWcld_amt":-0.012021158779568573,
-                        "LWcld_err":1.1322583190949233e-05,
-                        "LWcld_tau":0.0006422121516001084,
-                        "LWcld_tot":-0.009931332938666555,
-                        "LWdobsc_cov_fbk":1.1100206579105979e-20,
-                        "LWdobsc_fbk":0.003438197569004805,
-                        "LWdunobsc_fbk":-0.009931332938666557,
-                        "SWcld_alt":-0.00075068514515885,
-                        "SWcld_amt":0.073378481614812,
-                        "SWcld_err":1.3192206467045056e-06,
-                        "SWcld_tau":-0.004921155528683539,
-                        "SWcld_tot":0.0677079601616163,
-                        "SWdobsc_cov_fbk":-6.872860524771961e-20,
-                        "SWdobsc_fbk":-0.02289890713718275,
-                        "SWdunobsc_fbk":0.0677079601616163
-                    }
-                },
-                "ocn":{
-                    "LO680":{
-                        "LWcld_alt":0.010996651763000858,
-                        "LWcld_amt":-0.051930061380990544,
-                        "LWcld_err":0.00014846490335342245,
-                        "LWcld_tau":0.0016949591188464754,
-                        "LWcld_tot":-0.03908998559578979,
-                        "LWdobsc_cov_fbk":2.6248224386485444e-20,
-                        "LWdobsc_fbk":-0.002939567761993898,
-                        "LWdunobsc_fbk":-0.03908998559578979,
-                        "SWcld_alt":-0.004930081571967312,
-                        "SWcld_amt":0.3246607246368392,
-                        "SWcld_err":4.490712198640053e-05,
-                        "SWcld_tau":0.014591403618201139,
-                        "SWcld_tot":0.33436695380505954,
-                        "SWdobsc_cov_fbk":-1.5292705486781742e-19,
-                        "SWdobsc_fbk":0.02426370713210935,
-                        "SWdunobsc_fbk":0.33436695380505954
-                    }
-                },
-                "ocn_asc":{
-                    "LO680":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "LWdobsc_cov_fbk":NaN,
-                        "LWdobsc_fbk":NaN,
-                        "LWdunobsc_fbk":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN,
-                        "SWdobsc_cov_fbk":NaN,
-                        "SWdobsc_fbk":NaN,
-                        "SWdunobsc_fbk":NaN
-                    }
-                },
-                "ocn_dsc":{
-                    "LO680":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "LWdobsc_cov_fbk":NaN,
-                        "LWdobsc_fbk":NaN,
-                        "LWdunobsc_fbk":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN,
-                        "SWdobsc_cov_fbk":NaN,
-                        "SWdobsc_fbk":NaN,
-                        "SWdunobsc_fbk":NaN
-                    }
-                }
-            },
-            "eq50":{
-                "all":{
-                    "LO680":{
-                        "LWcld_alt":0.015516877240378674,
-                        "LWcld_amt":-0.08599104517957001,
-                        "LWcld_err":-0.0007164238296448721,
-                        "LWcld_tau":0.009287868818206943,
-                        "LWcld_tot":-0.06190272295062926,
-                        "LWdobsc_cov_fbk":2.7043260186918194e-20,
-                        "LWdobsc_fbk":-0.002889133069740353,
-                        "LWdunobsc_fbk":-0.06190272295062926,
-                        "SWcld_alt":-0.0071436448174997174,
-                        "SWcld_amt":0.5275175467788277,
-                        "SWcld_err":0.0005271403747108785,
-                        "SWcld_tau":-0.0476894215600856,
-                        "SWcld_tot":0.4732116207759532,
-                        "SWdobsc_cov_fbk":-1.4131312638578134e-19,
-                        "SWdobsc_fbk":0.0201792111365131,
-                        "SWdunobsc_fbk":0.4732116207759532
-                    }
-                },
-                "lnd":{
-                    "LO680":{
-                        "LWcld_alt":0.0015766324657875497,
-                        "LWcld_amt":-0.015359251990468338,
-                        "LWcld_err":-1.4382620727992065e-05,
-                        "LWcld_tau":0.001886550699439296,
-                        "LWcld_tot":-0.011910451445969484,
-                        "LWdobsc_cov_fbk":1.2407056230141942e-20,
-                        "LWdobsc_fbk":0.003678259267327828,
-                        "LWdunobsc_fbk":-0.011910451445969484,
-                        "SWcld_alt":-0.0008053954749739038,
-                        "SWcld_amt":0.09314567391552098,
-                        "SWcld_err":1.2968244497884768e-05,
-                        "SWcld_tau":-0.011119033272820584,
-                        "SWcld_tot":0.08123421341222435,
-                        "SWdobsc_cov_fbk":-7.675607960952666e-20,
-                        "SWdobsc_fbk":-0.02476523310600746,
-                        "SWdunobsc_fbk":0.08123421341222438
-                    }
-                },
-                "ocn":{
-                    "LO680":{
-                        "LWcld_alt":0.013940244774591125,
-                        "LWcld_amt":-0.07063179318910166,
-                        "LWcld_err":-0.0007020412089168799,
-                        "LWcld_tau":0.007401318118767646,
-                        "LWcld_tot":-0.049992271504659784,
-                        "LWdobsc_cov_fbk":1.4636203956776248e-20,
-                        "LWdobsc_fbk":-0.006567392337068181,
-                        "LWdunobsc_fbk":-0.049992271504659784,
-                        "SWcld_alt":-0.006338249342525813,
-                        "SWcld_amt":0.4343718728633066,
-                        "SWcld_err":0.0005141721302129937,
-                        "SWcld_tau":-0.03657038828726501,
-                        "SWcld_tot":0.3919774073637288,
-                        "SWdobsc_cov_fbk":-6.455704677625465e-20,
-                        "SWdobsc_fbk":0.04494444424252056,
-                        "SWdunobsc_fbk":0.39197740736372894
-                    }
-                },
-                "ocn_asc":{
-                    "LO680":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "LWdobsc_cov_fbk":NaN,
-                        "LWdobsc_fbk":NaN,
-                        "LWdunobsc_fbk":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN,
-                        "SWdobsc_cov_fbk":NaN,
-                        "SWdobsc_fbk":NaN,
-                        "SWdunobsc_fbk":NaN
-                    }
-                },
-                "ocn_dsc":{
-                    "LO680":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "LWdobsc_cov_fbk":NaN,
-                        "LWdobsc_fbk":NaN,
-                        "LWdunobsc_fbk":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN,
-                        "SWdobsc_cov_fbk":NaN,
-                        "SWdobsc_fbk":NaN,
-                        "SWdunobsc_fbk":NaN
-                    }
-                }
-            },
-            "eq60":{
-                "all":{
-                    "LO680":{
-                        "LWcld_alt":0.01681592881632722,
-                        "LWcld_amt":-0.09371269158383605,
-                        "LWcld_err":-0.0019045135198694273,
-                        "LWcld_tau":0.017322539075355945,
-                        "LWcld_tot":-0.061478737212022315,
-                        "LWdobsc_cov_fbk":3.434665352002726e-20,
-                        "LWdobsc_fbk":-0.011821106736851215,
-                        "LWdunobsc_fbk":-0.061478737212022315,
-                        "SWcld_alt":-0.008206544904112522,
-                        "SWcld_amt":0.5778214100036858,
-                        "SWcld_err":0.0012070869535204607,
-                        "SWcld_tau":-0.12535342660402388,
-                        "SWcld_tot":0.4454685254490698,
-                        "SWdobsc_cov_fbk":-1.8518080671009196e-19,
-                        "SWdobsc_fbk":0.06241140965296058,
-                        "SWdunobsc_fbk":0.4454685254490698
-                    }
-                },
-                "lnd":{
-                    "LO680":{
-                        "LWcld_alt":0.0016757156840640152,
-                        "LWcld_amt":-0.01717213610596975,
-                        "LWcld_err":-9.017288353931638e-05,
-                        "LWcld_tau":0.0035866517092546335,
-                        "LWcld_tot":-0.011999941596190418,
-                        "LWdobsc_cov_fbk":1.0852038820314595e-20,
-                        "LWdobsc_fbk":0.0027935921393364405,
-                        "LWdunobsc_fbk":-0.011999941596190422,
-                        "SWcld_alt":-0.000833658569231311,
-                        "SWcld_amt":0.10954194494183515,
-                        "SWcld_err":3.909564273964139e-05,
-                        "SWcld_tau":-0.02055631783229846,
-                        "SWcld_tot":0.08819106418304502,
-                        "SWdobsc_cov_fbk":-6.145709097401859e-20,
-                        "SWdobsc_fbk":-0.022148493120811106,
-                        "SWdunobsc_fbk":0.08819106418304502
-                    }
-                },
-                "ocn":{
-                    "LO680":{
-                        "LWcld_alt":0.015140213132263202,
-                        "LWcld_amt":-0.07654055547786631,
-                        "LWcld_err":-0.0018143406363301106,
-                        "LWcld_tau":0.013735887366101311,
-                        "LWcld_tot":-0.049478795615831905,
-                        "LWdobsc_cov_fbk":2.3494614699712673e-20,
-                        "LWdobsc_fbk":-0.014614698876187654,
-                        "LWdunobsc_fbk":-0.049478795615831905,
-                        "SWcld_alt":-0.007372886334881214,
-                        "SWcld_amt":0.46827946506185053,
-                        "SWcld_err":0.0011679913107808194,
-                        "SWcld_tau":-0.10479710877172543,
-                        "SWcld_tot":0.3572774612660248,
-                        "SWdobsc_cov_fbk":-1.2372371573607344e-19,
-                        "SWdobsc_fbk":0.08455990277377169,
-                        "SWdunobsc_fbk":0.3572774612660248
-                    }
-                },
-                "ocn_asc":{
-                    "LO680":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "LWdobsc_cov_fbk":NaN,
-                        "LWdobsc_fbk":NaN,
-                        "LWdunobsc_fbk":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN,
-                        "SWdobsc_cov_fbk":NaN,
-                        "SWdobsc_fbk":NaN,
-                        "SWdunobsc_fbk":NaN
-                    }
-                },
-                "ocn_dsc":{
-                    "LO680":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "LWdobsc_cov_fbk":NaN,
-                        "LWdobsc_fbk":NaN,
-                        "LWdunobsc_fbk":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN,
-                        "SWdobsc_cov_fbk":NaN,
-                        "SWdobsc_fbk":NaN,
-                        "SWdunobsc_fbk":NaN
-                    }
-                }
-            },
-            "eq70":{
-                "all":{
-                    "LO680":{
-                        "LWcld_alt":0.01755215343095518,
-                        "LWcld_amt":-0.09625034396424388,
-                        "LWcld_err":-0.0024263622263049375,
-                        "LWcld_tau":0.021661531082556418,
-                        "LWcld_tot":-0.059463021677037216,
-                        "LWdobsc_cov_fbk":3.50878169955066e-20,
-                        "LWdobsc_fbk":-0.017910149248620704,
-                        "LWdunobsc_fbk":-0.059463021677037216,
-                        "SWcld_alt":-0.008648654116849423,
-                        "SWcld_amt":0.5942090406188947,
-                        "SWcld_err":0.0015459152157569465,
-                        "SWcld_tau":-0.1679026053406163,
-                        "SWcld_tot":0.41920369637718574,
-                        "SWdobsc_cov_fbk":-1.9802026646672552e-19,
-                        "SWdobsc_fbk":0.09449261487365801,
-                        "SWdunobsc_fbk":0.41920369637718574
-                    }
-                },
-                "lnd":{
-                    "LO680":{
-                        "LWcld_alt":0.0017536649145208853,
-                        "LWcld_amt":-0.01819464642892106,
-                        "LWcld_err":-0.0001701779816423142,
-                        "LWcld_tau":0.0050477964312585855,
-                        "LWcld_tot":-0.011563363064783906,
-                        "LWdobsc_cov_fbk":1.1330493381574966e-20,
-                        "LWdobsc_fbk":0.001429251084676323,
-                        "LWdunobsc_fbk":-0.011563363064783908,
-                        "SWcld_alt":-0.0008950778630491272,
-                        "SWcld_amt":0.11911271631539612,
-                        "SWcld_err":7.2011889015037e-05,
-                        "SWcld_tau":-0.0295705948219966,
-                        "SWcld_tot":0.08871905551936547,
-                        "SWdobsc_cov_fbk":-6.1677518050681e-20,
-                        "SWdobsc_fbk":-0.015280379550709352,
-                        "SWdunobsc_fbk":0.08871905551936544
-                    }
-                },
-                "ocn":{
-                    "LO680":{
-                        "LWcld_alt":0.015798488516434294,
-                        "LWcld_amt":-0.07805569753532281,
-                        "LWcld_err":-0.002256184244662623,
-                        "LWcld_tau":0.01661373465129783,
-                        "LWcld_tot":-0.04789965861225331,
-                        "LWdobsc_cov_fbk":2.375732361393163e-20,
-                        "LWdobsc_fbk":-0.019339400333297025,
-                        "LWdunobsc_fbk":-0.04789965861225331,
-                        "SWcld_alt":-0.007753576253800296,
-                        "SWcld_amt":0.4750963243034984,
-                        "SWcld_err":0.0014739033267419097,
-                        "SWcld_tau":-0.13833201051861968,
-                        "SWcld_tot":0.33048464085782037,
-                        "SWdobsc_cov_fbk":-1.3634274841604463e-19,
-                        "SWdobsc_fbk":0.10977299442436737,
-                        "SWdunobsc_fbk":0.33048464085782037
-                    }
-                },
-                "ocn_asc":{
-                    "LO680":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "LWdobsc_cov_fbk":NaN,
-                        "LWdobsc_fbk":NaN,
-                        "LWdunobsc_fbk":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN,
-                        "SWdobsc_cov_fbk":NaN,
-                        "SWdobsc_fbk":NaN,
-                        "SWdunobsc_fbk":NaN
-                    }
-                },
-                "ocn_dsc":{
-                    "LO680":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "LWdobsc_cov_fbk":NaN,
-                        "LWdobsc_fbk":NaN,
-                        "LWdunobsc_fbk":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN,
-                        "SWdobsc_cov_fbk":NaN,
-                        "SWdobsc_fbk":NaN,
-                        "SWdunobsc_fbk":NaN
-                    }
-                }
-            },
-            "eq80":{
-                "all":{
-                    "LO680":{
-                        "LWcld_alt":0.0183368954154619,
-                        "LWcld_amt":-0.09658777544717105,
-                        "LWcld_err":-0.0025839130459866877,
-                        "LWcld_tau":0.022699916419055877,
-                        "LWcld_tot":-0.05813487665863996,
-                        "LWdobsc_cov_fbk":3.6669655746993e-20,
-                        "LWdobsc_fbk":-0.02014489824787616,
-                        "LWdunobsc_fbk":-0.05813487665863996,
-                        "SWcld_alt":-0.009018307409586894,
-                        "SWcld_amt":0.5959687501187575,
-                        "SWcld_err":0.0016452518228100195,
-                        "SWcld_tau":-0.17596258585860955,
-                        "SWcld_tot":0.4126331086733712,
-                        "SWdobsc_cov_fbk":-2.0428658478831513e-19,
-                        "SWdobsc_fbk":0.1067949074568111,
-                        "SWdunobsc_fbk":0.4126331086733712
-                    }
-                },
-                "lnd":{
-                    "LO680":{
-                        "LWcld_alt":0.0018604308498702043,
-                        "LWcld_amt":-0.01832754040685709,
-                        "LWcld_err":-0.0001875103938159629,
-                        "LWcld_tau":0.00531410081889272,
-                        "LWcld_tot":-0.011340519131910129,
-                        "LWdobsc_cov_fbk":1.2473964043083918e-20,
-                        "LWdobsc_fbk":0.0010259578130324095,
-                        "LWdunobsc_fbk":-0.01134051913191013,
-                        "SWcld_alt":-0.0009392634913183359,
-                        "SWcld_amt":0.11998474214118592,
-                        "SWcld_err":8.27492980187586e-05,
-                        "SWcld_tau":-0.03054955678732364,
-                        "SWcld_tot":0.08857867116056269,
-                        "SWdobsc_cov_fbk":-6.826028859310702e-20,
-                        "SWdobsc_fbk":-0.013600746235530405,
-                        "SWdunobsc_fbk":0.08857867116056269
-                    }
-                },
-                "ocn":{
-                    "LO680":{
-                        "LWcld_alt":0.016476464565591698,
-                        "LWcld_amt":-0.07826023504031396,
-                        "LWcld_err":-0.002396402652170725,
-                        "LWcld_tau":0.017385815600163158,
-                        "LWcld_tot":-0.04679435752672983,
-                        "LWdobsc_cov_fbk":2.4195691703909084e-20,
-                        "LWdobsc_fbk":-0.02117085606090857,
-                        "LWdunobsc_fbk":-0.04679435752672983,
-                        "SWcld_alt":-0.008079043918268561,
-                        "SWcld_amt":0.47598400797757173,
-                        "SWcld_err":0.0015625025247912605,
-                        "SWcld_tau":-0.14541302907128592,
-                        "SWcld_tot":0.32405443751280855,
-                        "SWdobsc_cov_fbk":-1.3602629619520822e-19,
-                        "SWdobsc_fbk":0.12039565369234151,
-                        "SWdunobsc_fbk":0.3240544375128085
-                    }
-                },
-                "ocn_asc":{
-                    "LO680":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "LWdobsc_cov_fbk":NaN,
-                        "LWdobsc_fbk":NaN,
-                        "LWdunobsc_fbk":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN,
-                        "SWdobsc_cov_fbk":NaN,
-                        "SWdobsc_fbk":NaN,
-                        "SWdunobsc_fbk":NaN
-                    }
-                },
-                "ocn_dsc":{
-                    "LO680":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "LWdobsc_cov_fbk":NaN,
-                        "LWdobsc_fbk":NaN,
-                        "LWdunobsc_fbk":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN,
-                        "SWdobsc_cov_fbk":NaN,
-                        "SWdobsc_fbk":NaN,
-                        "SWdunobsc_fbk":NaN
-                    }
-                }
-            },
-            "eq90":{
-                "all":{
-                    "LO680":{
-                        "LWcld_alt":0.018615170656549582,
-                        "LWcld_amt":-0.09656372165225524,
-                        "LWcld_err":-0.002624252325109463,
-                        "LWcld_tau":0.022885195918516888,
-                        "LWcld_tot":-0.05768760740229823,
-                        "LWdobsc_cov_fbk":3.691994122271269e-20,
-                        "LWdobsc_fbk":-0.020801702629816086,
-                        "LWdunobsc_fbk":-0.057687607402298224,
-                        "SWcld_alt":-0.00917096454227722,
-                        "SWcld_amt":0.5959014312124967,
-                        "SWcld_err":0.001682349032432072,
-                        "SWcld_tau":-0.1776506294336284,
-                        "SWcld_tot":0.410762186269023,
-                        "SWdobsc_cov_fbk":-2.0497053748153436e-19,
-                        "SWdobsc_fbk":0.1110020249202685,
-                        "SWdunobsc_fbk":0.410762186269023
-                    }
-                },
-                "lnd":{
-                    "LO680":{
-                        "LWcld_alt":0.0018661907815878409,
-                        "LWcld_amt":-0.018336418867488567,
-                        "LWcld_err":-0.00018714619025148236,
-                        "LWcld_tau":0.005344617201711868,
-                        "LWcld_tot":-0.01131275707444034,
-                        "LWdobsc_cov_fbk":1.2396933778565345e-20,
-                        "LWdobsc_fbk":0.0009767928756470067,
-                        "LWdunobsc_fbk":-0.01131275707444034,
-                        "SWcld_alt":-0.0009425295467605043,
-                        "SWcld_amt":0.12002264769622811,
-                        "SWcld_err":8.352964088680017e-05,
-                        "SWcld_tau":-0.030621448892430157,
-                        "SWcld_tot":0.08854219889792425,
-                        "SWdobsc_cov_fbk":-6.818533744709097e-20,
-                        "SWdobsc_fbk":-0.013489579352563176,
-                        "SWdunobsc_fbk":0.08854219889792425
-                    }
-                },
-                "ocn":{
-                    "LO680":{
-                        "LWcld_alt":0.016748979874961743,
-                        "LWcld_amt":-0.07822730278476668,
-                        "LWcld_err":-0.0024371061348579803,
-                        "LWcld_tau":0.017540578716805023,
-                        "LWcld_tot":-0.046374850327857896,
-                        "LWdobsc_cov_fbk":2.4523007444147354e-20,
-                        "LWdobsc_fbk":-0.021778495505463093,
-                        "LWdunobsc_fbk":-0.046374850327857896,
-                        "SWcld_alt":-0.008228434995516716,
-                        "SWcld_amt":0.4758787835162685,
-                        "SWcld_err":0.0015988193915452715,
-                        "SWcld_tau":-0.1470291805411982,
-                        "SWcld_tot":0.3222199873710988,
-                        "SWdobsc_cov_fbk":-1.3678520003444348e-19,
-                        "SWdobsc_fbk":0.12449160427283168,
-                        "SWdunobsc_fbk":0.3222199873710988
-                    }
-                },
-                "ocn_asc":{
-                    "LO680":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "LWdobsc_cov_fbk":NaN,
-                        "LWdobsc_fbk":NaN,
-                        "LWdunobsc_fbk":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN,
-                        "SWdobsc_cov_fbk":NaN,
-                        "SWdobsc_fbk":NaN,
-                        "SWdunobsc_fbk":NaN
-                    }
-                },
-                "ocn_dsc":{
-                    "LO680":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "LWdobsc_cov_fbk":NaN,
-                        "LWdobsc_fbk":NaN,
-                        "LWdunobsc_fbk":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN,
-                        "SWdobsc_cov_fbk":NaN,
-                        "SWdobsc_fbk":NaN,
-                        "SWdunobsc_fbk":NaN
-                    }
-                }
-            }
-        }
-    },
-    "CNRM-CM6-1":{
-        "r1i1p1f2":{
-            "Arctic":{
-                "all":{
-                    "LO680":{
-                        "LWcld_alt":0.0004825701170198378,
-                        "LWcld_amt":0.0009501105056000633,
-                        "LWcld_err":-2.2450973290645095e-06,
-                        "LWcld_tau":0.0011567714001941695,
-                        "LWcld_tot":0.002371606348361256,
-                        "LWdobsc_cov_fbk":1.1796744639554547e-21,
-                        "LWdobsc_fbk":-0.0012271562400896004,
-                        "LWdunobsc_fbk":0.0025872069254850067,
-                        "SWcld_alt":-9.626423681230016e-05,
-                        "SWcld_amt":-0.004078984079681222,
-                        "SWcld_err":5.8551729038948386e-06,
-                        "SWcld_tau":-0.0059082001307160245,
-                        "SWcld_tot":-0.010077593274305649,
-                        "SWdobsc_cov_fbk":7.188751048652307e-21,
-                        "SWdobsc_fbk":0.003999298546722087,
-                        "SWdunobsc_fbk":-0.010993738117424346
-                    }
-                },
-                "lnd":{
-                    "LO680":{
-                        "LWcld_alt":7.409788341281857e-05,
-                        "LWcld_amt":0.00018429359690049497,
-                        "LWcld_err":-2.7887275545432375e-07,
-                        "LWcld_tau":0.0001976315901019999,
-                        "LWcld_tot":0.000455744197659859,
-                        "LWdobsc_cov_fbk":1.1581356024570036e-21,
-                        "LWdobsc_fbk":-0.0001402909329246433,
-                        "LWdunobsc_fbk":0.000455744197659859,
-                        "SWcld_alt":-1.3652112543071194e-05,
-                        "SWcld_amt":-0.0013082241958324808,
-                        "SWcld_err":-6.877890934818062e-07,
-                        "SWcld_tau":-0.0009880425739237223,
-                        "SWcld_tot":-0.0023106066713927567,
-                        "SWdobsc_cov_fbk":-7.330847787115685e-23,
-                        "SWdobsc_fbk":0.00037379907648217857,
-                        "SWdunobsc_fbk":-0.0023106066713927567
-                    }
-                },
-                "ocn":{
-                    "LO680":{
-                        "LWcld_alt":0.0004084722336070193,
-                        "LWcld_amt":0.0007658169086995685,
-                        "LWcld_err":-1.966224573610185e-06,
-                        "LWcld_tau":0.00095913981009217,
-                        "LWcld_tot":0.0021314627278251476,
-                        "LWdobsc_cov_fbk":2.1538861498450828e-23,
-                        "LWdobsc_fbk":-0.001086865307164957,
-                        "LWdunobsc_fbk":0.0021314627278251476,
-                        "SWcld_alt":-9.13634185248926e-05,
-                        "SWcld_amt":-0.0031415766183652146,
-                        "SWcld_err":7.075250443185267e-06,
-                        "SWcld_tau":-0.005457266659584667,
-                        "SWcld_tot":-0.00868313144603159,
-                        "SWdobsc_cov_fbk":7.262059526523463e-21,
-                        "SWdobsc_fbk":0.003625499470239908,
-                        "SWdunobsc_fbk":-0.00868313144603159
-                    }
-                },
-                "ocn_asc":{
-                    "LO680":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "LWdobsc_cov_fbk":NaN,
-                        "LWdobsc_fbk":NaN,
-                        "LWdunobsc_fbk":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN,
-                        "SWdobsc_cov_fbk":NaN,
-                        "SWdobsc_fbk":NaN,
-                        "SWdunobsc_fbk":NaN
-                    }
-                },
-                "ocn_dsc":{
-                    "LO680":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "LWdobsc_cov_fbk":NaN,
-                        "LWdobsc_fbk":NaN,
-                        "LWdunobsc_fbk":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN,
-                        "SWdobsc_cov_fbk":NaN,
-                        "SWdobsc_fbk":NaN,
-                        "SWdunobsc_fbk":NaN
-                    }
-                }
-            },
-            "eq10":{
-                "all":{
-                    "LO680":{
-                        "LWcld_alt":0.004184417851240459,
-                        "LWcld_amt":-0.0074587391690510004,
-                        "LWcld_err":9.391801376598594e-05,
-                        "LWcld_tau":-0.0004208607467817551,
-                        "LWcld_tot":-0.0036012640508263105,
-                        "LWdobsc_cov_fbk":1.8488207167897207e-21,
-                        "LWdobsc_fbk":-0.0024558602896335977,
-                        "LWdunobsc_fbk":-0.00360126405082631,
-                        "SWcld_alt":-0.0018430057432889417,
-                        "SWcld_amt":0.0527568220961059,
-                        "SWcld_err":-0.00013118380607657058,
-                        "SWcld_tau":2.4990089538952837e-05,
-                        "SWcld_tot":0.05080762263627934,
-                        "SWdobsc_cov_fbk":-2.8920308261278273e-20,
-                        "SWdobsc_fbk":0.018918888367884343,
-                        "SWdunobsc_fbk":0.05080762263627935
-                    }
-                },
-                "lnd":{
-                    "LO680":{
-                        "LWcld_alt":0.0008842827859511935,
-                        "LWcld_amt":-0.001989195422823665,
-                        "LWcld_err":4.904384152197729e-06,
-                        "LWcld_tau":4.168948424372184e-05,
-                        "LWcld_tot":-0.001058318768476552,
-                        "LWdobsc_cov_fbk":1.6138624454591317e-21,
-                        "LWdobsc_fbk":0.00019277044777980847,
-                        "LWdunobsc_fbk":-0.0010583187684765523,
-                        "SWcld_alt":-0.0004243249884906489,
-                        "SWcld_amt":0.012912392013671012,
-                        "SWcld_err":1.031252284965753e-05,
-                        "SWcld_tau":-0.0017545658562469829,
-                        "SWcld_tot":0.010743813691783044,
-                        "SWdobsc_cov_fbk":-7.652602812997348e-21,
-                        "SWdobsc_fbk":-0.0011616000140891073,
-                        "SWdunobsc_fbk":0.010743813691783044
-                    }
-                },
-                "ocn":{
-                    "LO680":{
-                        "LWcld_alt":0.0033001350652892662,
-                        "LWcld_amt":-0.005469543746227336,
-                        "LWcld_err":8.901362961378823e-05,
-                        "LWcld_tau":-0.00046255023102547696,
-                        "LWcld_tot":-0.002542945282349758,
-                        "LWdobsc_cov_fbk":2.34958271330589e-22,
-                        "LWdobsc_fbk":-0.002648630737413407,
-                        "LWdunobsc_fbk":-0.002542945282349758,
-                        "SWcld_alt":-0.0014186807547982927,
-                        "SWcld_amt":0.039844430082434885,
-                        "SWcld_err":-0.0001414963289262281,
-                        "SWcld_tau":0.0017795559457859358,
-                        "SWcld_tot":0.0400638089444963,
-                        "SWdobsc_cov_fbk":-2.1267705448280924e-20,
-                        "SWdobsc_fbk":0.02008048838197345,
-                        "SWdunobsc_fbk":0.0400638089444963
-                    }
-                },
-                "ocn_asc":{
-                    "LO680":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "LWdobsc_cov_fbk":NaN,
-                        "LWdobsc_fbk":NaN,
-                        "LWdunobsc_fbk":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN,
-                        "SWdobsc_cov_fbk":NaN,
-                        "SWdobsc_fbk":NaN,
-                        "SWdunobsc_fbk":NaN
-                    }
-                },
-                "ocn_dsc":{
-                    "LO680":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "LWdobsc_cov_fbk":NaN,
-                        "LWdobsc_fbk":NaN,
-                        "LWdunobsc_fbk":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN,
-                        "SWdobsc_cov_fbk":NaN,
-                        "SWdobsc_fbk":NaN,
-                        "SWdunobsc_fbk":NaN
-                    }
-                }
-            },
-            "eq20":{
-                "all":{
-                    "LO680":{
-                        "LWcld_alt":0.007719066833221743,
-                        "LWcld_amt":-0.012252371729163684,
-                        "LWcld_err":0.00011874063673178388,
-                        "LWcld_tau":-0.00046254731015207855,
-                        "LWcld_tot":-0.004877111569362235,
-                        "LWdobsc_cov_fbk":1.3781996701386901e-21,
-                        "LWdobsc_fbk":-0.004987130568870682,
-                        "LWdunobsc_fbk":-0.004877111569362235,
-                        "SWcld_alt":-0.003371009449606421,
-                        "SWcld_amt":0.08363434544475963,
-                        "SWcld_err":-0.00017464360079216413,
-                        "SWcld_tau":-0.0036829252049383576,
-                        "SWcld_tot":0.07640576718942269,
-                        "SWdobsc_cov_fbk":-2.060934457408946e-20,
-                        "SWdobsc_fbk":0.03803892160911009,
-                        "SWdunobsc_fbk":0.07640576718942269
-                    }
-                },
-                "lnd":{
-                    "LO680":{
-                        "LWcld_alt":0.0013603567065261421,
-                        "LWcld_amt":-0.003414815798658698,
-                        "LWcld_err":4.793855976777152e-07,
-                        "LWcld_tau":0.00010986294445460606,
-                        "LWcld_tot":-0.001944116762080272,
-                        "LWdobsc_cov_fbk":1.1773666285405005e-21,
-                        "LWdobsc_fbk":0.00011306817645106907,
-                        "LWdunobsc_fbk":-0.001944116762080272,
-                        "SWcld_alt":-0.0006478858889824766,
-                        "SWcld_amt":0.021470850137076173,
-                        "SWcld_err":1.6931796658839362e-05,
-                        "SWcld_tau":-0.0033557655023509062,
-                        "SWcld_tot":0.017484130542401624,
-                        "SWdobsc_cov_fbk":-3.6397384548792974e-21,
-                        "SWdobsc_fbk":-0.0007274731501340191,
-                        "SWdunobsc_fbk":0.017484130542401628
-                    }
-                },
-                "ocn":{
-                    "LO680":{
-                        "LWcld_alt":0.006358710126695601,
-                        "LWcld_amt":-0.008837555930504986,
-                        "LWcld_err":0.00011826125113410618,
-                        "LWcld_tau":-0.0005724102546066846,
-                        "LWcld_tot":-0.0029329948072819635,
-                        "LWdobsc_cov_fbk":2.0083304159818945e-22,
-                        "LWdobsc_fbk":-0.005100198745321751,
-                        "LWdunobsc_fbk":-0.0029329948072819635,
-                        "SWcld_alt":-0.0027231235606239453,
-                        "SWcld_amt":0.06216349530768345,
-                        "SWcld_err":-0.00019157539745100349,
-                        "SWcld_tau":-0.0003271597025874514,
-                        "SWcld_tot":0.05892163664702105,
-                        "SWdobsc_cov_fbk":-1.6969606119210165e-20,
-                        "SWdobsc_fbk":0.0387663947592441,
-                        "SWdunobsc_fbk":0.058921636647021054
-                    }
-                },
-                "ocn_asc":{
-                    "LO680":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "LWdobsc_cov_fbk":NaN,
-                        "LWdobsc_fbk":NaN,
-                        "LWdunobsc_fbk":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN,
-                        "SWdobsc_cov_fbk":NaN,
-                        "SWdobsc_fbk":NaN,
-                        "SWdunobsc_fbk":NaN
-                    }
-                },
-                "ocn_dsc":{
-                    "LO680":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "LWdobsc_cov_fbk":NaN,
-                        "LWdobsc_fbk":NaN,
-                        "LWdunobsc_fbk":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN,
-                        "SWdobsc_cov_fbk":NaN,
-                        "SWdobsc_fbk":NaN,
-                        "SWdunobsc_fbk":NaN
-                    }
-                }
-            },
-            "eq30":{
-                "all":{
-                    "LO680":{
-                        "LWcld_alt":0.009967584412642371,
-                        "LWcld_amt":-0.018485172972610375,
-                        "LWcld_err":0.00019002000105089513,
-                        "LWcld_tau":-0.00026159216323889847,
-                        "LWcld_tot":-0.008589160722156007,
-                        "LWdobsc_cov_fbk":-3.503855727183506e-21,
-                        "LWdobsc_fbk":-0.006925291085559049,
-                        "LWdunobsc_fbk":-0.008589160722156009,
-                        "SWcld_alt":-0.004262607247304975,
-                        "SWcld_amt":0.1177172669709124,
-                        "SWcld_err":-0.0002499141442977506,
-                        "SWcld_tau":-0.010270318774372981,
-                        "SWcld_tot":0.10293442680493668,
-                        "SWdobsc_cov_fbk":-2.986781149570213e-21,
-                        "SWdobsc_fbk":0.04890006945794002,
-                        "SWdunobsc_fbk":0.10293442680493668
-                    }
-                },
-                "lnd":{
-                    "LO680":{
-                        "LWcld_alt":0.001655874520679893,
-                        "LWcld_amt":-0.005550749432182026,
-                        "LWcld_err":-6.519820928298046e-06,
-                        "LWcld_tau":0.00017053247928161108,
-                        "LWcld_tot":-0.0037308622531488206,
-                        "LWdobsc_cov_fbk":5.89840056730913e-22,
-                        "LWdobsc_fbk":0.00035588823457195204,
-                        "LWdunobsc_fbk":-0.0037308622531488198,
-                        "SWcld_alt":-0.0007805980321694104,
-                        "SWcld_amt":0.031551878556360984,
-                        "SWcld_err":2.560913799813721e-05,
-                        "SWcld_tau":-0.004410792361335694,
-                        "SWcld_tot":0.02638609730085401,
-                        "SWdobsc_cov_fbk":-5.4558441141542406e-21,
-                        "SWdobsc_fbk":-0.0018030274611679272,
-                        "SWdunobsc_fbk":0.026386097300854016
-                    }
-                },
-                "ocn":{
-                    "LO680":{
-                        "LWcld_alt":0.008311709891962478,
-                        "LWcld_amt":-0.01293442354042835,
-                        "LWcld_err":0.00019653982197919318,
-                        "LWcld_tau":-0.0004321246425205096,
-                        "LWcld_tot":-0.004858298469007187,
-                        "LWdobsc_cov_fbk":-4.09369578391442e-21,
-                        "LWdobsc_fbk":-0.0072811793201309994,
-                        "LWdunobsc_fbk":-0.004858298469007189,
-                        "SWcld_alt":-0.0034820092151355646,
-                        "SWcld_amt":0.08616538841455139,
-                        "SWcld_err":-0.0002755232822958879,
-                        "SWcld_tau":-0.005859526413037286,
-                        "SWcld_tot":0.07654832950408265,
-                        "SWdobsc_cov_fbk":2.4690629645840413e-21,
-                        "SWdobsc_fbk":0.05070309691910794,
-                        "SWdunobsc_fbk":0.07654832950408266
-                    }
-                },
-                "ocn_asc":{
-                    "LO680":{
-                        "LWcld_alt":0.0029290336125930923,
-                        "LWcld_amt":-0.004522455288986049,
-                        "LWcld_err":3.205050279508607e-05,
-                        "LWcld_tau":0.00017339179391841108,
-                        "LWcld_tot":-0.0013879793796794605,
-                        "LWdobsc_cov_fbk":5.2573849269213135e-21,
-                        "LWdobsc_fbk":-7.621055473728722e-05,
-                        "LWdunobsc_fbk":-0.0013879793796794605,
-                        "SWcld_alt":-0.0012420913238564235,
-                        "SWcld_amt":0.03465095833008442,
-                        "SWcld_err":-0.0001029144748365534,
-                        "SWcld_tau":-0.008370096309584947,
-                        "SWcld_tot":0.024935856221806496,
-                        "SWdobsc_cov_fbk":-4.072815709736282e-20,
-                        "SWdobsc_fbk":0.0005515817522274404,
-                        "SWdunobsc_fbk":0.024935856221806496
-                    }
-                },
-                "ocn_dsc":{
-                    "LO680":{
-                        "LWcld_alt":0.007402729001553164,
-                        "LWcld_amt":-0.018633301523911922,
-                        "LWcld_err":1.2057562766268561e-05,
-                        "LWcld_tau":-5.919392162192761e-05,
-                        "LWcld_tot":-0.011277708881214411,
-                        "LWdobsc_cov_fbk":2.8469115307513727e-19,
-                        "LWdobsc_fbk":-8.063885201129989e-05,
-                        "LWdunobsc_fbk":-0.01127770888121441,
-                        "SWcld_alt":-0.003015827258494925,
-                        "SWcld_amt":0.0981141043383158,
-                        "SWcld_err":6.226645364174621e-05,
-                        "SWcld_tau":-0.004089226203693736,
-                        "SWcld_tot":0.09107131732976886,
-                        "SWdobsc_cov_fbk":-1.3560700742573123e-18,
-                        "SWdobsc_fbk":0.0006434565755257349,
-                        "SWdunobsc_fbk":0.09107131732976886
-                    }
-                }
-            },
-            "eq40":{
-                "all":{
-                    "LO680":{
-                        "LWcld_alt":0.011092039877960933,
-                        "LWcld_amt":-0.02606474260396809,
-                        "LWcld_err":0.00027183298796330156,
-                        "LWcld_tau":-0.0002145904331856811,
-                        "LWcld_tot":-0.01491546017122953,
-                        "LWdobsc_cov_fbk":4.246219426156766e-23,
-                        "LWdobsc_fbk":-0.00591081423463801,
-                        "LWdunobsc_fbk":-0.01491546017122953,
-                        "SWcld_alt":-0.0047343924404346345,
-                        "SWcld_amt":0.15718789430379945,
-                        "SWcld_err":-0.00030027391044631837,
-                        "SWcld_tau":-0.01150486460890717,
-                        "SWcld_tot":0.14064836334401135,
-                        "SWdobsc_cov_fbk":-2.4481573867367953e-20,
-                        "SWdobsc_fbk":0.04516260255509483,
-                        "SWdunobsc_fbk":0.14064836334401135
-                    }
-                },
-                "lnd":{
-                    "LO680":{
-                        "LWcld_alt":0.0017148652746926168,
-                        "LWcld_amt":-0.007336766152066059,
-                        "LWcld_err":-1.5908389196391312e-05,
-                        "LWcld_tau":0.00029882270056264996,
-                        "LWcld_tot":-0.005338986566007186,
-                        "LWdobsc_cov_fbk":3.979927047935739e-21,
-                        "LWdobsc_fbk":0.000613812756173704,
-                        "LWdunobsc_fbk":-0.005338986566007186,
-                        "SWcld_alt":-0.0008150072611143233,
-                        "SWcld_amt":0.0389818982137358,
-                        "SWcld_err":3.066800150377343e-05,
-                        "SWcld_tau":-0.005702698473776802,
-                        "SWcld_tot":0.03249486048034845,
-                        "SWdobsc_cov_fbk":-2.3188867400672994e-20,
-                        "SWdobsc_fbk":-0.0028705817816152604,
-                        "SWdunobsc_fbk":0.03249486048034845
-                    }
-                },
-                "ocn":{
-                    "LO680":{
-                        "LWcld_alt":0.00937717460326832,
-                        "LWcld_amt":-0.018727976451902027,
-                        "LWcld_err":0.00028774137715969296,
-                        "LWcld_tau":-0.0005134131337483311,
-                        "LWcld_tot":-0.009576473605222346,
-                        "LWdobsc_cov_fbk":-3.937464853674174e-21,
-                        "LWdobsc_fbk":-0.0065246269908117134,
-                        "LWdunobsc_fbk":-0.009576473605222346,
-                        "SWcld_alt":-0.003919385179320312,
-                        "SWcld_amt":0.11820599609006367,
-                        "SWcld_err":-0.0003309419119500918,
-                        "SWcld_tau":-0.005802166135130367,
-                        "SWcld_tot":0.10815350286366292,
-                        "SWdobsc_cov_fbk":-1.2927064666949536e-21,
-                        "SWdobsc_fbk":0.04803318433671008,
-                        "SWdunobsc_fbk":0.10815350286366292
-                    }
-                },
-                "ocn_asc":{
-                    "LO680":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "LWdobsc_cov_fbk":NaN,
-                        "LWdobsc_fbk":NaN,
-                        "LWdunobsc_fbk":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN,
-                        "SWdobsc_cov_fbk":NaN,
-                        "SWdobsc_fbk":NaN,
-                        "SWdunobsc_fbk":NaN
-                    }
-                },
-                "ocn_dsc":{
-                    "LO680":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "LWdobsc_cov_fbk":NaN,
-                        "LWdobsc_fbk":NaN,
-                        "LWdunobsc_fbk":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN,
-                        "SWdobsc_cov_fbk":NaN,
-                        "SWdobsc_fbk":NaN,
-                        "SWdunobsc_fbk":NaN
-                    }
-                }
-            },
-            "eq50":{
-                "all":{
-                    "LO680":{
-                        "LWcld_alt":0.009784164920814529,
-                        "LWcld_amt":-0.03026192423183099,
-                        "LWcld_err":0.0002734871420725959,
-                        "LWcld_tau":0.0004999376767141117,
-                        "LWcld_tot":-0.01970433449222975,
-                        "LWdobsc_cov_fbk":-1.3868472249330589e-21,
-                        "LWdobsc_fbk":-0.004506883979182956,
-                        "LWdunobsc_fbk":-0.019704334492229747,
-                        "SWcld_alt":-0.00432127650064535,
-                        "SWcld_amt":0.17806963757404556,
-                        "SWcld_err":-0.00028821481614392866,
-                        "SWcld_tau":-0.015972223717768907,
-                        "SWcld_tot":0.15748792253948732,
-                        "SWdobsc_cov_fbk":2.4371776117959132e-21,
-                        "SWdobsc_fbk":0.03598223256327684,
-                        "SWdunobsc_fbk":0.15748792253948732
-                    }
-                },
-                "lnd":{
-                    "LO680":{
-                        "LWcld_alt":0.0015977495823807468,
-                        "LWcld_amt":-0.008208800763065722,
-                        "LWcld_err":-2.4628780672437023e-05,
-                        "LWcld_tau":0.0008258708624077836,
-                        "LWcld_tot":-0.00580980909894963,
-                        "LWdobsc_cov_fbk":4.770048797913524e-21,
-                        "LWdobsc_fbk":0.0006773831734641547,
-                        "LWdunobsc_fbk":-0.00580980909894963,
-                        "SWcld_alt":-0.0007782482934527808,
-                        "SWcld_amt":0.04250749776743121,
-                        "SWcld_err":3.1276076402618854e-05,
-                        "SWcld_tau":-0.00817225722059914,
-                        "SWcld_tot":0.033588268329781915,
-                        "SWdobsc_cov_fbk":-2.826484131826223e-20,
-                        "SWdobsc_fbk":-0.00384874472481231,
-                        "SWdunobsc_fbk":0.03358826832978191
-                    }
-                },
-                "ocn":{
-                    "LO680":{
-                        "LWcld_alt":0.008186415338433785,
-                        "LWcld_amt":-0.02205312346876526,
-                        "LWcld_err":0.00029811592274503294,
-                        "LWcld_tau":-0.0003259331856936717,
-                        "LWcld_tot":-0.013894525393280116,
-                        "LWdobsc_cov_fbk":-6.156896022846584e-21,
-                        "LWdobsc_fbk":-0.0051842671526471104,
-                        "LWdunobsc_fbk":-0.013894525393280116,
-                        "SWcld_alt":-0.0035430282071925684,
-                        "SWcld_amt":0.13556213980661433,
-                        "SWcld_err":-0.00031949089254654743,
-                        "SWcld_tau":-0.007799966497169768,
-                        "SWcld_tot":0.12389965420970546,
-                        "SWdobsc_cov_fbk":3.0702018930058146e-20,
-                        "SWdobsc_fbk":0.03983097728808916,
-                        "SWdunobsc_fbk":0.12389965420970546
-                    }
-                },
-                "ocn_asc":{
-                    "LO680":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "LWdobsc_cov_fbk":NaN,
-                        "LWdobsc_fbk":NaN,
-                        "LWdunobsc_fbk":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN,
-                        "SWdobsc_cov_fbk":NaN,
-                        "SWdobsc_fbk":NaN,
-                        "SWdunobsc_fbk":NaN
-                    }
-                },
-                "ocn_dsc":{
-                    "LO680":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "LWdobsc_cov_fbk":NaN,
-                        "LWdobsc_fbk":NaN,
-                        "LWdunobsc_fbk":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN,
-                        "SWdobsc_cov_fbk":NaN,
-                        "SWdobsc_fbk":NaN,
-                        "SWdunobsc_fbk":NaN
-                    }
-                }
-            },
-            "eq60":{
-                "all":{
-                    "LO680":{
-                        "LWcld_alt":0.00764926928649418,
-                        "LWcld_amt":-0.030394735531510227,
-                        "LWcld_err":0.00022218201636314472,
-                        "LWcld_tau":0.0032634400825599756,
-                        "LWcld_tot":-0.01925984414609292,
-                        "LWdobsc_cov_fbk":3.5023101293750454e-21,
-                        "LWdobsc_fbk":-0.006287657944655255,
-                        "LWdunobsc_fbk":-0.01925984414609292,
-                        "SWcld_alt":-0.003195165192430979,
-                        "SWcld_amt":0.17596912228223135,
-                        "SWcld_err":-0.00025346352478019103,
-                        "SWcld_tau":-0.03911251805709592,
-                        "SWcld_tot":0.13340797550792427,
-                        "SWdobsc_cov_fbk":-9.350846748025825e-21,
-                        "SWdobsc_fbk":0.04113556934366713,
-                        "SWdunobsc_fbk":0.13340797550792427
-                    }
-                },
-                "lnd":{
-                    "LO680":{
-                        "LWcld_alt":0.00140509938402308,
-                        "LWcld_amt":-0.007820236816820538,
-                        "LWcld_err":-3.9573065371527516e-05,
-                        "LWcld_tau":0.001711367956952939,
-                        "LWcld_tot":-0.004743342541216048,
-                        "LWdobsc_cov_fbk":4.457348300097265e-21,
-                        "LWdobsc_fbk":0.0003461342887323396,
-                        "LWdunobsc_fbk":-0.004743342541216047,
-                        "SWcld_alt":-0.0006857435223483619,
-                        "SWcld_amt":0.03994857681203373,
-                        "SWcld_err":2.9940516258756354e-05,
-                        "SWcld_tau":-0.011196454098273015,
-                        "SWcld_tot":0.02809631970767111,
-                        "SWdobsc_cov_fbk":-2.6123782042297528e-20,
-                        "SWdobsc_fbk":-0.0038665948534078766,
-                        "SWdunobsc_fbk":0.02809631970767111
-                    }
-                },
-                "ocn":{
-                    "LO680":{
-                        "LWcld_alt":0.006244169902471101,
-                        "LWcld_amt":-0.02257449871468969,
-                        "LWcld_err":0.00026175508173467227,
-                        "LWcld_tau":0.0015520721256070368,
-                        "LWcld_tot":-0.014516501604876877,
-                        "LWdobsc_cov_fbk":-9.550381707222177e-22,
-                        "LWdobsc_fbk":-0.006633792233387594,
-                        "LWdunobsc_fbk":-0.014516501604876877,
-                        "SWcld_alt":-0.0025094216700826174,
-                        "SWcld_amt":0.13602054547019765,
-                        "SWcld_err":-0.0002834040410389473,
-                        "SWcld_tau":-0.0279160639588229,
-                        "SWcld_tot":0.10531165580025316,
-                        "SWdobsc_cov_fbk":1.677293529427167e-20,
-                        "SWdobsc_fbk":0.045002164197075006,
-                        "SWdunobsc_fbk":0.10531165580025316
-                    }
-                },
-                "ocn_asc":{
-                    "LO680":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "LWdobsc_cov_fbk":NaN,
-                        "LWdobsc_fbk":NaN,
-                        "LWdunobsc_fbk":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN,
-                        "SWdobsc_cov_fbk":NaN,
-                        "SWdobsc_fbk":NaN,
-                        "SWdunobsc_fbk":NaN
-                    }
-                },
-                "ocn_dsc":{
-                    "LO680":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "LWdobsc_cov_fbk":NaN,
-                        "LWdobsc_fbk":NaN,
-                        "LWdunobsc_fbk":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN,
-                        "SWdobsc_cov_fbk":NaN,
-                        "SWdobsc_fbk":NaN,
-                        "SWdunobsc_fbk":NaN
-                    }
-                }
-            },
-            "eq70":{
-                "all":{
-                    "LO680":{
-                        "LWcld_alt":0.007960428215815234,
-                        "LWcld_amt":-0.030076235996111315,
-                        "LWcld_err":9.61068976546218e-05,
-                        "LWcld_tau":0.005847618586039396,
-                        "LWcld_tot":-0.016172082296602067,
-                        "LWdobsc_cov_fbk":-4.7173945603167756e-21,
-                        "LWdobsc_fbk":-0.008356906999265853,
-                        "LWdunobsc_fbk":-0.016172082296602067,
-                        "SWcld_alt":-0.00319792715057109,
-                        "SWcld_amt":0.17375355728983544,
-                        "SWcld_err":-0.00020447748376256245,
-                        "SWcld_tau":-0.05718090769172455,
-                        "SWcld_tot":0.11317024496377724,
-                        "SWdobsc_cov_fbk":5.189446815972864e-20,
-                        "SWdobsc_fbk":0.048631240764657906,
-                        "SWdunobsc_fbk":0.11317024496377724
-                    }
-                },
-                "lnd":{
-                    "LO680":{
-                        "LWcld_alt":0.0014055932440415586,
-                        "LWcld_amt":-0.007438811338836994,
-                        "LWcld_err":-4.7194260934730395e-05,
-                        "LWcld_tau":0.002484343943738935,
-                        "LWcld_tot":-0.0035960684119912315,
-                        "LWdobsc_cov_fbk":2.633726918910102e-21,
-                        "LWdobsc_fbk":-5.7199901098071776e-05,
-                        "LWdunobsc_fbk":-0.0035960684119912315,
-                        "SWcld_alt":-0.0006519426535218226,
-                        "SWcld_amt":0.038150960865443416,
-                        "SWcld_err":2.7863638218064352e-05,
-                        "SWcld_tau":-0.014183606654806067,
-                        "SWcld_tot":0.023343275195333593,
-                        "SWdobsc_cov_fbk":-1.5220532396699676e-20,
-                        "SWdobsc_fbk":-0.00348457262578997,
-                        "SWdunobsc_fbk":0.023343275195333593
-                    }
-                },
-                "ocn":{
-                    "LO680":{
-                        "LWcld_alt":0.006554834971773677,
-                        "LWcld_amt":-0.02263742465727432,
-                        "LWcld_err":0.0001433011585893522,
-                        "LWcld_tau":0.00336327464230046,
-                        "LWcld_tot":-0.012576013884610833,
-                        "LWdobsc_cov_fbk":-7.351121479226876e-21,
-                        "LWdobsc_fbk":-0.008299707098167781,
-                        "LWdunobsc_fbk":-0.012576013884610833,
-                        "SWcld_alt":-0.002545984497049267,
-                        "SWcld_amt":0.135602596424392,
-                        "SWcld_err":-0.0002323411219806268,
-                        "SWcld_tau":-0.04299730103691849,
-                        "SWcld_tot":0.08982696976844363,
-                        "SWdobsc_cov_fbk":6.711500055642828e-20,
-                        "SWdobsc_fbk":0.05211581339044787,
-                        "SWdunobsc_fbk":0.08982696976844362
-                    }
-                },
-                "ocn_asc":{
-                    "LO680":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "LWdobsc_cov_fbk":NaN,
-                        "LWdobsc_fbk":NaN,
-                        "LWdunobsc_fbk":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN,
-                        "SWdobsc_cov_fbk":NaN,
-                        "SWdobsc_fbk":NaN,
-                        "SWdunobsc_fbk":NaN
-                    }
-                },
-                "ocn_dsc":{
-                    "LO680":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "LWdobsc_cov_fbk":NaN,
-                        "LWdobsc_fbk":NaN,
-                        "LWdunobsc_fbk":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN,
-                        "SWdobsc_cov_fbk":NaN,
-                        "SWdobsc_fbk":NaN,
-                        "SWdunobsc_fbk":NaN
-                    }
-                }
-            },
-            "eq80":{
-                "all":{
-                    "LO680":{
-                        "LWcld_alt":0.008448370636874947,
-                        "LWcld_amt":-0.02935929180069723,
-                        "LWcld_err":7.081910812343549e-05,
-                        "LWcld_tau":0.006997924620644472,
-                        "LWcld_tot":-0.013842177435054377,
-                        "LWdobsc_cov_fbk":-3.58826411795753e-21,
-                        "LWdobsc_fbk":-0.009354819606828093,
-                        "LWdunobsc_fbk":-0.013842177435054379,
-                        "SWcld_alt":-0.003315214537198558,
-                        "SWcld_amt":0.1697230717601158,
-                        "SWcld_err":-0.00019314119585732802,
-                        "SWcld_tau":-0.06322723089580093,
-                        "SWcld_tot":0.10298748513125898,
-                        "SWdobsc_cov_fbk":5.848611528716177e-20,
-                        "SWdobsc_fbk":0.05209327785715965,
-                        "SWdunobsc_fbk":0.10298748513125898
-                    }
-                },
-                "lnd":{
-                    "LO680":{
-                        "LWcld_alt":0.001481680969880614,
-                        "LWcld_amt":-0.0073283680134483995,
-                        "LWcld_err":-4.678599801607428e-05,
-                        "LWcld_tau":0.0027164505917128194,
-                        "LWcld_tot":-0.0031770224498710396,
-                        "LWdobsc_cov_fbk":3.4937996529361475e-21,
-                        "LWdobsc_fbk":-0.00018878314960264143,
-                        "LWdunobsc_fbk":-0.00317702244987104,
-                        "SWcld_alt":-0.0006639827704378753,
-                        "SWcld_amt":0.037002933373896534,
-                        "SWcld_err":2.714284312759644e-05,
-                        "SWcld_tau":-0.015092452034045708,
-                        "SWcld_tot":0.021273641412540543,
-                        "SWdobsc_cov_fbk":-1.4741807206990068e-20,
-                        "SWdobsc_fbk":-0.003152580831016063,
-                        "SWdunobsc_fbk":0.021273641412540543
-                    }
-                },
-                "ocn":{
-                    "LO680":{
-                        "LWcld_alt":0.006966689666994333,
-                        "LWcld_amt":-0.02203092378724884,
-                        "LWcld_err":0.00011760510613950975,
-                        "LWcld_tau":0.004281474028931654,
-                        "LWcld_tot":-0.010665154985183339,
-                        "LWdobsc_cov_fbk":-7.082063770893674e-21,
-                        "LWdobsc_fbk":-0.009166036457225453,
-                        "LWdunobsc_fbk":-0.010665154985183339,
-                        "SWcld_alt":-0.0026512317667606827,
-                        "SWcld_amt":0.13272013838621924,
-                        "SWcld_err":-0.00022028403898492443,
-                        "SWcld_tau":-0.04813477886175522,
-                        "SWcld_tot":0.08171384371871841,
-                        "SWdobsc_cov_fbk":7.32279224941518e-20,
-                        "SWdobsc_fbk":0.05524585868817572,
-                        "SWdunobsc_fbk":0.08171384371871841
-                    }
-                },
-                "ocn_asc":{
-                    "LO680":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "LWdobsc_cov_fbk":NaN,
-                        "LWdobsc_fbk":NaN,
-                        "LWdunobsc_fbk":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN,
-                        "SWdobsc_cov_fbk":NaN,
-                        "SWdobsc_fbk":NaN,
-                        "SWdunobsc_fbk":NaN
-                    }
-                },
-                "ocn_dsc":{
-                    "LO680":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "LWdobsc_cov_fbk":NaN,
-                        "LWdobsc_fbk":NaN,
-                        "LWdunobsc_fbk":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN,
-                        "SWdobsc_cov_fbk":NaN,
-                        "SWdobsc_fbk":NaN,
-                        "SWdunobsc_fbk":NaN
-                    }
-                }
-            },
-            "eq90":{
-                "all":{
-                    "LO680":{
-                        "LWcld_alt":0.008614480637008147,
-                        "LWcld_amt":-0.029223480641343422,
-                        "LWcld_err":7.838346014567793e-05,
-                        "LWcld_tau":0.007345547342139275,
-                        "LWcld_tot":-0.013185069202050322,
-                        "LWdobsc_cov_fbk":-2.9127677312096578e-21,
-                        "LWdobsc_fbk":-0.00972267381520941,
-                        "LWdunobsc_fbk":-0.013185069202050322,
-                        "SWcld_alt":-0.003343466977262082,
-                        "SWcld_amt":0.16940183734515102,
-                        "SWcld_err":-0.00019258772248172354,
-                        "SWcld_tau":-0.06444758311297183,
-                        "SWcld_tot":0.1014181995324354,
-                        "SWdobsc_cov_fbk":5.736273524274088e-20,
-                        "SWdobsc_fbk":0.05293239419400387,
-                        "SWdunobsc_fbk":0.1014181995324354
-                    }
-                },
-                "lnd":{
-                    "LO680":{
-                        "LWcld_alt":0.0014921250877273993,
-                        "LWcld_amt":-0.007342085063937342,
-                        "LWcld_err":-4.668361404317685e-05,
-                        "LWcld_tau":0.0027635026711461974,
-                        "LWcld_tot":-0.0031331409191069225,
-                        "LWdobsc_cov_fbk":3.751129260181497e-21,
-                        "LWdobsc_fbk":-0.00020471803827782058,
-                        "LWdunobsc_fbk":-0.003133140919106922,
-                        "SWcld_alt":-0.0006654731527230454,
-                        "SWcld_amt":0.036983710864872975,
-                        "SWcld_err":2.697497018839221e-05,
-                        "SWcld_tau":-0.015176715416358484,
-                        "SWcld_tot":0.021168497265979833,
-                        "SWdobsc_cov_fbk":-1.539986311251619e-20,
-                        "SWdobsc_fbk":-0.00312306325584819,
-                        "SWdunobsc_fbk":0.021168497265979833
-                    }
-                },
-                "ocn":{
-                    "LO680":{
-                        "LWcld_alt":0.007122355549280745,
-                        "LWcld_amt":-0.021881395577406076,
-                        "LWcld_err":0.00012506707418885477,
-                        "LWcld_tau":0.004582044670993078,
-                        "LWcld_tot":-0.010051928282943398,
-                        "LWdobsc_cov_fbk":-6.663896991391155e-21,
-                        "LWdobsc_fbk":-0.009517955776931588,
-                        "LWdunobsc_fbk":-0.010051928282943398,
-                        "SWcld_alt":-0.0026779938245390365,
-                        "SWcld_amt":0.13241812648027806,
-                        "SWcld_err":-0.0002195626926701157,
-                        "SWcld_tau":-0.04927086769661335,
-                        "SWcld_tot":0.08024970226645556,
-                        "SWdobsc_cov_fbk":7.276259835525703e-20,
-                        "SWdobsc_fbk":0.056055457449852054,
-                        "SWdunobsc_fbk":0.08024970226645554
-                    }
-                },
-                "ocn_asc":{
-                    "LO680":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "LWdobsc_cov_fbk":NaN,
-                        "LWdobsc_fbk":NaN,
-                        "LWdunobsc_fbk":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN,
-                        "SWdobsc_cov_fbk":NaN,
-                        "SWdobsc_fbk":NaN,
-                        "SWdunobsc_fbk":NaN
-                    }
-                },
-                "ocn_dsc":{
-                    "LO680":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "LWdobsc_cov_fbk":NaN,
-                        "LWdobsc_fbk":NaN,
-                        "LWdunobsc_fbk":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN,
-                        "SWdobsc_cov_fbk":NaN,
-                        "SWdobsc_fbk":NaN,
-                        "SWdunobsc_fbk":NaN
-                    }
-                }
-            }
-        }
-    },
-    "CanESM5":{
-        "r1i1p2f1":{
-            "Arctic":{
-                "all":{
-                    "LO680":{
-                        "LWcld_alt":-8.972575355976228e-05,
-                        "LWcld_amt":3.862136572653236e-06,
-                        "LWcld_err":1.4896486258642927e-05,
-                        "LWcld_tau":0.0008828779698987524,
-                        "LWcld_tot":0.0005412738927801909,
-                        "LWdobsc_cov_fbk":1.3625984866986907e-22,
-                        "LWdobsc_fbk":-0.0013334866876287363,
-                        "LWdunobsc_fbk":0.0008119108391702863,
-                        "SWcld_alt":0.0001121455144667618,
-                        "SWcld_amt":0.0019405034816178269,
-                        "SWcld_err":-2.7715910041039706e-05,
-                        "SWcld_tau":-0.0035665555185453077,
-                        "SWcld_tot":-0.0015416224325017593,
-                        "SWdobsc_cov_fbk":-1.8187626806000115e-20,
-                        "SWdobsc_fbk":0.008750093700297305,
-                        "SWdunobsc_fbk":-0.0023124336487526384
-                    }
-                },
-                "lnd":{
-                    "LO680":{
-                        "LWcld_alt":4.980951745609113e-06,
-                        "LWcld_amt":7.943857697259788e-05,
-                        "LWcld_err":4.647674461357799e-06,
-                        "LWcld_tau":0.00020395762672746105,
-                        "LWcld_tot":0.0002930248299070258,
-                        "LWdobsc_cov_fbk":-1.53399829132488e-21,
-                        "LWdobsc_fbk":-0.00026432139129718533,
-                        "LWdunobsc_fbk":0.0002930248299070258,
-                        "SWcld_alt":8.730858919615814e-06,
-                        "SWcld_amt":0.0003524337326309908,
-                        "SWcld_err":-5.366556959558367e-06,
-                        "SWcld_tau":-0.0008817358035216355,
-                        "SWcld_tot":-0.0005259377689305874,
-                        "SWdobsc_cov_fbk":-4.309083487563349e-21,
-                        "SWdobsc_fbk":0.0010417786330387717,
-                        "SWdunobsc_fbk":-0.0005259377689305873
-                    }
-                },
-                "ocn":{
-                    "LO680":{
-                        "LWcld_alt":-9.470670530537136e-05,
-                        "LWcld_amt":-7.557644039994471e-05,
-                        "LWcld_err":1.024881179728513e-05,
-                        "LWcld_tau":0.0006789203431712915,
-                        "LWcld_tot":0.0005188860092632605,
-                        "LWdobsc_cov_fbk":1.6702581399947483e-21,
-                        "LWdobsc_fbk":-0.0010691652963315508,
-                        "LWdunobsc_fbk":0.0005188860092632605,
-                        "SWcld_alt":0.00015948741278052687,
-                        "SWcld_amt":0.0025583214897957492,
-                        "SWcld_err":-3.6207308102001195e-05,
-                        "SWcld_tau":-0.004468097474296326,
-                        "SWcld_tot":-0.001786495879822051,
-                        "SWdobsc_cov_fbk":-1.3878543318436768e-20,
-                        "SWdobsc_fbk":0.0077083150672585315,
-                        "SWdunobsc_fbk":-0.0017864958798220515
-                    }
-                },
-                "ocn_asc":{
-                    "LO680":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "LWdobsc_cov_fbk":NaN,
-                        "LWdobsc_fbk":NaN,
-                        "LWdunobsc_fbk":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN,
-                        "SWdobsc_cov_fbk":NaN,
-                        "SWdobsc_fbk":NaN,
-                        "SWdunobsc_fbk":NaN
-                    }
-                },
-                "ocn_dsc":{
-                    "LO680":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "LWdobsc_cov_fbk":NaN,
-                        "LWdobsc_fbk":NaN,
-                        "LWdunobsc_fbk":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN,
-                        "SWdobsc_cov_fbk":NaN,
-                        "SWdobsc_fbk":NaN,
-                        "SWdunobsc_fbk":NaN
-                    }
-                }
-            },
-            "eq10":{
-                "all":{
-                    "LO680":{
-                        "LWcld_alt":0.005692109448641028,
-                        "LWcld_amt":-0.01646235092718863,
-                        "LWcld_err":0.00017815101388670655,
-                        "LWcld_tau":-0.00114463519201882,
-                        "LWcld_tot":-0.011736725656679714,
-                        "LWdobsc_cov_fbk":-6.286658048526366e-21,
-                        "LWdobsc_fbk":0.0010888017740313667,
-                        "LWdunobsc_fbk":-0.011736725656679714,
-                        "SWcld_alt":-0.0028017635024902023,
-                        "SWcld_amt":0.1394885767753225,
-                        "SWcld_err":-0.0002194609259457957,
-                        "SWcld_tau":0.006327940257095207,
-                        "SWcld_tot":0.1427952926039817,
-                        "SWdobsc_cov_fbk":6.905499070103891e-20,
-                        "SWdobsc_fbk":-0.006437442756894197,
-                        "SWdunobsc_fbk":0.1427952926039817
-                    }
-                },
-                "lnd":{
-                    "LO680":{
-                        "LWcld_alt":0.0010054502354579237,
-                        "LWcld_amt":-0.004294749997915288,
-                        "LWcld_err":-3.3710906888742133e-06,
-                        "LWcld_tau":-0.00023633826805242566,
-                        "LWcld_tot":-0.003529009121198665,
-                        "LWdobsc_cov_fbk":-3.4493199296513666e-21,
-                        "LWdobsc_fbk":0.0014840382417607462,
-                        "LWdunobsc_fbk":-0.003529009121198665,
-                        "SWcld_alt":-0.0005184608555626314,
-                        "SWcld_amt":0.032675413344544484,
-                        "SWcld_err":5.626847900953067e-07,
-                        "SWcld_tau":0.002814278285512931,
-                        "SWcld_tot":0.034971793459284875,
-                        "SWdobsc_cov_fbk":4.0242631046587875e-20,
-                        "SWdobsc_fbk":-0.01132235972784934,
-                        "SWdunobsc_fbk":0.034971793459284875
-                    }
-                },
-                "ocn":{
-                    "LO680":{
-                        "LWcld_alt":0.004686659213183105,
-                        "LWcld_amt":-0.01216760092927334,
-                        "LWcld_err":0.00018152210457558077,
-                        "LWcld_tau":-0.0009082969239663944,
-                        "LWcld_tot":-0.008207716535481047,
-                        "LWdobsc_cov_fbk":-2.8373381188750007e-21,
-                        "LWdobsc_fbk":-0.00039523646772937957,
-                        "LWdunobsc_fbk":-0.008207716535481047,
-                        "SWcld_alt":-0.002283302646927571,
-                        "SWcld_amt":0.10681316343077801,
-                        "SWcld_err":-0.00022002361073589105,
-                        "SWcld_tau":0.0035136619715822764,
-                        "SWcld_tot":0.10782349914469681,
-                        "SWdobsc_cov_fbk":2.881235965445102e-20,
-                        "SWdobsc_fbk":0.004884916970955145,
-                        "SWdunobsc_fbk":0.10782349914469681
-                    }
-                },
-                "ocn_asc":{
-                    "LO680":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "LWdobsc_cov_fbk":NaN,
-                        "LWdobsc_fbk":NaN,
-                        "LWdunobsc_fbk":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN,
-                        "SWdobsc_cov_fbk":NaN,
-                        "SWdobsc_fbk":NaN,
-                        "SWdunobsc_fbk":NaN
-                    }
-                },
-                "ocn_dsc":{
-                    "LO680":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "LWdobsc_cov_fbk":NaN,
-                        "LWdobsc_fbk":NaN,
-                        "LWdunobsc_fbk":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN,
-                        "SWdobsc_cov_fbk":NaN,
-                        "SWdobsc_fbk":NaN,
-                        "SWdunobsc_fbk":NaN
-                    }
-                }
-            },
-            "eq20":{
-                "all":{
-                    "LO680":{
-                        "LWcld_alt":0.012553645472214306,
-                        "LWcld_amt":-0.02831193754148613,
-                        "LWcld_err":0.0003385055575745768,
-                        "LWcld_tau":-0.0018542585126706736,
-                        "LWcld_tot":-0.017274045024367923,
-                        "LWdobsc_cov_fbk":-1.5683857063910803e-20,
-                        "LWdobsc_fbk":-3.385053128873729e-05,
-                        "LWdunobsc_fbk":-0.017274045024367923,
-                        "SWcld_alt":-0.0059563334316261735,
-                        "SWcld_amt":0.23224868461345916,
-                        "SWcld_err":-0.0003497424310044549,
-                        "SWcld_tau":0.0082787822804648,
-                        "SWcld_tot":0.23422139103129333,
-                        "SWdobsc_cov_fbk":8.56076663860855e-20,
-                        "SWdobsc_fbk":0.0044483621920656975,
-                        "SWdunobsc_fbk":0.23422139103129333
-                    }
-                },
-                "lnd":{
-                    "LO680":{
-                        "LWcld_alt":0.0015238767948377084,
-                        "LWcld_amt":-0.007464297033669465,
-                        "LWcld_err":-1.2047785742978464e-05,
-                        "LWcld_tau":-0.0003330462320188395,
-                        "LWcld_tot":-0.006285514256593575,
-                        "LWdobsc_cov_fbk":-5.235739210499174e-21,
-                        "LWdobsc_fbk":0.001849754101551101,
-                        "LWdunobsc_fbk":-0.006285514256593575,
-                        "SWcld_alt":-0.0007746670145759848,
-                        "SWcld_amt":0.054142504148091526,
-                        "SWcld_err":8.052746221589253e-06,
-                        "SWcld_tau":0.003961017881895331,
-                        "SWcld_tot":0.05733690776163247,
-                        "SWdobsc_cov_fbk":4.805997995204537e-20,
-                        "SWdobsc_fbk":-0.013727152954624582,
-                        "SWdunobsc_fbk":0.057336907761632465
-                    }
-                },
-                "ocn":{
-                    "LO680":{
-                        "LWcld_alt":0.011029768677376596,
-                        "LWcld_amt":-0.02084764050781666,
-                        "LWcld_err":0.0003505533433175552,
-                        "LWcld_tau":-0.0015212122806518343,
-                        "LWcld_tot":-0.010988530767774346,
-                        "LWdobsc_cov_fbk":-1.044811785341163e-20,
-                        "LWdobsc_fbk":-0.0018836046328398384,
-                        "LWdunobsc_fbk":-0.010988530767774346,
-                        "SWcld_alt":-0.00518166641705019,
-                        "SWcld_amt":0.17810618046536764,
-                        "SWcld_err":-0.0003577951772260441,
-                        "SWcld_tau":0.00431776439856947,
-                        "SWcld_tot":0.17688448326966086,
-                        "SWdobsc_cov_fbk":3.754768643404012e-20,
-                        "SWdobsc_fbk":0.018175515146690278,
-                        "SWdunobsc_fbk":0.17688448326966086
-                    }
-                },
-                "ocn_asc":{
-                    "LO680":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "LWdobsc_cov_fbk":NaN,
-                        "LWdobsc_fbk":NaN,
-                        "LWdunobsc_fbk":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN,
-                        "SWdobsc_cov_fbk":NaN,
-                        "SWdobsc_fbk":NaN,
-                        "SWdunobsc_fbk":NaN
-                    }
-                },
-                "ocn_dsc":{
-                    "LO680":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "LWdobsc_cov_fbk":NaN,
-                        "LWdobsc_fbk":NaN,
-                        "LWdunobsc_fbk":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN,
-                        "SWdobsc_cov_fbk":NaN,
-                        "SWdobsc_fbk":NaN,
-                        "SWdunobsc_fbk":NaN
-                    }
-                }
-            },
-            "eq30":{
-                "all":{
-                    "LO680":{
-                        "LWcld_alt":0.016672050643544895,
-                        "LWcld_amt":-0.04339787880338983,
-                        "LWcld_err":0.0005357600618316567,
-                        "LWcld_tau":-0.0024078485688738597,
-                        "LWcld_tot":-0.028597916666887144,
-                        "LWdobsc_cov_fbk":-7.972971726209766e-21,
-                        "LWdobsc_fbk":-0.0015462514625103143,
-                        "LWdunobsc_fbk":-0.028597916666887144,
-                        "SWcld_alt":-0.007718775963097629,
-                        "SWcld_amt":0.3420026612397942,
-                        "SWcld_err":-0.0005639696024492821,
-                        "SWcld_tau":0.0064029991891027885,
-                        "SWcld_tot":0.3401229148633502,
-                        "SWdobsc_cov_fbk":7.004270619651935e-21,
-                        "SWdobsc_fbk":0.01650624958401214,
-                        "SWdunobsc_fbk":0.3401229148633502
-                    }
-                },
-                "lnd":{
-                    "LO680":{
-                        "LWcld_alt":0.0018935274482873236,
-                        "LWcld_amt":-0.009987892311841757,
-                        "LWcld_err":-1.2602680170966403e-05,
-                        "LWcld_tau":-0.0003893098896205396,
-                        "LWcld_tot":-0.00849627743334594,
-                        "LWdobsc_cov_fbk":-5.425745454336605e-21,
-                        "LWdobsc_fbk":0.002073896324412025,
-                        "LWdunobsc_fbk":-0.00849627743334594,
-                        "SWcld_alt":-0.000957927246412697,
-                        "SWcld_amt":0.06928064267091151,
-                        "SWcld_err":9.06727026839668e-06,
-                        "SWcld_tau":0.004619671089642781,
-                        "SWcld_tot":0.07295145378441,
-                        "SWdobsc_cov_fbk":4.630376806344248e-20,
-                        "SWdobsc_fbk":-0.014943958038849818,
-                        "SWdunobsc_fbk":0.07295145378440998
-                    }
-                },
-                "ocn":{
-                    "LO680":{
-                        "LWcld_alt":0.014778523195257574,
-                        "LWcld_amt":-0.03340998649154808,
-                        "LWcld_err":0.0005483627420026231,
-                        "LWcld_tau":-0.00201853867925332,
-                        "LWcld_tot":-0.020101639233541198,
-                        "LWdobsc_cov_fbk":-2.5472262718731602e-21,
-                        "LWdobsc_fbk":-0.003620147786922339,
-                        "LWdunobsc_fbk":-0.020101639233541198,
-                        "SWcld_alt":-0.0067608487166849335,
-                        "SWcld_amt":0.2727220185688828,
-                        "SWcld_err":-0.0005730368727176787,
-                        "SWcld_tau":0.001783328099460008,
-                        "SWcld_tot":0.2671714610789402,
-                        "SWdobsc_cov_fbk":-3.929949744379054e-20,
-                        "SWdobsc_fbk":0.03145020762286196,
-                        "SWdunobsc_fbk":0.26717146107894013
-                    }
-                },
-                "ocn_asc":{
-                    "LO680":{
-                        "LWcld_alt":0.003953679391781046,
-                        "LWcld_amt":-0.008547227432238793,
-                        "LWcld_err":0.00028948347664939947,
-                        "LWcld_tau":-0.00013487694183114048,
-                        "LWcld_tot":-0.004438941505639486,
-                        "LWdobsc_cov_fbk":4.197099285995786e-21,
-                        "LWdobsc_fbk":-5.0471875428020993e-05,
-                        "LWdunobsc_fbk":-0.004438941505639486,
-                        "SWcld_alt":-0.00187319038598541,
-                        "SWcld_amt":0.07566538308005079,
-                        "SWcld_err":-0.0002229839678391295,
-                        "SWcld_tau":-0.011192070555005637,
-                        "SWcld_tot":0.06237713817122061,
-                        "SWdobsc_cov_fbk":-8.690759510501851e-21,
-                        "SWdobsc_fbk":0.0004357820355372725,
-                        "SWdunobsc_fbk":0.06237713817122061
-                    }
-                },
-                "ocn_dsc":{
-                    "LO680":{
-                        "LWcld_alt":0.011961196714261808,
-                        "LWcld_amt":-0.030833030377902314,
-                        "LWcld_err":0.0002608383636962193,
-                        "LWcld_tau":-0.0008354616038891699,
-                        "LWcld_tot":-0.019446456903833457,
-                        "LWdobsc_cov_fbk":-4.816832473351108e-19,
-                        "LWdobsc_fbk":1.872310176078624e-05,
-                        "LWdunobsc_fbk":-0.019446456903833464,
-                        "SWcld_alt":-0.005301239629402554,
-                        "SWcld_amt":0.22371593252796307,
-                        "SWcld_err":3.457488569794258e-05,
-                        "SWcld_tau":0.004033764404397794,
-                        "SWcld_tot":0.22248303218865625,
-                        "SWdobsc_cov_fbk":2.5738696617724767e-18,
-                        "SWdobsc_fbk":-3.3507772510625926e-05,
-                        "SWdunobsc_fbk":0.22248303218865625
-                    }
-                }
-            },
-            "eq40":{
-                "all":{
-                    "LO680":{
-                        "LWcld_alt":0.019335681474577534,
-                        "LWcld_amt":-0.05921633018765036,
-                        "LWcld_err":0.0007926188529943451,
-                        "LWcld_tau":-0.002790253409388748,
-                        "LWcld_tot":-0.041878283269467236,
-                        "LWdobsc_cov_fbk":6.510334579553549e-21,
-                        "LWdobsc_fbk":-0.0005023754865979816,
-                        "LWdunobsc_fbk":-0.041878283269467236,
-                        "SWcld_alt":-0.008834373978848514,
-                        "SWcld_amt":0.44696838226313557,
-                        "SWcld_err":-0.0007866087324044121,
-                        "SWcld_tau":0.009462925032440695,
-                        "SWcld_tot":0.4468103245843233,
-                        "SWdobsc_cov_fbk":-4.4483816215146665e-20,
-                        "SWdobsc_fbk":0.012754375907453675,
-                        "SWdunobsc_fbk":0.4468103245843232
-                    }
-                },
-                "lnd":{
-                    "LO680":{
-                        "LWcld_alt":0.0019465470063329687,
-                        "LWcld_amt":-0.011855749122256084,
-                        "LWcld_err":-8.680558661204672e-06,
-                        "LWcld_tau":-0.0003370722746026695,
-                        "LWcld_tot":-0.010254954949186988,
-                        "LWdobsc_cov_fbk":1.2485313016632374e-21,
-                        "LWdobsc_fbk":0.0024106379662088913,
-                        "LWdunobsc_fbk":-0.01025495494918699,
-                        "SWcld_alt":-0.0009914531416873734,
-                        "SWcld_amt":0.07887746593332312,
-                        "SWcld_err":5.8467684939570325e-06,
-                        "SWcld_tau":0.004592837265977516,
-                        "SWcld_tot":0.08248469682610722,
-                        "SWdobsc_cov_fbk":3.505398125564936e-21,
-                        "SWdobsc_fbk":-0.016449926181330933,
-                        "SWdunobsc_fbk":0.08248469682610722
-                    }
-                },
-                "ocn":{
-                    "LO680":{
-                        "LWcld_alt":0.01738913446824457,
-                        "LWcld_amt":-0.04736058106539428,
-                        "LWcld_err":0.0008012994116555499,
-                        "LWcld_tau":-0.0024531811347860786,
-                        "LWcld_tot":-0.031623328320280246,
-                        "LWdobsc_cov_fbk":5.261803277890314e-21,
-                        "LWdobsc_fbk":-0.0029130134528068726,
-                        "LWdunobsc_fbk":-0.031623328320280246,
-                        "SWcld_alt":-0.00784292083716114,
-                        "SWcld_amt":0.36809091632981233,
-                        "SWcld_err":-0.0007924555008983692,
-                        "SWcld_tau":0.004870087766463179,
-                        "SWcld_tot":0.36432562775821603,
-                        "SWdobsc_cov_fbk":-4.798921434071163e-20,
-                        "SWdobsc_fbk":0.029204302088784606,
-                        "SWdunobsc_fbk":0.36432562775821603
-                    }
-                },
-                "ocn_asc":{
-                    "LO680":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "LWdobsc_cov_fbk":NaN,
-                        "LWdobsc_fbk":NaN,
-                        "LWdunobsc_fbk":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN,
-                        "SWdobsc_cov_fbk":NaN,
-                        "SWdobsc_fbk":NaN,
-                        "SWdunobsc_fbk":NaN
-                    }
-                },
-                "ocn_dsc":{
-                    "LO680":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "LWdobsc_cov_fbk":NaN,
-                        "LWdobsc_fbk":NaN,
-                        "LWdunobsc_fbk":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN,
-                        "SWdobsc_cov_fbk":NaN,
-                        "SWdobsc_fbk":NaN,
-                        "SWdunobsc_fbk":NaN
-                    }
-                }
-            },
-            "eq50":{
-                "all":{
-                    "LO680":{
-                        "LWcld_alt":0.02150105368608991,
-                        "LWcld_amt":-0.0653903928915521,
-                        "LWcld_err":0.0010930612379490394,
-                        "LWcld_tau":-0.0016085236802873286,
-                        "LWcld_tot":-0.04440480164780048,
-                        "LWdobsc_cov_fbk":4.085032344471984e-21,
-                        "LWdobsc_fbk":-0.0006556253668815589,
-                        "LWdunobsc_fbk":-0.04440480164780048,
-                        "SWcld_alt":-0.009813604831357603,
-                        "SWcld_amt":0.49724573326611843,
-                        "SWcld_err":-0.0008919740102669634,
-                        "SWcld_tau":0.006378097152103454,
-                        "SWcld_tot":0.4929182515765973,
-                        "SWdobsc_cov_fbk":-7.591383482315121e-20,
-                        "SWdobsc_fbk":0.015245804475677305,
-                        "SWdunobsc_fbk":0.4929182515765973
-                    }
-                },
-                "lnd":{
-                    "LO680":{
-                        "LWcld_alt":0.0017894527754551137,
-                        "LWcld_amt":-0.013097023302143997,
-                        "LWcld_err":-1.5576631254367217e-05,
-                        "LWcld_tau":0.000184550207218788,
-                        "LWcld_tot":-0.011138596950724463,
-                        "LWdobsc_cov_fbk":-1.3411076420224538e-21,
-                        "LWdobsc_fbk":0.0027204385016945756,
-                        "LWdunobsc_fbk":-0.011138596950724463,
-                        "SWcld_alt":-0.0009434494315266004,
-                        "SWcld_amt":0.09056696496891618,
-                        "SWcld_err":7.200827462563226e-06,
-                        "SWcld_tau":0.0029275881548687216,
-                        "SWcld_tot":0.09255830451972086,
-                        "SWdobsc_cov_fbk":2.643422485751547e-20,
-                        "SWdobsc_fbk":-0.01838739014393585,
-                        "SWdunobsc_fbk":0.09255830451972086
-                    }
-                },
-                "ocn":{
-                    "LO680":{
-                        "LWcld_alt":0.019711600910634796,
-                        "LWcld_amt":-0.05229336958940811,
-                        "LWcld_err":0.001108637869203407,
-                        "LWcld_tau":-0.0017930738875061164,
-                        "LWcld_tot":-0.033266204697076016,
-                        "LWdobsc_cov_fbk":5.4261399864944355e-21,
-                        "LWdobsc_fbk":-0.003376063868576135,
-                        "LWdunobsc_fbk":-0.033266204697076016,
-                        "SWcld_alt":-0.008870155399831003,
-                        "SWcld_amt":0.40667876829720223,
-                        "SWcld_err":-0.0008991748377295268,
-                        "SWcld_tau":0.003450508997234734,
-                        "SWcld_tot":0.40035994705687644,
-                        "SWdobsc_cov_fbk":-1.0234805968066665e-19,
-                        "SWdobsc_fbk":0.03363319461961315,
-                        "SWdunobsc_fbk":0.40035994705687644
-                    }
-                },
-                "ocn_asc":{
-                    "LO680":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "LWdobsc_cov_fbk":NaN,
-                        "LWdobsc_fbk":NaN,
-                        "LWdunobsc_fbk":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN,
-                        "SWdobsc_cov_fbk":NaN,
-                        "SWdobsc_fbk":NaN,
-                        "SWdunobsc_fbk":NaN
-                    }
-                },
-                "ocn_dsc":{
-                    "LO680":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "LWdobsc_cov_fbk":NaN,
-                        "LWdobsc_fbk":NaN,
-                        "LWdunobsc_fbk":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN,
-                        "SWdobsc_cov_fbk":NaN,
-                        "SWdobsc_fbk":NaN,
-                        "SWdunobsc_fbk":NaN
-                    }
-                }
-            },
-            "eq60":{
-                "all":{
-                    "LO680":{
-                        "LWcld_alt":0.021610355032872442,
-                        "LWcld_amt":-0.06465112256676772,
-                        "LWcld_err":0.0014530922671315849,
-                        "LWcld_tau":0.001770173114974771,
-                        "LWcld_tot":-0.03981750215178892,
-                        "LWdobsc_cov_fbk":8.530622195408669e-21,
-                        "LWdobsc_fbk":-0.0038228298017897334,
-                        "LWdunobsc_fbk":-0.039817502151788926,
-                        "SWcld_alt":-0.010047539327487065,
-                        "SWcld_amt":0.49996452889465487,
-                        "SWcld_err":-0.001070067734437297,
-                        "SWcld_tau":-0.020460296023246453,
-                        "SWcld_tot":0.46838662580948415,
-                        "SWdobsc_cov_fbk":-1.0993730028255038e-19,
-                        "SWdobsc_fbk":0.03859189998288643,
-                        "SWdunobsc_fbk":0.46838662580948415
-                    }
-                },
-                "lnd":{
-                    "LO680":{
-                        "LWcld_alt":0.0015598771165614795,
-                        "LWcld_amt":-0.012005423513640923,
-                        "LWcld_err":-3.806785565262184e-05,
-                        "LWcld_tau":0.0009448900400804908,
-                        "LWcld_tot":-0.009538724212651574,
-                        "LWdobsc_cov_fbk":9.90954234213111e-22,
-                        "LWdobsc_fbk":0.0021761321728376976,
-                        "LWdunobsc_fbk":-0.009538724212651574,
-                        "SWcld_alt":-0.0008079572207308323,
-                        "SWcld_amt":0.08774648203648662,
-                        "SWcld_err":1.7234565560196346e-05,
-                        "SWcld_tau":0.0010499941441644888,
-                        "SWcld_tot":0.08800575352548047,
-                        "SWdobsc_cov_fbk":-4.223809121061369e-21,
-                        "SWdobsc_fbk":-0.01646341879744825,
-                        "SWdunobsc_fbk":0.08800575352548046
-                    }
-                },
-                "ocn":{
-                    "LO680":{
-                        "LWcld_alt":0.02005047791631097,
-                        "LWcld_amt":-0.052645699053126806,
-                        "LWcld_err":0.0014911601227842071,
-                        "LWcld_tau":0.0008252830748942801,
-                        "LWcld_tot":-0.030278777939137348,
-                        "LWdobsc_cov_fbk":7.53966796119556e-21,
-                        "LWdobsc_fbk":-0.005998961974627432,
-                        "LWdunobsc_fbk":-0.030278777939137348,
-                        "SWcld_alt":-0.009239582106756232,
-                        "SWcld_amt":0.41221804685816826,
-                        "SWcld_err":-0.0010873022999974933,
-                        "SWcld_tau":-0.021510290167410944,
-                        "SWcld_tot":0.3803808722840037,
-                        "SWdobsc_cov_fbk":-1.0571349116148902e-19,
-                        "SWdobsc_fbk":0.055055318780334685,
-                        "SWdunobsc_fbk":0.3803808722840037
-                    }
-                },
-                "ocn_asc":{
-                    "LO680":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "LWdobsc_cov_fbk":NaN,
-                        "LWdobsc_fbk":NaN,
-                        "LWdunobsc_fbk":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN,
-                        "SWdobsc_cov_fbk":NaN,
-                        "SWdobsc_fbk":NaN,
-                        "SWdunobsc_fbk":NaN
-                    }
-                },
-                "ocn_dsc":{
-                    "LO680":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "LWdobsc_cov_fbk":NaN,
-                        "LWdobsc_fbk":NaN,
-                        "LWdunobsc_fbk":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN,
-                        "SWdobsc_cov_fbk":NaN,
-                        "SWdobsc_fbk":NaN,
-                        "SWdunobsc_fbk":NaN
-                    }
-                }
-            },
-            "eq70":{
-                "all":{
-                    "LO680":{
-                        "LWcld_alt":0.021604213707734004,
-                        "LWcld_amt":-0.06327382637890858,
-                        "LWcld_err":0.0015529783747569399,
-                        "LWcld_tau":0.004266668186388788,
-                        "LWcld_tot":-0.03584996611002884,
-                        "LWdobsc_cov_fbk":1.1508196591092045e-20,
-                        "LWdobsc_fbk":-0.006150207660948376,
-                        "LWdunobsc_fbk":-0.03584996611002884,
-                        "SWcld_alt":-0.010059899626515953,
-                        "SWcld_amt":0.4923786599173716,
-                        "SWcld_err":-0.0011023988668158896,
-                        "SWcld_tau":-0.04830564417763533,
-                        "SWcld_tot":0.43291071724640445,
-                        "SWdobsc_cov_fbk":-1.4082351820563094e-19,
-                        "SWdobsc_fbk":0.055284490845871705,
-                        "SWdunobsc_fbk":0.43291071724640445
-                    }
-                },
-                "lnd":{
-                    "LO680":{
-                        "LWcld_alt":0.001480043904116279,
-                        "LWcld_amt":-0.0111341696061574,
-                        "LWcld_err":-4.328159703079009e-05,
-                        "LWcld_tau":0.00149171687685481,
-                        "LWcld_tot":-0.008205690422217098,
-                        "LWdobsc_cov_fbk":-8.266266836302654e-22,
-                        "LWdobsc_fbk":0.001377055600374339,
-                        "LWdunobsc_fbk":-0.008205690422217098,
-                        "SWcld_alt":-0.0007487269637849913,
-                        "SWcld_amt":0.08378041000642023,
-                        "SWcld_err":1.7796307724869336e-05,
-                        "SWcld_tau":-0.001068605465572921,
-                        "SWcld_tot":0.08198087388478718,
-                        "SWdobsc_cov_fbk":1.2219953651154028e-20,
-                        "SWdobsc_fbk":-0.012770303696447811,
-                        "SWdunobsc_fbk":0.0819808738847872
-                    }
-                },
-                "ocn":{
-                    "LO680":{
-                        "LWcld_alt":0.020124169803617725,
-                        "LWcld_amt":-0.052139656772751175,
-                        "LWcld_err":0.0015962599717877302,
-                        "LWcld_tau":0.0027749513095339773,
-                        "LWcld_tot":-0.027644275687811744,
-                        "LWdobsc_cov_fbk":1.2334823274722305e-20,
-                        "LWdobsc_fbk":-0.007527263261322715,
-                        "LWdunobsc_fbk":-0.027644275687811744,
-                        "SWcld_alt":-0.009311172662730962,
-                        "SWcld_amt":0.40859824991095134,
-                        "SWcld_err":-0.0011201951745407588,
-                        "SWcld_tau":-0.0472370387120624,
-                        "SWcld_tot":0.3509298433616172,
-                        "SWdobsc_cov_fbk":-1.5304347185678496e-19,
-                        "SWdobsc_fbk":0.06805479454231951,
-                        "SWdunobsc_fbk":0.3509298433616172
-                    }
-                },
-                "ocn_asc":{
-                    "LO680":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "LWdobsc_cov_fbk":NaN,
-                        "LWdobsc_fbk":NaN,
-                        "LWdunobsc_fbk":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN,
-                        "SWdobsc_cov_fbk":NaN,
-                        "SWdobsc_fbk":NaN,
-                        "SWdunobsc_fbk":NaN
-                    }
-                },
-                "ocn_dsc":{
-                    "LO680":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "LWdobsc_cov_fbk":NaN,
-                        "LWdobsc_fbk":NaN,
-                        "LWdunobsc_fbk":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN,
-                        "SWdobsc_cov_fbk":NaN,
-                        "SWdobsc_fbk":NaN,
-                        "SWdunobsc_fbk":NaN
-                    }
-                }
-            },
-            "eq80":{
-                "all":{
-                    "LO680":{
-                        "LWcld_alt":0.021693540285747232,
-                        "LWcld_amt":-0.06314733027806378,
-                        "LWcld_err":0.0015696439116241665,
-                        "LWcld_tau":0.0052825394543874445,
-                        "LWcld_tot":-0.03460160662630494,
-                        "LWdobsc_cov_fbk":1.1805175705958953e-20,
-                        "LWdobsc_fbk":-0.007077506574212879,
-                        "LWdunobsc_fbk":-0.03460160662630494,
-                        "SWcld_alt":-0.010038688262864722,
-                        "SWcld_amt":0.49313958007609776,
-                        "SWcld_err":-0.0011172423463287388,
-                        "SWcld_tau":-0.05428952835238732,
-                        "SWcld_tot":0.42769412111451693,
-                        "SWdobsc_cov_fbk":-1.5635250885412102e-19,
-                        "SWdobsc_fbk":0.06064902075416311,
-                        "SWdunobsc_fbk":0.42769412111451693
-                    }
-                },
-                "lnd":{
-                    "LO680":{
-                        "LWcld_alt":0.0014956683410561628,
-                        "LWcld_amt":-0.011126011005924844,
-                        "LWcld_err":-4.2396790724648084e-05,
-                        "LWcld_tau":0.001768976377914097,
-                        "LWcld_tot":-0.007903763077679232,
-                        "LWdobsc_cov_fbk":-2.2801008322879934e-21,
-                        "LWdobsc_fbk":0.0011525927873656903,
-                        "LWdunobsc_fbk":-0.00790376307767923,
-                        "SWcld_alt":-0.0007454060744345133,
-                        "SWcld_amt":0.08400785887477595,
-                        "SWcld_err":1.5677701003693362e-05,
-                        "SWcld_tau":-0.0019073149191444802,
-                        "SWcld_tot":0.08137081558220065,
-                        "SWdobsc_cov_fbk":9.627988129576786e-21,
-                        "SWdobsc_fbk":-0.012056358010343321,
-                        "SWdunobsc_fbk":0.08137081558220065
-                    }
-                },
-                "ocn":{
-                    "LO680":{
-                        "LWcld_alt":0.020197871944691068,
-                        "LWcld_amt":-0.052021319272138954,
-                        "LWcld_err":0.0016120407023488147,
-                        "LWcld_tau":0.003513563076473349,
-                        "LWcld_tot":-0.02669784354862571,
-                        "LWdobsc_cov_fbk":1.4085276538246948e-20,
-                        "LWdobsc_fbk":-0.00823009936157857,
-                        "LWdunobsc_fbk":-0.02669784354862571,
-                        "SWcld_alt":-0.009293282188430205,
-                        "SWcld_amt":0.40913172120132185,
-                        "SWcld_err":-0.001132920047332432,
-                        "SWcld_tau":-0.05238221343324284,
-                        "SWcld_tot":0.34632330553231644,
-                        "SWdobsc_cov_fbk":-1.659804969836977e-19,
-                        "SWdobsc_fbk":0.07270537876450642,
-                        "SWdunobsc_fbk":0.34632330553231633
-                    }
-                },
-                "ocn_asc":{
-                    "LO680":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "LWdobsc_cov_fbk":NaN,
-                        "LWdobsc_fbk":NaN,
-                        "LWdunobsc_fbk":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN,
-                        "SWdobsc_cov_fbk":NaN,
-                        "SWdobsc_fbk":NaN,
-                        "SWdunobsc_fbk":NaN
-                    }
-                },
-                "ocn_dsc":{
-                    "LO680":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "LWdobsc_cov_fbk":NaN,
-                        "LWdobsc_fbk":NaN,
-                        "LWdunobsc_fbk":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN,
-                        "SWdobsc_cov_fbk":NaN,
-                        "SWdobsc_fbk":NaN,
-                        "SWdunobsc_fbk":NaN
-                    }
-                }
-            },
-            "eq90":{
-                "all":{
-                    "LO680":{
-                        "LWcld_alt":0.021641216675720804,
-                        "LWcld_amt":-0.06324135911280211,
-                        "LWcld_err":0.0015590633997049628,
-                        "LWcld_tau":0.005511730571622582,
-                        "LWcld_tot":-0.03452934846575376,
-                        "LWdobsc_cov_fbk":1.2228650210564825e-20,
-                        "LWdobsc_fbk":-0.007313088370252677,
-                        "LWdunobsc_fbk":-0.03452934846575376,
-                        "SWcld_alt":-0.00996536937525784,
-                        "SWcld_amt":0.49387634399426933,
-                        "SWcld_err":-0.0011228526685099064,
-                        "SWcld_tau":-0.05528812770420428,
-                        "SWcld_tot":0.42749999424629737,
-                        "SWdobsc_cov_fbk":-1.57440259376118e-19,
-                        "SWdobsc_fbk":0.06177051123862331,
-                        "SWdunobsc_fbk":0.4274999942462974
-                    }
-                },
-                "lnd":{
-                    "LO680":{
-                        "LWcld_alt":0.0014983888504464771,
-                        "LWcld_amt":-0.011164892199484985,
-                        "LWcld_err":-4.2998631617797036e-05,
-                        "LWcld_tau":0.0018283757628317814,
-                        "LWcld_tot":-0.007881126217824523,
-                        "LWdobsc_cov_fbk":-2.4205812013706387e-21,
-                        "LWdobsc_fbk":0.0011215164738747057,
-                        "LWdunobsc_fbk":-0.007881126217824521,
-                        "SWcld_alt":-0.0007438640053621377,
-                        "SWcld_amt":0.08408056615028901,
-                        "SWcld_err":1.5679463225380004e-05,
-                        "SWcld_tau":-0.002033438705287487,
-                        "SWcld_tot":0.08131894290286476,
-                        "SWdobsc_cov_fbk":9.856439761044011e-21,
-                        "SWdobsc_fbk":-0.011996504781034195,
-                        "SWdunobsc_fbk":0.08131894290286476
-                    }
-                },
-                "ocn":{
-                    "LO680":{
-                        "LWcld_alt":0.020142827825274327,
-                        "LWcld_amt":-0.05207646691331713,
-                        "LWcld_err":0.0016020620313227597,
-                        "LWcld_tau":0.003683354808790801,
-                        "LWcld_tot":-0.026648222247929242,
-                        "LWdobsc_cov_fbk":1.4649231411935462e-20,
-                        "LWdobsc_fbk":-0.008434604844127383,
-                        "LWdunobsc_fbk":-0.026648222247929246,
-                        "SWcld_alt":-0.0092215053698957,
-                        "SWcld_amt":0.4097957778439803,
-                        "SWcld_err":-0.0011385321317352866,
-                        "SWcld_tau":-0.0532546889989168,
-                        "SWcld_tot":0.34618105134343263,
-                        "SWdobsc_cov_fbk":-1.6729669913716194e-19,
-                        "SWdobsc_fbk":0.0737670160196575,
-                        "SWdunobsc_fbk":0.34618105134343263
-                    }
-                },
-                "ocn_asc":{
-                    "LO680":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "LWdobsc_cov_fbk":NaN,
-                        "LWdobsc_fbk":NaN,
-                        "LWdunobsc_fbk":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN,
-                        "SWdobsc_cov_fbk":NaN,
-                        "SWdobsc_fbk":NaN,
-                        "SWdunobsc_fbk":NaN
-                    }
-                },
-                "ocn_dsc":{
-                    "LO680":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "LWdobsc_cov_fbk":NaN,
-                        "LWdobsc_fbk":NaN,
-                        "LWdunobsc_fbk":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN,
-                        "SWdobsc_cov_fbk":NaN,
-                        "SWdobsc_fbk":NaN,
-                        "SWdunobsc_fbk":NaN
-                    }
-                }
-            }
-        }
-    },
-    "E3SM-1-0":{
-        "r2i1p1f1":{
-            "Arctic":{
-                "all":{
-                    "LO680":{
-                        "LWcld_alt":0.0010530109174048832,
-                        "LWcld_amt":0.00024200516188286014,
-                        "LWcld_err":-0.00014271338224807557,
-                        "LWcld_tau":0.0003721368195120658,
-                        "LWcld_tot":0.0013974028901724226,
-                        "LWdobsc_cov_fbk":1.2735364369301506e-21,
-                        "LWdobsc_fbk":-0.0027527587981721253,
-                        "LWdunobsc_fbk":0.0015244395165517342,
-                        "SWcld_alt":-0.000359268038278474,
-                        "SWcld_amt":0.00018405750532709655,
-                        "SWcld_err":7.800208533150995e-05,
-                        "SWcld_tau":-0.0013198284594801704,
-                        "SWcld_tot":-0.001417036907100038,
-                        "SWdobsc_cov_fbk":-5.410639610109459e-21,
-                        "SWdobsc_fbk":0.013274524067066336,
-                        "SWdunobsc_fbk":-0.0015458584441091318
-                    }
-                },
-                "lnd":{
-                    "LO680":{
-                        "LWcld_alt":0.00016269153385358533,
-                        "LWcld_amt":-5.099086134708611e-05,
-                        "LWcld_err":-2.2855768391399263e-05,
-                        "LWcld_tau":0.00014050388662660042,
-                        "LWcld_tot":0.00022934879074170038,
-                        "LWdobsc_cov_fbk":1.1632963322909984e-21,
-                        "LWdobsc_fbk":-0.00046955250975587107,
-                        "LWdunobsc_fbk":0.00022934879074170038,
-                        "SWcld_alt":-4.9344632979702216e-05,
-                        "SWcld_amt":0.0008730743346193711,
-                        "SWcld_err":1.0729041398699922e-05,
-                        "SWcld_tau":-0.0005711530318920322,
-                        "SWcld_tot":0.00026330571114633667,
-                        "SWdobsc_cov_fbk":-7.051262118611338e-22,
-                        "SWdobsc_fbk":0.0015159951153026895,
-                        "SWdunobsc_fbk":0.00026330571114633705
-                    }
-                },
-                "ocn":{
-                    "LO680":{
-                        "LWcld_alt":0.0008903193835512979,
-                        "LWcld_amt":0.0002929960232299463,
-                        "LWcld_err":-0.00011985761385667628,
-                        "LWcld_tau":0.00023163293288546542,
-                        "LWcld_tot":0.0012950907258100332,
-                        "LWdobsc_cov_fbk":1.1024010463915244e-22,
-                        "LWdobsc_fbk":-0.0022832062884162543,
-                        "LWdunobsc_fbk":0.0012950907258100332,
-                        "SWcld_alt":-0.0003425841360513604,
-                        "SWcld_amt":-0.0006722843288079931,
-                        "SWcld_err":7.436414259931093e-05,
-                        "SWcld_tau":-0.0008686598329954265,
-                        "SWcld_tot":-0.0018091641552554693,
-                        "SWdobsc_cov_fbk":-4.705513398248325e-21,
-                        "SWdobsc_fbk":0.011758528951763646,
-                        "SWdunobsc_fbk":-0.001809164155255469
-                    }
-                },
-                "ocn_asc":{
-                    "LO680":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "LWdobsc_cov_fbk":NaN,
-                        "LWdobsc_fbk":NaN,
-                        "LWdunobsc_fbk":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN,
-                        "SWdobsc_cov_fbk":NaN,
-                        "SWdobsc_fbk":NaN,
-                        "SWdunobsc_fbk":NaN
-                    }
-                },
-                "ocn_dsc":{
-                    "LO680":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "LWdobsc_cov_fbk":NaN,
-                        "LWdobsc_fbk":NaN,
-                        "LWdunobsc_fbk":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN,
-                        "SWdobsc_cov_fbk":NaN,
-                        "SWdobsc_fbk":NaN,
-                        "SWdunobsc_fbk":NaN
-                    }
-                }
-            },
-            "eq10":{
-                "all":{
-                    "LO680":{
-                        "LWcld_alt":0.004227639545693686,
-                        "LWcld_amt":-0.016130012458578074,
-                        "LWcld_err":-1.8586399776028082e-05,
-                        "LWcld_tau":-0.00013351399189542485,
-                        "LWcld_tot":-0.012054473304555842,
-                        "LWdobsc_cov_fbk":4.608284393522959e-21,
-                        "LWdobsc_fbk":-0.00017248199244478875,
-                        "LWdunobsc_fbk":-0.012054473304555842,
-                        "SWcld_alt":-0.0021462728971174214,
-                        "SWcld_amt":0.11240099088890602,
-                        "SWcld_err":9.656388521838929e-05,
-                        "SWcld_tau":-0.0032875765794246575,
-                        "SWcld_tot":0.10706370529758232,
-                        "SWdobsc_cov_fbk":-1.113946248666431e-20,
-                        "SWdobsc_fbk":0.0020153346867310194,
-                        "SWdunobsc_fbk":0.10706370529758232
-                    }
-                },
-                "lnd":{
-                    "LO680":{
-                        "LWcld_alt":0.0008133260947534203,
-                        "LWcld_amt":-0.003773213069272259,
-                        "LWcld_err":2.242255941390254e-05,
-                        "LWcld_tau":-9.86751384939317e-05,
-                        "LWcld_tot":-0.003036139553598868,
-                        "LWdobsc_cov_fbk":2.473927823063968e-21,
-                        "LWdobsc_fbk":0.000895512344731239,
-                        "LWdunobsc_fbk":-0.003036139553598868,
-                        "SWcld_alt":-0.0004297455247107144,
-                        "SWcld_amt":0.02515272707461881,
-                        "SWcld_err":-1.5623250344139385e-06,
-                        "SWcld_tau":0.002750501228963725,
-                        "SWcld_tot":0.027471920453837406,
-                        "SWdobsc_cov_fbk":-1.2005985937865504e-20,
-                        "SWdobsc_fbk":-0.006077104380015703,
-                        "SWdunobsc_fbk":0.02747192045383741
-                    }
-                },
-                "ocn":{
-                    "LO680":{
-                        "LWcld_alt":0.0034143134509402647,
-                        "LWcld_amt":-0.012356799389305817,
-                        "LWcld_err":-4.1008959189930626e-05,
-                        "LWcld_tau":-3.483885340149314e-05,
-                        "LWcld_tot":-0.009018333750956975,
-                        "LWdobsc_cov_fbk":2.1343565704589935e-21,
-                        "LWdobsc_fbk":-0.0010679943371760278,
-                        "LWdunobsc_fbk":-0.009018333750956975,
-                        "SWcld_alt":-0.0017165273724067077,
-                        "SWcld_amt":0.08724826381428719,
-                        "SWcld_err":9.812621025280322e-05,
-                        "SWcld_tau":-0.006038077808388381,
-                        "SWcld_tot":0.0795917848437449,
-                        "SWdobsc_cov_fbk":8.665234512011854e-22,
-                        "SWdobsc_fbk":0.008092439066746721,
-                        "SWdunobsc_fbk":0.0795917848437449
-                    }
-                },
-                "ocn_asc":{
-                    "LO680":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "LWdobsc_cov_fbk":NaN,
-                        "LWdobsc_fbk":NaN,
-                        "LWdunobsc_fbk":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN,
-                        "SWdobsc_cov_fbk":NaN,
-                        "SWdobsc_fbk":NaN,
-                        "SWdunobsc_fbk":NaN
-                    }
-                },
-                "ocn_dsc":{
-                    "LO680":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "LWdobsc_cov_fbk":NaN,
-                        "LWdobsc_fbk":NaN,
-                        "LWdunobsc_fbk":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN,
-                        "SWdobsc_cov_fbk":NaN,
-                        "SWdobsc_fbk":NaN,
-                        "SWdunobsc_fbk":NaN
-                    }
-                }
-            },
-            "eq20":{
-                "all":{
-                    "LO680":{
-                        "LWcld_alt":0.0068882462500237,
-                        "LWcld_amt":-0.03206246092199035,
-                        "LWcld_err":-7.211103409354548e-05,
-                        "LWcld_tau":-0.0003515115219410539,
-                        "LWcld_tot":-0.025597837228001255,
-                        "LWdobsc_cov_fbk":1.609627596892127e-20,
-                        "LWdobsc_fbk":-0.0031652632281730657,
-                        "LWdunobsc_fbk":-0.025597837228001255,
-                        "SWcld_alt":-0.003407527958652632,
-                        "SWcld_amt":0.21064314020979302,
-                        "SWcld_err":0.00018696429711181307,
-                        "SWcld_tau":-0.0043755239626923756,
-                        "SWcld_tot":0.2030470525855598,
-                        "SWdobsc_cov_fbk":-6.480473521362065e-20,
-                        "SWdobsc_fbk":0.022289743457177855,
-                        "SWdunobsc_fbk":0.2030470525855598
-                    }
-                },
-                "lnd":{
-                    "LO680":{
-                        "LWcld_alt":0.0012481145583815238,
-                        "LWcld_amt":-0.005824549223014698,
-                        "LWcld_err":1.6774930464402225e-05,
-                        "LWcld_tau":-0.0001325442602255553,
-                        "LWcld_tot":-0.004692203994394325,
-                        "LWdobsc_cov_fbk":4.581051665966265e-21,
-                        "LWdobsc_fbk":0.0007523239382277983,
-                        "LWdunobsc_fbk":-0.004692203994394326,
-                        "SWcld_alt":-0.0006442702709066389,
-                        "SWcld_amt":0.03739358450280219,
-                        "SWcld_err":1.4230867421021017e-05,
-                        "SWcld_tau":0.00372841622407976,
-                        "SWcld_tot":0.04049196132339633,
-                        "SWdobsc_cov_fbk":-3.433849877732755e-20,
-                        "SWdobsc_fbk":-0.0054143140265234075,
-                        "SWdunobsc_fbk":0.04049196132339633
-                    }
-                },
-                "ocn":{
-                    "LO680":{
-                        "LWcld_alt":0.005640131691642177,
-                        "LWcld_amt":-0.026237911698975658,
-                        "LWcld_err":-8.888596455794771e-05,
-                        "LWcld_tau":-0.00021896726171549862,
-                        "LWcld_tot":-0.02090563323360693,
-                        "LWdobsc_cov_fbk":1.1515224302955e-20,
-                        "LWdobsc_fbk":-0.003917587166400864,
-                        "LWdunobsc_fbk":-0.02090563323360693,
-                        "SWcld_alt":-0.0027632576877459933,
-                        "SWcld_amt":0.17324955570699083,
-                        "SWcld_err":0.00017273342969079206,
-                        "SWcld_tau":-0.008103940186772135,
-                        "SWcld_tot":0.16255509126216347,
-                        "SWdobsc_cov_fbk":-3.0466236436293077e-20,
-                        "SWdobsc_fbk":0.027704057483701264,
-                        "SWdunobsc_fbk":0.16255509126216347
-                    }
-                },
-                "ocn_asc":{
-                    "LO680":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "LWdobsc_cov_fbk":NaN,
-                        "LWdobsc_fbk":NaN,
-                        "LWdunobsc_fbk":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN,
-                        "SWdobsc_cov_fbk":NaN,
-                        "SWdobsc_fbk":NaN,
-                        "SWdunobsc_fbk":NaN
-                    }
-                },
-                "ocn_dsc":{
-                    "LO680":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "LWdobsc_cov_fbk":NaN,
-                        "LWdobsc_fbk":NaN,
-                        "LWdunobsc_fbk":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN,
-                        "SWdobsc_cov_fbk":NaN,
-                        "SWdobsc_fbk":NaN,
-                        "SWdunobsc_fbk":NaN
-                    }
-                }
-            },
-            "eq30":{
-                "all":{
-                    "LO680":{
-                        "LWcld_alt":0.008461620568428987,
-                        "LWcld_amt":-0.04803685929274826,
-                        "LWcld_err":-8.6112239213195e-05,
-                        "LWcld_tau":-0.00016350514783668722,
-                        "LWcld_tot":-0.03982485611136915,
-                        "LWdobsc_cov_fbk":1.6623908194904823e-20,
-                        "LWdobsc_fbk":-0.007909114945742575,
-                        "LWdunobsc_fbk":-0.039824856111369156,
-                        "SWcld_alt":-0.004102832256853194,
-                        "SWcld_amt":0.2917708044418232,
-                        "SWcld_err":0.0002257128702457216,
-                        "SWcld_tau":-0.011517864378449537,
-                        "SWcld_tot":0.2763758206767662,
-                        "SWdobsc_cov_fbk":-6.615445577447063e-20,
-                        "SWdobsc_fbk":0.046608795266555814,
-                        "SWdunobsc_fbk":0.2763758206767662
-                    }
-                },
-                "lnd":{
-                    "LO680":{
-                        "LWcld_alt":0.0015323869984723464,
-                        "LWcld_amt":-0.00862790767455362,
-                        "LWcld_err":1.4178521497942276e-05,
-                        "LWcld_tau":-0.00014936943208857146,
-                        "LWcld_tot":-0.007230711586671903,
-                        "LWdobsc_cov_fbk":1.1024301929423947e-20,
-                        "LWdobsc_fbk":0.0010068998014327334,
-                        "LWdunobsc_fbk":-0.007230711586671903,
-                        "SWcld_alt":-0.0007806551429419923,
-                        "SWcld_amt":0.05164349828011013,
-                        "SWcld_err":2.4671645100376863e-05,
-                        "SWcld_tau":0.0046072613399600415,
-                        "SWcld_tot":0.05549477612222855,
-                        "SWdobsc_cov_fbk":-7.02319399769783e-20,
-                        "SWdobsc_fbk":-0.006838528428432195,
-                        "SWdunobsc_fbk":0.055494776122228535
-                    }
-                },
-                "ocn":{
-                    "LO680":{
-                        "LWcld_alt":0.006929233569956643,
-                        "LWcld_amt":-0.039408951618194646,
-                        "LWcld_err":-0.00010029076071113731,
-                        "LWcld_tau":-1.4135715748115722e-05,
-                        "LWcld_tot":-0.032594144524697256,
-                        "LWdobsc_cov_fbk":5.599606265480881e-21,
-                        "LWdobsc_fbk":-0.00891601474717531,
-                        "LWdunobsc_fbk":-0.032594144524697256,
-                        "SWcld_alt":-0.003322177113911202,
-                        "SWcld_amt":0.24012730616171307,
-                        "SWcld_err":0.00020104122514534473,
-                        "SWcld_tau":-0.016125125718409575,
-                        "SWcld_tot":0.2208810445545376,
-                        "SWdobsc_cov_fbk":4.077484202507653e-21,
-                        "SWdobsc_fbk":0.05344732369498801,
-                        "SWdunobsc_fbk":0.2208810445545376
-                    }
-                },
-                "ocn_asc":{
-                    "LO680":{
-                        "LWcld_alt":0.0031598510140225106,
-                        "LWcld_amt":-0.010642199783001705,
-                        "LWcld_err":-4.558612597429137e-05,
-                        "LWcld_tau":0.0003162937286487822,
-                        "LWcld_tot":-0.007211641166304704,
-                        "LWdobsc_cov_fbk":-6.551053653687498e-20,
-                        "LWdobsc_fbk":-6.772728467133259e-05,
-                        "LWdunobsc_fbk":-0.007211641166304705,
-                        "SWcld_alt":-0.0015672461060295517,
-                        "SWcld_amt":0.07443444784550658,
-                        "SWcld_err":9.133812404326843e-05,
-                        "SWcld_tau":-0.014960219078010055,
-                        "SWcld_tot":0.05799832078551026,
-                        "SWdobsc_cov_fbk":5.358762803463861e-19,
-                        "SWdobsc_fbk":0.00047476480698050995,
-                        "SWdunobsc_fbk":0.05799832078551026
-                    }
-                },
-                "ocn_dsc":{
-                    "LO680":{
-                        "LWcld_alt":0.004920388427588918,
-                        "LWcld_amt":-0.039138753133609976,
-                        "LWcld_err":-0.00010878053045888623,
-                        "LWcld_tau":-0.0004760384892234794,
-                        "LWcld_tot":-0.03480318372570342,
-                        "LWdobsc_cov_fbk":4.349755881886523e-19,
-                        "LWdobsc_fbk":-0.00016252308866145497,
-                        "LWdunobsc_fbk":-0.03480318372570342,
-                        "SWcld_alt":-0.0022048945450434225,
-                        "SWcld_amt":0.20090582365412224,
-                        "SWcld_err":0.00021055586380934203,
-                        "SWcld_tau":0.005291883543072119,
-                        "SWcld_tot":0.20420336851596024,
-                        "SWdobsc_cov_fbk":-2.399676316363364e-18,
-                        "SWdobsc_fbk":0.0010049526700648848,
-                        "SWdunobsc_fbk":0.20420336851596024
-                    }
-                }
-            },
-            "eq40":{
-                "all":{
-                    "LO680":{
-                        "LWcld_alt":0.00900103336555576,
-                        "LWcld_amt":-0.07452542417247447,
-                        "LWcld_err":2.5343461369334584e-05,
-                        "LWcld_tau":0.0014748946237580576,
-                        "LWcld_tot":-0.06402415272179132,
-                        "LWdobsc_cov_fbk":4.19791795221248e-22,
-                        "LWdobsc_fbk":-0.00985810754128763,
-                        "LWdunobsc_fbk":-0.06402415272179132,
-                        "SWcld_alt":-0.004421271858209144,
-                        "SWcld_amt":0.4061956058628175,
-                        "SWcld_err":0.00013514367054966327,
-                        "SWcld_tau":-0.03069001287178449,
-                        "SWcld_tot":0.37121946480337353,
-                        "SWdobsc_cov_fbk":-1.7858164402216572e-20,
-                        "SWdobsc_fbk":0.061812586719809604,
-                        "SWdunobsc_fbk":0.37121946480337353
-                    }
-                },
-                "lnd":{
-                    "LO680":{
-                        "LWcld_alt":0.0015030751840450576,
-                        "LWcld_amt":-0.01332172505009834,
-                        "LWcld_err":1.4956666339500814e-05,
-                        "LWcld_tau":2.2548676114976155e-06,
-                        "LWcld_tot":-0.011801438332102282,
-                        "LWdobsc_cov_fbk":1.3872756608360784e-20,
-                        "LWdobsc_fbk":0.0016137088105579265,
-                        "LWdunobsc_fbk":-0.011801438332102282,
-                        "SWcld_alt":-0.000783956918626361,
-                        "SWcld_amt":0.07076124446169149,
-                        "SWcld_err":2.3887519623443446e-05,
-                        "SWcld_tau":0.004282619377161023,
-                        "SWcld_tot":0.07428379443984959,
-                        "SWdobsc_cov_fbk":-8.461729718113023e-20,
-                        "SWdobsc_fbk":-0.009235915666053003,
-                        "SWdunobsc_fbk":0.07428379443984959
-                    }
-                },
-                "ocn":{
-                    "LO680":{
-                        "LWcld_alt":0.007497958181510704,
-                        "LWcld_amt":-0.061203699122376144,
-                        "LWcld_err":1.0386795029833766e-05,
-                        "LWcld_tau":0.00147263975614656,
-                        "LWcld_tot":-0.052222714389689034,
-                        "LWdobsc_cov_fbk":-1.3452964813139543e-20,
-                        "LWdobsc_fbk":-0.011471816351845557,
-                        "LWdunobsc_fbk":-0.052222714389689034,
-                        "SWcld_alt":-0.0036373149395827835,
-                        "SWcld_amt":0.335434361401126,
-                        "SWcld_err":0.00011125615092621987,
-                        "SWcld_tau":-0.03497263224894552,
-                        "SWcld_tot":0.2969356703635239,
-                        "SWdobsc_cov_fbk":6.675913277891369e-20,
-                        "SWdobsc_fbk":0.0710485023858626,
-                        "SWdunobsc_fbk":0.2969356703635239
-                    }
-                },
-                "ocn_asc":{
-                    "LO680":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "LWdobsc_cov_fbk":NaN,
-                        "LWdobsc_fbk":NaN,
-                        "LWdunobsc_fbk":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN,
-                        "SWdobsc_cov_fbk":NaN,
-                        "SWdobsc_fbk":NaN,
-                        "SWdunobsc_fbk":NaN
-                    }
-                },
-                "ocn_dsc":{
-                    "LO680":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "LWdobsc_cov_fbk":NaN,
-                        "LWdobsc_fbk":NaN,
-                        "LWdunobsc_fbk":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN,
-                        "SWdobsc_cov_fbk":NaN,
-                        "SWdobsc_fbk":NaN,
-                        "SWdunobsc_fbk":NaN
-                    }
-                }
-            },
-            "eq50":{
-                "all":{
-                    "LO680":{
-                        "LWcld_alt":0.01118015527947983,
-                        "LWcld_amt":-0.09181519232435781,
-                        "LWcld_err":-1.699471337749097e-05,
-                        "LWcld_tau":0.004893122989064934,
-                        "LWcld_tot":-0.07575890876919054,
-                        "LWdobsc_cov_fbk":2.997378265319964e-20,
-                        "LWdobsc_fbk":-0.015820220017322573,
-                        "LWdunobsc_fbk":-0.07575890876919054,
-                        "SWcld_alt":-0.00539704318566955,
-                        "SWcld_amt":0.49009816896690883,
-                        "SWcld_err":0.00016030620785061514,
-                        "SWcld_tau":-0.06281322227447218,
-                        "SWcld_tot":0.4220482097146179,
-                        "SWdobsc_cov_fbk":-1.19727727182763e-19,
-                        "SWdobsc_fbk":0.08989434792625985,
-                        "SWdunobsc_fbk":0.4220482097146179
-                    }
-                },
-                "lnd":{
-                    "LO680":{
-                        "LWcld_alt":0.0015774022817523668,
-                        "LWcld_amt":-0.016490731605507097,
-                        "LWcld_err":-1.8526955600001227e-05,
-                        "LWcld_tau":0.0005457804122664338,
-                        "LWcld_tot":-0.014386075867088293,
-                        "LWdobsc_cov_fbk":2.0290290581731187e-20,
-                        "LWdobsc_fbk":0.0014306398419009503,
-                        "LWdunobsc_fbk":-0.014386075867088295,
-                        "SWcld_alt":-0.0008049426677638679,
-                        "SWcld_amt":0.087525899281238,
-                        "SWcld_err":3.0012423355329546e-05,
-                        "SWcld_tau":0.0019411269311889595,
-                        "SWcld_tot":0.08869209596801841,
-                        "SWdobsc_cov_fbk":-1.1041237442193382e-19,
-                        "SWdobsc_fbk":-0.010257422192850833,
-                        "SWdunobsc_fbk":0.08869209596801841
-                    }
-                },
-                "ocn":{
-                    "LO680":{
-                        "LWcld_alt":0.009602752997727464,
-                        "LWcld_amt":-0.07532446071885072,
-                        "LWcld_err":1.532242222510252e-06,
-                        "LWcld_tau":0.004347342576798499,
-                        "LWcld_tot":-0.061372832902102235,
-                        "LWdobsc_cov_fbk":9.683492071468452e-21,
-                        "LWdobsc_fbk":-0.01725085985922353,
-                        "LWdunobsc_fbk":-0.061372832902102235,
-                        "SWcld_alt":-0.004592100517905681,
-                        "SWcld_amt":0.402572269685671,
-                        "SWcld_err":0.00013029378449528562,
-                        "SWcld_tau":-0.06475434920566114,
-                        "SWcld_tot":0.3333561137465994,
-                        "SWdobsc_cov_fbk":-9.31535276082918e-21,
-                        "SWdobsc_fbk":0.1001517701191107,
-                        "SWdunobsc_fbk":0.3333561137465994
-                    }
-                },
-                "ocn_asc":{
-                    "LO680":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "LWdobsc_cov_fbk":NaN,
-                        "LWdobsc_fbk":NaN,
-                        "LWdunobsc_fbk":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN,
-                        "SWdobsc_cov_fbk":NaN,
-                        "SWdobsc_fbk":NaN,
-                        "SWdunobsc_fbk":NaN
-                    }
-                },
-                "ocn_dsc":{
-                    "LO680":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "LWdobsc_cov_fbk":NaN,
-                        "LWdobsc_fbk":NaN,
-                        "LWdunobsc_fbk":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN,
-                        "SWdobsc_cov_fbk":NaN,
-                        "SWdobsc_fbk":NaN,
-                        "SWdunobsc_fbk":NaN
-                    }
-                }
-            },
-            "eq60":{
-                "all":{
-                    "LO680":{
-                        "LWcld_alt":0.013891052246278991,
-                        "LWcld_amt":-0.09849379541715042,
-                        "LWcld_err":-0.0002973645261561867,
-                        "LWcld_tau":0.009050065035768417,
-                        "LWcld_tot":-0.0758500426612592,
-                        "LWdobsc_cov_fbk":3.93215032103739e-20,
-                        "LWdobsc_fbk":-0.02516741542338163,
-                        "LWdunobsc_fbk":-0.0758500426612592,
-                        "SWcld_alt":-0.006787878839674325,
-                        "SWcld_amt":0.5236492295824546,
-                        "SWcld_err":0.00028682595385947935,
-                        "SWcld_tau":-0.10882404497357116,
-                        "SWcld_tot":0.40832413172306864,
-                        "SWdobsc_cov_fbk":-1.7214480320225228e-19,
-                        "SWdobsc_fbk":0.12830116919778442,
-                        "SWdunobsc_fbk":0.40832413172306864
-                    }
-                },
-                "lnd":{
-                    "LO680":{
-                        "LWcld_alt":0.0016541884833615322,
-                        "LWcld_amt":-0.017896374626442692,
-                        "LWcld_err":-6.160715838916492e-05,
-                        "LWcld_tau":0.0012970659210926705,
-                        "LWcld_tot":-0.015006727380377656,
-                        "LWdobsc_cov_fbk":2.0942752225220325e-20,
-                        "LWdobsc_fbk":0.00021327739907790042,
-                        "LWdunobsc_fbk":-0.015006727380377656,
-                        "SWcld_alt":-0.0007996555954012776,
-                        "SWcld_amt":0.0982521626776404,
-                        "SWcld_err":3.214006658461946e-05,
-                        "SWcld_tau":-0.0026615652960643253,
-                        "SWcld_tot":0.09482308185275941,
-                        "SWdobsc_cov_fbk":-1.3054375071285566e-19,
-                        "SWdobsc_fbk":-0.00797234549066208,
-                        "SWdunobsc_fbk":0.09482308185275941
-                    }
-                },
-                "ocn":{
-                    "LO680":{
-                        "LWcld_alt":0.012236863762917457,
-                        "LWcld_amt":-0.08059742079070774,
-                        "LWcld_err":-0.00023575736776702176,
-                        "LWcld_tau":0.007752999114675747,
-                        "LWcld_tot":-0.06084331528088157,
-                        "LWdobsc_cov_fbk":1.837875098515357e-20,
-                        "LWdobsc_fbk":-0.02538069282245953,
-                        "LWdunobsc_fbk":-0.06084331528088157,
-                        "SWcld_alt":-0.005988223244273048,
-                        "SWcld_amt":0.4253970669048142,
-                        "SWcld_err":0.00025468588727485986,
-                        "SWcld_tau":-0.10616247967750685,
-                        "SWcld_tot":0.3135010498703092,
-                        "SWdobsc_cov_fbk":-4.160105248939663e-20,
-                        "SWdobsc_fbk":0.1362735146884465,
-                        "SWdunobsc_fbk":0.31350104987030913
-                    }
-                },
-                "ocn_asc":{
-                    "LO680":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "LWdobsc_cov_fbk":NaN,
-                        "LWdobsc_fbk":NaN,
-                        "LWdunobsc_fbk":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN,
-                        "SWdobsc_cov_fbk":NaN,
-                        "SWdobsc_fbk":NaN,
-                        "SWdunobsc_fbk":NaN
-                    }
-                },
-                "ocn_dsc":{
-                    "LO680":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "LWdobsc_cov_fbk":NaN,
-                        "LWdobsc_fbk":NaN,
-                        "LWdunobsc_fbk":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN,
-                        "SWdobsc_cov_fbk":NaN,
-                        "SWdobsc_fbk":NaN,
-                        "SWdunobsc_fbk":NaN
-                    }
-                }
-            },
-            "eq70":{
-                "all":{
-                    "LO680":{
-                        "LWcld_alt":0.015503096992757481,
-                        "LWcld_amt":-0.10078471334714212,
-                        "LWcld_err":-0.0005377581194378255,
-                        "LWcld_tau":0.011609193215014342,
-                        "LWcld_tot":-0.07421018125880811,
-                        "LWdobsc_cov_fbk":3.5787027767002564e-20,
-                        "LWdobsc_fbk":-0.031049892188488862,
-                        "LWdunobsc_fbk":-0.07421018125880811,
-                        "SWcld_alt":-0.007542165054046228,
-                        "SWcld_amt":0.5352772447184544,
-                        "SWcld_err":0.0003920490918975028,
-                        "SWcld_tau":-0.13711659408652296,
-                        "SWcld_tot":0.3910105346697826,
-                        "SWdobsc_cov_fbk":-1.5397750235624335e-19,
-                        "SWdobsc_fbk":0.15456477403482302,
-                        "SWdunobsc_fbk":0.3910105346697826
-                    }
-                },
-                "lnd":{
-                    "LO680":{
-                        "LWcld_alt":0.0018467357059574064,
-                        "LWcld_amt":-0.019057796698450966,
-                        "LWcld_err":-0.00010043752487940933,
-                        "LWcld_tau":0.0020161458163248653,
-                        "LWcld_tot":-0.015295352701048105,
-                        "LWdobsc_cov_fbk":1.9598317609305776e-20,
-                        "LWdobsc_fbk":-0.0015391216163682805,
-                        "LWdunobsc_fbk":-0.015295352701048107,
-                        "SWcld_alt":-0.0008400285546346912,
-                        "SWcld_amt":0.1080136848174043,
-                        "SWcld_err":3.825086384307105e-05,
-                        "SWcld_tau":-0.007436093404094651,
-                        "SWcld_tot":0.09977581372251802,
-                        "SWdobsc_cov_fbk":-1.2771695560334636e-19,
-                        "SWdobsc_fbk":-0.0027106139000520374,
-                        "SWdunobsc_fbk":0.09977581372251802
-                    }
-                },
-                "ocn":{
-                    "LO680":{
-                        "LWcld_alt":0.013656361286800074,
-                        "LWcld_amt":-0.08172691664869117,
-                        "LWcld_err":-0.0004373205945584163,
-                        "LWcld_tau":0.009593047398689478,
-                        "LWcld_tot":-0.058914828557760014,
-                        "LWdobsc_cov_fbk":1.618871015769677e-20,
-                        "LWdobsc_fbk":-0.029510770572120582,
-                        "LWdunobsc_fbk":-0.058914828557760014,
-                        "SWcld_alt":-0.006702136499411535,
-                        "SWcld_amt":0.42726355990105014,
-                        "SWcld_err":0.00035379822805443173,
-                        "SWcld_tau":-0.12968050068242834,
-                        "SWcld_tot":0.29123472094726466,
-                        "SWdobsc_cov_fbk":-2.6260546752897084e-20,
-                        "SWdobsc_fbk":0.15727538793487505,
-                        "SWdunobsc_fbk":0.2912347209472646
-                    }
-                },
-                "ocn_asc":{
-                    "LO680":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "LWdobsc_cov_fbk":NaN,
-                        "LWdobsc_fbk":NaN,
-                        "LWdunobsc_fbk":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN,
-                        "SWdobsc_cov_fbk":NaN,
-                        "SWdobsc_fbk":NaN,
-                        "SWdunobsc_fbk":NaN
-                    }
-                },
-                "ocn_dsc":{
-                    "LO680":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "LWdobsc_cov_fbk":NaN,
-                        "LWdobsc_fbk":NaN,
-                        "LWdunobsc_fbk":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN,
-                        "SWdobsc_cov_fbk":NaN,
-                        "SWdobsc_fbk":NaN,
-                        "SWdunobsc_fbk":NaN
-                    }
-                }
-            },
-            "eq80":{
-                "all":{
-                    "LO680":{
-                        "LWcld_alt":0.01640378076422503,
-                        "LWcld_amt":-0.10074643673183992,
-                        "LWcld_err":-0.0006460550622102182,
-                        "LWcld_tau":0.012302821842135145,
-                        "LWcld_tot":-0.07268588918768994,
-                        "LWdobsc_cov_fbk":3.587800095470132e-20,
-                        "LWdobsc_fbk":-0.03348032895342256,
-                        "LWdunobsc_fbk":-0.07268588918768992,
-                        "SWcld_alt":-0.007878641053132573,
-                        "SWcld_amt":0.535193551605235,
-                        "SWcld_err":0.00045213191052804417,
-                        "SWcld_tau":-0.14102271031365188,
-                        "SWcld_tot":0.3867443321489786,
-                        "SWdobsc_cov_fbk":-1.570929667361076e-19,
-                        "SWdobsc_fbk":0.16528344049018562,
-                        "SWdunobsc_fbk":0.3867443321489786
-                    }
-                },
-                "lnd":{
-                    "LO680":{
-                        "LWcld_alt":0.0019918591018201885,
-                        "LWcld_amt":-0.019142815370563428,
-                        "LWcld_err":-0.00011940343049780848,
-                        "LWcld_tau":0.0022279816564801155,
-                        "LWcld_tot":-0.015042378042760934,
-                        "LWdobsc_cov_fbk":2.0390335949368946e-20,
-                        "LWdobsc_fbk":-0.0020407193388697112,
-                        "LWdunobsc_fbk":-0.015042378042760934,
-                        "SWcld_alt":-0.0008826964373866718,
-                        "SWcld_amt":0.10882137852323533,
-                        "SWcld_err":4.641996204533067e-05,
-                        "SWcld_tau":-0.008108310807506852,
-                        "SWcld_tot":0.09987679124038713,
-                        "SWdobsc_cov_fbk":-1.3013812775379717e-19,
-                        "SWdobsc_fbk":-0.0013525043677495852,
-                        "SWdunobsc_fbk":0.09987679124038713
-                    }
-                },
-                "ocn":{
-                    "LO680":{
-                        "LWcld_alt":0.014411921662404842,
-                        "LWcld_amt":-0.08160362136127645,
-                        "LWcld_err":-0.0005266516317124096,
-                        "LWcld_tau":0.01007484018565503,
-                        "LWcld_tot":-0.057643511144928994,
-                        "LWdobsc_cov_fbk":1.5487665005332376e-20,
-                        "LWdobsc_fbk":-0.03143960961455285,
-                        "LWdunobsc_fbk":-0.057643511144928994,
-                        "SWcld_alt":-0.006995944615745903,
-                        "SWcld_amt":0.42637217308199965,
-                        "SWcld_err":0.0004057119484827136,
-                        "SWcld_tau":-0.13291439950614506,
-                        "SWcld_tot":0.2868675409085914,
-                        "SWdobsc_cov_fbk":-2.6954838982310434e-20,
-                        "SWdobsc_fbk":0.1666359448579352,
-                        "SWdunobsc_fbk":0.2868675409085914
-                    }
-                },
-                "ocn_asc":{
-                    "LO680":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "LWdobsc_cov_fbk":NaN,
-                        "LWdobsc_fbk":NaN,
-                        "LWdunobsc_fbk":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN,
-                        "SWdobsc_cov_fbk":NaN,
-                        "SWdobsc_fbk":NaN,
-                        "SWdunobsc_fbk":NaN
-                    }
-                },
-                "ocn_dsc":{
-                    "LO680":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "LWdobsc_cov_fbk":NaN,
-                        "LWdobsc_fbk":NaN,
-                        "LWdunobsc_fbk":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN,
-                        "SWdobsc_cov_fbk":NaN,
-                        "SWdobsc_fbk":NaN,
-                        "SWdunobsc_fbk":NaN
-                    }
-                }
-            },
-            "eq90":{
-                "all":{
-                    "LO680":{
-                        "LWcld_alt":0.016700967783728135,
-                        "LWcld_amt":-0.10061706102348798,
-                        "LWcld_err":-0.0006783707409460448,
-                        "LWcld_tau":0.012350433000555027,
-                        "LWcld_tot":-0.07224403098015086,
-                        "LWdobsc_cov_fbk":3.5099949263463465e-20,
-                        "LWdobsc_fbk":-0.03418740322691502,
-                        "LWdunobsc_fbk":-0.07224403098015086,
-                        "SWcld_alt":-0.007980202882203129,
-                        "SWcld_amt":0.5350894251326239,
-                        "SWcld_err":0.00047214301926881913,
-                        "SWcld_tau":-0.1406579008570833,
-                        "SWcld_tot":0.3869234644126063,
-                        "SWdobsc_cov_fbk":-1.535021283245106e-19,
-                        "SWdobsc_fbk":0.1685616791097135,
-                        "SWdunobsc_fbk":0.38692346441260633
-                    }
-                },
-                "lnd":{
-                    "LO680":{
-                        "LWcld_alt":0.0020038243670868323,
-                        "LWcld_amt":-0.019137888786457374,
-                        "LWcld_err":-0.00012072080721097366,
-                        "LWcld_tau":0.0022587608550089177,
-                        "LWcld_tot":-0.014996024371572596,
-                        "LWdobsc_cov_fbk":2.0421317828990088e-20,
-                        "LWdobsc_fbk":-0.0021151244928276215,
-                        "LWdunobsc_fbk":-0.014996024371572596,
-                        "SWcld_alt":-0.0008872872225418535,
-                        "SWcld_amt":0.10884145266253781,
-                        "SWcld_err":4.7343038535358e-05,
-                        "SWcld_tau":-0.008166279157158762,
-                        "SWcld_tot":0.09983522932137257,
-                        "SWdobsc_cov_fbk":-1.2803955837531565e-19,
-                        "SWdobsc_fbk":-0.0011802737018964977,
-                        "SWdunobsc_fbk":0.09983522932137257
-                    }
-                },
-                "ocn":{
-                    "LO680":{
-                        "LWcld_alt":0.014697143416641302,
-                        "LWcld_amt":-0.0814791722370306,
-                        "LWcld_err":-0.0005576499337350713,
-                        "LWcld_tau":0.010091672145546111,
-                        "LWcld_tot":-0.05724800660857826,
-                        "LWdobsc_cov_fbk":1.4678631434473374e-20,
-                        "LWdobsc_fbk":-0.032072278734087405,
-                        "LWdunobsc_fbk":-0.05724800660857826,
-                        "SWcld_alt":-0.007092915659661276,
-                        "SWcld_amt":0.42624797247008606,
-                        "SWcld_err":0.0004247999807334611,
-                        "SWcld_tau":-0.13249162169992454,
-                        "SWcld_tot":0.28708823509123377,
-                        "SWdobsc_cov_fbk":-2.5462569949194987e-20,
-                        "SWdobsc_fbk":0.16974195281160998,
-                        "SWdunobsc_fbk":0.28708823509123377
-                    }
-                },
-                "ocn_asc":{
-                    "LO680":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "LWdobsc_cov_fbk":NaN,
-                        "LWdobsc_fbk":NaN,
-                        "LWdunobsc_fbk":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN,
-                        "SWdobsc_cov_fbk":NaN,
-                        "SWdobsc_fbk":NaN,
-                        "SWdunobsc_fbk":NaN
-                    }
-                },
-                "ocn_dsc":{
-                    "LO680":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "LWdobsc_cov_fbk":NaN,
-                        "LWdobsc_fbk":NaN,
-                        "LWdunobsc_fbk":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN,
-                        "SWdobsc_cov_fbk":NaN,
-                        "SWdobsc_fbk":NaN,
-                        "SWdunobsc_fbk":NaN
-                    }
-                }
-            }
-        }
-    },
-    "GFDL-CM4":{
-        "r1i1p1f1":{
-            "Arctic":{
-                "all":{
-                    "LO680":{
-                        "LWcld_alt":0.0006859042947123526,
-                        "LWcld_amt":0.0008269167409260148,
-                        "LWcld_err":8.835100719188977e-05,
-                        "LWcld_tau":0.000976931717983905,
-                        "LWcld_tot":0.002363261780746315,
-                        "LWdobsc_cov_fbk":-8.326526118783821e-22,
-                        "LWdobsc_fbk":-0.0016919224549349727,
-                        "LWdunobsc_fbk":0.0025781037608141617,
-                        "SWcld_alt":-0.00031681465248742786,
-                        "SWcld_amt":-0.002189218379352044,
-                        "SWcld_err":-9.249823912319259e-06,
-                        "SWcld_tau":-0.004679401954447567,
-                        "SWcld_tot":-0.00719468481019936,
-                        "SWdobsc_cov_fbk":8.95703483941837e-21,
-                        "SWdobsc_fbk":0.011302287808998434,
-                        "SWdunobsc_fbk":-0.007848747065672028
-                    }
-                },
-                "lnd":{
-                    "LO680":{
-                        "LWcld_alt":6.437607362962942e-05,
-                        "LWcld_amt":0.00020487129493917234,
-                        "LWcld_err":1.3947758471266322e-05,
-                        "LWcld_tau":0.00024643138485055813,
-                        "LWcld_tot":0.0005296265118906263,
-                        "LWdobsc_cov_fbk":-8.745806735468354e-23,
-                        "LWdobsc_fbk":-0.00023606675780014403,
-                        "LWdunobsc_fbk":0.0005296265118906263,
-                        "SWcld_alt":-3.251408419255953e-05,
-                        "SWcld_amt":-0.0007006310398608123,
-                        "SWcld_err":-4.27454511273244e-06,
-                        "SWcld_tau":-0.0009707976815522973,
-                        "SWcld_tot":-0.0017082173507184015,
-                        "SWdobsc_cov_fbk":-6.075604252721159e-22,
-                        "SWdobsc_fbk":0.0013048731274099188,
-                        "SWdunobsc_fbk":-0.0017082173507184015
-                    }
-                },
-                "ocn":{
-                    "LO680":{
-                        "LWcld_alt":0.0006215282210827232,
-                        "LWcld_amt":0.0006220454459868425,
-                        "LWcld_err":7.440324872062343e-05,
-                        "LWcld_tau":0.0007305003331333468,
-                        "LWcld_tot":0.002048477248923536,
-                        "LWdobsc_cov_fbk":-7.451945445236986e-22,
-                        "LWdobsc_fbk":-0.0014558556971348289,
-                        "LWdunobsc_fbk":0.002048477248923536,
-                        "SWcld_alt":-0.0003131019003391801,
-                        "SWcld_amt":-0.0016876071921595982,
-                        "SWcld_err":-5.8161718825249315e-06,
-                        "SWcld_tau":-0.004134004450572323,
-                        "SWcld_tot":-0.0061405297149536246,
-                        "SWdobsc_cov_fbk":9.564595264690483e-21,
-                        "SWdobsc_fbk":0.009997414681588514,
-                        "SWdunobsc_fbk":-0.0061405297149536246
-                    }
-                },
-                "ocn_asc":{
-                    "LO680":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "LWdobsc_cov_fbk":NaN,
-                        "LWdobsc_fbk":NaN,
-                        "LWdunobsc_fbk":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN,
-                        "SWdobsc_cov_fbk":NaN,
-                        "SWdobsc_fbk":NaN,
-                        "SWdunobsc_fbk":NaN
-                    }
-                },
-                "ocn_dsc":{
-                    "LO680":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "LWdobsc_cov_fbk":NaN,
-                        "LWdobsc_fbk":NaN,
-                        "LWdunobsc_fbk":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN,
-                        "SWdobsc_cov_fbk":NaN,
-                        "SWdobsc_fbk":NaN,
-                        "SWdunobsc_fbk":NaN
-                    }
-                }
-            },
-            "eq10":{
-                "all":{
-                    "LO680":{
-                        "LWcld_alt":0.0025024842342911925,
-                        "LWcld_amt":-0.009423550806449835,
-                        "LWcld_err":6.686300858815548e-05,
-                        "LWcld_tau":-0.0003482335406734341,
-                        "LWcld_tot":-0.007202437104243919,
-                        "LWdobsc_cov_fbk":-5.368738924806355e-21,
-                        "LWdobsc_fbk":0.00011844879971344189,
-                        "LWdunobsc_fbk":-0.007202437104243919,
-                        "SWcld_alt":-0.0013135460954830976,
-                        "SWcld_amt":0.08062606452276827,
-                        "SWcld_err":-0.00011522324530813408,
-                        "SWcld_tau":0.003724709223135297,
-                        "SWcld_tot":0.08292200440511234,
-                        "SWdobsc_cov_fbk":1.8110237382055727e-20,
-                        "SWdobsc_fbk":-0.00021287156975274284,
-                        "SWdunobsc_fbk":0.08292200440511234
-                    }
-                },
-                "lnd":{
-                    "LO680":{
-                        "LWcld_alt":0.0005565157206346939,
-                        "LWcld_amt":-0.00284339902058965,
-                        "LWcld_err":-1.3799466830373037e-05,
-                        "LWcld_tau":-2.3699382045460513e-06,
-                        "LWcld_tot":-0.0023030527049898754,
-                        "LWdobsc_cov_fbk":2.3233706933423757e-21,
-                        "LWdobsc_fbk":0.0012799675226716316,
-                        "LWdunobsc_fbk":-0.0023030527049898754,
-                        "SWcld_alt":-0.00030977989077806263,
-                        "SWcld_amt":0.022007193419242937,
-                        "SWcld_err":1.9493687484122332e-05,
-                        "SWcld_tau":-0.0007627955410931636,
-                        "SWcld_tot":0.020954111674855832,
-                        "SWdobsc_cov_fbk":-1.3305724011871446e-20,
-                        "SWdobsc_fbk":-0.010423204904131018,
-                        "SWdunobsc_fbk":0.020954111674855832
-                    }
-                },
-                "ocn":{
-                    "LO680":{
-                        "LWcld_alt":0.0019459685136564988,
-                        "LWcld_amt":-0.006580151785860183,
-                        "LWcld_err":8.066247541852851e-05,
-                        "LWcld_tau":-0.000345863602468888,
-                        "LWcld_tot":-0.0048993843992540444,
-                        "LWdobsc_cov_fbk":-7.692109618148731e-21,
-                        "LWdobsc_fbk":-0.0011615187229581897,
-                        "LWdunobsc_fbk":-0.0048993843992540444,
-                        "SWcld_alt":-0.001003766204705035,
-                        "SWcld_amt":0.05861887110352534,
-                        "SWcld_err":-0.00013471693279225644,
-                        "SWcld_tau":0.004487504764228461,
-                        "SWcld_tot":0.06196789273025651,
-                        "SWdobsc_cov_fbk":3.1415961393927176e-20,
-                        "SWdobsc_fbk":0.010210333334378275,
-                        "SWdunobsc_fbk":0.06196789273025651
-                    }
-                },
-                "ocn_asc":{
-                    "LO680":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "LWdobsc_cov_fbk":NaN,
-                        "LWdobsc_fbk":NaN,
-                        "LWdunobsc_fbk":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN,
-                        "SWdobsc_cov_fbk":NaN,
-                        "SWdobsc_fbk":NaN,
-                        "SWdunobsc_fbk":NaN
-                    }
-                },
-                "ocn_dsc":{
-                    "LO680":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "LWdobsc_cov_fbk":NaN,
-                        "LWdobsc_fbk":NaN,
-                        "LWdunobsc_fbk":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN,
-                        "SWdobsc_cov_fbk":NaN,
-                        "SWdobsc_fbk":NaN,
-                        "SWdunobsc_fbk":NaN
-                    }
-                }
-            },
-            "eq20":{
-                "all":{
-                    "LO680":{
-                        "LWcld_alt":0.004264838276340069,
-                        "LWcld_amt":-0.020985864816892328,
-                        "LWcld_err":6.448404126775099e-05,
-                        "LWcld_tau":-0.0008807608460106218,
-                        "LWcld_tot":-0.01753730334529513,
-                        "LWdobsc_cov_fbk":1.2695831714678743e-20,
-                        "LWdobsc_fbk":-0.002127180814196048,
-                        "LWdunobsc_fbk":-0.01753730334529513,
-                        "SWcld_alt":-0.002169774146338652,
-                        "SWcld_amt":0.1615014177070862,
-                        "SWcld_err":-0.00011517941381555222,
-                        "SWcld_tau":0.013605386847101419,
-                        "SWcld_tot":0.17282185099403344,
-                        "SWdobsc_cov_fbk":-1.0781061006866865e-19,
-                        "SWdobsc_fbk":0.013793184690228619,
-                        "SWdunobsc_fbk":0.17282185099403347
-                    }
-                },
-                "lnd":{
-                    "LO680":{
-                        "LWcld_alt":0.0008649727798861309,
-                        "LWcld_amt":-0.005452885282714335,
-                        "LWcld_err":-2.3490335553307312e-05,
-                        "LWcld_tau":1.8751617292465747e-06,
-                        "LWcld_tot":-0.004609527676652264,
-                        "LWdobsc_cov_fbk":7.185236645839623e-21,
-                        "LWdobsc_fbk":0.001789253946849435,
-                        "LWdunobsc_fbk":-0.004609527676652265,
-                        "SWcld_alt":-0.0004679530281779811,
-                        "SWcld_amt":0.039503987536534384,
-                        "SWcld_err":3.7199213795636306e-05,
-                        "SWcld_tau":-0.0012741540971111817,
-                        "SWcld_tot":0.03779907962504085,
-                        "SWdobsc_cov_fbk":-4.034591001560787e-20,
-                        "SWdobsc_fbk":-0.014407395211258242,
-                        "SWdunobsc_fbk":0.03779907962504086
-                    }
-                },
-                "ocn":{
-                    "LO680":{
-                        "LWcld_alt":0.003399865496453937,
-                        "LWcld_amt":-0.015532979534177994,
-                        "LWcld_err":8.797437682105829e-05,
-                        "LWcld_tau":-0.0008826360077398683,
-                        "LWcld_tot":-0.012927775668642866,
-                        "LWdobsc_cov_fbk":5.510595068839122e-21,
-                        "LWdobsc_fbk":-0.0039164347610454835,
-                        "LWdunobsc_fbk":-0.012927775668642866,
-                        "SWcld_alt":-0.0017018211181606705,
-                        "SWcld_amt":0.12199743017055185,
-                        "SWcld_err":-0.00015237862761118852,
-                        "SWcld_tau":0.0148795409442126,
-                        "SWcld_tot":0.1350227713689926,
-                        "SWdobsc_cov_fbk":-6.746470005306079e-20,
-                        "SWdobsc_fbk":0.028200579901486864,
-                        "SWdunobsc_fbk":0.1350227713689926
-                    }
-                },
-                "ocn_asc":{
-                    "LO680":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "LWdobsc_cov_fbk":NaN,
-                        "LWdobsc_fbk":NaN,
-                        "LWdunobsc_fbk":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN,
-                        "SWdobsc_cov_fbk":NaN,
-                        "SWdobsc_fbk":NaN,
-                        "SWdunobsc_fbk":NaN
-                    }
-                },
-                "ocn_dsc":{
-                    "LO680":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "LWdobsc_cov_fbk":NaN,
-                        "LWdobsc_fbk":NaN,
-                        "LWdunobsc_fbk":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN,
-                        "SWdobsc_cov_fbk":NaN,
-                        "SWdobsc_fbk":NaN,
-                        "SWdunobsc_fbk":NaN
-                    }
-                }
-            },
-            "eq30":{
-                "all":{
-                    "LO680":{
-                        "LWcld_alt":0.006482257484956236,
-                        "LWcld_amt":-0.03322638050219668,
-                        "LWcld_err":0.00018111868670672783,
-                        "LWcld_tau":-0.0009604361452950939,
-                        "LWcld_tot":-0.027523440475828817,
-                        "LWdobsc_cov_fbk":1.7450872682187333e-20,
-                        "LWdobsc_fbk":-0.005905596730064112,
-                        "LWdunobsc_fbk":-0.027523440475828817,
-                        "SWcld_alt":-0.003129512264614777,
-                        "SWcld_amt":0.23814618024768053,
-                        "SWcld_err":-0.00021172864396628785,
-                        "SWcld_tau":0.01171016442925086,
-                        "SWcld_tot":0.2465151037683503,
-                        "SWdobsc_cov_fbk":-1.2822008828723913e-19,
-                        "SWdobsc_fbk":0.03445436464405961,
-                        "SWdunobsc_fbk":0.2465151037683503
-                    }
-                },
-                "lnd":{
-                    "LO680":{
-                        "LWcld_alt":0.0011914166247028138,
-                        "LWcld_amt":-0.00802361997889509,
-                        "LWcld_err":-2.903720869470384e-05,
-                        "LWcld_tau":-3.274752722977767e-06,
-                        "LWcld_tot":-0.006864515315609957,
-                        "LWdobsc_cov_fbk":1.087637922992992e-20,
-                        "LWdobsc_fbk":0.0024292781266050134,
-                        "LWdunobsc_fbk":-0.006864515315609956,
-                        "SWcld_alt":-0.0006290039098493389,
-                        "SWcld_amt":0.05430673315764411,
-                        "SWcld_err":4.945570615242931e-05,
-                        "SWcld_tau":-0.0010636037850534701,
-                        "SWcld_tot":0.052663581168893726,
-                        "SWdobsc_cov_fbk":-6.252005033595021e-20,
-                        "SWdobsc_fbk":-0.01853061057999837,
-                        "SWdunobsc_fbk":0.052663581168893726
-                    }
-                },
-                "ocn":{
-                    "LO680":{
-                        "LWcld_alt":0.005290840860253422,
-                        "LWcld_amt":-0.025202760523301598,
-                        "LWcld_err":0.00021015589540143164,
-                        "LWcld_tau":-0.0009571613925721161,
-                        "LWcld_tot":-0.02065892516021886,
-                        "LWdobsc_cov_fbk":6.5744934522574126e-21,
-                        "LWdobsc_fbk":-0.008334874856669125,
-                        "LWdunobsc_fbk":-0.02065892516021886,
-                        "SWcld_alt":-0.002500508354765438,
-                        "SWcld_amt":0.1838394470900364,
-                        "SWcld_err":-0.0002611843501187172,
-                        "SWcld_tau":0.012773768214304329,
-                        "SWcld_tot":0.19385152259945662,
-                        "SWdobsc_cov_fbk":-6.570003795128891e-20,
-                        "SWdobsc_fbk":0.052984975224057984,
-                        "SWdunobsc_fbk":0.19385152259945662
-                    }
-                },
-                "ocn_asc":{
-                    "LO680":{
-                        "LWcld_alt":0.0017113574762331683,
-                        "LWcld_amt":-0.0033918513508106895,
-                        "LWcld_err":3.143463245255877e-05,
-                        "LWcld_tau":0.00014808047930991482,
-                        "LWcld_tot":-0.0015009787628150475,
-                        "LWdobsc_cov_fbk":8.181653913317558e-21,
-                        "LWdobsc_fbk":-4.074947417507884e-05,
-                        "LWdunobsc_fbk":-0.0015009787628150475,
-                        "SWcld_alt":-0.0008853944850154456,
-                        "SWcld_amt":0.030465376866355007,
-                        "SWcld_err":-7.605033670648655e-05,
-                        "SWcld_tau":-0.007154944763026637,
-                        "SWcld_tot":0.022348987281606437,
-                        "SWdobsc_cov_fbk":-9.293801298612807e-20,
-                        "SWdobsc_fbk":0.00034162538431640197,
-                        "SWdunobsc_fbk":0.022348987281606433
-                    }
-                },
-                "ocn_dsc":{
-                    "LO680":{
-                        "LWcld_alt":0.006427425054611631,
-                        "LWcld_amt":-0.033582774293088806,
-                        "LWcld_err":0.00010122449345337069,
-                        "LWcld_tau":-0.0006885343084384604,
-                        "LWcld_tot":-0.027742659053462265,
-                        "LWdobsc_cov_fbk":-1.501061120584192e-19,
-                        "LWdobsc_fbk":-0.00018265645277061102,
-                        "LWdunobsc_fbk":-0.027742659053462265,
-                        "SWcld_alt":-0.002977612692456158,
-                        "SWcld_amt":0.1918767181977519,
-                        "SWcld_err":4.531677983823937e-05,
-                        "SWcld_tau":0.016180513506837357,
-                        "SWcld_tot":0.20512493579197133,
-                        "SWdobsc_cov_fbk":6.499943023805388e-19,
-                        "SWdobsc_fbk":0.001371207008894249,
-                        "SWdunobsc_fbk":0.20512493579197133
-                    }
-                }
-            },
-            "eq40":{
-                "all":{
-                    "LO680":{
-                        "LWcld_alt":0.010010334556761553,
-                        "LWcld_amt":-0.043222660626880426,
-                        "LWcld_err":0.00037559553902782785,
-                        "LWcld_tau":-0.0008785062777782239,
-                        "LWcld_tot":-0.03371523680886928,
-                        "LWdobsc_cov_fbk":2.902577430455869e-20,
-                        "LWdobsc_fbk":-0.004524605701422413,
-                        "LWdunobsc_fbk":-0.03371523680886928,
-                        "SWcld_alt":-0.00462706382934856,
-                        "SWcld_amt":0.2956471525374352,
-                        "SWcld_err":-0.0003478091190123342,
-                        "SWcld_tau":0.009242142102006174,
-                        "SWcld_tot":0.29991442169108046,
-                        "SWdobsc_cov_fbk":-1.8945326971465075e-19,
-                        "SWdobsc_fbk":0.028231746729411613,
-                        "SWdunobsc_fbk":0.29991442169108046
-                    }
-                },
-                "lnd":{
-                    "LO680":{
-                        "LWcld_alt":0.0012806963616289477,
-                        "LWcld_amt":-0.010130214602044534,
-                        "LWcld_err":-2.3955169140313748e-05,
-                        "LWcld_tau":3.178125200509736e-05,
-                        "LWcld_tot":-0.008841692157550804,
-                        "LWdobsc_cov_fbk":1.565840326276352e-20,
-                        "LWdobsc_fbk":0.0028936776789097243,
-                        "LWdunobsc_fbk":-0.008841692157550804,
-                        "SWcld_alt":-0.0006747005935630355,
-                        "SWcld_amt":0.06435789384080307,
-                        "SWcld_err":4.6671531273094535e-05,
-                        "SWcld_tau":-0.001026360575816649,
-                        "SWcld_tot":0.06270350420269646,
-                        "SWdobsc_cov_fbk":-9.209344921892615e-20,
-                        "SWdobsc_fbk":-0.0207093410675248,
-                        "SWdunobsc_fbk":0.06270350420269648
-                    }
-                },
-                "ocn":{
-                    "LO680":{
-                        "LWcld_alt":0.008729638195132604,
-                        "LWcld_amt":-0.033092446024835895,
-                        "LWcld_err":0.0003995507081681415,
-                        "LWcld_tau":-0.0009102875297833211,
-                        "LWcld_tot":-0.024873544651318465,
-                        "LWdobsc_cov_fbk":1.336737104179518e-20,
-                        "LWdobsc_fbk":-0.0074182833803321385,
-                        "LWdunobsc_fbk":-0.024873544651318465,
-                        "SWcld_alt":-0.003952363235785525,
-                        "SWcld_amt":0.2312892586966321,
-                        "SWcld_err":-0.00039448065028542865,
-                        "SWcld_tau":0.010268502677822824,
-                        "SWcld_tot":0.23721091748838394,
-                        "SWdobsc_cov_fbk":-9.735982049572458e-20,
-                        "SWdobsc_fbk":0.04894108779693641,
-                        "SWdunobsc_fbk":0.23721091748838397
-                    }
-                },
-                "ocn_asc":{
-                    "LO680":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "LWdobsc_cov_fbk":NaN,
-                        "LWdobsc_fbk":NaN,
-                        "LWdunobsc_fbk":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN,
-                        "SWdobsc_cov_fbk":NaN,
-                        "SWdobsc_fbk":NaN,
-                        "SWdunobsc_fbk":NaN
-                    }
-                },
-                "ocn_dsc":{
-                    "LO680":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "LWdobsc_cov_fbk":NaN,
-                        "LWdobsc_fbk":NaN,
-                        "LWdunobsc_fbk":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN,
-                        "SWdobsc_cov_fbk":NaN,
-                        "SWdobsc_fbk":NaN,
-                        "SWdunobsc_fbk":NaN
-                    }
-                }
-            },
-            "eq50":{
-                "all":{
-                    "LO680":{
-                        "LWcld_alt":0.011909007743999138,
-                        "LWcld_amt":-0.05247867500812233,
-                        "LWcld_err":0.0006027119461828886,
-                        "LWcld_tau":2.5676428443327412e-05,
-                        "LWcld_tot":-0.03994127888949698,
-                        "LWdobsc_cov_fbk":3.8249109323227646e-20,
-                        "LWdobsc_fbk":-0.003099395845421514,
-                        "LWdunobsc_fbk":-0.03994127888949698,
-                        "SWcld_alt":-0.0055392437521066895,
-                        "SWcld_amt":0.34849375179744996,
-                        "SWcld_err":-0.0004487271871797117,
-                        "SWcld_tau":0.008551125208047858,
-                        "SWcld_tot":0.35105690606621137,
-                        "SWdobsc_cov_fbk":-2.162305737595423e-19,
-                        "SWdobsc_fbk":0.018154621269205626,
-                        "SWdunobsc_fbk":0.35105690606621137
-                    }
-                },
-                "lnd":{
-                    "LO680":{
-                        "LWcld_alt":0.0012661868137132965,
-                        "LWcld_amt":-0.011846535341879788,
-                        "LWcld_err":-3.1771068086621292e-06,
-                        "LWcld_tau":0.0004995577056060062,
-                        "LWcld_tot":-0.01008396792936915,
-                        "LWdobsc_cov_fbk":2.0352501954936273e-20,
-                        "LWdobsc_fbk":0.003099328800690332,
-                        "LWdunobsc_fbk":-0.010083967929369149,
-                        "SWcld_alt":-0.0006667931085568003,
-                        "SWcld_amt":0.07505045692873945,
-                        "SWcld_err":3.669490777953951e-05,
-                        "SWcld_tau":-0.002659392662914283,
-                        "SWcld_tot":0.0717609660650479,
-                        "SWdobsc_cov_fbk":-1.1743413756698647e-19,
-                        "SWdobsc_fbk":-0.022870314151666075,
-                        "SWdunobsc_fbk":0.0717609660650479
-                    }
-                },
-                "ocn":{
-                    "LO680":{
-                        "LWcld_alt":0.010642820930285839,
-                        "LWcld_amt":-0.040632139666242545,
-                        "LWcld_err":0.0006058890529915506,
-                        "LWcld_tau":-0.00047388127716267866,
-                        "LWcld_tot":-0.029857310960127836,
-                        "LWdobsc_cov_fbk":1.7896607368291373e-20,
-                        "LWdobsc_fbk":-0.006198724646111845,
-                        "LWdunobsc_fbk":-0.029857310960127833,
-                        "SWcld_alt":-0.004872450643549889,
-                        "SWcld_amt":0.27344329486871044,
-                        "SWcld_err":-0.0004854220949592511,
-                        "SWcld_tau":0.01121051787096214,
-                        "SWcld_tot":0.2792959400011634,
-                        "SWdobsc_cov_fbk":-9.879643619255584e-20,
-                        "SWdobsc_fbk":0.041024935420871704,
-                        "SWdunobsc_fbk":0.2792959400011634
-                    }
-                },
-                "ocn_asc":{
-                    "LO680":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "LWdobsc_cov_fbk":NaN,
-                        "LWdobsc_fbk":NaN,
-                        "LWdunobsc_fbk":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN,
-                        "SWdobsc_cov_fbk":NaN,
-                        "SWdobsc_fbk":NaN,
-                        "SWdunobsc_fbk":NaN
-                    }
-                },
-                "ocn_dsc":{
-                    "LO680":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "LWdobsc_cov_fbk":NaN,
-                        "LWdobsc_fbk":NaN,
-                        "LWdunobsc_fbk":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN,
-                        "SWdobsc_cov_fbk":NaN,
-                        "SWdobsc_fbk":NaN,
-                        "SWdunobsc_fbk":NaN
-                    }
-                }
-            },
-            "eq60":{
-                "all":{
-                    "LO680":{
-                        "LWcld_alt":0.012906334988995835,
-                        "LWcld_amt":-0.058461925275780126,
-                        "LWcld_err":0.0009168746253170371,
-                        "LWcld_tau":0.002940683241011712,
-                        "LWcld_tot":-0.041698032420455534,
-                        "LWdobsc_cov_fbk":3.701563142644184e-20,
-                        "LWdobsc_fbk":-0.006409327702484008,
-                        "LWdunobsc_fbk":-0.04169803242045555,
-                        "SWcld_alt":-0.00599483284616021,
-                        "SWcld_amt":0.38446190178038736,
-                        "SWcld_err":-0.0004992520786017335,
-                        "SWcld_tau":-0.021251910358822342,
-                        "SWcld_tot":0.35671590649680307,
-                        "SWdobsc_cov_fbk":-2.3242567894520917e-19,
-                        "SWdobsc_fbk":0.03677442818727463,
-                        "SWdunobsc_fbk":0.35671590649680307
-                    }
-                },
-                "lnd":{
-                    "LO680":{
-                        "LWcld_alt":0.0010937755796350131,
-                        "LWcld_amt":-0.01211423250073692,
-                        "LWcld_err":4.657483356611665e-05,
-                        "LWcld_tau":0.0015877473850747696,
-                        "LWcld_tot":-0.009386134702461019,
-                        "LWdobsc_cov_fbk":2.4168028218740647e-20,
-                        "LWdobsc_fbk":0.0027411212897940386,
-                        "LWdunobsc_fbk":-0.009386134702461019,
-                        "SWcld_alt":-0.0005874814675652129,
-                        "SWcld_amt":0.07964883138775936,
-                        "SWcld_err":1.498120719798302e-05,
-                        "SWcld_tau":-0.0060169464152355955,
-                        "SWcld_tot":0.07305938471215652,
-                        "SWdobsc_cov_fbk":-1.2855792241901754e-19,
-                        "SWdobsc_fbk":-0.022403992107877514,
-                        "SWdunobsc_fbk":0.07305938471215652
-                    }
-                },
-                "ocn":{
-                    "LO680":{
-                        "LWcld_alt":0.011812559409360823,
-                        "LWcld_amt":-0.04634769277504321,
-                        "LWcld_err":0.0008702997917509205,
-                        "LWcld_tau":0.0013529358559369422,
-                        "LWcld_tot":-0.03231189771799452,
-                        "LWdobsc_cov_fbk":1.2847603207701192e-20,
-                        "LWdobsc_fbk":-0.009150448992278046,
-                        "LWdunobsc_fbk":-0.032311897717994524,
-                        "SWcld_alt":-0.005407351378594998,
-                        "SWcld_amt":0.304813070392628,
-                        "SWcld_err":-0.0005142332857997165,
-                        "SWcld_tau":-0.01523496394358675,
-                        "SWcld_tot":0.28365652178464656,
-                        "SWdobsc_cov_fbk":-1.0386775652619169e-19,
-                        "SWdobsc_fbk":0.059178420295152155,
-                        "SWdunobsc_fbk":0.28365652178464656
-                    }
-                },
-                "ocn_asc":{
-                    "LO680":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "LWdobsc_cov_fbk":NaN,
-                        "LWdobsc_fbk":NaN,
-                        "LWdunobsc_fbk":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN,
-                        "SWdobsc_cov_fbk":NaN,
-                        "SWdobsc_fbk":NaN,
-                        "SWdunobsc_fbk":NaN
-                    }
-                },
-                "ocn_dsc":{
-                    "LO680":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "LWdobsc_cov_fbk":NaN,
-                        "LWdobsc_fbk":NaN,
-                        "LWdunobsc_fbk":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN,
-                        "SWdobsc_cov_fbk":NaN,
-                        "SWdobsc_fbk":NaN,
-                        "SWdunobsc_fbk":NaN
-                    }
-                }
-            },
-            "eq70":{
-                "all":{
-                    "LO680":{
-                        "LWcld_alt":0.013710723508277971,
-                        "LWcld_amt":-0.059689657717654,
-                        "LWcld_err":0.0011092512150437932,
-                        "LWcld_tau":0.005524080261885414,
-                        "LWcld_tot":-0.039345602732446826,
-                        "LWdobsc_cov_fbk":3.5815328499432313e-20,
-                        "LWdobsc_fbk":-0.009366089822596986,
-                        "LWdunobsc_fbk":-0.039345602732446826,
-                        "SWcld_alt":-0.006330855658075548,
-                        "SWcld_amt":0.395854222852317,
-                        "SWcld_err":-0.0005281906986816283,
-                        "SWcld_tau":-0.04844457019840187,
-                        "SWcld_tot":0.340550606297158,
-                        "SWdobsc_cov_fbk":-2.241936341675447e-19,
-                        "SWdobsc_fbk":0.05579452286110841,
-                        "SWdunobsc_fbk":0.34055060629715794
-                    }
-                },
-                "lnd":{
-                    "LO680":{
-                        "LWcld_alt":0.0010386137819893015,
-                        "LWcld_amt":-0.012065478406457815,
-                        "LWcld_err":9.492476083097811e-05,
-                        "LWcld_tau":0.0026270992188127014,
-                        "LWcld_tot":-0.008304840644824833,
-                        "LWdobsc_cov_fbk":2.3769271162742658e-20,
-                        "LWdobsc_fbk":0.0020863633505770677,
-                        "LWdunobsc_fbk":-0.008304840644824833,
-                        "SWcld_alt":-0.0005461883357826685,
-                        "SWcld_amt":0.08218255695268736,
-                        "SWcld_err":-8.868799397043161e-06,
-                        "SWcld_tau":-0.009833581769443812,
-                        "SWcld_tot":0.07179391804806384,
-                        "SWdobsc_cov_fbk":-1.2050168666653684e-19,
-                        "SWdobsc_fbk":-0.01962154319871359,
-                        "SWdunobsc_fbk":0.07179391804806384
-                    }
-                },
-                "ocn":{
-                    "LO680":{
-                        "LWcld_alt":0.012672109726288669,
-                        "LWcld_amt":-0.04762417931119619,
-                        "LWcld_err":0.0010143264542128152,
-                        "LWcld_tau":0.002896981043072712,
-                        "LWcld_tot":-0.031040762087621996,
-                        "LWdobsc_cov_fbk":1.2046057336689654e-20,
-                        "LWdobsc_fbk":-0.011452453173174055,
-                        "LWdunobsc_fbk":-0.031040762087621993,
-                        "SWcld_alt":-0.00578466732229288,
-                        "SWcld_amt":0.31367166589962964,
-                        "SWcld_err":-0.0005193218992845852,
-                        "SWcld_tau":-0.038610988428958055,
-                        "SWcld_tot":0.2687566882490941,
-                        "SWdobsc_cov_fbk":-1.0369194750100785e-19,
-                        "SWdobsc_fbk":0.075416066059822,
-                        "SWdunobsc_fbk":0.2687566882490941
-                    }
-                },
-                "ocn_asc":{
-                    "LO680":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "LWdobsc_cov_fbk":NaN,
-                        "LWdobsc_fbk":NaN,
-                        "LWdunobsc_fbk":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN,
-                        "SWdobsc_cov_fbk":NaN,
-                        "SWdobsc_fbk":NaN,
-                        "SWdunobsc_fbk":NaN
-                    }
-                },
-                "ocn_dsc":{
-                    "LO680":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "LWdobsc_cov_fbk":NaN,
-                        "LWdobsc_fbk":NaN,
-                        "LWdunobsc_fbk":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN,
-                        "SWdobsc_cov_fbk":NaN,
-                        "SWdobsc_fbk":NaN,
-                        "SWdunobsc_fbk":NaN
-                    }
-                }
-            },
-            "eq80":{
-                "all":{
-                    "LO680":{
-                        "LWcld_alt":0.014284321642890806,
-                        "LWcld_amt":-0.058944879431321096,
-                        "LWcld_err":0.0011928118062455536,
-                        "LWcld_tau":0.006563818132456101,
-                        "LWcld_tot":-0.036903927849728645,
-                        "LWdobsc_cov_fbk":3.5824357899148416e-20,
-                        "LWdobsc_fbk":-0.010730645390184193,
-                        "LWdunobsc_fbk":-0.036903927849728645,
-                        "SWcld_alt":-0.006592239232159988,
-                        "SWcld_amt":0.3942348619557603,
-                        "SWcld_err":-0.0005470064000774067,
-                        "SWcld_tau":-0.05414613459904713,
-                        "SWcld_tot":0.33294948172447575,
-                        "SWdobsc_cov_fbk":-2.191807635054439e-19,
-                        "SWdobsc_fbk":0.06443946420070405,
-                        "SWdunobsc_fbk":0.33294948172447575
-                    }
-                },
-                "lnd":{
-                    "LO680":{
-                        "LWcld_alt":0.0010919138600610345,
-                        "LWcld_amt":-0.011851672725713762,
-                        "LWcld_err":0.00010930081466986137,
-                        "LWcld_tau":0.0029275701477182395,
-                        "LWcld_tot":-0.00772288790326463,
-                        "LWdobsc_cov_fbk":2.3383121958335002e-20,
-                        "LWdobsc_fbk":0.0018237679734686282,
-                        "LWdunobsc_fbk":-0.00772288790326463,
-                        "SWcld_alt":-0.000571623280700192,
-                        "SWcld_amt":0.08160263072941006,
-                        "SWcld_err":-1.3614542832366927e-05,
-                        "SWcld_tau":-0.010833319168437702,
-                        "SWcld_tot":0.07018407373743979,
-                        "SWdobsc_cov_fbk":-1.2086432761474075e-19,
-                        "SWdobsc_fbk":-0.018458515219036117,
-                        "SWdunobsc_fbk":0.07018407373743979
-                    }
-                },
-                "ocn":{
-                    "LO680":{
-                        "LWcld_alt":0.01319240778282977,
-                        "LWcld_amt":-0.04709320670560733,
-                        "LWcld_err":0.0010835109915756922,
-                        "LWcld_tau":0.003636247984737861,
-                        "LWcld_tot":-0.02918103994646401,
-                        "LWdobsc_cov_fbk":1.2441235940813423e-20,
-                        "LWdobsc_fbk":-0.012554413363652818,
-                        "LWdunobsc_fbk":-0.02918103994646401,
-                        "SWcld_alt":-0.0060206159514597955,
-                        "SWcld_amt":0.3126322312263502,
-                        "SWcld_err":-0.0005333918572450398,
-                        "SWcld_tau":-0.04331281543060942,
-                        "SWcld_tot":0.26276540798703596,
-                        "SWdobsc_cov_fbk":-9.831643589070314e-20,
-                        "SWdobsc_fbk":0.08289797941974016,
-                        "SWdunobsc_fbk":0.26276540798703596
-                    }
-                },
-                "ocn_asc":{
-                    "LO680":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "LWdobsc_cov_fbk":NaN,
-                        "LWdobsc_fbk":NaN,
-                        "LWdunobsc_fbk":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN,
-                        "SWdobsc_cov_fbk":NaN,
-                        "SWdobsc_fbk":NaN,
-                        "SWdunobsc_fbk":NaN
-                    }
-                },
-                "ocn_dsc":{
-                    "LO680":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "LWdobsc_cov_fbk":NaN,
-                        "LWdobsc_fbk":NaN,
-                        "LWdunobsc_fbk":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN,
-                        "SWdobsc_cov_fbk":NaN,
-                        "SWdobsc_fbk":NaN,
-                        "SWdunobsc_fbk":NaN
-                    }
-                }
-            },
-            "eq90":{
-                "all":{
-                    "LO680":{
-                        "LWcld_alt":0.01446693635912328,
-                        "LWcld_amt":-0.05872395466755989,
-                        "LWcld_err":0.0012203236073336894,
-                        "LWcld_tau":0.00686770331764086,
-                        "LWcld_tot":-0.03616899138346207,
-                        "LWdobsc_cov_fbk":3.537395121213821e-20,
-                        "LWdobsc_fbk":-0.011239013324425388,
-                        "LWdunobsc_fbk":-0.03616899138346208,
-                        "SWcld_alt":-0.006688668976908764,
-                        "SWcld_amt":0.39367261594599806,
-                        "SWcld_err":-0.0005484569057037887,
-                        "SWcld_tau":-0.05534579693871522,
-                        "SWcld_tot":0.3310896931246703,
-                        "SWdobsc_cov_fbk":-2.1737009624430284e-19,
-                        "SWdobsc_fbk":0.06714354742953928,
-                        "SWdunobsc_fbk":0.3310896931246703
-                    }
-                },
-                "lnd":{
-                    "LO680":{
-                        "LWcld_alt":0.0011042840512463941,
-                        "LWcld_amt":-0.011811107894062525,
-                        "LWcld_err":0.00011117223268177238,
-                        "LWcld_tau":0.0029884177003277807,
-                        "LWcld_tot":-0.007607233909806574,
-                        "LWdobsc_cov_fbk":2.3302916001915767e-20,
-                        "LWdobsc_fbk":0.0017718419973214948,
-                        "LWdunobsc_fbk":-0.007607233909806575,
-                        "SWcld_alt":-0.0005765744009868964,
-                        "SWcld_amt":0.08149546217485144,
-                        "SWcld_err":-1.3754823049847439e-05,
-                        "SWcld_tau":-0.01093764255654788,
-                        "SWcld_tot":0.06996749039426682,
-                        "SWdobsc_cov_fbk":-1.2057149268163673e-19,
-                        "SWdobsc_fbk":-0.018336535525975736,
-                        "SWdunobsc_fbk":0.06996749039426682
-                    }
-                },
-                "ocn":{
-                    "LO680":{
-                        "LWcld_alt":0.013362652307876885,
-                        "LWcld_amt":-0.04691284677349738,
-                        "LWcld_err":0.001109151374651917,
-                        "LWcld_tau":0.003879285617313079,
-                        "LWcld_tot":-0.0285617574736555,
-                        "LWdobsc_cov_fbk":1.2071035210222444e-20,
-                        "LWdobsc_fbk":-0.013010855321746886,
-                        "LWdunobsc_fbk":-0.028561757473655497,
-                        "SWcld_alt":-0.006112094575921868,
-                        "SWcld_amt":0.3121771537711467,
-                        "SWcld_err":-0.0005347020826539412,
-                        "SWcld_tau":-0.04440815438216733,
-                        "SWcld_tot":0.2611222027304035,
-                        "SWdobsc_cov_fbk":-9.679860356266609e-20,
-                        "SWdobsc_fbk":0.08548008295551501,
-                        "SWdunobsc_fbk":0.2611222027304035
-                    }
-                },
-                "ocn_asc":{
-                    "LO680":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "LWdobsc_cov_fbk":NaN,
-                        "LWdobsc_fbk":NaN,
-                        "LWdunobsc_fbk":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN,
-                        "SWdobsc_cov_fbk":NaN,
-                        "SWdobsc_fbk":NaN,
-                        "SWdunobsc_fbk":NaN
-                    }
-                },
-                "ocn_dsc":{
-                    "LO680":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "LWdobsc_cov_fbk":NaN,
-                        "LWdobsc_fbk":NaN,
-                        "LWdunobsc_fbk":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN,
-                        "SWdobsc_cov_fbk":NaN,
-                        "SWdobsc_fbk":NaN,
-                        "SWdunobsc_fbk":NaN
-                    }
-                }
-            }
-        }
-    },
-    "HadGEM3-GC31-LL":{
-        "r5i1p1f3":{
-            "Arctic":{
-                "all":{
-                    "LO680":{
-                        "LWcld_alt":0.0003747411302023392,
-                        "LWcld_amt":-0.0007202288801465708,
-                        "LWcld_err":-0.00015582282695879342,
-                        "LWcld_tau":0.0006117038352343797,
-                        "LWcld_tot":0.0001011938201370752,
-                        "LWdobsc_cov_fbk":-2.9341185429299195e-21,
-                        "LWdobsc_fbk":-0.0015565251936173004,
-                        "LWdunobsc_fbk":0.0001103932583313547,
-                        "SWcld_alt":-6.905449907705599e-05,
-                        "SWcld_amt":0.005980172800576028,
-                        "SWcld_err":8.80497621310829e-05,
-                        "SWcld_tau":-6.831437905275468e-05,
-                        "SWcld_tot":0.005930853684577298,
-                        "SWdobsc_cov_fbk":2.6132637320266038e-20,
-                        "SWdobsc_fbk":0.005060052620294613,
-                        "SWdunobsc_fbk":0.006470022201357053
-                    }
-                },
-                "lnd":{
-                    "LO680":{
-                        "LWcld_alt":4.2698307943053805e-05,
-                        "LWcld_amt":-0.000407935693278581,
-                        "LWcld_err":-1.6148706377559268e-05,
-                        "LWcld_tau":0.00013184091221956652,
-                        "LWcld_tot":-0.00024954517949352,
-                        "LWdobsc_cov_fbk":-2.0418197243157712e-21,
-                        "LWdobsc_fbk":-0.0001963250903135225,
-                        "LWdunobsc_fbk":-0.00024954517949352003,
-                        "SWcld_alt":-5.727155069437182e-06,
-                        "SWcld_amt":0.003705139636700359,
-                        "SWcld_err":9.241661701453327e-06,
-                        "SWcld_tau":-0.00012031885510083096,
-                        "SWcld_tot":0.003588335288231544,
-                        "SWdobsc_cov_fbk":2.6021759388653165e-20,
-                        "SWdobsc_fbk":0.0005957817833535948,
-                        "SWdunobsc_fbk":0.0035883352882315435
-                    }
-                },
-                "ocn":{
-                    "LO680":{
-                        "LWcld_alt":0.00033204282225928546,
-                        "LWcld_amt":-0.0003122931868679898,
-                        "LWcld_err":-0.00013967412058123416,
-                        "LWcld_tau":0.00047986292301481335,
-                        "LWcld_tot":0.00035993843782487474,
-                        "LWdobsc_cov_fbk":-8.922988186141485e-22,
-                        "LWdobsc_fbk":-0.0013602001033037776,
-                        "LWdunobsc_fbk":0.00035993843782487474,
-                        "SWcld_alt":-6.960502574189663e-05,
-                        "SWcld_amt":0.0028186852366553082,
-                        "SWcld_err":8.681262425972804e-05,
-                        "SWcld_tau":4.579407795237112e-05,
-                        "SWcld_tot":0.00288168691312551,
-                        "SWdobsc_cov_fbk":1.1087793161286861e-22,
-                        "SWdobsc_fbk":0.004464270836941018,
-                        "SWdunobsc_fbk":0.00288168691312551
-                    }
-                },
-                "ocn_asc":{
-                    "LO680":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "LWdobsc_cov_fbk":NaN,
-                        "LWdobsc_fbk":NaN,
-                        "LWdunobsc_fbk":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN,
-                        "SWdobsc_cov_fbk":NaN,
-                        "SWdobsc_fbk":NaN,
-                        "SWdunobsc_fbk":NaN
-                    }
-                },
-                "ocn_dsc":{
-                    "LO680":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "LWdobsc_cov_fbk":NaN,
-                        "LWdobsc_fbk":NaN,
-                        "LWdunobsc_fbk":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN,
-                        "SWdobsc_cov_fbk":NaN,
-                        "SWdobsc_fbk":NaN,
-                        "SWdunobsc_fbk":NaN
-                    }
-                }
-            },
-            "eq10":{
-                "all":{
-                    "LO680":{
-                        "LWcld_alt":0.004823737560859607,
-                        "LWcld_amt":-0.015391915425545397,
-                        "LWcld_err":0.00016243501456371216,
-                        "LWcld_tau":-0.0007534598089187,
-                        "LWcld_tot":-0.011159202659040779,
-                        "LWdobsc_cov_fbk":5.1736126275355315e-21,
-                        "LWdobsc_fbk":0.010094861584519008,
-                        "LWdunobsc_fbk":-0.01115920265904078,
-                        "SWcld_alt":-0.002412337705819718,
-                        "SWcld_amt":0.11942458440586702,
-                        "SWcld_err":-0.000177136572954253,
-                        "SWcld_tau":0.012245722442644644,
-                        "SWcld_tot":0.12908083256973768,
-                        "SWdobsc_cov_fbk":-9.17203918248428e-21,
-                        "SWdobsc_fbk":-0.08640349656058714,
-                        "SWdunobsc_fbk":0.12908083256973768
-                    }
-                },
-                "lnd":{
-                    "LO680":{
-                        "LWcld_alt":0.001189822414717623,
-                        "LWcld_amt":-0.004414828742221301,
-                        "LWcld_err":9.124733550817085e-06,
-                        "LWcld_tau":0.0001361085289173422,
-                        "LWcld_tot":-0.0030797730650355183,
-                        "LWdobsc_cov_fbk":3.043468662028965e-21,
-                        "LWdobsc_fbk":0.0025207038588888125,
-                        "LWdunobsc_fbk":-0.0030797730650355183,
-                        "SWcld_alt":-0.0005810586210555792,
-                        "SWcld_amt":0.028483194010644072,
-                        "SWcld_err":-4.822559449615271e-06,
-                        "SWcld_tau":-0.0017798228194461987,
-                        "SWcld_tot":0.026117490010692676,
-                        "SWdobsc_cov_fbk":-1.9335493132839205e-20,
-                        "SWdobsc_fbk":-0.016837429657833563,
-                        "SWdunobsc_fbk":0.026117490010692676
-                    }
-                },
-                "ocn":{
-                    "LO680":{
-                        "LWcld_alt":0.0036339151461419836,
-                        "LWcld_amt":-0.010977086683324097,
-                        "LWcld_err":0.0001533102810128951,
-                        "LWcld_tau":-0.0008895683378360421,
-                        "LWcld_tot":-0.008079429594005262,
-                        "LWdobsc_cov_fbk":2.1301439655065662e-21,
-                        "LWdobsc_fbk":0.007574157725630194,
-                        "LWdunobsc_fbk":-0.008079429594005262,
-                        "SWcld_alt":-0.001831279084764138,
-                        "SWcld_amt":0.09094139039522292,
-                        "SWcld_err":-0.00017231401350463772,
-                        "SWcld_tau":0.014025545262090844,
-                        "SWcld_tot":0.10296334255904499,
-                        "SWdobsc_cov_fbk":1.016345395035493e-20,
-                        "SWdobsc_fbk":-0.06956606690275356,
-                        "SWdunobsc_fbk":0.10296334255904499
-                    }
-                },
-                "ocn_asc":{
-                    "LO680":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "LWdobsc_cov_fbk":NaN,
-                        "LWdobsc_fbk":NaN,
-                        "LWdunobsc_fbk":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN,
-                        "SWdobsc_cov_fbk":NaN,
-                        "SWdobsc_fbk":NaN,
-                        "SWdunobsc_fbk":NaN
-                    }
-                },
-                "ocn_dsc":{
-                    "LO680":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "LWdobsc_cov_fbk":NaN,
-                        "LWdobsc_fbk":NaN,
-                        "LWdunobsc_fbk":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN,
-                        "SWdobsc_cov_fbk":NaN,
-                        "SWdobsc_fbk":NaN,
-                        "SWdunobsc_fbk":NaN
-                    }
-                }
-            },
-            "eq20":{
-                "all":{
-                    "LO680":{
-                        "LWcld_alt":0.00767957671824637,
-                        "LWcld_amt":-0.02616972979141076,
-                        "LWcld_err":0.00012849813704161252,
-                        "LWcld_tau":-0.0014486925524572795,
-                        "LWcld_tot":-0.019810347488580054,
-                        "LWdobsc_cov_fbk":1.641048112001746e-20,
-                        "LWdobsc_fbk":0.016665207335933466,
-                        "LWdunobsc_fbk":-0.019810347488580054,
-                        "SWcld_alt":-0.0037974524823336455,
-                        "SWcld_amt":0.19451250064296474,
-                        "SWcld_err":-0.00012135801704568607,
-                        "SWcld_tau":0.024330548286364096,
-                        "SWcld_tot":0.21492423842994948,
-                        "SWdobsc_cov_fbk":-6.92514355384145e-20,
-                        "SWdobsc_fbk":-0.136871763404691,
-                        "SWdunobsc_fbk":0.21492423842994948
-                    }
-                },
-                "lnd":{
-                    "LO680":{
-                        "LWcld_alt":0.0016955393965654992,
-                        "LWcld_amt":-0.007290685699067622,
-                        "LWcld_err":-9.962664173035917e-06,
-                        "LWcld_tau":0.00019050586136703452,
-                        "LWcld_tot":-0.005414603105308123,
-                        "LWdobsc_cov_fbk":3.079904195707949e-21,
-                        "LWdobsc_fbk":0.0036034860817369886,
-                        "LWdunobsc_fbk":-0.005414603105308123,
-                        "SWcld_alt":-0.0008193078303085671,
-                        "SWcld_amt":0.04455837905302711,
-                        "SWcld_err":1.2422780909734503e-05,
-                        "SWcld_tau":-0.002142883808984719,
-                        "SWcld_tot":0.041608610194643555,
-                        "SWdobsc_cov_fbk":-1.5461782044995847e-20,
-                        "SWdobsc_fbk":-0.023252000925995494,
-                        "SWdunobsc_fbk":0.04160861019464356
-                    }
-                },
-                "ocn":{
-                    "LO680":{
-                        "LWcld_alt":0.005984037321680873,
-                        "LWcld_amt":-0.018879044092343134,
-                        "LWcld_err":0.00013846080121464847,
-                        "LWcld_tau":-0.0016391984138243141,
-                        "LWcld_tot":-0.014395744383271928,
-                        "LWdobsc_cov_fbk":1.3330576924309508e-20,
-                        "LWdobsc_fbk":0.013061721254196476,
-                        "LWdunobsc_fbk":-0.014395744383271928,
-                        "SWcld_alt":-0.0029781446520250783,
-                        "SWcld_amt":0.1499541215899376,
-                        "SWcld_err":-0.00013378079795542058,
-                        "SWcld_tau":0.026473432095348817,
-                        "SWcld_tot":0.1733156282353059,
-                        "SWdobsc_cov_fbk":-5.3789653493418684e-20,
-                        "SWdobsc_fbk":-0.11361976247869553,
-                        "SWdunobsc_fbk":0.1733156282353059
-                    }
-                },
-                "ocn_asc":{
-                    "LO680":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "LWdobsc_cov_fbk":NaN,
-                        "LWdobsc_fbk":NaN,
-                        "LWdunobsc_fbk":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN,
-                        "SWdobsc_cov_fbk":NaN,
-                        "SWdobsc_fbk":NaN,
-                        "SWdunobsc_fbk":NaN
-                    }
-                },
-                "ocn_dsc":{
-                    "LO680":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "LWdobsc_cov_fbk":NaN,
-                        "LWdobsc_fbk":NaN,
-                        "LWdunobsc_fbk":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN,
-                        "SWdobsc_cov_fbk":NaN,
-                        "SWdobsc_fbk":NaN,
-                        "SWdunobsc_fbk":NaN
-                    }
-                }
-            },
-            "eq30":{
-                "all":{
-                    "LO680":{
-                        "LWcld_alt":0.007279123181066705,
-                        "LWcld_amt":-0.03949943770180799,
-                        "LWcld_err":-0.00011421866178391126,
-                        "LWcld_tau":-0.0029450554955838203,
-                        "LWcld_tot":-0.03527958867810902,
-                        "LWdobsc_cov_fbk":1.121733856843365e-20,
-                        "LWdobsc_fbk":0.022459950486950597,
-                        "LWdunobsc_fbk":-0.03527958867810902,
-                        "SWcld_alt":-0.0036952114349356724,
-                        "SWcld_amt":0.2759919331646648,
-                        "SWcld_err":7.395268730636812e-05,
-                        "SWcld_tau":0.04371434264938474,
-                        "SWcld_tot":0.3160850170664202,
-                        "SWdobsc_cov_fbk":-5.889827865672949e-20,
-                        "SWdobsc_fbk":-0.1755564029728025,
-                        "SWdunobsc_fbk":0.31608501706642017
-                    }
-                },
-                "lnd":{
-                    "LO680":{
-                        "LWcld_alt":0.002007579852279815,
-                        "LWcld_amt":-0.01038752554171095,
-                        "LWcld_err":-2.455509795544341e-05,
-                        "LWcld_tau":5.49318560897914e-05,
-                        "LWcld_tot":-0.008349568931296786,
-                        "LWdobsc_cov_fbk":-4.9286342294698e-21,
-                        "LWdobsc_fbk":0.004681011381506033,
-                        "LWdunobsc_fbk":-0.008349568931296788,
-                        "SWcld_alt":-0.000961154965945764,
-                        "SWcld_amt":0.059878852560039726,
-                        "SWcld_err":2.7644596153277658e-05,
-                        "SWcld_tau":-0.0007463888394515485,
-                        "SWcld_tot":0.05819895335079572,
-                        "SWdobsc_cov_fbk":2.4511379974521804e-20,
-                        "SWdobsc_fbk":-0.028607948200497627,
-                        "SWdunobsc_fbk":0.05819895335079572
-                    }
-                },
-                "ocn":{
-                    "LO680":{
-                        "LWcld_alt":0.005271543328786891,
-                        "LWcld_amt":-0.02911191216009705,
-                        "LWcld_err":-8.96635638284679e-05,
-                        "LWcld_tau":-0.0029999873516736116,
-                        "LWcld_tot":-0.026930019746812233,
-                        "LWdobsc_cov_fbk":1.614597279790345e-20,
-                        "LWdobsc_fbk":0.017778939105444567,
-                        "LWdunobsc_fbk":-0.026930019746812233,
-                        "SWcld_alt":-0.0027340564689899084,
-                        "SWcld_amt":0.21611308060462506,
-                        "SWcld_err":4.630809115309048e-05,
-                        "SWcld_tau":0.04446073148883628,
-                        "SWcld_tot":0.25788606371562445,
-                        "SWdobsc_cov_fbk":-8.340965863125124e-20,
-                        "SWdobsc_fbk":-0.14694845477230487,
-                        "SWdunobsc_fbk":0.25788606371562445
-                    }
-                },
-                "ocn_asc":{
-                    "LO680":{
-                        "LWcld_alt":0.002505997461769852,
-                        "LWcld_amt":0.001787713630851363,
-                        "LWcld_err":-6.23739554388446e-05,
-                        "LWcld_tau":-0.0003607697577249773,
-                        "LWcld_tot":0.0038705673794573927,
-                        "LWdobsc_cov_fbk":1.7642405254887747e-20,
-                        "LWdobsc_fbk":5.203493539925293e-05,
-                        "LWdunobsc_fbk":0.0038705673794573927,
-                        "SWcld_alt":-0.0012216571356814352,
-                        "SWcld_amt":-0.01451293834621307,
-                        "SWcld_err":4.804746196518688e-05,
-                        "SWcld_tau":0.0061670089239355234,
-                        "SWcld_tot":-0.009519539095993792,
-                        "SWdobsc_cov_fbk":-6.686426270331496e-20,
-                        "SWdobsc_fbk":-0.0004735626637090951,
-                        "SWdunobsc_fbk":-0.009519539095993795
-                    }
-                },
-                "ocn_dsc":{
-                    "LO680":{
-                        "LWcld_alt":0.0034421365969667193,
-                        "LWcld_amt":-0.01438904381604142,
-                        "LWcld_err":-0.0003168564120689203,
-                        "LWcld_tau":-0.002086360409439892,
-                        "LWcld_tot":-0.013350124040583514,
-                        "LWdobsc_cov_fbk":-9.139835169373878e-20,
-                        "LWdobsc_fbk":0.00047275416535811883,
-                        "LWdunobsc_fbk":-0.013350124040583519,
-                        "SWcld_alt":-0.0016415612785361695,
-                        "SWcld_amt":0.08096942372020495,
-                        "SWcld_err":0.0003510358217952063,
-                        "SWcld_tau":0.03395132886476149,
-                        "SWcld_tot":0.11363022712822546,
-                        "SWdobsc_cov_fbk":4.325928773688987e-19,
-                        "SWdobsc_fbk":-0.003211808045119497,
-                        "SWdunobsc_fbk":0.1136302271282255
-                    }
-                }
-            },
-            "eq40":{
-                "all":{
-                    "LO680":{
-                        "LWcld_alt":0.003788231871549044,
-                        "LWcld_amt":-0.06321354219116994,
-                        "LWcld_err":-0.000252539162795299,
-                        "LWcld_tau":-0.005910318564452035,
-                        "LWcld_tot":-0.06558816804686823,
-                        "LWdobsc_cov_fbk":1.3630806294684845e-21,
-                        "LWdobsc_fbk":0.03355853092437299,
-                        "LWdunobsc_fbk":-0.06558816804686823,
-                        "SWcld_alt":-0.002518048265150777,
-                        "SWcld_amt":0.4064582734062574,
-                        "SWcld_err":0.00016900934304264932,
-                        "SWcld_tau":0.07606468655113875,
-                        "SWcld_tot":0.48017392103528805,
-                        "SWdobsc_cov_fbk":-2.585744756737274e-20,
-                        "SWdobsc_fbk":-0.23814604427051375,
-                        "SWdunobsc_fbk":0.48017392103528805
-                    }
-                },
-                "lnd":{
-                    "LO680":{
-                        "LWcld_alt":0.001949692522256884,
-                        "LWcld_amt":-0.013680580292061659,
-                        "LWcld_err":-2.6316606755418905e-05,
-                        "LWcld_tau":-0.00011971162681083287,
-                        "LWcld_tot":-0.011876916003371025,
-                        "LWdobsc_cov_fbk":-1.024826107530214e-21,
-                        "LWdobsc_fbk":0.005843584990751,
-                        "LWdunobsc_fbk":-0.011876916003371025,
-                        "SWcld_alt":-0.0009602170247204736,
-                        "SWcld_amt":0.07358882164425291,
-                        "SWcld_err":3.213345056295785e-05,
-                        "SWcld_tau":0.0007312127054568519,
-                        "SWcld_tot":0.07339195077555223,
-                        "SWdobsc_cov_fbk":5.067132322317419e-21,
-                        "SWdobsc_fbk":-0.03341023169712813,
-                        "SWdunobsc_fbk":0.07339195077555223
-                    }
-                },
-                "ocn":{
-                    "LO680":{
-                        "LWcld_alt":0.0018385393492921602,
-                        "LWcld_amt":-0.049532961899108274,
-                        "LWcld_err":-0.00022622255603988015,
-                        "LWcld_tau":-0.005790606937641202,
-                        "LWcld_tot":-0.05371125204349719,
-                        "LWdobsc_cov_fbk":2.3879067369987e-21,
-                        "LWdobsc_fbk":0.027714945933621987,
-                        "LWdunobsc_fbk":-0.05371125204349719,
-                        "SWcld_alt":-0.0015578312404303032,
-                        "SWcld_amt":0.3328694517620045,
-                        "SWcld_err":0.00013687589247969145,
-                        "SWcld_tau":0.07533347384568191,
-                        "SWcld_tot":0.4067819702597359,
-                        "SWdobsc_cov_fbk":-3.092457988969025e-20,
-                        "SWdobsc_fbk":-0.2047358125733856,
-                        "SWdunobsc_fbk":0.4067819702597359
-                    }
-                },
-                "ocn_asc":{
-                    "LO680":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "LWdobsc_cov_fbk":NaN,
-                        "LWdobsc_fbk":NaN,
-                        "LWdunobsc_fbk":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN,
-                        "SWdobsc_cov_fbk":NaN,
-                        "SWdobsc_fbk":NaN,
-                        "SWdunobsc_fbk":NaN
-                    }
-                },
-                "ocn_dsc":{
-                    "LO680":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "LWdobsc_cov_fbk":NaN,
-                        "LWdobsc_fbk":NaN,
-                        "LWdunobsc_fbk":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN,
-                        "SWdobsc_cov_fbk":NaN,
-                        "SWdobsc_fbk":NaN,
-                        "SWdunobsc_fbk":NaN
-                    }
-                }
-            },
-            "eq50":{
-                "all":{
-                    "LO680":{
-                        "LWcld_alt":0.0009788474407318299,
-                        "LWcld_amt":-0.08539361380650046,
-                        "LWcld_err":-0.0001649333343870967,
-                        "LWcld_tau":-0.006323260333327887,
-                        "LWcld_tot":-0.0909029600334836,
-                        "LWdobsc_cov_fbk":-8.767489685843392e-21,
-                        "LWdobsc_fbk":0.04160797408436258,
-                        "LWdunobsc_fbk":-0.0909029600334836,
-                        "SWcld_alt":-0.001654827587371445,
-                        "SWcld_amt":0.5263208171058441,
-                        "SWcld_err":0.00018613060093931522,
-                        "SWcld_tau":0.08397587812280266,
-                        "SWcld_tot":0.6088279982422146,
-                        "SWdobsc_cov_fbk":4.383396827346367e-20,
-                        "SWdobsc_fbk":-0.2851602313477117,
-                        "SWdunobsc_fbk":0.6088279982422146
-                    }
-                },
-                "lnd":{
-                    "LO680":{
-                        "LWcld_alt":0.0018373265489522884,
-                        "LWcld_amt":-0.018225763470845698,
-                        "LWcld_err":-1.637233006632299e-05,
-                        "LWcld_tau":0.0005221736013190143,
-                        "LWcld_tot":-0.015882635650640718,
-                        "LWdobsc_cov_fbk":5.1584235845207815e-21,
-                        "LWdobsc_fbk":0.007415961348750405,
-                        "LWdunobsc_fbk":-0.015882635650640718,
-                        "SWcld_alt":-0.0009226062623601761,
-                        "SWcld_amt":0.09593948953443941,
-                        "SWcld_err":3.260958670897305e-05,
-                        "SWcld_tau":-0.0002529984158001555,
-                        "SWcld_tot":0.09479649444298806,
-                        "SWdobsc_cov_fbk":-3.3006513479696136e-20,
-                        "SWdobsc_fbk":-0.0402711584044961,
-                        "SWdunobsc_fbk":0.09479649444298806
-                    }
-                },
-                "ocn":{
-                    "LO680":{
-                        "LWcld_alt":-0.0008584791082204584,
-                        "LWcld_amt":-0.06716785033565474,
-                        "LWcld_err":-0.0001485610043207737,
-                        "LWcld_tau":-0.006845433934646903,
-                        "LWcld_tot":-0.07502032438284288,
-                        "LWdobsc_cov_fbk":-1.3925913270364166e-20,
-                        "LWdobsc_fbk":0.03419201273561217,
-                        "LWdunobsc_fbk":-0.07502032438284288,
-                        "SWcld_alt":-0.000732221325011269,
-                        "SWcld_amt":0.43038132757140474,
-                        "SWcld_err":0.0001535210142303422,
-                        "SWcld_tau":0.08422887653860282,
-                        "SWcld_tot":0.5140315037992265,
-                        "SWdobsc_cov_fbk":7.684048175315978e-20,
-                        "SWdobsc_fbk":-0.2448890729432156,
-                        "SWdunobsc_fbk":0.5140315037992265
-                    }
-                },
-                "ocn_asc":{
-                    "LO680":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "LWdobsc_cov_fbk":NaN,
-                        "LWdobsc_fbk":NaN,
-                        "LWdunobsc_fbk":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN,
-                        "SWdobsc_cov_fbk":NaN,
-                        "SWdobsc_fbk":NaN,
-                        "SWdunobsc_fbk":NaN
-                    }
-                },
-                "ocn_dsc":{
-                    "LO680":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "LWdobsc_cov_fbk":NaN,
-                        "LWdobsc_fbk":NaN,
-                        "LWdunobsc_fbk":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN,
-                        "SWdobsc_cov_fbk":NaN,
-                        "SWdobsc_fbk":NaN,
-                        "SWdunobsc_fbk":NaN
-                    }
-                }
-            },
-            "eq60":{
-                "all":{
-                    "LO680":{
-                        "LWcld_alt":-0.00036416069422380825,
-                        "LWcld_amt":-0.09597885319512789,
-                        "LWcld_err":-0.00030771542534993465,
-                        "LWcld_tau":-0.0038083225524837712,
-                        "LWcld_tot":-0.1004590518671854,
-                        "LWdobsc_cov_fbk":-9.422168558843474e-21,
-                        "LWdobsc_fbk":0.04050169353278767,
-                        "LWdunobsc_fbk":-0.1004590518671854,
-                        "SWcld_alt":-0.0013276202981897854,
-                        "SWcld_amt":0.5859943404399202,
-                        "SWcld_err":0.00034247251924804573,
-                        "SWcld_tau":0.059753871652673145,
-                        "SWcld_tot":0.6447630643136517,
-                        "SWdobsc_cov_fbk":3.2863305648572463e-20,
-                        "SWdobsc_fbk":-0.28449064866218393,
-                        "SWdunobsc_fbk":0.6447630643136516
-                    }
-                },
-                "lnd":{
-                    "LO680":{
-                        "LWcld_alt":0.0015852232053820854,
-                        "LWcld_amt":-0.021621608857128174,
-                        "LWcld_err":-8.61142995189422e-06,
-                        "LWcld_tau":0.001648695842497701,
-                        "LWcld_tot":-0.018396301239200278,
-                        "LWdobsc_cov_fbk":3.861362074254579e-21,
-                        "LWdobsc_fbk":0.008332242414911168,
-                        "LWdunobsc_fbk":-0.018396301239200278,
-                        "SWcld_alt":-0.0008164682456727749,
-                        "SWcld_amt":0.11702109472965866,
-                        "SWcld_err":3.515653973498832e-05,
-                        "SWcld_tau":-0.00218092918200779,
-                        "SWcld_tot":0.1140588538417131,
-                        "SWdobsc_cov_fbk":-2.7287911437978973e-20,
-                        "SWdobsc_fbk":-0.0449399205466591,
-                        "SWdunobsc_fbk":0.11405885384171309
-                    }
-                },
-                "ocn":{
-                    "LO680":{
-                        "LWcld_alt":-0.0019493838996058933,
-                        "LWcld_amt":-0.07435724433799971,
-                        "LWcld_err":-0.00029910399539804047,
-                        "LWcld_tau":-0.005457018394981472,
-                        "LWcld_tot":-0.08206275062798511,
-                        "LWdobsc_cov_fbk":-1.328353063309805e-20,
-                        "LWdobsc_fbk":0.03216945111787651,
-                        "LWdunobsc_fbk":-0.08206275062798511,
-                        "SWcld_alt":-0.0005111520525170109,
-                        "SWcld_amt":0.4689732457102616,
-                        "SWcld_err":0.00030731597951305737,
-                        "SWcld_tau":0.06193480083468094,
-                        "SWcld_tot":0.5307042104719385,
-                        "SWdobsc_cov_fbk":6.015121708655143e-20,
-                        "SWdobsc_fbk":-0.23955072811552483,
-                        "SWdunobsc_fbk":0.5307042104719385
-                    }
-                },
-                "ocn_asc":{
-                    "LO680":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "LWdobsc_cov_fbk":NaN,
-                        "LWdobsc_fbk":NaN,
-                        "LWdunobsc_fbk":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN,
-                        "SWdobsc_cov_fbk":NaN,
-                        "SWdobsc_fbk":NaN,
-                        "SWdunobsc_fbk":NaN
-                    }
-                },
-                "ocn_dsc":{
-                    "LO680":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "LWdobsc_cov_fbk":NaN,
-                        "LWdobsc_fbk":NaN,
-                        "LWdunobsc_fbk":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN,
-                        "SWdobsc_cov_fbk":NaN,
-                        "SWdobsc_fbk":NaN,
-                        "SWdunobsc_fbk":NaN
-                    }
-                }
-            },
-            "eq70":{
-                "all":{
-                    "LO680":{
-                        "LWcld_alt":-0.0008322563323237518,
-                        "LWcld_amt":-0.09956112032632382,
-                        "LWcld_err":-0.0004390569989213389,
-                        "LWcld_tau":-0.0020460855894295226,
-                        "LWcld_tot":-0.10287851924699844,
-                        "LWdobsc_cov_fbk":-1.33076786755822e-20,
-                        "LWdobsc_fbk":0.03880311091682346,
-                        "LWdunobsc_fbk":-0.10287851924699844,
-                        "SWcld_alt":-0.0011831648816103373,
-                        "SWcld_amt":0.6084557362494034,
-                        "SWcld_err":0.0004242894997928911,
-                        "SWcld_tau":0.04414100010449263,
-                        "SWcld_tot":0.6518378609720785,
-                        "SWdobsc_cov_fbk":6.538049858250433e-20,
-                        "SWdobsc_fbk":-0.27944280649072345,
-                        "SWdunobsc_fbk":0.6518378609720785
-                    }
-                },
-                "lnd":{
-                    "LO680":{
-                        "LWcld_alt":0.001433970693893726,
-                        "LWcld_amt":-0.024057025499507164,
-                        "LWcld_err":-2.128059691099425e-05,
-                        "LWcld_tau":0.002369583646651176,
-                        "LWcld_tot":-0.020274751755873253,
-                        "LWdobsc_cov_fbk":2.393927117015216e-21,
-                        "LWdobsc_fbk":0.008263146955362741,
-                        "LWdunobsc_fbk":-0.020274751755873253,
-                        "SWcld_alt":-0.0007471810689454555,
-                        "SWcld_amt":0.13439302904597447,
-                        "SWcld_err":4.2779458536416686e-05,
-                        "SWcld_tau":-0.0037284269824142953,
-                        "SWcld_tot":0.12996020045315115,
-                        "SWdobsc_cov_fbk":-1.2966388278515049e-20,
-                        "SWdobsc_fbk":-0.045527132046827345,
-                        "SWdunobsc_fbk":0.12996020045315115
-                    }
-                },
-                "ocn":{
-                    "LO680":{
-                        "LWcld_alt":-0.0022662270262174774,
-                        "LWcld_amt":-0.07550409482681668,
-                        "LWcld_err":-0.00041777640201034463,
-                        "LWcld_tau":-0.004415669236080698,
-                        "LWcld_tot":-0.08260376749112519,
-                        "LWdobsc_cov_fbk":-1.5701605792597418e-20,
-                        "LWdobsc_fbk":0.030539963961460723,
-                        "LWdunobsc_fbk":-0.08260376749112519,
-                        "SWcld_alt":-0.0004359838126648818,
-                        "SWcld_amt":0.4740627072034289,
-                        "SWcld_err":0.0003815100412564744,
-                        "SWcld_tau":0.04786942708690692,
-                        "SWcld_tot":0.5218776605189274,
-                        "SWdobsc_cov_fbk":7.834688686101938e-20,
-                        "SWdobsc_fbk":-0.23391567444389605,
-                        "SWdunobsc_fbk":0.5218776605189274
-                    }
-                },
-                "ocn_asc":{
-                    "LO680":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "LWdobsc_cov_fbk":NaN,
-                        "LWdobsc_fbk":NaN,
-                        "LWdunobsc_fbk":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN,
-                        "SWdobsc_cov_fbk":NaN,
-                        "SWdobsc_fbk":NaN,
-                        "SWdunobsc_fbk":NaN
-                    }
-                },
-                "ocn_dsc":{
-                    "LO680":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "LWdobsc_cov_fbk":NaN,
-                        "LWdobsc_fbk":NaN,
-                        "LWdunobsc_fbk":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN,
-                        "SWdobsc_cov_fbk":NaN,
-                        "SWdobsc_fbk":NaN,
-                        "SWdunobsc_fbk":NaN
-                    }
-                }
-            },
-            "eq80":{
-                "all":{
-                    "LO680":{
-                        "LWcld_alt":-0.0006284779912092208,
-                        "LWcld_amt":-0.10015777078095361,
-                        "LWcld_err":-0.0005550823714766391,
-                        "LWcld_tau":-0.0014736175895136716,
-                        "LWcld_tot":-0.10281494873315317,
-                        "LWdobsc_cov_fbk":-1.6244160406099437e-20,
-                        "LWdobsc_fbk":0.03760576472566518,
-                        "LWdunobsc_fbk":-0.10281494873315317,
-                        "SWcld_alt":-0.0012174293185916757,
-                        "SWcld_amt":0.6138154626308768,
-                        "SWcld_err":0.0004914598404181246,
-                        "SWcld_tau":0.04339728658379113,
-                        "SWcld_tot":0.6564867797364944,
-                        "SWdobsc_cov_fbk":9.204984278536343e-20,
-                        "SWdobsc_fbk":-0.2758031797682004,
-                        "SWdunobsc_fbk":0.6564867797364943
-                    }
-                },
-                "lnd":{
-                    "LO680":{
-                        "LWcld_alt":0.0014739725151659796,
-                        "LWcld_amt":-0.024443519203089775,
-                        "LWcld_err":-3.624010762871378e-05,
-                        "LWcld_tau":0.002566719819146119,
-                        "LWcld_tot":-0.020439066976406392,
-                        "LWdobsc_cov_fbk":4.949100033841086e-22,
-                        "LWdobsc_fbk":0.00806786889384367,
-                        "LWdunobsc_fbk":-0.020439066976406392,
-                        "SWcld_alt":-0.0007527263192159133,
-                        "SWcld_amt":0.13776278291003052,
-                        "SWcld_err":5.085511093456356e-05,
-                        "SWcld_tau":-0.0039328200620458624,
-                        "SWcld_tot":0.1331280916397033,
-                        "SWdobsc_cov_fbk":1.106718655798634e-20,
-                        "SWdobsc_fbk":-0.04499422069041125,
-                        "SWdunobsc_fbk":0.13312809163970332
-                    }
-                },
-                "ocn":{
-                    "LO680":{
-                        "LWcld_alt":-0.0021024505063752005,
-                        "LWcld_amt":-0.07571425157786386,
-                        "LWcld_err":-0.0005188422638479252,
-                        "LWcld_tau":-0.004040337408659791,
-                        "LWcld_tot":-0.08237588175674677,
-                        "LWdobsc_cov_fbk":-1.6739070409483545e-20,
-                        "LWdobsc_fbk":0.02953789583182151,
-                        "LWdunobsc_fbk":-0.08237588175674677,
-                        "SWcld_alt":-0.00046470299937576253,
-                        "SWcld_amt":0.47605267972084625,
-                        "SWcld_err":0.000440604729483561,
-                        "SWcld_tau":0.047330106645837,
-                        "SWcld_tot":0.5233586880967911,
-                        "SWdobsc_cov_fbk":8.098265622737706e-20,
-                        "SWdobsc_fbk":-0.23080895907778917,
-                        "SWdunobsc_fbk":0.523358688096791
-                    }
-                },
-                "ocn_asc":{
-                    "LO680":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "LWdobsc_cov_fbk":NaN,
-                        "LWdobsc_fbk":NaN,
-                        "LWdunobsc_fbk":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN,
-                        "SWdobsc_cov_fbk":NaN,
-                        "SWdobsc_fbk":NaN,
-                        "SWdunobsc_fbk":NaN
-                    }
-                },
-                "ocn_dsc":{
-                    "LO680":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "LWdobsc_cov_fbk":NaN,
-                        "LWdobsc_fbk":NaN,
-                        "LWdunobsc_fbk":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN,
-                        "SWdobsc_cov_fbk":NaN,
-                        "SWdobsc_fbk":NaN,
-                        "SWdunobsc_fbk":NaN
-                    }
-                }
-            },
-            "eq90":{
-                "all":{
-                    "LO680":{
-                        "LWcld_alt":-0.0004477414494981735,
-                        "LWcld_amt":-0.10005023640039379,
-                        "LWcld_err":-0.0005917464527736171,
-                        "LWcld_tau":-0.0012106134035308055,
-                        "LWcld_tot":-0.1023003377061964,
-                        "LWdobsc_cov_fbk":-1.6366315469438444e-20,
-                        "LWdobsc_fbk":0.03718352963144448,
-                        "LWdunobsc_fbk":-0.10230033770619638,
-                        "SWcld_alt":-0.0012574381555788053,
-                        "SWcld_amt":0.6137308485592509,
-                        "SWcld_err":0.0005139519748091357,
-                        "SWcld_tau":0.04331900815488726,
-                        "SWcld_tot":0.6563063705333683,
-                        "SWdobsc_cov_fbk":9.131378563417788e-20,
-                        "SWdobsc_fbk":-0.274583663108717,
-                        "SWdunobsc_fbk":0.6563063705333684
-                    }
-                },
-                "lnd":{
-                    "LO680":{
-                        "LWcld_alt":0.0014766056327733682,
-                        "LWcld_amt":-0.02443285274052726,
-                        "LWcld_err":-3.728957770008359e-05,
-                        "LWcld_tau":0.0026159307529069428,
-                        "LWcld_tot":-0.020377605932547036,
-                        "LWdobsc_cov_fbk":2.761705326635847e-22,
-                        "LWdobsc_fbk":0.008031565073916423,
-                        "LWdunobsc_fbk":-0.020377605932547036,
-                        "SWcld_alt":-0.000752697840088393,
-                        "SWcld_amt":0.13777628148512236,
-                        "SWcld_err":5.104077539507488e-05,
-                        "SWcld_tau":-0.0039801431958629155,
-                        "SWcld_tot":0.1330944812245661,
-                        "SWdobsc_cov_fbk":1.1192345343063525e-20,
-                        "SWdobsc_fbk":-0.044926132020614906,
-                        "SWdunobsc_fbk":0.1330944812245661
-                    }
-                },
-                "ocn":{
-                    "LO680":{
-                        "LWcld_alt":-0.0019243470822715417,
-                        "LWcld_amt":-0.07561738365986652,
-                        "LWcld_err":-0.0005544568750735335,
-                        "LWcld_tau":-0.003826544156437748,
-                        "LWcld_tot":-0.08192273177364935,
-                        "LWdobsc_cov_fbk":-1.6642486002102025e-20,
-                        "LWdobsc_fbk":0.029151964557528057,
-                        "LWdunobsc_fbk":-0.08192273177364935,
-                        "SWcld_alt":-0.0005047403154904121,
-                        "SWcld_amt":0.4759545670741285,
-                        "SWcld_err":0.00046291119941406066,
-                        "SWcld_tau":0.04729915135075018,
-                        "SWcld_tot":0.5232118893088024,
-                        "SWdobsc_cov_fbk":8.012144029111434e-20,
-                        "SWdobsc_fbk":-0.22965753108810213,
-                        "SWdunobsc_fbk":0.5232118893088022
-                    }
-                },
-                "ocn_asc":{
-                    "LO680":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "LWdobsc_cov_fbk":NaN,
-                        "LWdobsc_fbk":NaN,
-                        "LWdunobsc_fbk":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN,
-                        "SWdobsc_cov_fbk":NaN,
-                        "SWdobsc_fbk":NaN,
-                        "SWdunobsc_fbk":NaN
-                    }
-                },
-                "ocn_dsc":{
-                    "LO680":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "LWdobsc_cov_fbk":NaN,
-                        "LWdobsc_fbk":NaN,
-                        "LWdunobsc_fbk":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN,
-                        "SWdobsc_cov_fbk":NaN,
-                        "SWdobsc_fbk":NaN,
-                        "SWdunobsc_fbk":NaN
-                    }
-                }
-            }
-        }
-    },
-    "IPSL-CM6A-LR":{
-        "r1i1p1f1":{
-            "Arctic":{
-                "all":{
-                    "LO680":{
-                        "LWcld_alt":0.0004257009064295393,
-                        "LWcld_amt":-0.00011382574671393513,
-                        "LWcld_err":-0.00012464435412435883,
-                        "LWcld_tau":0.0010971037006053655,
-                        "LWcld_tot":0.0011773066306802265,
-                        "LWdobsc_cov_fbk":-2.540780838091127e-21,
-                        "LWdobsc_fbk":-0.0015214743857159452,
-                        "LWdunobsc_fbk":0.0012843345061966107,
-                        "SWcld_alt":-8.416815996440293e-05,
-                        "SWcld_amt":0.0002934142764285634,
-                        "SWcld_err":2.947487325034435e-05,
-                        "SWcld_tau":-0.005147130455455811,
-                        "SWcld_tot":-0.0049084094657413065,
-                        "SWdobsc_cov_fbk":1.2853596474873218e-20,
-                        "SWdobsc_fbk":0.0056377358453167095,
-                        "SWdunobsc_fbk":-0.005354628508081425
-                    }
-                },
-                "lnd":{
-                    "LO680":{
-                        "LWcld_alt":9.602703827596929e-05,
-                        "LWcld_amt":-0.0001541651850020617,
-                        "LWcld_err":-1.6773366991460862e-05,
-                        "LWcld_tau":0.00018733405486353743,
-                        "LWcld_tot":0.00011242254114598414,
-                        "LWdobsc_cov_fbk":-7.707184210047523e-22,
-                        "LWdobsc_fbk":-0.00015197150719015968,
-                        "LWdunobsc_fbk":0.00011242254114598414,
-                        "SWcld_alt":-2.7743585870389537e-05,
-                        "SWcld_amt":0.0009403274246102896,
-                        "SWcld_err":5.939156332049352e-06,
-                        "SWcld_tau":-0.0007597567403679537,
-                        "SWcld_tot":0.00015876625470399576,
-                        "SWdobsc_cov_fbk":6.4721532334615155e-21,
-                        "SWdobsc_fbk":0.000505020078609338,
-                        "SWdunobsc_fbk":0.00015876625470399584
-                    }
-                },
-                "ocn":{
-                    "LO680":{
-                        "LWcld_alt":0.00032967386815357014,
-                        "LWcld_amt":4.033943828812662e-05,
-                        "LWcld_err":-0.00010787098713289796,
-                        "LWcld_tau":0.0009097696457418279,
-                        "LWcld_tot":0.0011719119650506265,
-                        "LWdobsc_cov_fbk":-1.770062417086375e-21,
-                        "LWdobsc_fbk":-0.0013695028785257854,
-                        "LWdunobsc_fbk":0.0011719119650506267,
-                        "SWcld_alt":-6.407622499986821e-05,
-                        "SWcld_amt":-0.0006202391230518568,
-                        "SWcld_err":2.6215250850144478e-05,
-                        "SWcld_tau":-0.00485529466558384,
-                        "SWcld_tot":-0.00551339476278542,
-                        "SWdobsc_cov_fbk":6.3814432414117045e-21,
-                        "SWdobsc_fbk":0.005132715766707371,
-                        "SWdunobsc_fbk":-0.00551339476278542
-                    }
-                },
-                "ocn_asc":{
-                    "LO680":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "LWdobsc_cov_fbk":NaN,
-                        "LWdobsc_fbk":NaN,
-                        "LWdunobsc_fbk":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN,
-                        "SWdobsc_cov_fbk":NaN,
-                        "SWdobsc_fbk":NaN,
-                        "SWdunobsc_fbk":NaN
-                    }
-                },
-                "ocn_dsc":{
-                    "LO680":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "LWdobsc_cov_fbk":NaN,
-                        "LWdobsc_fbk":NaN,
-                        "LWdunobsc_fbk":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN,
-                        "SWdobsc_cov_fbk":NaN,
-                        "SWdobsc_fbk":NaN,
-                        "SWdunobsc_fbk":NaN
-                    }
-                }
-            },
-            "eq10":{
-                "all":{
-                    "LO680":{
-                        "LWcld_alt":-0.0027044944962863575,
-                        "LWcld_amt":-0.015070734248105434,
-                        "LWcld_err":0.00035405486381386936,
-                        "LWcld_tau":-0.001825568728303328,
-                        "LWcld_tot":-0.01924674260888125,
-                        "LWdobsc_cov_fbk":-2.4578128611633086e-20,
-                        "LWdobsc_fbk":0.003502314792808233,
-                        "LWdunobsc_fbk":-0.01924674260888125,
-                        "SWcld_alt":0.0012190608497309133,
-                        "SWcld_amt":0.09976766699577172,
-                        "SWcld_err":-0.0004782057807832059,
-                        "SWcld_tau":0.020267461563726476,
-                        "SWcld_tot":0.1207759836284459,
-                        "SWdobsc_cov_fbk":1.385127562992561e-19,
-                        "SWdobsc_fbk":-0.024667350599264313,
-                        "SWdunobsc_fbk":0.12077598362844592
-                    }
-                },
-                "lnd":{
-                    "LO680":{
-                        "LWcld_alt":0.00046226209313147173,
-                        "LWcld_amt":-0.0026935564001413902,
-                        "LWcld_err":0.0001141588105889337,
-                        "LWcld_tau":0.00010600514788719418,
-                        "LWcld_tot":-0.002011130348533791,
-                        "LWdobsc_cov_fbk":-9.575585589420151e-21,
-                        "LWdobsc_fbk":0.00045977983849467024,
-                        "LWdunobsc_fbk":-0.0020111303485337907,
-                        "SWcld_alt":-0.0002241374565366322,
-                        "SWcld_amt":0.019188463566134312,
-                        "SWcld_err":-9.10656905073982e-05,
-                        "SWcld_tau":-0.005231468179344149,
-                        "SWcld_tot":0.01364179223974613,
-                        "SWdobsc_cov_fbk":4.953509481297682e-20,
-                        "SWdobsc_fbk":-0.003293571799773977,
-                        "SWdunobsc_fbk":0.01364179223974613
-                    }
-                },
-                "ocn":{
-                    "LO680":{
-                        "LWcld_alt":-0.0031667565894178303,
-                        "LWcld_amt":-0.012377177847964044,
-                        "LWcld_err":0.0002398960532249357,
-                        "LWcld_tau":-0.0019315738761905225,
-                        "LWcld_tot":-0.01723561226034746,
-                        "LWdobsc_cov_fbk":-1.5002543022212933e-20,
-                        "LWdobsc_fbk":0.003042534954313563,
-                        "LWdunobsc_fbk":-0.017235612260347463,
-                        "SWcld_alt":0.0014431983062675457,
-                        "SWcld_amt":0.08057920342963741,
-                        "SWcld_err":-0.00038714009027580767,
-                        "SWcld_tau":0.025498929743070622,
-                        "SWcld_tot":0.10713419138869977,
-                        "SWdobsc_cov_fbk":8.897766148627928e-20,
-                        "SWdobsc_fbk":-0.021373778799490335,
-                        "SWdunobsc_fbk":0.10713419138869977
-                    }
-                },
-                "ocn_asc":{
-                    "LO680":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "LWdobsc_cov_fbk":NaN,
-                        "LWdobsc_fbk":NaN,
-                        "LWdunobsc_fbk":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN,
-                        "SWdobsc_cov_fbk":NaN,
-                        "SWdobsc_fbk":NaN,
-                        "SWdunobsc_fbk":NaN
-                    }
-                },
-                "ocn_dsc":{
-                    "LO680":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "LWdobsc_cov_fbk":NaN,
-                        "LWdobsc_fbk":NaN,
-                        "LWdunobsc_fbk":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN,
-                        "SWdobsc_cov_fbk":NaN,
-                        "SWdobsc_fbk":NaN,
-                        "SWdunobsc_fbk":NaN
-                    }
-                }
-            },
-            "eq20":{
-                "all":{
-                    "LO680":{
-                        "LWcld_alt":-0.00589982084794569,
-                        "LWcld_amt":-0.027870349315322257,
-                        "LWcld_err":0.0004714215215853524,
-                        "LWcld_tau":-0.0030955521373046547,
-                        "LWcld_tot":-0.036394300778987254,
-                        "LWdobsc_cov_fbk":-2.5407825456099118e-20,
-                        "LWdobsc_fbk":0.006387218311692481,
-                        "LWdunobsc_fbk":-0.036394300778987254,
-                        "SWcld_alt":0.002659889379312318,
-                        "SWcld_amt":0.17793373017115474,
-                        "SWcld_err":-0.0007067788383800193,
-                        "SWcld_tau":0.04025673583136403,
-                        "SWcld_tot":0.22014357654345104,
-                        "SWdobsc_cov_fbk":1.3112646185930168e-19,
-                        "SWdobsc_fbk":-0.044038877794991264,
-                        "SWdunobsc_fbk":0.22014357654345107
-                    }
-                },
-                "lnd":{
-                    "LO680":{
-                        "LWcld_alt":0.0006172277910102111,
-                        "LWcld_amt":-0.004022228702928443,
-                        "LWcld_err":0.0001223751387712019,
-                        "LWcld_tau":0.00015620676384245293,
-                        "LWcld_tot":-0.0031264190093045776,
-                        "LWdobsc_cov_fbk":-1.0597393257135428e-20,
-                        "LWdobsc_fbk":0.0007803964015441591,
-                        "LWdunobsc_fbk":-0.0031264190093045776,
-                        "SWcld_alt":-0.00029674597471731706,
-                        "SWcld_amt":0.02776203372489747,
-                        "SWcld_err":-0.00010299053533049334,
-                        "SWcld_tau":-0.00685718373423395,
-                        "SWcld_tot":0.020505113480615712,
-                        "SWdobsc_cov_fbk":5.295846023682453e-20,
-                        "SWdobsc_fbk":-0.005240140027490136,
-                        "SWdunobsc_fbk":0.020505113480615716
-                    }
-                },
-                "ocn":{
-                    "LO680":{
-                        "LWcld_alt":-0.006517048638955902,
-                        "LWcld_amt":-0.023848120612393817,
-                        "LWcld_err":0.00034904638281415043,
-                        "LWcld_tau":-0.0032517589011471074,
-                        "LWcld_tot":-0.03326788176968268,
-                        "LWdobsc_cov_fbk":-1.481043219896369e-20,
-                        "LWdobsc_fbk":0.005606821910148323,
-                        "LWdunobsc_fbk":-0.03326788176968268,
-                        "SWcld_alt":0.0029566353540296346,
-                        "SWcld_amt":0.15017169644625725,
-                        "SWcld_err":-0.0006037883030495261,
-                        "SWcld_tau":0.04711391956559798,
-                        "SWcld_tot":0.1996384630628353,
-                        "SWdobsc_cov_fbk":7.816800162247715e-20,
-                        "SWdobsc_fbk":-0.03879873776750113,
-                        "SWdunobsc_fbk":0.1996384630628353
-                    }
-                },
-                "ocn_asc":{
-                    "LO680":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "LWdobsc_cov_fbk":NaN,
-                        "LWdobsc_fbk":NaN,
-                        "LWdunobsc_fbk":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN,
-                        "SWdobsc_cov_fbk":NaN,
-                        "SWdobsc_fbk":NaN,
-                        "SWdunobsc_fbk":NaN
-                    }
-                },
-                "ocn_dsc":{
-                    "LO680":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "LWdobsc_cov_fbk":NaN,
-                        "LWdobsc_fbk":NaN,
-                        "LWdunobsc_fbk":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN,
-                        "SWdobsc_cov_fbk":NaN,
-                        "SWdobsc_fbk":NaN,
-                        "SWdunobsc_fbk":NaN
-                    }
-                }
-            },
-            "eq30":{
-                "all":{
-                    "LO680":{
-                        "LWcld_alt":-0.009679744450172797,
-                        "LWcld_amt":-0.04991585094477045,
-                        "LWcld_err":0.0006113379182286577,
-                        "LWcld_tau":-0.004190662897758132,
-                        "LWcld_tot":-0.06317492037447273,
-                        "LWdobsc_cov_fbk":-2.5296138184673843e-20,
-                        "LWdobsc_fbk":0.007873876891291463,
-                        "LWdunobsc_fbk":-0.06317492037447271,
-                        "SWcld_alt":0.004252152876359791,
-                        "SWcld_amt":0.30030455390448324,
-                        "SWcld_err":-0.0008607517033981171,
-                        "SWcld_tau":0.05345378270417153,
-                        "SWcld_tot":0.35714973778161646,
-                        "SWdobsc_cov_fbk":1.4551707136977685e-19,
-                        "SWdobsc_fbk":-0.05449513397096432,
-                        "SWdunobsc_fbk":0.35714973778161646
-                    }
-                },
-                "lnd":{
-                    "LO680":{
-                        "LWcld_alt":0.0006938907792071474,
-                        "LWcld_amt":-0.006099172261337236,
-                        "LWcld_err":0.00012620123548132142,
-                        "LWcld_tau":0.00012028034014731494,
-                        "LWcld_tot":-0.0051587999065014515,
-                        "LWdobsc_cov_fbk":-1.313194921430621e-20,
-                        "LWdobsc_fbk":0.0012713584347627542,
-                        "LWdunobsc_fbk":-0.0051587999065014515,
-                        "SWcld_alt":-0.0003310396639891299,
-                        "SWcld_amt":0.0383966821070078,
-                        "SWcld_err":-0.00010173075508178112,
-                        "SWcld_tau":-0.006735430340126698,
-                        "SWcld_tot":0.03122848134781019,
-                        "SWdobsc_cov_fbk":6.428798792245291e-20,
-                        "SWdobsc_fbk":-0.0078574120551451,
-                        "SWdunobsc_fbk":0.031228481347810196
-                    }
-                },
-                "ocn":{
-                    "LO680":{
-                        "LWcld_alt":-0.010373635229379943,
-                        "LWcld_amt":-0.043816678683433224,
-                        "LWcld_err":0.00048513668274733626,
-                        "LWcld_tau":-0.004310943237905446,
-                        "LWcld_tot":-0.058016120467971256,
-                        "LWdobsc_cov_fbk":-1.2164188970367643e-20,
-                        "LWdobsc_fbk":0.006602518456528706,
-                        "LWdunobsc_fbk":-0.058016120467971256,
-                        "SWcld_alt":0.004583192540348921,
-                        "SWcld_amt":0.2619078717974755,
-                        "SWcld_err":-0.000759020948316336,
-                        "SWcld_tau":0.06018921304429823,
-                        "SWcld_tot":0.32592125643380626,
-                        "SWdobsc_cov_fbk":8.122908344732391e-20,
-                        "SWdobsc_fbk":-0.04663772191581922,
-                        "SWdunobsc_fbk":0.32592125643380626
-                    }
-                },
-                "ocn_asc":{
-                    "LO680":{
-                        "LWcld_alt":-0.0024573780643887045,
-                        "LWcld_amt":-0.0029043496647806217,
-                        "LWcld_err":0.00018828019090387708,
-                        "LWcld_tau":-0.0005363421711598054,
-                        "LWcld_tot":-0.0057097897094252545,
-                        "LWdobsc_cov_fbk":-1.03257788813227e-20,
-                        "LWdobsc_fbk":-8.028365898118393e-05,
-                        "LWdunobsc_fbk":-0.005709789709425255,
-                        "SWcld_alt":0.0011282608489597253,
-                        "SWcld_amt":0.023870496100703954,
-                        "SWcld_err":-0.000455597710612562,
-                        "SWcld_tau":0.0036624895647155366,
-                        "SWcld_tot":0.02820564880376665,
-                        "SWdobsc_cov_fbk":8.405926802547515e-20,
-                        "SWdobsc_fbk":0.0006176996519101581,
-                        "SWdunobsc_fbk":0.028205648803766654
-                    }
-                },
-                "ocn_dsc":{
-                    "LO680":{
-                        "LWcld_alt":-0.007319284425900748,
-                        "LWcld_amt":-0.035767197617781556,
-                        "LWcld_err":0.00024367757309115478,
-                        "LWcld_tau":-0.003474100957150001,
-                        "LWcld_tot":-0.04631690542774115,
-                        "LWdobsc_cov_fbk":3.9773862613638406e-19,
-                        "LWdobsc_fbk":0.0002606694712537535,
-                        "LWdunobsc_fbk":-0.04631690542774115,
-                        "SWcld_alt":0.003198199437454821,
-                        "SWcld_amt":0.19566138666060529,
-                        "SWcld_err":-0.00023874883413994697,
-                        "SWcld_tau":0.05054633624317508,
-                        "SWcld_tot":0.24916717350709527,
-                        "SWdobsc_cov_fbk":-2.30126500568554e-18,
-                        "SWdobsc_fbk":-0.0014589543637891116,
-                        "SWdunobsc_fbk":0.24916717350709527
-                    }
-                }
-            },
-            "eq40":{
-                "all":{
-                    "LO680":{
-                        "LWcld_alt":-0.012446147226633425,
-                        "LWcld_amt":-0.08474527849275944,
-                        "LWcld_err":0.00083878890728014,
-                        "LWcld_tau":-0.004837373584107168,
-                        "LWcld_tot":-0.10119001039621989,
-                        "LWdobsc_cov_fbk":1.1474099516637968e-20,
-                        "LWdobsc_fbk":0.00755861000534342,
-                        "LWdunobsc_fbk":-0.1011900103962199,
-                        "SWcld_alt":0.0052421515536903275,
-                        "SWcld_amt":0.47130453958195967,
-                        "SWcld_err":-0.0010529097175480471,
-                        "SWcld_tau":0.05865589148884601,
-                        "SWcld_tot":0.5341496729069478,
-                        "SWdobsc_cov_fbk":-1.6716373096835687e-20,
-                        "SWdobsc_fbk":-0.04559196597623915,
-                        "SWdunobsc_fbk":0.5341496729069479
-                    }
-                },
-                "lnd":{
-                    "LO680":{
-                        "LWcld_alt":0.00066312161069303,
-                        "LWcld_amt":-0.009224203897476297,
-                        "LWcld_err":0.00013463697150167765,
-                        "LWcld_tau":0.00021138538811752566,
-                        "LWcld_tot":-0.008215059927164063,
-                        "LWdobsc_cov_fbk":-1.2837395333711474e-20,
-                        "LWdobsc_fbk":0.0017838729147108192,
-                        "LWdunobsc_fbk":-0.008215059927164065,
-                        "SWcld_alt":-0.0003214547890770581,
-                        "SWcld_amt":0.051174897728394786,
-                        "SWcld_err":-0.00010426424714014875,
-                        "SWcld_tau":-0.006943106201430423,
-                        "SWcld_tot":0.04380607249074716,
-                        "SWdobsc_cov_fbk":6.992279908270572e-20,
-                        "SWdobsc_fbk":-0.010083992615072776,
-                        "SWdunobsc_fbk":0.04380607249074716
-                    }
-                },
-                "ocn":{
-                    "LO680":{
-                        "LWcld_alt":-0.013109268837326457,
-                        "LWcld_amt":-0.07552107459528316,
-                        "LWcld_err":0.0007041519357784625,
-                        "LWcld_tau":-0.005048758972224693,
-                        "LWcld_tot":-0.09297495046905584,
-                        "LWdobsc_cov_fbk":2.4311494850349442e-20,
-                        "LWdobsc_fbk":0.005774737090632601,
-                        "LWdunobsc_fbk":-0.09297495046905584,
-                        "SWcld_alt":0.005563606342767386,
-                        "SWcld_amt":0.4201296418535649,
-                        "SWcld_err":-0.0009486454704078984,
-                        "SWcld_tau":0.06559899769027644,
-                        "SWcld_tot":0.49034360041620073,
-                        "SWdobsc_cov_fbk":-8.663917217954139e-20,
-                        "SWdobsc_fbk":-0.03550797336116637,
-                        "SWdunobsc_fbk":0.4903436004162007
-                    }
-                },
-                "ocn_asc":{
-                    "LO680":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "LWdobsc_cov_fbk":NaN,
-                        "LWdobsc_fbk":NaN,
-                        "LWdunobsc_fbk":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN,
-                        "SWdobsc_cov_fbk":NaN,
-                        "SWdobsc_fbk":NaN,
-                        "SWdunobsc_fbk":NaN
-                    }
-                },
-                "ocn_dsc":{
-                    "LO680":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "LWdobsc_cov_fbk":NaN,
-                        "LWdobsc_fbk":NaN,
-                        "LWdunobsc_fbk":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN,
-                        "SWdobsc_cov_fbk":NaN,
-                        "SWdobsc_fbk":NaN,
-                        "SWdunobsc_fbk":NaN
-                    }
-                }
-            },
-            "eq50":{
-                "all":{
-                    "LO680":{
-                        "LWcld_alt":-0.013428036942068391,
-                        "LWcld_amt":-0.1095800599155672,
-                        "LWcld_err":0.000993260767916373,
-                        "LWcld_tau":-0.0038077600590618673,
-                        "LWcld_tot":-0.12582259614878108,
-                        "LWdobsc_cov_fbk":-1.871607048619409e-20,
-                        "LWdobsc_fbk":0.006934192792727293,
-                        "LWdunobsc_fbk":-0.12582259614878108,
-                        "SWcld_alt":0.005619276614318128,
-                        "SWcld_amt":0.5846359883665878,
-                        "SWcld_err":-0.001125851333049423,
-                        "SWcld_tau":0.058600574915277946,
-                        "SWcld_tot":0.6477299885631344,
-                        "SWdobsc_cov_fbk":1.533851794185621e-19,
-                        "SWdobsc_fbk":-0.04003360955213659,
-                        "SWdunobsc_fbk":0.6477299885631344
-                    }
-                },
-                "lnd":{
-                    "LO680":{
-                        "LWcld_alt":0.0008517432543004676,
-                        "LWcld_amt":-0.01310880849097836,
-                        "LWcld_err":0.00012999862017389928,
-                        "LWcld_tau":0.0014476199361460591,
-                        "LWcld_tot":-0.010679446680357934,
-                        "LWdobsc_cov_fbk":-1.3691392902817775e-20,
-                        "LWdobsc_fbk":0.002149691672046844,
-                        "LWdunobsc_fbk":-0.010679446680357934,
-                        "SWcld_alt":-0.00035986858788131975,
-                        "SWcld_amt":0.06820179321726715,
-                        "SWcld_err":-0.00010438684091946118,
-                        "SWcld_tau":-0.011164135207743911,
-                        "SWcld_tot":0.05657340258072246,
-                        "SWdobsc_cov_fbk":8.490656010959088e-20,
-                        "SWdobsc_fbk":-0.01237751763437473,
-                        "SWdunobsc_fbk":0.056573402580722444
-                    }
-                },
-                "ocn":{
-                    "LO680":{
-                        "LWcld_alt":-0.014279780196368858,
-                        "LWcld_amt":-0.09647125142458886,
-                        "LWcld_err":0.0008632621477424736,
-                        "LWcld_tau":-0.005255379995207927,
-                        "LWcld_tot":-0.11514314946842315,
-                        "LWdobsc_cov_fbk":-5.0246775833763235e-21,
-                        "LWdobsc_fbk":0.004784501120680449,
-                        "LWdunobsc_fbk":-0.11514314946842315,
-                        "SWcld_alt":0.005979145202199449,
-                        "SWcld_amt":0.5164341951493207,
-                        "SWcld_err":-0.0010214644921299617,
-                        "SWcld_tau":0.06976471012302184,
-                        "SWcld_tot":0.5911565859824119,
-                        "SWdobsc_cov_fbk":6.847861930897123e-20,
-                        "SWdobsc_fbk":-0.02765609191776186,
-                        "SWdunobsc_fbk":0.5911565859824119
-                    }
-                },
-                "ocn_asc":{
-                    "LO680":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "LWdobsc_cov_fbk":NaN,
-                        "LWdobsc_fbk":NaN,
-                        "LWdunobsc_fbk":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN,
-                        "SWdobsc_cov_fbk":NaN,
-                        "SWdobsc_fbk":NaN,
-                        "SWdunobsc_fbk":NaN
-                    }
-                },
-                "ocn_dsc":{
-                    "LO680":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "LWdobsc_cov_fbk":NaN,
-                        "LWdobsc_fbk":NaN,
-                        "LWdunobsc_fbk":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN,
-                        "SWdobsc_cov_fbk":NaN,
-                        "SWdobsc_fbk":NaN,
-                        "SWdunobsc_fbk":NaN
-                    }
-                }
-            },
-            "eq60":{
-                "all":{
-                    "LO680":{
-                        "LWcld_alt":-0.014656289595996219,
-                        "LWcld_amt":-0.12279922010441074,
-                        "LWcld_err":0.001037564125564928,
-                        "LWcld_tau":-0.0005331213574538072,
-                        "LWcld_tot":-0.13695106693229583,
-                        "LWdobsc_cov_fbk":-1.6211327742947665e-20,
-                        "LWdobsc_fbk":0.0004898828199389394,
-                        "LWdunobsc_fbk":-0.13695106693229583,
-                        "SWcld_alt":0.006194419331491931,
-                        "SWcld_amt":0.6413823223441795,
-                        "SWcld_err":-0.001084570918262205,
-                        "SWcld_tau":0.0297470283331883,
-                        "SWcld_tot":0.6762391990905975,
-                        "SWdobsc_cov_fbk":1.6597372426213883e-19,
-                        "SWdobsc_fbk":-0.018080256796292462,
-                        "SWdunobsc_fbk":0.6762391990905975
-                    }
-                },
-                "lnd":{
-                    "LO680":{
-                        "LWcld_alt":0.0010873825604994192,
-                        "LWcld_amt":-0.016289044235088743,
-                        "LWcld_err":0.00011676806443698895,
-                        "LWcld_tau":0.0032314467487860443,
-                        "LWcld_tot":-0.01185344686136629,
-                        "LWdobsc_cov_fbk":-1.0919878685812166e-20,
-                        "LWdobsc_fbk":0.0019343337366100769,
-                        "LWdunobsc_fbk":-0.01185344686136629,
-                        "SWcld_alt":-0.0003977695089378102,
-                        "SWcld_amt":0.08448950041833951,
-                        "SWcld_err":-0.00010780404801654003,
-                        "SWcld_tau":-0.01693734217115279,
-                        "SWcld_tot":0.06704658469023236,
-                        "SWdobsc_cov_fbk":8.425103331407446e-20,
-                        "SWdobsc_fbk":-0.012328886333997811,
-                        "SWdunobsc_fbk":0.06704658469023236
-                    }
-                },
-                "ocn":{
-                    "LO680":{
-                        "LWcld_alt":-0.015743672156495636,
-                        "LWcld_amt":-0.10651017586932203,
-                        "LWcld_err":0.0009207960611279387,
-                        "LWcld_tau":-0.003764568106239852,
-                        "LWcld_tot":-0.1250976200709296,
-                        "LWdobsc_cov_fbk":-5.2914490571355034e-21,
-                        "LWdobsc_fbk":-0.0014444509166711376,
-                        "LWdunobsc_fbk":-0.1250976200709296,
-                        "SWcld_alt":0.006592188840429742,
-                        "SWcld_amt":0.5568928219258399,
-                        "SWcld_err":-0.000976766870245665,
-                        "SWcld_tau":0.0466843705043411,
-                        "SWcld_tot":0.609192614400365,
-                        "SWdobsc_cov_fbk":8.172269094806432e-20,
-                        "SWdobsc_fbk":-0.00575137046229465,
-                        "SWdunobsc_fbk":0.6091926144003651
-                    }
-                },
-                "ocn_asc":{
-                    "LO680":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "LWdobsc_cov_fbk":NaN,
-                        "LWdobsc_fbk":NaN,
-                        "LWdunobsc_fbk":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN,
-                        "SWdobsc_cov_fbk":NaN,
-                        "SWdobsc_fbk":NaN,
-                        "SWdunobsc_fbk":NaN
-                    }
-                },
-                "ocn_dsc":{
-                    "LO680":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "LWdobsc_cov_fbk":NaN,
-                        "LWdobsc_fbk":NaN,
-                        "LWdunobsc_fbk":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN,
-                        "SWdobsc_cov_fbk":NaN,
-                        "SWdobsc_fbk":NaN,
-                        "SWdunobsc_fbk":NaN
-                    }
-                }
-            },
-            "eq70":{
-                "all":{
-                    "LO680":{
-                        "LWcld_alt":-0.014703725546623676,
-                        "LWcld_amt":-0.12670460487065258,
-                        "LWcld_err":0.0008579673009908931,
-                        "LWcld_tau":0.0018202037647529572,
-                        "LWcld_tot":-0.1387301593515324,
-                        "LWdobsc_cov_fbk":-1.8827764835232653e-20,
-                        "LWdobsc_fbk":-0.002446833564904723,
-                        "LWdunobsc_fbk":-0.1387301593515324,
-                        "SWcld_alt":0.00634794836638193,
-                        "SWcld_amt":0.6585341548033484,
-                        "SWcld_err":-0.0010054101955411156,
-                        "SWcld_tau":0.013195740182305687,
-                        "SWcld_tot":0.6770724331564949,
-                        "SWdobsc_cov_fbk":1.9726136092278349e-19,
-                        "SWdobsc_fbk":-0.007637738310671635,
-                        "SWdunobsc_fbk":0.6770724331564949
-                    }
-                },
-                "lnd":{
-                    "LO680":{
-                        "LWcld_alt":0.0013115726077212008,
-                        "LWcld_amt":-0.018188609143882546,
-                        "LWcld_err":7.409764541999529e-05,
-                        "LWcld_tau":0.004327140004909097,
-                        "LWcld_tot":-0.012475798885832253,
-                        "LWdobsc_cov_fbk":-1.6745284708300695e-20,
-                        "LWdobsc_fbk":0.0016134195760362064,
-                        "LWdunobsc_fbk":-0.012475798885832253,
-                        "SWcld_alt":-0.00046802000429967735,
-                        "SWcld_amt":0.09539986122197497,
-                        "SWcld_err":-9.742448761116441e-05,
-                        "SWcld_tau":-0.020973600650364327,
-                        "SWcld_tot":0.07386081607969981,
-                        "SWdobsc_cov_fbk":1.140872908201346e-19,
-                        "SWdobsc_fbk":-0.011571647847444319,
-                        "SWdunobsc_fbk":0.07386081607969981
-                    }
-                },
-                "ocn":{
-                    "LO680":{
-                        "LWcld_alt":-0.016015298154344874,
-                        "LWcld_amt":-0.10851599572677008,
-                        "LWcld_err":0.0007838696555708978,
-                        "LWcld_tau":-0.002506936240156139,
-                        "LWcld_tot":-0.12625436046570018,
-                        "LWdobsc_cov_fbk":-2.0824801269319623e-21,
-                        "LWdobsc_fbk":-0.00406025314094093,
-                        "LWdunobsc_fbk":-0.12625436046570018,
-                        "SWcld_alt":0.0068159683706816064,
-                        "SWcld_amt":0.5631342935813735,
-                        "SWcld_err":-0.0009079857079299512,
-                        "SWcld_tau":0.034169340832670016,
-                        "SWcld_tot":0.603211617076795,
-                        "SWdobsc_cov_fbk":8.317407010264886e-20,
-                        "SWdobsc_fbk":0.003933909536772685,
-                        "SWdunobsc_fbk":0.603211617076795
-                    }
-                },
-                "ocn_asc":{
-                    "LO680":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "LWdobsc_cov_fbk":NaN,
-                        "LWdobsc_fbk":NaN,
-                        "LWdunobsc_fbk":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN,
-                        "SWdobsc_cov_fbk":NaN,
-                        "SWdobsc_fbk":NaN,
-                        "SWdunobsc_fbk":NaN
-                    }
-                },
-                "ocn_dsc":{
-                    "LO680":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "LWdobsc_cov_fbk":NaN,
-                        "LWdobsc_fbk":NaN,
-                        "LWdunobsc_fbk":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN,
-                        "SWdobsc_cov_fbk":NaN,
-                        "SWdobsc_fbk":NaN,
-                        "SWdunobsc_fbk":NaN
-                    }
-                }
-            },
-            "eq80":{
-                "all":{
-                    "LO680":{
-                        "LWcld_alt":-0.014367943269557782,
-                        "LWcld_amt":-0.12708468354819347,
-                        "LWcld_err":0.0007520081312002786,
-                        "LWcld_tau":0.0026911362686422523,
-                        "LWcld_tot":-0.13800948241790875,
-                        "LWdobsc_cov_fbk":-2.1246795065766644e-20,
-                        "LWdobsc_fbk":-0.0036984252385343767,
-                        "LWdunobsc_fbk":-0.13800948241790875,
-                        "SWcld_alt":0.006281205778288675,
-                        "SWcld_amt":0.6593483611740635,
-                        "SWcld_err":-0.0009765501535604506,
-                        "SWcld_tau":0.009427237056695124,
-                        "SWcld_tot":0.6740802538554869,
-                        "SWdobsc_cov_fbk":2.160695485483339e-19,
-                        "SWdobsc_fbk":-0.003171305091276449,
-                        "SWdunobsc_fbk":0.6740802538554869
-                    }
-                },
-                "lnd":{
-                    "LO680":{
-                        "LWcld_alt":0.001411704986462069,
-                        "LWcld_amt":-0.018413078868258076,
-                        "LWcld_err":5.650740379866361e-05,
-                        "LWcld_tau":0.0045504960284564475,
-                        "LWcld_tot":-0.012394370449540894,
-                        "LWdobsc_cov_fbk":-1.7125126135751e-20,
-                        "LWdobsc_fbk":0.0014444216295809243,
-                        "LWdunobsc_fbk":-0.012394370449540892,
-                        "SWcld_alt":-0.0004932240174164947,
-                        "SWcld_amt":0.09627982890226432,
-                        "SWcld_err":-9.213351904791016e-05,
-                        "SWcld_tau":-0.021669694839650987,
-                        "SWcld_tot":0.0740247765261489,
-                        "SWdobsc_cov_fbk":1.2017820642848325e-19,
-                        "SWdobsc_fbk":-0.011107532679644093,
-                        "SWdunobsc_fbk":0.0740247765261489
-                    }
-                },
-                "ocn":{
-                    "LO680":{
-                        "LWcld_alt":-0.01577964825601985,
-                        "LWcld_amt":-0.10867160467993542,
-                        "LWcld_err":0.0006955007274016147,
-                        "LWcld_tau":-0.001859359759814195,
-                        "LWcld_tot":-0.12561511196836786,
-                        "LWdobsc_cov_fbk":-4.121668930015634e-21,
-                        "LWdobsc_fbk":-0.0051428468681153005,
-                        "LWdunobsc_fbk":-0.12561511196836786,
-                        "SWcld_alt":0.0067744297957051695,
-                        "SWcld_amt":0.5630685322717993,
-                        "SWcld_err":-0.0008844166345125406,
-                        "SWcld_tau":0.031096931896346106,
-                        "SWcld_tot":0.6000554773293378,
-                        "SWdobsc_cov_fbk":9.589134211985063e-20,
-                        "SWdobsc_fbk":0.007936227588367643,
-                        "SWdunobsc_fbk":0.6000554773293378
-                    }
-                },
-                "ocn_asc":{
-                    "LO680":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "LWdobsc_cov_fbk":NaN,
-                        "LWdobsc_fbk":NaN,
-                        "LWdunobsc_fbk":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN,
-                        "SWdobsc_cov_fbk":NaN,
-                        "SWdobsc_fbk":NaN,
-                        "SWdunobsc_fbk":NaN
-                    }
-                },
-                "ocn_dsc":{
-                    "LO680":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "LWdobsc_cov_fbk":NaN,
-                        "LWdobsc_fbk":NaN,
-                        "LWdunobsc_fbk":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN,
-                        "SWdobsc_cov_fbk":NaN,
-                        "SWdobsc_fbk":NaN,
-                        "SWdunobsc_fbk":NaN
-                    }
-                }
-            },
-            "eq90":{
-                "all":{
-                    "LO680":{
-                        "LWcld_alt":-0.014200311734510036,
-                        "LWcld_amt":-0.12710558604690644,
-                        "LWcld_err":0.0007087994355307186,
-                        "LWcld_tau":0.0030203584280819444,
-                        "LWcld_tot":-0.1375767399178038,
-                        "LWdobsc_cov_fbk":-2.06643409998491e-20,
-                        "LWdobsc_fbk":-0.004099003360780355,
-                        "LWdunobsc_fbk":-0.13757673991780378,
-                        "SWcld_alt":0.006247809008500711,
-                        "SWcld_amt":0.6590871987412019,
-                        "SWcld_err":-0.0009665763480433024,
-                        "SWcld_tau":0.008083022622108436,
-                        "SWcld_tot":0.6724514540237677,
-                        "SWdobsc_cov_fbk":2.1080927671890592e-19,
-                        "SWdobsc_fbk":-0.0018516996670433366,
-                        "SWdunobsc_fbk":0.6724514540237677
-                    }
-                },
-                "lnd":{
-                    "LO680":{
-                        "LWcld_alt":0.0014216170039335046,
-                        "LWcld_amt":-0.018464501342701927,
-                        "LWcld_err":5.452755011247662e-05,
-                        "LWcld_tau":0.004585771587478347,
-                        "LWcld_tot":-0.012402585201177601,
-                        "LWdobsc_cov_fbk":-1.708456526970879e-20,
-                        "LWdobsc_fbk":0.0014346463111467487,
-                        "LWdunobsc_fbk":-0.012402585201177598,
-                        "SWcld_alt":-0.000494114518798633,
-                        "SWcld_amt":0.09632040383357417,
-                        "SWcld_err":-9.180320413661887e-05,
-                        "SWcld_tau":-0.021716960967398048,
-                        "SWcld_tot":0.07401752514324086,
-                        "SWdobsc_cov_fbk":1.200579070906861e-19,
-                        "SWdobsc_fbk":-0.011091899918020237,
-                        "SWdunobsc_fbk":0.07401752514324086
-                    }
-                },
-                "ocn":{
-                    "LO680":{
-                        "LWcld_alt":-0.015621928738443541,
-                        "LWcld_amt":-0.10864108470420449,
-                        "LWcld_err":0.0006542718854182419,
-                        "LWcld_tau":-0.0015654131593964022,
-                        "LWcld_tot":-0.12517415471662618,
-                        "LWdobsc_cov_fbk":-3.579775730140294e-21,
-                        "LWdobsc_fbk":-0.005533649671927103,
-                        "LWdunobsc_fbk":-0.12517415471662618,
-                        "SWcld_alt":0.006741923527299344,
-                        "SWcld_amt":0.5627667949076277,
-                        "SWcld_err":-0.0008747731439066837,
-                        "SWcld_tau":0.029799983589506484,
-                        "SWcld_tot":0.5984339288805268,
-                        "SWdobsc_cov_fbk":9.075136962821971e-20,
-                        "SWdobsc_fbk":0.009240200250976898,
-                        "SWdunobsc_fbk":0.5984339288805268
-                    }
-                },
-                "ocn_asc":{
-                    "LO680":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "LWdobsc_cov_fbk":NaN,
-                        "LWdobsc_fbk":NaN,
-                        "LWdunobsc_fbk":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN,
-                        "SWdobsc_cov_fbk":NaN,
-                        "SWdobsc_fbk":NaN,
-                        "SWdunobsc_fbk":NaN
-                    }
-                },
-                "ocn_dsc":{
-                    "LO680":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "LWdobsc_cov_fbk":NaN,
-                        "LWdobsc_fbk":NaN,
-                        "LWdunobsc_fbk":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN,
-                        "SWdobsc_cov_fbk":NaN,
-                        "SWdobsc_fbk":NaN,
-                        "SWdunobsc_fbk":NaN
-                    }
-                }
-            }
-        }
-    },
-    "MIROC6":{
-        "r1i1p1f1":{
-            "Arctic":{
-                "all":{
-                    "LO680":{
-                        "LWcld_alt":0.0016431377020264088,
-                        "LWcld_amt":0.0018686520042113471,
-                        "LWcld_err":2.057113944801956e-06,
-                        "LWcld_tau":0.0004555363954411268,
-                        "LWcld_tot":0.003638601280988378,
-                        "LWdobsc_cov_fbk":5.142631188138472e-22,
-                        "LWdobsc_fbk":-0.0019788614606297845,
-                        "LWdunobsc_fbk":0.003969383215623685,
-                        "SWcld_alt":-0.000624225050514426,
-                        "SWcld_amt":-0.0034830081266227146,
-                        "SWcld_err":2.898449642014705e-05,
-                        "SWcld_tau":-0.002024265503398538,
-                        "SWcld_tot":-0.006102514184115532,
-                        "SWdobsc_cov_fbk":6.106495808937695e-22,
-                        "SWdobsc_fbk":0.008777755836602197,
-                        "SWdunobsc_fbk":-0.006657288200853309
-                    }
-                },
-                "lnd":{
-                    "LO680":{
-                        "LWcld_alt":0.000177817102516756,
-                        "LWcld_amt":0.0004216482389330579,
-                        "LWcld_err":4.3667642344217774e-06,
-                        "LWcld_tau":0.0001078260863335292,
-                        "LWcld_tot":0.0007116581920177649,
-                        "LWdobsc_cov_fbk":-3.3689210872625263e-22,
-                        "LWdobsc_fbk":-0.00019284498600246198,
-                        "LWdunobsc_fbk":0.0007116581920177649,
-                        "SWcld_alt":-9.224436194265442e-05,
-                        "SWcld_amt":-0.0005766995016561295,
-                        "SWcld_err":2.7451963851758414e-06,
-                        "SWcld_tau":-0.0006132584911129743,
-                        "SWcld_tot":-0.0012794571583265822,
-                        "SWdobsc_cov_fbk":7.472799662352107e-22,
-                        "SWdobsc_fbk":0.0008452592750166359,
-                        "SWdunobsc_fbk":-0.0012794571583265824
-                    }
-                },
-                "ocn":{
-                    "LO680":{
-                        "LWcld_alt":0.0014653205995096535,
-                        "LWcld_amt":0.0014470037652782894,
-                        "LWcld_err":-2.3096502896198232e-06,
-                        "LWcld_tau":0.0003477103091075976,
-                        "LWcld_tot":0.0032577250236059205,
-                        "LWdobsc_cov_fbk":8.511552275400986e-22,
-                        "LWdobsc_fbk":-0.0017860164746273228,
-                        "LWdunobsc_fbk":0.0032577250236059205,
-                        "SWcld_alt":-0.0005887284204367194,
-                        "SWcld_amt":-0.003222945727386832,
-                        "SWcld_err":2.887425425498458e-05,
-                        "SWcld_tau":-0.0015950311489581583,
-                        "SWcld_tot":-0.005377831042526725,
-                        "SWdobsc_cov_fbk":-1.3663038534144277e-22,
-                        "SWdobsc_fbk":0.00793249656158556,
-                        "SWdunobsc_fbk":-0.005377831042526724
-                    }
-                },
-                "ocn_asc":{
-                    "LO680":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "LWdobsc_cov_fbk":NaN,
-                        "LWdobsc_fbk":NaN,
-                        "LWdunobsc_fbk":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN,
-                        "SWdobsc_cov_fbk":NaN,
-                        "SWdobsc_fbk":NaN,
-                        "SWdunobsc_fbk":NaN
-                    }
-                },
-                "ocn_dsc":{
-                    "LO680":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "LWdobsc_cov_fbk":NaN,
-                        "LWdobsc_fbk":NaN,
-                        "LWdunobsc_fbk":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN,
-                        "SWdobsc_cov_fbk":NaN,
-                        "SWdobsc_fbk":NaN,
-                        "SWdunobsc_fbk":NaN
-                    }
-                }
-            },
-            "eq10":{
-                "all":{
-                    "LO680":{
-                        "LWcld_alt":0.010914775979631862,
-                        "LWcld_amt":-0.006528092347278039,
-                        "LWcld_err":0.00013087934212393954,
-                        "LWcld_tau":0.00034430827493348487,
-                        "LWcld_tot":0.004861871249411248,
-                        "LWdobsc_cov_fbk":6.4726205765421434e-21,
-                        "LWdobsc_fbk":0.0051455000361023346,
-                        "LWdunobsc_fbk":0.004861871249411248,
-                        "SWcld_alt":-0.00578595268675882,
-                        "SWcld_amt":0.059605508987227405,
-                        "SWcld_err":-0.0004159607799181679,
-                        "SWcld_tau":-0.02766037906848377,
-                        "SWcld_tot":0.025743216452066647,
-                        "SWdobsc_cov_fbk":3.686435152377572e-21,
-                        "SWdobsc_fbk":-0.05178912645641521,
-                        "SWdunobsc_fbk":0.025743216452066647
-                    }
-                },
-                "lnd":{
-                    "LO680":{
-                        "LWcld_alt":0.0026675839875785873,
-                        "LWcld_amt":-0.003375977809313101,
-                        "LWcld_err":1.7740822626803495e-05,
-                        "LWcld_tau":4.422323941213709e-05,
-                        "LWcld_tot":-0.0006464297596955738,
-                        "LWdobsc_cov_fbk":5.412521337265562e-21,
-                        "LWdobsc_fbk":0.001930506600042413,
-                        "LWdunobsc_fbk":-0.0006464297596955739,
-                        "SWcld_alt":-0.0015193861504634546,
-                        "SWcld_amt":0.028637103826927895,
-                        "SWcld_err":1.1344901524763287e-05,
-                        "SWcld_tau":-0.0034156225655261664,
-                        "SWcld_tot":0.02371344001246303,
-                        "SWdobsc_cov_fbk":-3.160879712589608e-20,
-                        "SWdobsc_fbk":-0.016863169972537027,
-                        "SWdunobsc_fbk":0.023713440012463033
-                    }
-                },
-                "ocn":{
-                    "LO680":{
-                        "LWcld_alt":0.008247191992053276,
-                        "LWcld_amt":-0.003152114537964939,
-                        "LWcld_err":0.00011313851949713604,
-                        "LWcld_tau":0.00030008503552134776,
-                        "LWcld_tot":0.005508301009106822,
-                        "LWdobsc_cov_fbk":1.0600992392765835e-21,
-                        "LWdobsc_fbk":0.003214993436059922,
-                        "LWdunobsc_fbk":0.005508301009106822,
-                        "SWcld_alt":-0.004266566536295366,
-                        "SWcld_amt":0.03096840516029951,
-                        "SWcld_err":-0.00042730568144293117,
-                        "SWcld_tau":-0.0242447565029576,
-                        "SWcld_tot":0.002029776439603613,
-                        "SWdobsc_cov_fbk":3.5295232278273655e-20,
-                        "SWdobsc_fbk":-0.03492595648387818,
-                        "SWdunobsc_fbk":0.0020297764396036105
-                    }
-                },
-                "ocn_asc":{
-                    "LO680":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "LWdobsc_cov_fbk":NaN,
-                        "LWdobsc_fbk":NaN,
-                        "LWdunobsc_fbk":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN,
-                        "SWdobsc_cov_fbk":NaN,
-                        "SWdobsc_fbk":NaN,
-                        "SWdunobsc_fbk":NaN
-                    }
-                },
-                "ocn_dsc":{
-                    "LO680":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "LWdobsc_cov_fbk":NaN,
-                        "LWdobsc_fbk":NaN,
-                        "LWdunobsc_fbk":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN,
-                        "SWdobsc_cov_fbk":NaN,
-                        "SWdobsc_fbk":NaN,
-                        "SWdunobsc_fbk":NaN
-                    }
-                }
-            },
-            "eq20":{
-                "all":{
-                    "LO680":{
-                        "LWcld_alt":0.018174669624527618,
-                        "LWcld_amt":-0.011707750535608392,
-                        "LWcld_err":0.00016587227007552427,
-                        "LWcld_tau":0.00046141471285768743,
-                        "LWcld_tot":0.007094206071852439,
-                        "LWdobsc_cov_fbk":1.0821965329948433e-20,
-                        "LWdobsc_fbk":0.007295724955795922,
-                        "LWdunobsc_fbk":0.007094206071852439,
-                        "SWcld_alt":-0.009545321703210005,
-                        "SWcld_amt":0.1029080223735232,
-                        "SWcld_err":-0.0005318929609654605,
-                        "SWcld_tau":-0.039520198793915405,
-                        "SWcld_tot":0.05331060891543233,
-                        "SWdobsc_cov_fbk":-2.50225887942006e-20,
-                        "SWdobsc_fbk":-0.06882839248634778,
-                        "SWdunobsc_fbk":0.05331060891543234
-                    }
-                },
-                "lnd":{
-                    "LO680":{
-                        "LWcld_alt":0.00362370675244632,
-                        "LWcld_amt":-0.006132286174481565,
-                        "LWcld_err":1.1381373761870578e-05,
-                        "LWcld_tau":6.907079531017232e-05,
-                        "LWcld_tot":-0.0024281272529632024,
-                        "LWdobsc_cov_fbk":7.210861094884328e-21,
-                        "LWdobsc_fbk":0.002657541374095662,
-                        "LWdunobsc_fbk":-0.0024281272529632024,
-                        "SWcld_alt":-0.0020674543878573554,
-                        "SWcld_amt":0.04857228836765417,
-                        "SWcld_err":4.47229557323045e-05,
-                        "SWcld_tau":-0.004524828402801216,
-                        "SWcld_tot":0.0420247285327279,
-                        "SWdobsc_cov_fbk":-3.9980235393227347e-20,
-                        "SWdobsc_fbk":-0.022580576565098414,
-                        "SWdunobsc_fbk":0.0420247285327279
-                    }
-                },
-                "ocn":{
-                    "LO680":{
-                        "LWcld_alt":0.014550962872081298,
-                        "LWcld_amt":-0.005575464361126825,
-                        "LWcld_err":0.00015449089631365366,
-                        "LWcld_tau":0.0003923439175475151,
-                        "LWcld_tot":0.009522333324815642,
-                        "LWdobsc_cov_fbk":3.611104235064104e-21,
-                        "LWdobsc_fbk":0.0046381835817002615,
-                        "LWdunobsc_fbk":0.009522333324815642,
-                        "SWcld_alt":-0.007477867315352649,
-                        "SWcld_amt":0.05433573400586905,
-                        "SWcld_err":-0.0005766159166977649,
-                        "SWcld_tau":-0.03499537039111419,
-                        "SWcld_tot":0.011285880382704437,
-                        "SWdobsc_cov_fbk":1.4957646599026766e-20,
-                        "SWdobsc_fbk":-0.04624781592124938,
-                        "SWdunobsc_fbk":0.011285880382704436
-                    }
-                },
-                "ocn_asc":{
-                    "LO680":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "LWdobsc_cov_fbk":NaN,
-                        "LWdobsc_fbk":NaN,
-                        "LWdunobsc_fbk":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN,
-                        "SWdobsc_cov_fbk":NaN,
-                        "SWdobsc_fbk":NaN,
-                        "SWdunobsc_fbk":NaN
-                    }
-                },
-                "ocn_dsc":{
-                    "LO680":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "LWdobsc_cov_fbk":NaN,
-                        "LWdobsc_fbk":NaN,
-                        "LWdunobsc_fbk":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN,
-                        "SWdobsc_cov_fbk":NaN,
-                        "SWdobsc_fbk":NaN,
-                        "SWdunobsc_fbk":NaN
-                    }
-                }
-            },
-            "eq30":{
-                "all":{
-                    "LO680":{
-                        "LWcld_alt":0.021877554460790833,
-                        "LWcld_amt":-0.018387569199467973,
-                        "LWcld_err":0.00019310687036659744,
-                        "LWcld_tau":0.0003552620220794607,
-                        "LWcld_tot":0.00403835415376892,
-                        "LWdobsc_cov_fbk":9.209584185876996e-21,
-                        "LWdobsc_fbk":0.004461042226964394,
-                        "LWdunobsc_fbk":0.0040383541537689195,
-                        "SWcld_alt":-0.01136749361482312,
-                        "SWcld_amt":0.15312958475103117,
-                        "SWcld_err":-0.0005505649039066036,
-                        "SWcld_tau":-0.03952435137846524,
-                        "SWcld_tot":0.10168717485383623,
-                        "SWdobsc_cov_fbk":1.4897280236461448e-20,
-                        "SWdobsc_fbk":-0.05079190261195112,
-                        "SWdunobsc_fbk":0.10168717485383623
-                    }
-                },
-                "lnd":{
-                    "LO680":{
-                        "LWcld_alt":0.004079569923250744,
-                        "LWcld_amt":-0.009021595914663198,
-                        "LWcld_err":5.355700414267122e-06,
-                        "LWcld_tau":7.828804455528933e-05,
-                        "LWcld_tot":-0.004858382246442894,
-                        "LWdobsc_cov_fbk":5.137618734408598e-21,
-                        "LWdobsc_fbk":0.0032104273098305255,
-                        "LWdunobsc_fbk":-0.004858382246442894,
-                        "SWcld_alt":-0.0023218488338615866,
-                        "SWcld_amt":0.06743381593019747,
-                        "SWcld_err":6.747747772103792e-05,
-                        "SWcld_tau":-0.004671497106989264,
-                        "SWcld_tot":0.06050794746706766,
-                        "SWdobsc_cov_fbk":-1.8108509752910068e-20,
-                        "SWdobsc_fbk":-0.02657855410113712,
-                        "SWdunobsc_fbk":0.06050794746706766
-                    }
-                },
-                "ocn":{
-                    "LO680":{
-                        "LWcld_alt":0.017797984537540088,
-                        "LWcld_amt":-0.00936597328480478,
-                        "LWcld_err":0.0001877511699523303,
-                        "LWcld_tau":0.0002769739775241714,
-                        "LWcld_tot":0.008896736400211816,
-                        "LWdobsc_cov_fbk":4.071965451468398e-21,
-                        "LWdobsc_fbk":0.001250614917133868,
-                        "LWdunobsc_fbk":0.008896736400211816,
-                        "SWcld_alt":-0.009045644780961532,
-                        "SWcld_amt":0.08569576882083373,
-                        "SWcld_err":-0.0006180423816276413,
-                        "SWcld_tau":-0.034852854271475976,
-                        "SWcld_tot":0.04117922738676858,
-                        "SWdobsc_cov_fbk":3.3005789989371495e-20,
-                        "SWdobsc_fbk":-0.024213348510813994,
-                        "SWdunobsc_fbk":0.041179227386768574
-                    }
-                },
-                "ocn_asc":{
-                    "LO680":{
-                        "LWcld_alt":0.010143376353251899,
-                        "LWcld_amt":0.004203222134957023,
-                        "LWcld_err":9.321125745697094e-05,
-                        "LWcld_tau":0.0005119790503673725,
-                        "LWcld_tot":0.014951788796033268,
-                        "LWdobsc_cov_fbk":1.2172504992724878e-19,
-                        "LWdobsc_fbk":3.976416573479224e-06,
-                        "LWdunobsc_fbk":0.014951788796033266,
-                        "SWcld_alt":-0.005126879424048021,
-                        "SWcld_amt":-0.041515605122188486,
-                        "SWcld_err":-0.00042330979054347825,
-                        "SWcld_tau":-0.03219060140168679,
-                        "SWcld_tot":-0.07925639573846675,
-                        "SWdobsc_cov_fbk":-8.283737481640271e-19,
-                        "SWdobsc_fbk":-5.7991341362481875e-05,
-                        "SWdunobsc_fbk":-0.07925639573846677
-                    }
-                },
-                "ocn_dsc":{
-                    "LO680":{
-                        "LWcld_alt":0.01186337080146274,
-                        "LWcld_amt":-0.01800348579850571,
-                        "LWcld_err":-1.6559779098440952e-05,
-                        "LWcld_tau":0.0002276533504335129,
-                        "LWcld_tot":-0.005929021425707899,
-                        "LWdobsc_cov_fbk":6.352605320082811e-19,
-                        "LWdobsc_fbk":0.00012283596184382183,
-                        "LWdunobsc_fbk":-0.005929021425707909,
-                        "SWcld_alt":-0.00570345687948865,
-                        "SWcld_amt":0.10412933103185157,
-                        "SWcld_err":0.00019459430693669893,
-                        "SWcld_tau":-0.01564173298147831,
-                        "SWcld_tot":0.0829787354778213,
-                        "SWdobsc_cov_fbk":-4.476937808646372e-18,
-                        "SWdobsc_fbk":-0.0006569335536282445,
-                        "SWdunobsc_fbk":0.08297873547782135
-                    }
-                }
-            },
-            "eq40":{
-                "all":{
-                    "LO680":{
-                        "LWcld_alt":0.027157961504097206,
-                        "LWcld_amt":-0.021422855567485962,
-                        "LWcld_err":0.00029275837616970444,
-                        "LWcld_tau":0.0003876566755134812,
-                        "LWcld_tot":0.006415520988294429,
-                        "LWdobsc_cov_fbk":8.035679841668465e-21,
-                        "LWdobsc_fbk":0.005907959056139718,
-                        "LWdunobsc_fbk":0.00641552098829443,
-                        "SWcld_alt":-0.013763226175150805,
-                        "SWcld_amt":0.1757510146486261,
-                        "SWcld_err":-0.0006463100133167285,
-                        "SWcld_tau":-0.04141800419738107,
-                        "SWcld_tot":0.1199234742627775,
-                        "SWdobsc_cov_fbk":3.3415970401213136e-20,
-                        "SWdobsc_fbk":-0.05937805713983576,
-                        "SWdunobsc_fbk":0.1199234742627775
-                    }
-                },
-                "lnd":{
-                    "LO680":{
-                        "LWcld_alt":0.004213798867274093,
-                        "LWcld_amt":-0.010689896243918924,
-                        "LWcld_err":3.163122519275201e-06,
-                        "LWcld_tau":0.00013578910113614922,
-                        "LWcld_tot":-0.006337145152989406,
-                        "LWdobsc_cov_fbk":8.499587597289223e-21,
-                        "LWdobsc_fbk":0.003401077349130779,
-                        "LWdunobsc_fbk":-0.006337145152989406,
-                        "SWcld_alt":-0.0024029342597807267,
-                        "SWcld_amt":0.0773593826088848,
-                        "SWcld_err":7.374346698307207e-05,
-                        "SWcld_tau":-0.005124374991341221,
-                        "SWcld_tot":0.06990581682474592,
-                        "SWdobsc_cov_fbk":-3.005915625986457e-20,
-                        "SWdobsc_fbk":-0.027777947851891114,
-                        "SWdunobsc_fbk":0.06990581682474592
-                    }
-                },
-                "ocn":{
-                    "LO680":{
-                        "LWcld_alt":0.022944162636823112,
-                        "LWcld_amt":-0.01073295932356704,
-                        "LWcld_err":0.00028959525365042924,
-                        "LWcld_tau":0.000251867574377332,
-                        "LWcld_tot":0.012752666141283836,
-                        "LWdobsc_cov_fbk":-4.639077556207623e-22,
-                        "LWdobsc_fbk":0.0025068817070089387,
-                        "LWdunobsc_fbk":0.012752666141283836,
-                        "SWcld_alt":-0.011360291915370078,
-                        "SWcld_amt":0.09839163203974129,
-                        "SWcld_err":-0.0007200534802998005,
-                        "SWcld_tau":-0.03629362920603986,
-                        "SWcld_tot":0.050017657438031576,
-                        "SWdobsc_cov_fbk":6.34751266610777e-20,
-                        "SWdobsc_fbk":-0.03160010928794463,
-                        "SWdunobsc_fbk":0.050017657438031576
-                    }
-                },
-                "ocn_asc":{
-                    "LO680":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "LWdobsc_cov_fbk":NaN,
-                        "LWdobsc_fbk":NaN,
-                        "LWdunobsc_fbk":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN,
-                        "SWdobsc_cov_fbk":NaN,
-                        "SWdobsc_fbk":NaN,
-                        "SWdunobsc_fbk":NaN
-                    }
-                },
-                "ocn_dsc":{
-                    "LO680":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "LWdobsc_cov_fbk":NaN,
-                        "LWdobsc_fbk":NaN,
-                        "LWdunobsc_fbk":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN,
-                        "SWdobsc_cov_fbk":NaN,
-                        "SWdobsc_fbk":NaN,
-                        "SWdunobsc_fbk":NaN
-                    }
-                }
-            },
-            "eq50":{
-                "all":{
-                    "LO680":{
-                        "LWcld_alt":0.031743569749523005,
-                        "LWcld_amt":-0.02434529477657356,
-                        "LWcld_err":0.0003217178662062272,
-                        "LWcld_tau":0.0011213671186099913,
-                        "LWcld_tot":0.008841359957765655,
-                        "LWdobsc_cov_fbk":1.243982797498629e-20,
-                        "LWdobsc_fbk":0.006512853562681374,
-                        "LWdunobsc_fbk":0.008841359957765657,
-                        "SWcld_alt":-0.015860228305983517,
-                        "SWcld_amt":0.20048798394043102,
-                        "SWcld_err":-0.0006527264328730917,
-                        "SWcld_tau":-0.04400354629700213,
-                        "SWcld_tot":0.13997148290457231,
-                        "SWdobsc_cov_fbk":2.3611982227907828e-21,
-                        "SWdobsc_fbk":-0.06509059691020999,
-                        "SWdunobsc_fbk":0.13997148290457231
-                    }
-                },
-                "lnd":{
-                    "LO680":{
-                        "LWcld_alt":0.004450459806783889,
-                        "LWcld_amt":-0.01209326077591679,
-                        "LWcld_err":-6.272325705059479e-07,
-                        "LWcld_tau":0.00036340940960477027,
-                        "LWcld_tot":-0.007280018792098636,
-                        "LWdobsc_cov_fbk":9.860053522520377e-21,
-                        "LWdobsc_fbk":0.003382019911569534,
-                        "LWdunobsc_fbk":-0.007280018792098635,
-                        "SWcld_alt":-0.002494119984141985,
-                        "SWcld_amt":0.08777997261200911,
-                        "SWcld_err":7.719917276594088e-05,
-                        "SWcld_tau":-0.006472793236065211,
-                        "SWcld_tot":0.07889025856456784,
-                        "SWdobsc_cov_fbk":-5.081121460932771e-20,
-                        "SWdobsc_fbk":-0.028691126792239983,
-                        "SWdunobsc_fbk":0.07889025856456784
-                    }
-                },
-                "ocn":{
-                    "LO680":{
-                        "LWcld_alt":0.02729310994273911,
-                        "LWcld_amt":-0.01225203400065677,
-                        "LWcld_err":0.0003223450987767332,
-                        "LWcld_tau":0.0007579577090052209,
-                        "LWcld_tot":0.01612137874986429,
-                        "LWdobsc_cov_fbk":2.5797744524659094e-21,
-                        "LWdobsc_fbk":0.0031308336511118393,
-                        "LWdunobsc_fbk":0.01612137874986429,
-                        "SWcld_alt":-0.013366108321841532,
-                        "SWcld_amt":0.11270801132842197,
-                        "SWcld_err":-0.0007299256056390326,
-                        "SWcld_tau":-0.03753075306093692,
-                        "SWcld_tot":0.06108122434000446,
-                        "SWdobsc_cov_fbk":5.3172412832118506e-20,
-                        "SWdobsc_fbk":-0.036399470117970005,
-                        "SWdunobsc_fbk":0.06108122434000446
-                    }
-                },
-                "ocn_asc":{
-                    "LO680":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "LWdobsc_cov_fbk":NaN,
-                        "LWdobsc_fbk":NaN,
-                        "LWdunobsc_fbk":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN,
-                        "SWdobsc_cov_fbk":NaN,
-                        "SWdobsc_fbk":NaN,
-                        "SWdunobsc_fbk":NaN
-                    }
-                },
-                "ocn_dsc":{
-                    "LO680":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "LWdobsc_cov_fbk":NaN,
-                        "LWdobsc_fbk":NaN,
-                        "LWdunobsc_fbk":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN,
-                        "SWdobsc_cov_fbk":NaN,
-                        "SWdobsc_fbk":NaN,
-                        "SWdunobsc_fbk":NaN
-                    }
-                }
-            },
-            "eq60":{
-                "all":{
-                    "LO680":{
-                        "LWcld_alt":0.035036167099076435,
-                        "LWcld_amt":-0.021898416087519215,
-                        "LWcld_err":0.0003252462801661754,
-                        "LWcld_tau":0.0027374968831597826,
-                        "LWcld_tot":0.016200494174883177,
-                        "LWdobsc_cov_fbk":5.871345622162797e-21,
-                        "LWdobsc_fbk":0.0010438486789558765,
-                        "LWdunobsc_fbk":0.01620049417488318,
-                        "SWcld_alt":-0.017768667816376333,
-                        "SWcld_amt":0.19850445014234205,
-                        "SWcld_err":-0.0006937174846640661,
-                        "SWcld_tau":-0.06244890270590678,
-                        "SWcld_tot":0.11759316213539488,
-                        "SWdobsc_cov_fbk":3.048101279194896e-20,
-                        "SWdobsc_fbk":-0.03339143934228664,
-                        "SWdunobsc_fbk":0.11759316213539485
-                    }
-                },
-                "lnd":{
-                    "LO680":{
-                        "LWcld_alt":0.0045418340610720795,
-                        "LWcld_amt":-0.011188887714492569,
-                        "LWcld_err":1.7062311293994843e-06,
-                        "LWcld_tau":0.0007707091332820526,
-                        "LWcld_tot":-0.005874638289009038,
-                        "LWdobsc_cov_fbk":1.2254547858121368e-20,
-                        "LWdobsc_fbk":0.0028161977041136696,
-                        "LWdunobsc_fbk":-0.005874638289009038,
-                        "SWcld_alt":-0.002542152635129692,
-                        "SWcld_amt":0.09041511314485394,
-                        "SWcld_err":8.078771305951145e-05,
-                        "SWcld_tau":-0.008534771974917603,
-                        "SWcld_tot":0.07941897624786615,
-                        "SWdobsc_cov_fbk":-5.005913510251695e-20,
-                        "SWdobsc_fbk":-0.02753610584280064,
-                        "SWdunobsc_fbk":0.07941897624786615
-                    }
-                },
-                "ocn":{
-                    "LO680":{
-                        "LWcld_alt":0.03049433303800435,
-                        "LWcld_amt":-0.010709528373026642,
-                        "LWcld_err":0.000323540049036776,
-                        "LWcld_tau":0.0019667877498777304,
-                        "LWcld_tot":0.022075132463892217,
-                        "LWdobsc_cov_fbk":-6.383202235958577e-21,
-                        "LWdobsc_fbk":-0.0017723490251577937,
-                        "LWdunobsc_fbk":0.022075132463892217,
-                        "SWcld_alt":-0.015226515181246644,
-                        "SWcld_amt":0.10808933699748814,
-                        "SWcld_err":-0.0007745051977235775,
-                        "SWcld_tau":-0.05391413073098918,
-                        "SWcld_tot":0.038174185887528723,
-                        "SWdobsc_cov_fbk":8.054014789446599e-20,
-                        "SWdobsc_fbk":-0.005855333499485994,
-                        "SWdunobsc_fbk":0.038174185887528723
-                    }
-                },
-                "ocn_asc":{
-                    "LO680":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "LWdobsc_cov_fbk":NaN,
-                        "LWdobsc_fbk":NaN,
-                        "LWdunobsc_fbk":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN,
-                        "SWdobsc_cov_fbk":NaN,
-                        "SWdobsc_fbk":NaN,
-                        "SWdunobsc_fbk":NaN
-                    }
-                },
-                "ocn_dsc":{
-                    "LO680":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "LWdobsc_cov_fbk":NaN,
-                        "LWdobsc_fbk":NaN,
-                        "LWdunobsc_fbk":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN,
-                        "SWdobsc_cov_fbk":NaN,
-                        "SWdobsc_fbk":NaN,
-                        "SWdunobsc_fbk":NaN
-                    }
-                }
-            },
-            "eq70":{
-                "all":{
-                    "LO680":{
-                        "LWcld_alt":0.03733771276828996,
-                        "LWcld_amt":-0.01825562528332576,
-                        "LWcld_err":0.0004138512138758278,
-                        "LWcld_tau":0.003971080315531356,
-                        "LWcld_tot":0.023467019014371388,
-                        "LWdobsc_cov_fbk":7.713429024508446e-21,
-                        "LWdobsc_fbk":-0.0028007893179530455,
-                        "LWdunobsc_fbk":0.023467019014371388,
-                        "SWcld_alt":-0.018853692882894745,
-                        "SWcld_amt":0.1872662949789642,
-                        "SWcld_err":-0.0006791693964742512,
-                        "SWcld_tau":-0.07772925744218379,
-                        "SWcld_tot":0.09000417525741143,
-                        "SWdobsc_cov_fbk":2.1685161838072157e-20,
-                        "SWdobsc_fbk":-0.010367606937810902,
-                        "SWdunobsc_fbk":0.09000417525741143
-                    }
-                },
-                "lnd":{
-                    "LO680":{
-                        "LWcld_alt":0.004763819490045529,
-                        "LWcld_amt":-0.009501202507493135,
-                        "LWcld_err":1.964902443445525e-05,
-                        "LWcld_tau":0.0012149356012005799,
-                        "LWcld_tot":-0.0035027983918125703,
-                        "LWdobsc_cov_fbk":1.6475225662515453e-20,
-                        "LWdobsc_fbk":0.0018687822289490585,
-                        "LWdunobsc_fbk":-0.0035027983918125703,
-                        "SWcld_alt":-0.002637901006393788,
-                        "SWcld_amt":0.08899812202872437,
-                        "SWcld_err":8.420486033680662e-05,
-                        "SWcld_tau":-0.010678251955512086,
-                        "SWcld_tot":0.0757661739271553,
-                        "SWdobsc_cov_fbk":-5.681314827380212e-20,
-                        "SWdobsc_fbk":-0.024265915386007253,
-                        "SWdunobsc_fbk":0.0757661739271553
-                    }
-                },
-                "ocn":{
-                    "LO680":{
-                        "LWcld_alt":0.032573893278244434,
-                        "LWcld_amt":-0.008754422775832623,
-                        "LWcld_err":0.0003942021894413725,
-                        "LWcld_tau":0.002756144714330776,
-                        "LWcld_tot":0.026969817406183958,
-                        "LWdobsc_cov_fbk":-8.761796638007015e-21,
-                        "LWdobsc_fbk":-0.004669571546902103,
-                        "LWdunobsc_fbk":0.026969817406183965,
-                        "SWcld_alt":-0.01621579187650096,
-                        "SWcld_amt":0.09826817295023983,
-                        "SWcld_err":-0.0007633742568110579,
-                        "SWcld_tau":-0.0670510054866717,
-                        "SWcld_tot":0.014238001330256128,
-                        "SWdobsc_cov_fbk":7.849831011187429e-20,
-                        "SWdobsc_fbk":0.013898308448196353,
-                        "SWdunobsc_fbk":0.014238001330256123
-                    }
-                },
-                "ocn_asc":{
-                    "LO680":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "LWdobsc_cov_fbk":NaN,
-                        "LWdobsc_fbk":NaN,
-                        "LWdunobsc_fbk":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN,
-                        "SWdobsc_cov_fbk":NaN,
-                        "SWdobsc_fbk":NaN,
-                        "SWdunobsc_fbk":NaN
-                    }
-                },
-                "ocn_dsc":{
-                    "LO680":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "LWdobsc_cov_fbk":NaN,
-                        "LWdobsc_fbk":NaN,
-                        "LWdunobsc_fbk":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN,
-                        "SWdobsc_cov_fbk":NaN,
-                        "SWdobsc_fbk":NaN,
-                        "SWdunobsc_fbk":NaN
-                    }
-                }
-            },
-            "eq80":{
-                "all":{
-                    "LO680":{
-                        "LWcld_alt":0.03876768732710437,
-                        "LWcld_amt":-0.016069882170662273,
-                        "LWcld_err":0.00045040083387372625,
-                        "LWcld_tau":0.004593549137737338,
-                        "LWcld_tot":0.027741755128053172,
-                        "LWdobsc_cov_fbk":7.793830246199588e-21,
-                        "LWdobsc_fbk":-0.0044150633567298,
-                        "LWdunobsc_fbk":0.027741755128053172,
-                        "SWcld_alt":-0.01943616162337851,
-                        "SWcld_amt":0.18218127361464878,
-                        "SWcld_err":-0.0006715933302654781,
-                        "SWcld_tau":-0.08107249577424065,
-                        "SWcld_tot":0.08100102288676418,
-                        "SWdobsc_cov_fbk":2.1637466263543206e-20,
-                        "SWdobsc_fbk":-0.003057398723524226,
-                        "SWdunobsc_fbk":0.08100102288676418
-                    }
-                },
-                "lnd":{
-                    "LO680":{
-                        "LWcld_alt":0.0049307932484678,
-                        "LWcld_amt":-0.008804426041782751,
-                        "LWcld_err":2.9818871063183756e-05,
-                        "LWcld_tau":0.00142481651775277,
-                        "LWcld_tot":-0.002418997404498999,
-                        "LWdobsc_cov_fbk":1.5902995937873194e-20,
-                        "LWdobsc_fbk":0.0016349387103313103,
-                        "LWdunobsc_fbk":-0.002418997404498999,
-                        "SWcld_alt":-0.0027142402526494206,
-                        "SWcld_amt":0.08817586854761922,
-                        "SWcld_err":8.459381311650193e-05,
-                        "SWcld_tau":-0.011434523679819376,
-                        "SWcld_tot":0.07411169842826694,
-                        "SWdobsc_cov_fbk":-5.570078703741567e-20,
-                        "SWdobsc_fbk":-0.02354905629381387,
-                        "SWdunobsc_fbk":0.07411169842826693
-                    }
-                },
-                "ocn":{
-                    "LO680":{
-                        "LWcld_alt":0.03383689407863658,
-                        "LWcld_amt":-0.007265456128879521,
-                        "LWcld_err":0.00042058196281054256,
-                        "LWcld_tau":0.003168732619984569,
-                        "LWcld_tot":0.03016075253255218,
-                        "LWdobsc_cov_fbk":-8.109165691673604e-21,
-                        "LWdobsc_fbk":-0.0060500020670611115,
-                        "LWdunobsc_fbk":0.03016075253255217,
-                        "SWcld_alt":-0.016721921370729087,
-                        "SWcld_amt":0.09400540506702959,
-                        "SWcld_err":-0.0007561871433819801,
-                        "SWcld_tau":-0.06963797209442128,
-                        "SWcld_tot":0.006889324458497242,
-                        "SWdobsc_cov_fbk":7.733825330095897e-20,
-                        "SWdobsc_fbk":0.020491657570289642,
-                        "SWdunobsc_fbk":0.0068893244584972415
-                    }
-                },
-                "ocn_asc":{
-                    "LO680":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "LWdobsc_cov_fbk":NaN,
-                        "LWdobsc_fbk":NaN,
-                        "LWdunobsc_fbk":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN,
-                        "SWdobsc_cov_fbk":NaN,
-                        "SWdobsc_fbk":NaN,
-                        "SWdunobsc_fbk":NaN
-                    }
-                },
-                "ocn_dsc":{
-                    "LO680":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "LWdobsc_cov_fbk":NaN,
-                        "LWdobsc_fbk":NaN,
-                        "LWdunobsc_fbk":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN,
-                        "SWdobsc_cov_fbk":NaN,
-                        "SWdobsc_fbk":NaN,
-                        "SWdunobsc_fbk":NaN
-                    }
-                }
-            },
-            "eq90":{
-                "all":{
-                    "LO680":{
-                        "LWcld_alt":0.03929372085768118,
-                        "LWcld_amt":-0.015351958457356265,
-                        "LWcld_err":0.0004473952783493954,
-                        "LWcld_tau":0.00478688851443006,
-                        "LWcld_tot":0.029176046193104366,
-                        "LWdobsc_cov_fbk":9.586691954282912e-21,
-                        "LWdobsc_fbk":-0.005074037514047625,
-                        "LWdunobsc_fbk":0.029176046193104373,
-                        "SWcld_alt":-0.019610377829215233,
-                        "SWcld_amt":0.18080649173995297,
-                        "SWcld_err":-0.0006617990778695655,
-                        "SWcld_tau":-0.08164622143000545,
-                        "SWcld_tot":0.07888809340286272,
-                        "SWdobsc_cov_fbk":1.78395579245224e-20,
-                        "SWdobsc_fbk":-0.0008163087534794738,
-                        "SWdunobsc_fbk":0.07888809340286272
-                    }
-                },
-                "lnd":{
-                    "LO680":{
-                        "LWcld_alt":0.004964969066877978,
-                        "LWcld_amt":-0.008573978785806702,
-                        "LWcld_err":3.2166166042627906e-05,
-                        "LWcld_tau":0.001494689240893508,
-                        "LWcld_tot":-0.0020821543119925877,
-                        "LWdobsc_cov_fbk":1.573830873128278e-20,
-                        "LWdobsc_fbk":0.0015951895042784468,
-                        "LWdunobsc_fbk":-0.0020821543119925877,
-                        "SWcld_alt":-0.0027278786888058257,
-                        "SWcld_amt":0.08775277904235795,
-                        "SWcld_err":8.438466090922998e-05,
-                        "SWcld_tau":-0.011622913256241684,
-                        "SWcld_tot":0.07348637175821966,
-                        "SWdobsc_cov_fbk":-5.550821935033838e-20,
-                        "SWdobsc_fbk":-0.02337811398422074,
-                        "SWdunobsc_fbk":0.07348637175821966
-                    }
-                },
-                "ocn":{
-                    "LO680":{
-                        "LWcld_alt":0.034328751790803205,
-                        "LWcld_amt":-0.006777979671549563,
-                        "LWcld_err":0.00041522911230676743,
-                        "LWcld_tau":0.0032921992735365515,
-                        "LWcld_tot":0.031258200505096964,
-                        "LWdobsc_cov_fbk":-6.151616776999878e-21,
-                        "LWdobsc_fbk":-0.006669227018326072,
-                        "LWdunobsc_fbk":0.031258200505096964,
-                        "SWcld_alt":-0.016882499140409405,
-                        "SWcld_amt":0.09305371269759505,
-                        "SWcld_err":-0.0007461837387787956,
-                        "SWcld_tau":-0.07002330817376377,
-                        "SWcld_tot":0.005401721644643052,
-                        "SWdobsc_cov_fbk":7.334777727486081e-20,
-                        "SWdobsc_fbk":0.022561805230741267,
-                        "SWdunobsc_fbk":0.005401721644643057
-                    }
-                },
-                "ocn_asc":{
-                    "LO680":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "LWdobsc_cov_fbk":NaN,
-                        "LWdobsc_fbk":NaN,
-                        "LWdunobsc_fbk":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN,
-                        "SWdobsc_cov_fbk":NaN,
-                        "SWdobsc_fbk":NaN,
-                        "SWdunobsc_fbk":NaN
-                    }
-                },
-                "ocn_dsc":{
-                    "LO680":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "LWdobsc_cov_fbk":NaN,
-                        "LWdobsc_fbk":NaN,
-                        "LWdunobsc_fbk":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN,
-                        "SWdobsc_cov_fbk":NaN,
-                        "SWdobsc_fbk":NaN,
-                        "SWdunobsc_fbk":NaN
-                    }
-                }
-            }
-        }
-    },
-    "MRI-ESM2-0":{
-        "r1i1p1f1":{
-            "Arctic":{
-                "all":{
-                    "LO680":{
-                        "LWcld_alt":0.00022830262499913976,
-                        "LWcld_amt":0.0015199629923122225,
-                        "LWcld_err":2.024914522740577e-05,
-                        "LWcld_tau":0.0004393563772876098,
-                        "LWcld_tot":0.0020238818781741796,
-                        "LWdobsc_cov_fbk":1.2766007769097869e-21,
-                        "LWdobsc_fbk":-0.0013504065648416016,
-                        "LWdunobsc_fbk":0.002207871139826378,
-                        "SWcld_alt":-9.411551238420968e-05,
-                        "SWcld_amt":-0.003785655580581705,
-                        "SWcld_err":-8.875467699007331e-06,
-                        "SWcld_tau":-0.0032399914948065076,
-                        "SWcld_tot":-0.007128638055471432,
-                        "SWdobsc_cov_fbk":-4.41592457640028e-21,
-                        "SWdobsc_fbk":0.005609754743957209,
-                        "SWdunobsc_fbk":-0.007776696060514288
-                    }
-                },
-                "lnd":{
-                    "LO680":{
-                        "LWcld_alt":1.8292551844153534e-05,
-                        "LWcld_amt":0.00017154574061794436,
-                        "LWcld_err":5.168868161849244e-06,
-                        "LWcld_tau":9.180346510466184e-05,
-                        "LWcld_tot":0.00028681062572860893,
-                        "LWdobsc_cov_fbk":-1.0098215675584287e-21,
-                        "LWdobsc_fbk":-0.0002479614512844657,
-                        "LWdunobsc_fbk":0.00028681062572860893,
-                        "SWcld_alt":-8.810415552607795e-06,
-                        "SWcld_amt":-0.0002800783388017284,
-                        "SWcld_err":-3.1797669373155874e-06,
-                        "SWcld_tau":-0.0005199224853506437,
-                        "SWcld_tot":-0.0008119910066422952,
-                        "SWdobsc_cov_fbk":5.765385453864258e-21,
-                        "SWdobsc_fbk":0.0007595904045359379,
-                        "SWdunobsc_fbk":-0.0008119910066422955
-                    }
-                },
-                "ocn":{
-                    "LO680":{
-                        "LWcld_alt":0.00021001007315498614,
-                        "LWcld_amt":0.0013484172516942782,
-                        "LWcld_err":1.5080277065556526e-05,
-                        "LWcld_tau":0.0003475529121829479,
-                        "LWcld_tot":0.0019210605140977687,
-                        "LWdobsc_cov_fbk":2.286422344468215e-21,
-                        "LWdobsc_fbk":-0.001102445113557136,
-                        "LWdunobsc_fbk":0.0019210605140977687,
-                        "SWcld_alt":-9.386105250289368e-05,
-                        "SWcld_amt":-0.003849727749105587,
-                        "SWcld_err":-6.502561461601503e-06,
-                        "SWcld_tau":-0.00301461369080191,
-                        "SWcld_tot":-0.006964705053871992,
-                        "SWdobsc_cov_fbk":-1.018131003026454e-20,
-                        "SWdobsc_fbk":0.004850164339421271,
-                        "SWdunobsc_fbk":-0.006964705053871992
-                    }
-                },
-                "ocn_asc":{
-                    "LO680":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "LWdobsc_cov_fbk":NaN,
-                        "LWdobsc_fbk":NaN,
-                        "LWdunobsc_fbk":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN,
-                        "SWdobsc_cov_fbk":NaN,
-                        "SWdobsc_fbk":NaN,
-                        "SWdunobsc_fbk":NaN
-                    }
-                },
-                "ocn_dsc":{
-                    "LO680":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "LWdobsc_cov_fbk":NaN,
-                        "LWdobsc_fbk":NaN,
-                        "LWdunobsc_fbk":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN,
-                        "SWdobsc_cov_fbk":NaN,
-                        "SWdobsc_fbk":NaN,
-                        "SWdunobsc_fbk":NaN
-                    }
-                }
-            },
-            "eq10":{
-                "all":{
-                    "LO680":{
-                        "LWcld_alt":0.006874569499746103,
-                        "LWcld_amt":-0.006788998471828517,
-                        "LWcld_err":4.0355648806540925e-05,
-                        "LWcld_tau":0.00021216580145238365,
-                        "LWcld_tot":0.0003380924781765111,
-                        "LWdobsc_cov_fbk":-1.0510267736546667e-20,
-                        "LWdobsc_fbk":-0.001362419327014588,
-                        "LWdunobsc_fbk":0.00033809247817651137,
-                        "SWcld_alt":-0.0038867231151027636,
-                        "SWcld_amt":0.05017739800211757,
-                        "SWcld_err":1.9904290520146075e-06,
-                        "SWcld_tau":-0.009987055021206821,
-                        "SWcld_tot":0.03630561029485999,
-                        "SWdobsc_cov_fbk":9.079558611543937e-20,
-                        "SWdobsc_fbk":0.011645706782954257,
-                        "SWdunobsc_fbk":0.03630561029485999
-                    }
-                },
-                "lnd":{
-                    "LO680":{
-                        "LWcld_alt":0.0009559653338599423,
-                        "LWcld_amt":-0.0012725487439281117,
-                        "LWcld_err":2.3640181076867606e-06,
-                        "LWcld_tau":2.857725647395809e-05,
-                        "LWcld_tot":-0.00028564213548652463,
-                        "LWdobsc_cov_fbk":-4.438126714763795e-22,
-                        "LWdobsc_fbk":0.000979414049245579,
-                        "LWdunobsc_fbk":-0.00028564213548652463,
-                        "SWcld_alt":-0.0005534308425350173,
-                        "SWcld_amt":0.00858516553947905,
-                        "SWcld_err":7.247213521841995e-06,
-                        "SWcld_tau":-0.0012478717549319952,
-                        "SWcld_tot":0.0067911101555338796,
-                        "SWdobsc_cov_fbk":2.533999333401724e-21,
-                        "SWdobsc_fbk":-0.007231994452456952,
-                        "SWdunobsc_fbk":0.00679111015553388
-                    }
-                },
-                "ocn":{
-                    "LO680":{
-                        "LWcld_alt":0.00591860416588616,
-                        "LWcld_amt":-0.005516449727900404,
-                        "LWcld_err":3.799163069885416e-05,
-                        "LWcld_tau":0.0001835885449784256,
-                        "LWcld_tot":0.0006237346136630355,
-                        "LWdobsc_cov_fbk":-1.0066455065070285e-20,
-                        "LWdobsc_fbk":-0.0023418333762601673,
-                        "LWdunobsc_fbk":0.0006237346136630358,
-                        "SWcld_alt":-0.0033332922725677473,
-                        "SWcld_amt":0.041592232462638525,
-                        "SWcld_err":-5.256784469827394e-06,
-                        "SWcld_tau":-0.008739183266274828,
-                        "SWcld_tot":0.029514500139326123,
-                        "SWdobsc_cov_fbk":8.826158678203766e-20,
-                        "SWdobsc_fbk":0.01887770123541121,
-                        "SWdunobsc_fbk":0.02951450013932612
-                    }
-                },
-                "ocn_asc":{
-                    "LO680":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "LWdobsc_cov_fbk":NaN,
-                        "LWdobsc_fbk":NaN,
-                        "LWdunobsc_fbk":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN,
-                        "SWdobsc_cov_fbk":NaN,
-                        "SWdobsc_fbk":NaN,
-                        "SWdunobsc_fbk":NaN
-                    }
-                },
-                "ocn_dsc":{
-                    "LO680":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "LWdobsc_cov_fbk":NaN,
-                        "LWdobsc_fbk":NaN,
-                        "LWdunobsc_fbk":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN,
-                        "SWdobsc_cov_fbk":NaN,
-                        "SWdobsc_fbk":NaN,
-                        "SWdunobsc_fbk":NaN
-                    }
-                }
-            },
-            "eq20":{
-                "all":{
-                    "LO680":{
-                        "LWcld_alt":0.011824236377872109,
-                        "LWcld_amt":-0.014805857332925406,
-                        "LWcld_err":6.315405428441709e-05,
-                        "LWcld_tau":0.0005910263211052274,
-                        "LWcld_tot":-0.002327440579663652,
-                        "LWdobsc_cov_fbk":-2.111249688615103e-20,
-                        "LWdobsc_fbk":-0.00549241738394863,
-                        "LWdunobsc_fbk":-0.0023274405796636526,
-                        "SWcld_alt":-0.0064890225903234565,
-                        "SWcld_amt":0.09119317220072892,
-                        "SWcld_err":-2.439353264456525e-06,
-                        "SWcld_tau":-0.02444876790221487,
-                        "SWcld_tot":0.06025294235492614,
-                        "SWdobsc_cov_fbk":1.3023991427661783e-19,
-                        "SWdobsc_fbk":0.03927775621355636,
-                        "SWdunobsc_fbk":0.06025294235492614
-                    }
-                },
-                "lnd":{
-                    "LO680":{
-                        "LWcld_alt":0.0015929301746465915,
-                        "LWcld_amt":-0.002715688836396665,
-                        "LWcld_err":6.585640055077433e-06,
-                        "LWcld_tau":4.6244243207988315e-05,
-                        "LWcld_tot":-0.0010699287784870085,
-                        "LWdobsc_cov_fbk":1.1179238644171009e-21,
-                        "LWdobsc_fbk":0.0012060643179475855,
-                        "LWdunobsc_fbk":-0.0010699287784870085,
-                        "SWcld_alt":-0.000893476734788571,
-                        "SWcld_amt":0.017360210279036038,
-                        "SWcld_err":7.354422381902007e-06,
-                        "SWcld_tau":-0.001912970841621076,
-                        "SWcld_tot":0.014561117125008292,
-                        "SWdobsc_cov_fbk":-3.948234933464792e-21,
-                        "SWdobsc_fbk":-0.008701815534678952,
-                        "SWdunobsc_fbk":0.014561117125008292
-                    }
-                },
-                "ocn":{
-                    "LO680":{
-                        "LWcld_alt":0.010231306203225519,
-                        "LWcld_amt":-0.01209016849652874,
-                        "LWcld_err":5.656841422933966e-05,
-                        "LWcld_tau":0.0005447820778972391,
-                        "LWcld_tot":-0.0012575118011766441,
-                        "LWdobsc_cov_fbk":-2.2230420750568133e-20,
-                        "LWdobsc_fbk":-0.0066984817018962165,
-                        "LWdunobsc_fbk":-0.0012575118011766437,
-                        "SWcld_alt":-0.0055955458555348855,
-                        "SWcld_amt":0.07383296192169288,
-                        "SWcld_err":-9.79377564635853e-06,
-                        "SWcld_tau":-0.022535797060593794,
-                        "SWcld_tot":0.04569182522991785,
-                        "SWdobsc_cov_fbk":1.341881492100826e-19,
-                        "SWdobsc_fbk":0.0479795717482353,
-                        "SWdunobsc_fbk":0.04569182522991785
-                    }
-                },
-                "ocn_asc":{
-                    "LO680":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "LWdobsc_cov_fbk":NaN,
-                        "LWdobsc_fbk":NaN,
-                        "LWdunobsc_fbk":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN,
-                        "SWdobsc_cov_fbk":NaN,
-                        "SWdobsc_fbk":NaN,
-                        "SWdunobsc_fbk":NaN
-                    }
-                },
-                "ocn_dsc":{
-                    "LO680":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "LWdobsc_cov_fbk":NaN,
-                        "LWdobsc_fbk":NaN,
-                        "LWdunobsc_fbk":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN,
-                        "SWdobsc_cov_fbk":NaN,
-                        "SWdobsc_fbk":NaN,
-                        "SWdunobsc_fbk":NaN
-                    }
-                }
-            },
-            "eq30":{
-                "all":{
-                    "LO680":{
-                        "LWcld_alt":0.01348311569767184,
-                        "LWcld_amt":-0.030661953188032862,
-                        "LWcld_err":0.00012019511840002997,
-                        "LWcld_tau":0.0011117572767625975,
-                        "LWcld_tot":-0.015946885095198402,
-                        "LWdobsc_cov_fbk":-9.871823319900693e-21,
-                        "LWdobsc_fbk":-0.01092129626115299,
-                        "LWdunobsc_fbk":-0.015946885095198402,
-                        "SWcld_alt":-0.007326527092813017,
-                        "SWcld_amt":0.17565064545267797,
-                        "SWcld_err":-0.00013343145917530676,
-                        "SWcld_tau":-0.04777789278600609,
-                        "SWcld_tot":0.12041279411468354,
-                        "SWdobsc_cov_fbk":7.751372519727858e-20,
-                        "SWdobsc_fbk":0.06867160109375452,
-                        "SWdunobsc_fbk":0.12041279411468353
-                    }
-                },
-                "lnd":{
-                    "LO680":{
-                        "LWcld_alt":0.0019507562690694476,
-                        "LWcld_amt":-0.004206453937468209,
-                        "LWcld_err":8.050130828186542e-06,
-                        "LWcld_tau":6.302185616624264e-05,
-                        "LWcld_tot":-0.0021846256814043313,
-                        "LWdobsc_cov_fbk":1.1196986157891194e-21,
-                        "LWdobsc_fbk":0.0015671749084541456,
-                        "LWdunobsc_fbk":-0.002184625681404331,
-                        "SWcld_alt":-0.0010807889212952413,
-                        "SWcld_amt":0.02543241327501589,
-                        "SWcld_err":9.281267265107096e-06,
-                        "SWcld_tau":-0.002416084676487906,
-                        "SWcld_tot":0.02194482094449785,
-                        "SWdobsc_cov_fbk":-1.6264961428235442e-21,
-                        "SWdobsc_fbk":-0.010786812587673417,
-                        "SWdunobsc_fbk":0.02194482094449785
-                    }
-                },
-                "ocn":{
-                    "LO680":{
-                        "LWcld_alt":0.011532359428602389,
-                        "LWcld_amt":-0.02645549925056466,
-                        "LWcld_err":0.00011214498757184344,
-                        "LWcld_tau":0.0010487354205963547,
-                        "LWcld_tot":-0.013762259413794069,
-                        "LWdobsc_cov_fbk":-1.0991521935689815e-20,
-                        "LWdobsc_fbk":-0.012488471169607135,
-                        "LWdunobsc_fbk":-0.013762259413794073,
-                        "SWcld_alt":-0.006245738171517776,
-                        "SWcld_amt":0.1502182321776621,
-                        "SWcld_err":-0.00014271272644041384,
-                        "SWcld_tau":-0.0453618081095182,
-                        "SWcld_tot":0.0984679731701857,
-                        "SWdobsc_cov_fbk":7.91402213401021e-20,
-                        "SWdobsc_fbk":0.07945841368142793,
-                        "SWdunobsc_fbk":0.0984679731701857
-                    }
-                },
-                "ocn_asc":{
-                    "LO680":{
-                        "LWcld_alt":0.004750888477106571,
-                        "LWcld_amt":0.0002438916097131849,
-                        "LWcld_err":3.58217905680329e-06,
-                        "LWcld_tau":0.00025684743919388665,
-                        "LWcld_tot":0.005255209705070446,
-                        "LWdobsc_cov_fbk":-1.1540148376797433e-20,
-                        "LWdobsc_fbk":-7.575200455375864e-05,
-                        "LWdunobsc_fbk":0.005255209705070448,
-                        "SWcld_alt":-0.00259767100042934,
-                        "SWcld_amt":-0.0015244751206466018,
-                        "SWcld_err":4.394362776029378e-05,
-                        "SWcld_tau":-0.008672125587748885,
-                        "SWcld_tot":-0.01275032808106453,
-                        "SWdobsc_cov_fbk":2.2860403273072084e-19,
-                        "SWdobsc_fbk":0.0006351025792490423,
-                        "SWdunobsc_fbk":-0.012750328081064534
-                    }
-                },
-                "ocn_dsc":{
-                    "LO680":{
-                        "LWcld_alt":0.005240922996644081,
-                        "LWcld_amt":-0.037834246677374804,
-                        "LWcld_err":9.628851625264388e-05,
-                        "LWcld_tau":0.0008187886931330184,
-                        "LWcld_tot":-0.03167824647134506,
-                        "LWdobsc_cov_fbk":-4.8871642660657615e-19,
-                        "LWdobsc_fbk":-0.00011931780545149703,
-                        "LWdunobsc_fbk":-0.03167824647134507,
-                        "SWcld_alt":-0.002641632144465359,
-                        "SWcld_amt":0.21004884071333085,
-                        "SWcld_err":-0.00024295068696775254,
-                        "SWcld_tau":-0.034623463205801126,
-                        "SWcld_tot":0.17254079467609662,
-                        "SWdobsc_cov_fbk":3.309175933263811e-18,
-                        "SWdobsc_fbk":0.000991568523227017,
-                        "SWdunobsc_fbk":0.1725407946760966
-                    }
-                }
-            },
-            "eq40":{
-                "all":{
-                    "LO680":{
-                        "LWcld_alt":0.016538819953723093,
-                        "LWcld_amt":-0.048575843051338274,
-                        "LWcld_err":0.00022630805582180667,
-                        "LWcld_tau":0.0015943951354400385,
-                        "LWcld_tot":-0.03021631990635333,
-                        "LWdobsc_cov_fbk":-8.190311684443378e-21,
-                        "LWdobsc_fbk":-0.012442999446608387,
-                        "LWdunobsc_fbk":-0.03021631990635333,
-                        "SWcld_alt":-0.008548764276729149,
-                        "SWcld_amt":0.27296753945868657,
-                        "SWcld_err":-0.0002455019616517428,
-                        "SWcld_tau":-0.06671115403233645,
-                        "SWcld_tot":0.19746211918796922,
-                        "SWdobsc_cov_fbk":6.89822853090867e-20,
-                        "SWdobsc_fbk":0.08221672526886688,
-                        "SWdunobsc_fbk":0.19746211918796922
-                    }
-                },
-                "lnd":{
-                    "LO680":{
-                        "LWcld_alt":0.0020300183827834657,
-                        "LWcld_amt":-0.005835500487932157,
-                        "LWcld_err":9.227743780723641e-06,
-                        "LWcld_tau":0.0001128468051367348,
-                        "LWcld_tot":-0.0036834075562312326,
-                        "LWdobsc_cov_fbk":3.428394107779776e-21,
-                        "LWdobsc_fbk":0.0019629803758086355,
-                        "LWdunobsc_fbk":-0.0036834075562312326,
-                        "SWcld_alt":-0.0011263840053880086,
-                        "SWcld_amt":0.03317945021576183,
-                        "SWcld_err":9.803952998542176e-06,
-                        "SWcld_tau":-0.0029347503306789645,
-                        "SWcld_tot":0.029128119832693405,
-                        "SWdobsc_cov_fbk":-2.0745243544986776e-20,
-                        "SWdobsc_fbk":-0.012699864738957789,
-                        "SWdunobsc_fbk":0.029128119832693405
-                    }
-                },
-                "ocn":{
-                    "LO680":{
-                        "LWcld_alt":0.014508801570939625,
-                        "LWcld_amt":-0.042740342563406114,
-                        "LWcld_err":0.00021708031204108308,
-                        "LWcld_tau":0.0014815483303033039,
-                        "LWcld_tot":-0.0265329123501221,
-                        "LWdobsc_cov_fbk":-1.161870579222316e-20,
-                        "LWdobsc_fbk":-0.014405979822417024,
-                        "LWdunobsc_fbk":-0.026532912350122104,
-                        "SWcld_alt":-0.007422380271341141,
-                        "SWcld_amt":0.23978808924292472,
-                        "SWcld_err":-0.000255305914650285,
-                        "SWcld_tau":-0.06377640370165748,
-                        "SWcld_tot":0.16833399935527582,
-                        "SWdobsc_cov_fbk":8.97275288540735e-20,
-                        "SWdobsc_fbk":0.09491659000782467,
-                        "SWdunobsc_fbk":0.16833399935527582
-                    }
-                },
-                "ocn_asc":{
-                    "LO680":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "LWdobsc_cov_fbk":NaN,
-                        "LWdobsc_fbk":NaN,
-                        "LWdunobsc_fbk":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN,
-                        "SWdobsc_cov_fbk":NaN,
-                        "SWdobsc_fbk":NaN,
-                        "SWdunobsc_fbk":NaN
-                    }
-                },
-                "ocn_dsc":{
-                    "LO680":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "LWdobsc_cov_fbk":NaN,
-                        "LWdobsc_fbk":NaN,
-                        "LWdunobsc_fbk":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN,
-                        "SWdobsc_cov_fbk":NaN,
-                        "SWdobsc_fbk":NaN,
-                        "SWdunobsc_fbk":NaN
-                    }
-                }
-            },
-            "eq50":{
-                "all":{
-                    "LO680":{
-                        "LWcld_alt":0.0224197170993419,
-                        "LWcld_amt":-0.052805875815680685,
-                        "LWcld_err":0.00041684889751544,
-                        "LWcld_tau":0.0022490973969299995,
-                        "LWcld_tot":-0.027720212421893337,
-                        "LWdobsc_cov_fbk":-2.3672294433376e-21,
-                        "LWdobsc_fbk":-0.012703768107071687,
-                        "LWdunobsc_fbk":-0.027720212421893344,
-                        "SWcld_alt":-0.010954759055249293,
-                        "SWcld_amt":0.30189595420812887,
-                        "SWcld_err":-0.0003522697595555262,
-                        "SWcld_tau":-0.07471087825226881,
-                        "SWcld_tot":0.21587804714105527,
-                        "SWdobsc_cov_fbk":1.9383685213364744e-20,
-                        "SWdobsc_fbk":0.0866918388575898,
-                        "SWdunobsc_fbk":0.2158780471410552
-                    }
-                },
-                "lnd":{
-                    "LO680":{
-                        "LWcld_alt":0.0021190372138471174,
-                        "LWcld_amt":-0.005848149232495491,
-                        "LWcld_err":1.5247008088110283e-05,
-                        "LWcld_tau":0.00028741755755625143,
-                        "LWcld_tot":-0.003426447453004013,
-                        "LWdobsc_cov_fbk":8.444590391110754e-22,
-                        "LWdobsc_fbk":0.0021596577466129635,
-                        "LWdunobsc_fbk":-0.0034264474530040123,
-                        "SWcld_alt":-0.0011748251489904838,
-                        "SWcld_amt":0.03573834990238736,
-                        "SWcld_err":4.322402644543027e-06,
-                        "SWcld_tau":-0.003978496661237406,
-                        "SWcld_tot":0.030589350494804022,
-                        "SWdobsc_cov_fbk":-9.604372395773643e-21,
-                        "SWdobsc_fbk":-0.014450259078748676,
-                        "SWdunobsc_fbk":0.030589350494804022
-                    }
-                },
-                "ocn":{
-                    "LO680":{
-                        "LWcld_alt":0.02030067988549478,
-                        "LWcld_amt":-0.04695772658318519,
-                        "LWcld_err":0.0004016018894273297,
-                        "LWcld_tau":0.0019616798393737485,
-                        "LWcld_tot":-0.024293764968889325,
-                        "LWdobsc_cov_fbk":-3.211688482448679e-21,
-                        "LWdobsc_fbk":-0.01486342585368465,
-                        "LWdunobsc_fbk":-0.02429376496888933,
-                        "SWcld_alt":-0.009779933906258809,
-                        "SWcld_amt":0.26615760430574154,
-                        "SWcld_err":-0.00035659216220006927,
-                        "SWcld_tau":-0.07073238159103139,
-                        "SWcld_tot":0.18528869664625125,
-                        "SWdobsc_cov_fbk":2.898805760913839e-20,
-                        "SWdobsc_fbk":0.10114209793633848,
-                        "SWdunobsc_fbk":0.18528869664625125
-                    }
-                },
-                "ocn_asc":{
-                    "LO680":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "LWdobsc_cov_fbk":NaN,
-                        "LWdobsc_fbk":NaN,
-                        "LWdunobsc_fbk":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN,
-                        "SWdobsc_cov_fbk":NaN,
-                        "SWdobsc_fbk":NaN,
-                        "SWdunobsc_fbk":NaN
-                    }
-                },
-                "ocn_dsc":{
-                    "LO680":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "LWdobsc_cov_fbk":NaN,
-                        "LWdobsc_fbk":NaN,
-                        "LWdunobsc_fbk":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN,
-                        "SWdobsc_cov_fbk":NaN,
-                        "SWdobsc_fbk":NaN,
-                        "SWdunobsc_fbk":NaN
-                    }
-                }
-            },
-            "eq60":{
-                "all":{
-                    "LO680":{
-                        "LWcld_alt":0.024634992499651134,
-                        "LWcld_amt":-0.051587477657991626,
-                        "LWcld_err":0.0006407053780891803,
-                        "LWcld_tau":0.0035234112304813442,
-                        "LWcld_tot":-0.022788368549769965,
-                        "LWdobsc_cov_fbk":-4.392306207719002e-21,
-                        "LWdobsc_fbk":-0.01574677804380086,
-                        "LWdunobsc_fbk":-0.022788368549769965,
-                        "SWcld_alt":-0.01194226426821264,
-                        "SWcld_amt":0.30228664541407957,
-                        "SWcld_err":-0.00046860423473280314,
-                        "SWcld_tau":-0.09475401445137688,
-                        "SWcld_tot":0.19512176245975718,
-                        "SWdobsc_cov_fbk":2.5433640381124504e-20,
-                        "SWdobsc_fbk":0.10002980156607251,
-                        "SWdunobsc_fbk":0.19512176245975718
-                    }
-                },
-                "lnd":{
-                    "LO680":{
-                        "LWcld_alt":0.0020352483889457588,
-                        "LWcld_amt":-0.003917681766690994,
-                        "LWcld_err":3.5151913579888493e-05,
-                        "LWcld_tau":0.0006197021340640956,
-                        "LWcld_tot":-0.0012275793301012503,
-                        "LWdobsc_cov_fbk":-1.3237881573743846e-21,
-                        "LWdobsc_fbk":0.0013213802638497067,
-                        "LWdunobsc_fbk":-0.0012275793301012507,
-                        "SWcld_alt":-0.0011509867911153136,
-                        "SWcld_amt":0.028890340531671336,
-                        "SWcld_err":-8.009250441660032e-06,
-                        "SWcld_tau":-0.005450226746788102,
-                        "SWcld_tot":0.02228111774332626,
-                        "SWdobsc_cov_fbk":2.6784092851711324e-21,
-                        "SWdobsc_fbk":-0.01285477697389946,
-                        "SWdunobsc_fbk":0.022281117743326265
-                    }
-                },
-                "ocn":{
-                    "LO680":{
-                        "LWcld_alt":0.02259974411070538,
-                        "LWcld_amt":-0.04766979589130063,
-                        "LWcld_err":0.0006055534645092918,
-                        "LWcld_tau":0.0029037090964172487,
-                        "LWcld_tot":-0.021560789219668714,
-                        "LWdobsc_cov_fbk":-3.0685180503446104e-21,
-                        "LWdobsc_fbk":-0.017068158307650568,
-                        "LWdunobsc_fbk":-0.021560789219668714,
-                        "SWcld_alt":-0.010791277477097327,
-                        "SWcld_amt":0.2733963048824082,
-                        "SWcld_err":-0.00046059498429114315,
-                        "SWcld_tau":-0.0893037877045888,
-                        "SWcld_tot":0.1728406447164309,
-                        "SWdobsc_cov_fbk":2.275523109595337e-20,
-                        "SWdobsc_fbk":0.11288457853997197,
-                        "SWdunobsc_fbk":0.1728406447164309
-                    }
-                },
-                "ocn_asc":{
-                    "LO680":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "LWdobsc_cov_fbk":NaN,
-                        "LWdobsc_fbk":NaN,
-                        "LWdunobsc_fbk":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN,
-                        "SWdobsc_cov_fbk":NaN,
-                        "SWdobsc_fbk":NaN,
-                        "SWdunobsc_fbk":NaN
-                    }
-                },
-                "ocn_dsc":{
-                    "LO680":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "LWdobsc_cov_fbk":NaN,
-                        "LWdobsc_fbk":NaN,
-                        "LWdunobsc_fbk":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN,
-                        "SWdobsc_cov_fbk":NaN,
-                        "SWdobsc_fbk":NaN,
-                        "SWdunobsc_fbk":NaN
-                    }
-                }
-            },
-            "eq70":{
-                "all":{
-                    "LO680":{
-                        "LWcld_alt":0.024274475638021167,
-                        "LWcld_amt":-0.048700269874473366,
-                        "LWcld_err":0.0007396177665456838,
-                        "LWcld_tau":0.004515547598101303,
-                        "LWcld_tot":-0.01917062887180521,
-                        "LWdobsc_cov_fbk":-5.991386804553157e-21,
-                        "LWdobsc_fbk":-0.018298107304870723,
-                        "LWdunobsc_fbk":-0.01917062887180521,
-                        "SWcld_alt":-0.011784924312863107,
-                        "SWcld_amt":0.2915824059524377,
-                        "SWcld_err":-0.0005168855450416837,
-                        "SWcld_tau":-0.10812554732240003,
-                        "SWcld_tot":0.17115504877213286,
-                        "SWdobsc_cov_fbk":2.0522439830198382e-20,
-                        "SWdobsc_fbk":0.11164686085712129,
-                        "SWdunobsc_fbk":0.17115504877213292
-                    }
-                },
-                "lnd":{
-                    "LO680":{
-                        "LWcld_alt":0.001923231409144655,
-                        "LWcld_amt":-0.002214268654816674,
-                        "LWcld_err":5.78127962367235e-05,
-                        "LWcld_tau":0.0009609569265374059,
-                        "LWcld_tot":0.0007277324771021101,
-                        "LWdobsc_cov_fbk":1.1013873697646223e-21,
-                        "LWdobsc_fbk":0.0003966265432042886,
-                        "LWdunobsc_fbk":0.0007277324771021101,
-                        "SWcld_alt":-0.001132791363464813,
-                        "SWcld_amt":0.022358206802622144,
-                        "SWcld_err":-2.0708789972060835e-05,
-                        "SWcld_tau":-0.006921846945507937,
-                        "SWcld_tot":0.014282859703677334,
-                        "SWdobsc_cov_fbk":-1.2486478631745746e-20,
-                        "SWdobsc_fbk":-0.010786292233834182,
-                        "SWdunobsc_fbk":0.014282859703677328
-                    }
-                },
-                "ocn":{
-                    "LO680":{
-                        "LWcld_alt":0.02235124422887651,
-                        "LWcld_amt":-0.04648600121965668,
-                        "LWcld_err":0.0006818049703089603,
-                        "LWcld_tau":0.0035545906715638968,
-                        "LWcld_tot":-0.01989836134890732,
-                        "LWdobsc_cov_fbk":-7.092774174317765e-21,
-                        "LWdobsc_fbk":-0.018694733848075008,
-                        "LWdunobsc_fbk":-0.01989836134890732,
-                        "SWcld_alt":-0.010652132949398293,
-                        "SWcld_amt":0.2692241991498156,
-                        "SWcld_err":-0.0004961767550696227,
-                        "SWcld_tau":-0.10120370037689208,
-                        "SWcld_tot":0.15687218906845557,
-                        "SWdobsc_cov_fbk":3.3008918461944136e-20,
-                        "SWdobsc_fbk":0.12243315309095544,
-                        "SWdunobsc_fbk":0.15687218906845554
-                    }
-                },
-                "ocn_asc":{
-                    "LO680":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "LWdobsc_cov_fbk":NaN,
-                        "LWdobsc_fbk":NaN,
-                        "LWdunobsc_fbk":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN,
-                        "SWdobsc_cov_fbk":NaN,
-                        "SWdobsc_fbk":NaN,
-                        "SWdunobsc_fbk":NaN
-                    }
-                },
-                "ocn_dsc":{
-                    "LO680":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "LWdobsc_cov_fbk":NaN,
-                        "LWdobsc_fbk":NaN,
-                        "LWdunobsc_fbk":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN,
-                        "SWdobsc_cov_fbk":NaN,
-                        "SWdobsc_fbk":NaN,
-                        "SWdunobsc_fbk":NaN
-                    }
-                }
-            },
-            "eq80":{
-                "all":{
-                    "LO680":{
-                        "LWcld_alt":0.02434633657197525,
-                        "LWcld_amt":-0.04720005610055606,
-                        "LWcld_err":0.0007677401072729858,
-                        "LWcld_tau":0.004996396983303825,
-                        "LWcld_tot":-0.017089582438004,
-                        "LWdobsc_cov_fbk":-3.591134574739244e-21,
-                        "LWdobsc_fbk":-0.019461393177693128,
-                        "LWdunobsc_fbk":-0.017089582438004,
-                        "SWcld_alt":-0.011840804378426136,
-                        "SWcld_amt":0.28728869192386586,
-                        "SWcld_err":-0.0005302677603700135,
-                        "SWcld_tau":-0.111726998507306,
-                        "SWcld_tot":0.16319062127776376,
-                        "SWdobsc_cov_fbk":8.252302479059312e-21,
-                        "SWdobsc_fbk":0.11646079916658202,
-                        "SWdunobsc_fbk":0.16319062127776376
-                    }
-                },
-                "lnd":{
-                    "LO680":{
-                        "LWcld_alt":0.0019395454611326388,
-                        "LWcld_amt":-0.002047766723933912,
-                        "LWcld_err":6.251214082531686e-05,
-                        "LWcld_tau":0.001089626844414618,
-                        "LWcld_tot":0.001043917722438662,
-                        "LWdobsc_cov_fbk":-2.0754118524392422e-22,
-                        "LWdobsc_fbk":0.00010529600796567108,
-                        "LWdunobsc_fbk":0.0010439177224386622,
-                        "SWcld_alt":-0.0011403229868006146,
-                        "SWcld_amt":0.022150226742038027,
-                        "SWcld_err":-2.3530181692384567e-05,
-                        "SWcld_tau":-0.0074242968234443174,
-                        "SWcld_tot":0.01356207675010071,
-                        "SWdobsc_cov_fbk":-7.238879146580032e-21,
-                        "SWdobsc_fbk":-0.010100311508349285,
-                        "SWdunobsc_fbk":0.01356207675010071
-                    }
-                },
-                "ocn":{
-                    "LO680":{
-                        "LWcld_alt":0.02240679111084261,
-                        "LWcld_amt":-0.04515228937662214,
-                        "LWcld_err":0.0007052279664476689,
-                        "LWcld_tau":0.003906770138889207,
-                        "LWcld_tot":-0.01813350016044266,
-                        "LWdobsc_cov_fbk":-3.3835933894953167e-21,
-                        "LWdobsc_fbk":-0.019566689185658806,
-                        "LWdunobsc_fbk":-0.01813350016044266,
-                        "SWcld_alt":-0.010700481391625521,
-                        "SWcld_amt":0.2651384651818279,
-                        "SWcld_err":-0.0005067375786776289,
-                        "SWcld_tau":-0.10430270168386169,
-                        "SWcld_tot":0.14962854452766305,
-                        "SWdobsc_cov_fbk":1.5491181625639356e-20,
-                        "SWdobsc_fbk":0.12656111067493128,
-                        "SWdunobsc_fbk":0.14962854452766305
-                    }
-                },
-                "ocn_asc":{
-                    "LO680":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "LWdobsc_cov_fbk":NaN,
-                        "LWdobsc_fbk":NaN,
-                        "LWdunobsc_fbk":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN,
-                        "SWdobsc_cov_fbk":NaN,
-                        "SWdobsc_fbk":NaN,
-                        "SWdunobsc_fbk":NaN
-                    }
-                },
-                "ocn_dsc":{
-                    "LO680":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "LWdobsc_cov_fbk":NaN,
-                        "LWdobsc_fbk":NaN,
-                        "LWdunobsc_fbk":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN,
-                        "SWdobsc_cov_fbk":NaN,
-                        "SWdobsc_fbk":NaN,
-                        "SWdunobsc_fbk":NaN
-                    }
-                }
-            },
-            "eq90":{
-                "all":{
-                    "LO680":{
-                        "LWcld_alt":0.024426678233726462,
-                        "LWcld_amt":-0.0468121900652877,
-                        "LWcld_err":0.0007676549915780007,
-                        "LWcld_tau":0.005135208623259116,
-                        "LWcld_tot":-0.016482648216724124,
-                        "LWdobsc_cov_fbk":-4.095656898290358e-21,
-                        "LWdobsc_fbk":-0.019820139680841047,
-                        "LWdunobsc_fbk":-0.01648264821672412,
-                        "SWcld_alt":-0.01186072222967137,
-                        "SWcld_amt":0.2865062202731148,
-                        "SWcld_err":-0.0005298115744802935,
-                        "SWcld_tau":-0.11233347625056606,
-                        "SWcld_tot":0.1617822102183971,
-                        "SWdobsc_cov_fbk":8.429405635407378e-21,
-                        "SWdobsc_fbk":0.11714112408424122,
-                        "SWdunobsc_fbk":0.1617822102183971
-                    }
-                },
-                "lnd":{
-                    "LO680":{
-                        "LWcld_alt":0.001942436073609499,
-                        "LWcld_amt":-0.002033340478572438,
-                        "LWcld_err":6.278481753274342e-05,
-                        "LWcld_tau":0.0011215449023541082,
-                        "LWcld_tot":0.001093425314923913,
-                        "LWdobsc_cov_fbk":-1.6904127597181913e-22,
-                        "LWdobsc_fbk":5.4545142647086574e-05,
-                        "LWdunobsc_fbk":0.0010934253149239128,
-                        "SWcld_alt":-0.0011396934961664265,
-                        "SWcld_amt":0.022091616661616936,
-                        "SWcld_err":-2.376087685536478e-05,
-                        "SWcld_tau":-0.0074640839280161846,
-                        "SWcld_tot":0.013464078360578962,
-                        "SWdobsc_cov_fbk":-7.041133249264225e-21,
-                        "SWdobsc_fbk":-0.010054815885335454,
-                        "SWdunobsc_fbk":0.013464078360578965
-                    }
-                },
-                "ocn":{
-                    "LO680":{
-                        "LWcld_alt":0.022484242160116963,
-                        "LWcld_amt":-0.04477884958671526,
-                        "LWcld_err":0.0007048701740452575,
-                        "LWcld_tau":0.004013663720905008,
-                        "LWcld_tot":-0.017576073531648032,
-                        "LWdobsc_cov_fbk":-3.92661562231853e-21,
-                        "LWdobsc_fbk":-0.01987468482348814,
-                        "LWdunobsc_fbk":-0.017576073531648032,
-                        "SWcld_alt":-0.010721028733504945,
-                        "SWcld_amt":0.2644146036114978,
-                        "SWcld_err":-0.0005060506976249287,
-                        "SWcld_tau":-0.10486939232254988,
-                        "SWcld_tot":0.1483181318578181,
-                        "SWdobsc_cov_fbk":1.547053888467158e-20,
-                        "SWdobsc_fbk":0.1271959399695767,
-                        "SWdunobsc_fbk":0.1483181318578181
-                    }
-                },
-                "ocn_asc":{
-                    "LO680":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "LWdobsc_cov_fbk":NaN,
-                        "LWdobsc_fbk":NaN,
-                        "LWdunobsc_fbk":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN,
-                        "SWdobsc_cov_fbk":NaN,
-                        "SWdobsc_fbk":NaN,
-                        "SWdunobsc_fbk":NaN
-                    }
-                },
-                "ocn_dsc":{
-                    "LO680":{
-                        "LWcld_alt":NaN,
-                        "LWcld_amt":NaN,
-                        "LWcld_err":NaN,
-                        "LWcld_tau":NaN,
-                        "LWcld_tot":NaN,
-                        "LWdobsc_cov_fbk":NaN,
-                        "LWdobsc_fbk":NaN,
-                        "LWdunobsc_fbk":NaN,
-                        "SWcld_alt":NaN,
-                        "SWcld_amt":NaN,
-                        "SWcld_err":NaN,
-                        "SWcld_tau":NaN,
-                        "SWcld_tot":NaN,
-                        "SWdobsc_cov_fbk":NaN,
-                        "SWdobsc_fbk":NaN,
-                        "SWdunobsc_fbk":NaN
-                    }
-                }
-            }
-        }
-    },
-    "metadata":{}
-}
diff --git a/pcmdi_metrics/cloud_feedback/data/cmip6_amip_cld_errs.json b/pcmdi_metrics/cloud_feedback/data/cmip6_amip_cld_errs.json
deleted file mode 100644
index c0c6cae6f..000000000
--- a/pcmdi_metrics/cloud_feedback/data/cmip6_amip_cld_errs.json
+++ /dev/null
@@ -1,30413 +0,0 @@
-{
-    "BCC-CSM2-MR":{
-        "r1i1p1f1":{
-            "30-60":{
-                "all":{
-                    "ALL":{
-                        "E_LW":1.5829764522634808,
-                        "E_NET":1.6204448201660986,
-                        "E_SW":1.6461844844693856,
-                        "E_TCA":0.9750874973542317,
-                        "E_ctpt":1.4184378652459912
-                    },
-                    "HI680":{
-                        "E_LW":1.6040044260045272,
-                        "E_NET":1.689631333837487,
-                        "E_SW":1.7384557866974693,
-                        "E_TCA":0.8190241894203575,
-                        "E_ctpt":1.5711767465867188
-                    },
-                    "LO680":{
-                        "E_LW":1.2401597114228813,
-                        "E_NET":1.5497966095199038,
-                        "E_SW":1.484341106531429,
-                        "E_TCA":1.1324330536190923,
-                        "E_ctpt":1.2443215691466398
-                    }
-                },
-                "lnd":{
-                    "ALL":{
-                        "E_LW":0.7867844844103848,
-                        "E_NET":1.083887262241178,
-                        "E_SW":0.9854038701530143,
-                        "E_TCA":0.4199508505254939,
-                        "E_ctpt":0.9267974591091537
-                    },
-                    "HI680":{
-                        "E_LW":0.7768982617502275,
-                        "E_NET":0.9325598393720069,
-                        "E_SW":0.9029204652177201,
-                        "E_TCA":0.3270559659808098,
-                        "E_ctpt":0.7956958010556657
-                    },
-                    "LO680":{
-                        "E_LW":1.2517270798755997,
-                        "E_NET":1.4609411444001266,
-                        "E_SW":1.403982443811163,
-                        "E_TCA":1.0083242926495979,
-                        "E_ctpt":1.2857072535476268
-                    }
-                },
-                "ocn":{
-                    "ALL":{
-                        "E_LW":1.518129939960195,
-                        "E_NET":1.53781395047685,
-                        "E_SW":1.5615384534111985,
-                        "E_TCA":0.7369415294177188,
-                        "E_ctpt":1.3973393558063112
-                    },
-                    "HI680":{
-                        "E_LW":1.5313710550380353,
-                        "E_NET":1.568443516329492,
-                        "E_SW":1.6236129147642604,
-                        "E_TCA":0.6358931716207962,
-                        "E_ctpt":1.4860765517624768
-                    },
-                    "LO680":{
-                        "E_LW":1.274960242084145,
-                        "E_NET":1.5046889869417976,
-                        "E_SW":1.447670719447905,
-                        "E_TCA":1.0981323870186481,
-                        "E_ctpt":1.278326884228207
-                    }
-                },
-                "ocn_asc":{
-                    "ALL":{
-                        "E_LW":NaN,
-                        "E_NET":NaN,
-                        "E_SW":NaN,
-                        "E_TCA":NaN,
-                        "E_ctpt":NaN
-                    },
-                    "HI680":{
-                        "E_LW":NaN,
-                        "E_NET":NaN,
-                        "E_SW":NaN,
-                        "E_TCA":NaN,
-                        "E_ctpt":NaN
-                    },
-                    "LO680":{
-                        "E_LW":NaN,
-                        "E_NET":NaN,
-                        "E_SW":NaN,
-                        "E_TCA":NaN,
-                        "E_ctpt":NaN
-                    }
-                },
-                "ocn_dsc":{
-                    "ALL":{
-                        "E_LW":NaN,
-                        "E_NET":NaN,
-                        "E_SW":NaN,
-                        "E_TCA":NaN,
-                        "E_ctpt":NaN
-                    },
-                    "HI680":{
-                        "E_LW":NaN,
-                        "E_NET":NaN,
-                        "E_SW":NaN,
-                        "E_TCA":NaN,
-                        "E_ctpt":NaN
-                    },
-                    "LO680":{
-                        "E_LW":NaN,
-                        "E_NET":NaN,
-                        "E_SW":NaN,
-                        "E_TCA":NaN,
-                        "E_ctpt":NaN
-                    }
-                }
-            },
-            "30-80":{
-                "all":{
-                    "ALL":{
-                        "E_LW":1.4993171047158367,
-                        "E_NET":1.5653589949604931,
-                        "E_SW":1.5952904896448663,
-                        "E_TCA":0.9578753887423753,
-                        "E_ctpt":1.351328100161978
-                    },
-                    "HI680":{
-                        "E_LW":1.5161577671812556,
-                        "E_NET":1.6263837327146573,
-                        "E_SW":1.685790717238695,
-                        "E_TCA":0.7959349525958478,
-                        "E_ctpt":1.4168789022336779
-                    },
-                    "LO680":{
-                        "E_LW":1.212964540363325,
-                        "E_NET":1.5077036124259007,
-                        "E_SW":1.4500823383701618,
-                        "E_TCA":1.183888840653607,
-                        "E_ctpt":1.275718385166408
-                    }
-                },
-                "lnd":{
-                    "ALL":{
-                        "E_LW":0.8756219723456327,
-                        "E_NET":1.1727593858677048,
-                        "E_SW":1.0799141371730137,
-                        "E_TCA":0.45622457186075305,
-                        "E_ctpt":0.9913336574022843
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-                        "E_LW":0.5059363650088975,
-                        "E_NET":2.0494136199663435,
-                        "E_SW":0.8861259938305173,
-                        "E_TCA":1.6487322316509305,
-                        "E_ctpt":1.035035069539092
-                    },
-                    "HI680":{
-                        "E_LW":0.5030708621750393,
-                        "E_NET":1.6377071344126481,
-                        "E_SW":0.7619783437561879,
-                        "E_TCA":1.0896945228372221,
-                        "E_ctpt":0.9780994283611887
-                    },
-                    "LO680":{
-                        "E_LW":1.9364728556475364,
-                        "E_NET":3.58548049921786,
-                        "E_SW":3.277540653038117,
-                        "E_TCA":0.8357024035853556,
-                        "E_ctpt":1.8045504335303597
-                    }
-                },
-                "ocn_dsc":{
-                    "ALL":{
-                        "E_LW":0.7645298843797046,
-                        "E_NET":1.9666141040508216,
-                        "E_SW":1.6309608628415748,
-                        "E_TCA":2.8564705204047414,
-                        "E_ctpt":1.3478222008722216
-                    },
-                    "HI680":{
-                        "E_LW":0.6422551722372639,
-                        "E_NET":1.9280504942807208,
-                        "E_SW":1.0878933755886626,
-                        "E_TCA":1.2018311312123748,
-                        "E_ctpt":1.2468245298709317
-                    },
-                    "LO680":{
-                        "E_LW":1.4082258812294874,
-                        "E_NET":1.9700082571988218,
-                        "E_SW":1.8361990314859318,
-                        "E_TCA":1.3014374054243307,
-                        "E_ctpt":1.3744703274159886
-                    }
-                }
-            },
-            "eq60":{
-                "all":{
-                    "ALL":{
-                        "E_LW":0.6353978561752518,
-                        "E_NET":0.8087067126779321,
-                        "E_SW":0.7477938885817371,
-                        "E_TCA":0.7320470537608431,
-                        "E_ctpt":0.774503593231607
-                    },
-                    "HI680":{
-                        "E_LW":0.6259486486373231,
-                        "E_NET":0.7025877185247932,
-                        "E_SW":0.6752935880625267,
-                        "E_TCA":0.6577582698857852,
-                        "E_ctpt":0.7476415536345659
-                    },
-                    "LO680":{
-                        "E_LW":0.8308301917662239,
-                        "E_NET":0.8783755529425569,
-                        "E_SW":0.8578709024085527,
-                        "E_TCA":0.7167828085025576,
-                        "E_ctpt":0.8008830687150383
-                    }
-                },
-                "lnd":{
-                    "ALL":{
-                        "E_LW":0.504732552454938,
-                        "E_NET":0.6968429896732091,
-                        "E_SW":0.5895616292628488,
-                        "E_TCA":0.45068916847121215,
-                        "E_ctpt":0.6802757743828836
-                    },
-                    "HI680":{
-                        "E_LW":0.4999525443004064,
-                        "E_NET":0.6566623487355014,
-                        "E_SW":0.5578022189358094,
-                        "E_TCA":0.4026648322724333,
-                        "E_ctpt":0.6366810207211882
-                    },
-                    "LO680":{
-                        "E_LW":0.8422471619426263,
-                        "E_NET":0.776089477094323,
-                        "E_SW":0.7726380432886334,
-                        "E_TCA":0.7524566883817111,
-                        "E_ctpt":0.8036504904979361
-                    }
-                },
-                "ocn":{
-                    "ALL":{
-                        "E_LW":0.6003912382926391,
-                        "E_NET":0.7813021406996311,
-                        "E_SW":0.7190103901948172,
-                        "E_TCA":0.5654872374278667,
-                        "E_ctpt":0.7202967456069345
-                    },
-                    "HI680":{
-                        "E_LW":0.5882334284502972,
-                        "E_NET":0.6512642224373746,
-                        "E_SW":0.6285366495541512,
-                        "E_TCA":0.5790165205646679,
-                        "E_ctpt":0.6591425934924086
-                    },
-                    "LO680":{
-                        "E_LW":0.8122105547944656,
-                        "E_NET":0.8576140849243683,
-                        "E_SW":0.8375078210584086,
-                        "E_TCA":0.6328109526295398,
-                        "E_ctpt":0.772558696820811
-                    }
-                },
-                "ocn_asc":{
-                    "ALL":{
-                        "E_LW":NaN,
-                        "E_NET":NaN,
-                        "E_SW":NaN,
-                        "E_TCA":NaN,
-                        "E_ctpt":NaN
-                    },
-                    "HI680":{
-                        "E_LW":NaN,
-                        "E_NET":NaN,
-                        "E_SW":NaN,
-                        "E_TCA":NaN,
-                        "E_ctpt":NaN
-                    },
-                    "LO680":{
-                        "E_LW":NaN,
-                        "E_NET":NaN,
-                        "E_SW":NaN,
-                        "E_TCA":NaN,
-                        "E_ctpt":NaN
-                    }
-                },
-                "ocn_dsc":{
-                    "ALL":{
-                        "E_LW":NaN,
-                        "E_NET":NaN,
-                        "E_SW":NaN,
-                        "E_TCA":NaN,
-                        "E_ctpt":NaN
-                    },
-                    "HI680":{
-                        "E_LW":NaN,
-                        "E_NET":NaN,
-                        "E_SW":NaN,
-                        "E_TCA":NaN,
-                        "E_ctpt":NaN
-                    },
-                    "LO680":{
-                        "E_LW":NaN,
-                        "E_NET":NaN,
-                        "E_SW":NaN,
-                        "E_TCA":NaN,
-                        "E_ctpt":NaN
-                    }
-                }
-            },
-            "eq90":{
-                "all":{
-                    "ALL":{
-                        "E_LW":0.6513015339060677,
-                        "E_NET":0.8004583339971199,
-                        "E_SW":0.7465123703579186,
-                        "E_TCA":0.7553993073008406,
-                        "E_ctpt":0.8666442236893048
-                    },
-                    "HI680":{
-                        "E_LW":0.6398633196472756,
-                        "E_NET":0.6868727128074782,
-                        "E_SW":0.6670121333379978,
-                        "E_TCA":0.6660146568794232,
-                        "E_ctpt":0.8234720319663724
-                    },
-                    "LO680":{
-                        "E_LW":0.8814916130521562,
-                        "E_NET":0.8790410259125657,
-                        "E_SW":0.8658622980324098,
-                        "E_TCA":0.7832222204299203,
-                        "E_ctpt":0.9118559592395634
-                    }
-                },
-                "lnd":{
-                    "ALL":{
-                        "E_LW":0.5329693133205972,
-                        "E_NET":0.7077049505005238,
-                        "E_SW":0.5999905033552229,
-                        "E_TCA":0.44619334476386424,
-                        "E_ctpt":0.763403951271289
-                    },
-                    "HI680":{
-                        "E_LW":0.5265726365462783,
-                        "E_NET":0.6520899111953626,
-                        "E_SW":0.5562784807833785,
-                        "E_TCA":0.40492792265637323,
-                        "E_ctpt":0.7159768524746855
-                    },
-                    "LO680":{
-                        "E_LW":0.9109420515657707,
-                        "E_NET":0.8042562318749732,
-                        "E_SW":0.806574990683832,
-                        "E_TCA":0.7432602178795754,
-                        "E_ctpt":0.8863286631869223
-                    }
-                },
-                "ocn":{
-                    "ALL":{
-                        "E_LW":0.6017790161768655,
-                        "E_NET":0.7707063333386877,
-                        "E_SW":0.7122005698599693,
-                        "E_TCA":0.5612183103769928,
-                        "E_ctpt":0.7999287083395636
-                    },
-                    "HI680":{
-                        "E_LW":0.5877502732590081,
-                        "E_NET":0.6453384390626643,
-                        "E_SW":0.6200797637988076,
-                        "E_TCA":0.5587208470387122,
-                        "E_ctpt":0.7229620801374321
-                    },
-                    "LO680":{
-                        "E_LW":0.8404800065595975,
-                        "E_NET":0.8463360558079986,
-                        "E_SW":0.8317866031670539,
-                        "E_TCA":0.6891058781445603,
-                        "E_ctpt":0.870277872562572
-                    }
-                },
-                "ocn_asc":{
-                    "ALL":{
-                        "E_LW":NaN,
-                        "E_NET":NaN,
-                        "E_SW":NaN,
-                        "E_TCA":NaN,
-                        "E_ctpt":NaN
-                    },
-                    "HI680":{
-                        "E_LW":NaN,
-                        "E_NET":NaN,
-                        "E_SW":NaN,
-                        "E_TCA":NaN,
-                        "E_ctpt":NaN
-                    },
-                    "LO680":{
-                        "E_LW":NaN,
-                        "E_NET":NaN,
-                        "E_SW":NaN,
-                        "E_TCA":NaN,
-                        "E_ctpt":NaN
-                    }
-                },
-                "ocn_dsc":{
-                    "ALL":{
-                        "E_LW":NaN,
-                        "E_NET":NaN,
-                        "E_SW":NaN,
-                        "E_TCA":NaN,
-                        "E_ctpt":NaN
-                    },
-                    "HI680":{
-                        "E_LW":NaN,
-                        "E_NET":NaN,
-                        "E_SW":NaN,
-                        "E_TCA":NaN,
-                        "E_ctpt":NaN
-                    },
-                    "LO680":{
-                        "E_LW":NaN,
-                        "E_NET":NaN,
-                        "E_SW":NaN,
-                        "E_TCA":NaN,
-                        "E_ctpt":NaN
-                    }
-                }
-            }
-        }
-    },
-    "metadata":{
-        "author":"Mark D. Zelinka <zelinka1@llnl.gov>",
-        "date_modified":"2021-03-23"
-    }
-}

From 4e3276fddd8e34bf6583c00289c812532b15334d Mon Sep 17 00:00:00 2001
From: mzelinka <zelinka1@llnl.gov>
Date: Fri, 29 Sep 2023 16:07:55 -0700
Subject: [PATCH 075/133] additional changes, mostly to the font end

---
 .../cloud_feedback/cloud_feedback_driver.py   |  15 +-
 pcmdi_metrics/cloud_feedback/lib/__init__.py  |   4 +-
 .../lib/cal_CloudRadKernel_xr.py              |   4 +-
 .../cloud_feedback/lib/compute_ECS_xr.py      | 158 ++++++++----------
 .../cloud_feedback/param/my_param.py          |   1 -
 5 files changed, 79 insertions(+), 103 deletions(-)

diff --git a/pcmdi_metrics/cloud_feedback/cloud_feedback_driver.py b/pcmdi_metrics/cloud_feedback/cloud_feedback_driver.py
index f3a39953c..8e882dafd 100644
--- a/pcmdi_metrics/cloud_feedback/cloud_feedback_driver.py
+++ b/pcmdi_metrics/cloud_feedback/cloud_feedback_driver.py
@@ -25,7 +25,6 @@
 version = param.version
 input_files_json = param.input_files_json
 path = param.path
-xml_path = param.xml_path
 data_path = param.data_path
 figure_path = param.figure_path
 output_path = param.output_path
@@ -45,7 +44,6 @@
 print("version:", version)
 print("path:", path)
 print("input_files_json:", input_files_json)
-print("xml_path:", xml_path)
 print("figure_path:", figure_path)
 print("output_path:", output_path)
 print("output_json_filename:", output_json_filename)
@@ -57,9 +55,6 @@
 else:
     exps = ["amip", "amip-p4K"]
 
-# generate xmls pointing to the cmorized netcdf files
-os.makedirs(xml_path, exist_ok=True)
-
 filenames = dict()
 
 if input_files_json is not None:
@@ -108,9 +103,7 @@
             searchstring = os.path.join(
                 ncfiles[exp][field]["path"], ncfiles[exp][field]["file"]
             )
-        xmlname = os.path.join(xml_path, ".".join([exp, model, variant, field, "xml"]))
-        os.system("cdscan -x " + xmlname + " " + searchstring)
-        filenames[exp][field] = xmlname
+        filenames[exp][field] = searchstring
 
 if debug:
     with open(os.path.join(output_path, "filenames.json"), "w") as f:
@@ -123,9 +116,9 @@
 
 # add this model's results to the pre-existing json file containing other models' results:
 updated_fbk_dict, updated_obsc_fbk_dict = organize_fbk_jsons(
-    fbk_dict, obsc_fbk_dict, model, variant, datadir=data_path
+    fbk_dict, obsc_fbk_dict, model, variant
 )
-updated_err_dict = organize_err_jsons(err_dict, model, variant, datadir=data_path)
+updated_err_dict = organize_err_jsons(err_dict, model, variant)
 
 ecs = None
 if get_ecs:
@@ -133,7 +126,7 @@
     ecs = compute_ECS(filenames)
     print("calc ECS done")
     print("ecs: ", ecs)
-updated_ecs_dict = organize_ecs_jsons(ecs, model, variant, datadir=data_path)
+updated_ecs_dict = organize_ecs_jsons(ecs, model, variant)
 
 os.makedirs(output_path, exist_ok=True)
 if debug:
diff --git a/pcmdi_metrics/cloud_feedback/lib/__init__.py b/pcmdi_metrics/cloud_feedback/lib/__init__.py
index bc31dbc31..e409530f6 100644
--- a/pcmdi_metrics/cloud_feedback/lib/__init__.py
+++ b/pcmdi_metrics/cloud_feedback/lib/__init__.py
@@ -1,6 +1,6 @@
 from .argparse_functions import AddParserArgument  # noqa
-from .cal_CloudRadKernel import CloudRadKernel  # noqa
-from .compute_ECS import compute_ECS  # noqa
+from .cal_CloudRadKernel_xr import CloudRadKernel  # noqa
+from .compute_ECS_xr import compute_ECS  # noqa
 from .lib_cloud_feedback import cloud_feedback_metrics_to_json  # noqa
 from .organize_jsons import (  # noqa
     organize_ecs_jsons,
diff --git a/pcmdi_metrics/cloud_feedback/lib/cal_CloudRadKernel_xr.py b/pcmdi_metrics/cloud_feedback/lib/cal_CloudRadKernel_xr.py
index 1dfffbbf9..03b82248a 100644
--- a/pcmdi_metrics/cloud_feedback/lib/cal_CloudRadKernel_xr.py
+++ b/pcmdi_metrics/cloud_feedback/lib/cal_CloudRadKernel_xr.py
@@ -19,7 +19,7 @@
 # define necessary information
 # =============================================
 
-datadir = "../data/"
+datadir = "./data/"
 
 # Define a python dictionary containing the sections of the histogram to consider
 # These are the same as in Zelinka et al, GRL, 2016
@@ -190,7 +190,7 @@ def get_CRK_data(filepath):
 ###########################################################################
 def get_kernel_regrid(ctl):
     # Read in data and map kernels to lat/lon
-
+    
     f = xc.open_mfdataset(datadir + "cloud_kernels2.nc", decode_times=False)
     f = f.rename({"mo": "time", "tau_midpt": "tau", "p_midpt": "plev"})
     f["time"] = ctl["time"].copy()
diff --git a/pcmdi_metrics/cloud_feedback/lib/compute_ECS_xr.py b/pcmdi_metrics/cloud_feedback/lib/compute_ECS_xr.py
index 954032339..d257af846 100644
--- a/pcmdi_metrics/cloud_feedback/lib/compute_ECS_xr.py
+++ b/pcmdi_metrics/cloud_feedback/lib/compute_ECS_xr.py
@@ -6,20 +6,18 @@
 from scipy import stats
 import cftime
 
-
 def dict_to_dataset(DICT):
     # Convert a dictionary of Datasets to a single Dataset
-    data_vars = {}
+    data_vars={}
     for var in list(DICT.keys()):
         data_vars[var] = (["year"], DICT[var][var].data)
     ds = xr.Dataset(
         data_vars,
         coords=dict(year=DICT[var].year),
     )
-    return ds
-
+    return(ds)
 
-def get_branch_time(pi, ab):
+def get_branch_time(pi,ab):
     # Determine when abrupt overlaps with piControl
     # returns the following:
     # 1) st: the branch time in cftime.datetime format
@@ -27,111 +25,99 @@ def get_branch_time(pi, ab):
     # 3) extensions beyond the ~150-year overlap period to allow for 21-year rolling mean:
     #    3a) extend_st -- the date 10 years prior to st
     #    3b) extend_fn -- the date 10 years after fn
-
+    
     var = ab.variable_id
     source = ab.source_id
     experiment = ab.experiment_id
-    absub = ab.isel(time=slice(0, 12 * 150))
-    lenab = int(absub[var].shape[0] / 12) - 1
-    btip = int(absub.attrs["branch_time_in_parent"])  # timestep, not a date
+    absub = ab.isel(time=slice(0,12*150))
+    lenab = int(absub[var].shape[0]/12)-1
+    btip = int(absub.attrs['branch_time_in_parent']) # timestep, not a date
 
-    if source == "GFDL-CM4" and (
-        experiment == "1pctCO2"
-        or experiment == "abrupt-4xCO2"
-        or experiment == "historical"
-    ):
-        btip = 91250  # https://errata.es-doc.org/static/view.html?uid=2f6b5963-f87e-b2df-a5b0-2f12b6b68d32
-    ptu = absub.attrs["parent_time_units"]
+    if source=='GFDL-CM4' and (experiment=='1pctCO2' or experiment=='abrupt-4xCO2' or experiment=='historical'): 
+        btip = 91250 # https://errata.es-doc.org/static/view.html?uid=2f6b5963-f87e-b2df-a5b0-2f12b6b68d32    
+    ptu = absub.attrs['parent_time_units']
     # print('  branch time in parent:   '+str(btip)+' '+ptu)
     # print('  parent times:   '+str(pi.time.dt.year[0].values)+' - '+str(pi.time.dt.year[-1].values))
-    start = int(ptu.split(" ")[2][:4])
+    start = int(ptu.split(' ')[2][:4])
     cal = pi.time.dt.calendar
     # see http://cfconventions.org/cf-conventions/cf-conventions.html#calendar
-    if cal == "360_day":
-        dpy = 360
-    elif cal == "noleap":
-        dpy = 365
-    elif cal == "proleptic_gregorian" or cal == "standard" or cal == "julian":
-        dpy = 365.25
+    if cal=='360_day':
+        dpy=360
+    elif cal=='noleap':
+        dpy=365
+    elif cal=='proleptic_gregorian' or cal=='standard' or cal=='julian':
+        dpy=365.25
     else:
-        raise (Exception("Not sure how to handle " + cal + " calendar"))
-    if source == "CIESM" and (experiment == "abrupt-4xCO2" or experiment == "1pctCO2"):
+        raise(Exception('Not sure how to handle '+cal+' calendar'))
+    if source=='CIESM' and (experiment=='abrupt-4xCO2' or experiment=='1pctCO2'):
         # info from Yi Qin 10/1/20:
-        # "The branch_time for 4xCO2 should be the 101-yr of these published 500-yr piControl data,
+        # "The branch_time for 4xCO2 should be the 101-yr of these published 500-yr piControl data, 
         # but I wrongly regarded the “raw” piControl with piControl-spinup as the parent_case, which is a 1000-yr long run.
-        year0 = 101
-    elif source == "KACE-1-0-G" and (
-        experiment == "abrupt-4xCO2" or experiment == "1pctCO2"
-    ):
+        year0 = 101 
+    elif source=='KACE-1-0-G' and (experiment=='abrupt-4xCO2' or experiment=='1pctCO2'):
         # info from Jae-Hee Lee (via Gavin Schmidt on 1/26/22):
-        # "We used year 2150 of piControl as the initial condition for the KACE-1-0-G 1pctCO2 runs.
+        # "We used year 2150 of piControl as the initial condition for the KACE-1-0-G 1pctCO2 runs. 
         # so, you can assume that 1850 in the 1pctCO2 and 2150 in the piControl are the same
-        year0 = 2150
+        year0 = 2150         
     else:
-        year0 = int((btip / dpy) + start)
-    year150 = int(year0 + lenab)
-    st = cftime.datetime(year0, 1, 1).strftime("%Y-%m-%dT%H:%M:%S")
-    fn = cftime.datetime(year150, 12, 31, 23, 59, 59).strftime("%Y-%m-%dT%H:%M:%S")
+        year0 = int((btip/dpy)+start)
+    year150 = int(year0+lenab)
+    st=cftime.datetime(year0, 1, 1).strftime("%Y-%m-%dT%H:%M:%S")
+    fn=cftime.datetime(year150, 12, 31,23,59,59).strftime("%Y-%m-%dT%H:%M:%S")
     # Add on 10 years before st and after fn to assist in computing 21-year rolling means
-    extend_st = cftime.datetime(np.max((year0 - 10, 1)), 1, 1).strftime(
-        "%Y-%m-%dT%H:%M:%S"
-    )
-    extend_fn = cftime.datetime(
-        year150 + 10, 12, pi.time.dt.day[-1], 23, 59, 59
-    ).strftime("%Y-%m-%dT%H:%M:%S")
+    extend_st=cftime.datetime(np.max((year0-10,1)), 1, 1).strftime("%Y-%m-%dT%H:%M:%S")
+    extend_fn=cftime.datetime(year150+10, 12, pi.time.dt.day[-1],23,59,59).strftime("%Y-%m-%dT%H:%M:%S") 
 
-    return (extend_st, extend_fn, st, fn)  # ,year0,year150)
+    return(extend_st,extend_fn,st,fn)#,year0,year150)
 
-
-def get_data(pi, ab):
+def get_data(pi,ab):  
     # Return the piControl running mean and the abrupt deviation from this
 
-    var = ab.variable_id
-    extend_st, extend_fn, st, fn = get_branch_time(pi, ab)
-    absub = ab.isel(time=slice(0, 12 * 150))
-    pisub = pi.sel(time=slice(extend_st, extend_fn))
-    if len(pisub.time) < 1200:
-        print("len(time)<1200...skipping")
+    var=ab.variable_id
+    extend_st,extend_fn,st,fn = get_branch_time(pi,ab)
+    absub = ab.isel(time=slice(0,12*150))
+    pisub = pi.sel(time=slice(extend_st,extend_fn))
+    if len(pisub.time)<1200:
+        print('len(time)<1200...skipping')
         return None
 
-    bdate = pisub.time.dt.year.values
+    bdate = pisub.time.dt.year.values 
     # compute global means:
     GLcntl = pisub.spatial.average(var)
     GLpert = absub.spatial.average(var)
     # compute annual means:
-    annpert = GLpert.groupby("time.year").mean("time")
-    anncntl = GLcntl.groupby("time.year").mean("time")
+    annpert = GLpert.groupby('time.year').mean('time')
+    anncntl = GLcntl.groupby('time.year').mean('time')
     # compute 21-year centered rolling mean:
-    runcntl = anncntl.rolling(year=21, min_periods=1, center=True).mean()
+    runcntl = anncntl.rolling(year = 21, min_periods = 1, center = True).mean()
     # subset the rolling mean for the actual overlapping time:
-    runcntlsub = runcntl.sel(year=slice(int(st[:4]), int(fn[:4])))
+    runcntlsub = runcntl.sel(year=slice(int(st[:4]),int(fn[:4])))
     # set the abrupt run's year coord to match the overlapping piCon's year coord
-    annpert = annpert.assign_coords(year=runcntlsub.coords["year"])
+    annpert=annpert.assign_coords(year=runcntlsub.coords['year'])
     # compute abrupt anomalies from piControl running mean:
     annanom = annpert - runcntlsub
+    
+    return(runcntl,annanom)
 
-    return (runcntl, annanom)
-
-
-def compute_abrupt_anoms(pifilepath, abfilepath):
+def compute_ECS(filepath):
     # compute annual- anad global-mean tas and EEI anomalies in abrupt4xCO2 w.r.t. piControl climo, then compute ERF, lambda, and ECS via Gregory
-    cntl = {}
-    anom = {}
-    skip = False
-    variables = list(pifilepath.keys())
+    cntl={}
+    anom={}
+    skip=False
+    variables = list(filepath["piControl"].keys())
     for var in variables:
-        pi = xcdat.open_mfdataset(pifilepath[var], use_cftime=True)
-        ab = xcdat.open_mfdataset(abfilepath[var], use_cftime=True)
-
-        if len(ab.time) < 140 * 12:
-            print("   len(ab.time)<140*12")
-            skip = True
+        pi=xcdat.open_mfdataset(filepath["piControl"][var], use_cftime = True)
+        ab=xcdat.open_mfdataset(filepath["abrupt-4xCO2"][var], use_cftime = True)
+        
+        if len(ab.time)<140*12:
+            print('   len(ab.time)<140*12')
+            skip=True
             break
-        output = get_data(pi, ab)
+        output = get_data(pi,ab)
         if output:
-            cntl[var], anom[var] = output
+            cntl[var],anom[var] = output
         else:
-            skip = True
+            skip=True
             break
     if skip:
         # print('Skipping this model...')
@@ -140,18 +126,16 @@ def compute_abrupt_anoms(pifilepath, abfilepath):
     CNTL = dict_to_dataset(cntl)
     ANOM = dict_to_dataset(anom)
 
-    CNTL["eei"] = CNTL["rsdt"] - CNTL["rsut"] - CNTL["rlut"]
-    ANOM["eei"] = ANOM["rsdt"] - ANOM["rsut"] - ANOM["rlut"]
-
-    x = ANOM["tas"].load()
-    y = ANOM["eei"].load()
+    CNTL['eei'] = CNTL['rsdt']-CNTL['rsut']-CNTL['rlut']
+    ANOM['eei'] = ANOM['rsdt']-ANOM['rsut']-ANOM['rlut']
 
-    return (x, y)
-
-
-def gregory_calcs(x, y):
-    m, b, r, p, std_err = stats.linregress(x, y)
-    ERF = b / 2
+    x = ANOM['tas'].load()
+    y = ANOM['eei'].load()
+    
+    m, b, r, p, std_err = stats.linregress(x,y)
+    ERF = b/2
     lam = m
-    ECS = -ERF / lam
-    return (ERF, lam, ECS)
+    ecs = -ERF/lam
+    
+    return ecs
+
diff --git a/pcmdi_metrics/cloud_feedback/param/my_param.py b/pcmdi_metrics/cloud_feedback/param/my_param.py
index 35c13e080..a5c80a090 100644
--- a/pcmdi_metrics/cloud_feedback/param/my_param.py
+++ b/pcmdi_metrics/cloud_feedback/param/my_param.py
@@ -12,7 +12,6 @@
 get_ecs = False
 
 # Output directory path (directory will be generated if it does not exist yet.)
-xml_path = "./xmls/"
 figure_path = "./figures/"
 output_path = "./output"
 output_json_filename = "_".join(["cloud_feedback", model, variant]) + ".json"

From 69f60f9d9157ddb7b75e336d1a0ee92bba688a86 Mon Sep 17 00:00:00 2001
From: Jiwoo Lee <lee1043@users.noreply.github.com>
Date: Tue, 3 Oct 2023 16:31:23 -0700
Subject: [PATCH 076/133] Update issue templates

---
 .github/ISSUE_TEMPLATE/bug_report.md          | 38 +++++++++++++++++++
 .../ISSUE_TEMPLATE/documentation-update.md    | 10 +++++
 .github/ISSUE_TEMPLATE/feature_request.md     | 20 ++++++++++
 .github/ISSUE_TEMPLATE/questions.md           | 10 +++++
 4 files changed, 78 insertions(+)
 create mode 100644 .github/ISSUE_TEMPLATE/bug_report.md
 create mode 100644 .github/ISSUE_TEMPLATE/documentation-update.md
 create mode 100644 .github/ISSUE_TEMPLATE/feature_request.md
 create mode 100644 .github/ISSUE_TEMPLATE/questions.md

diff --git a/.github/ISSUE_TEMPLATE/bug_report.md b/.github/ISSUE_TEMPLATE/bug_report.md
new file mode 100644
index 000000000..a79b5cb8b
--- /dev/null
+++ b/.github/ISSUE_TEMPLATE/bug_report.md
@@ -0,0 +1,38 @@
+---
+name: Bug report
+about: " File a bug report to help us improve PMP "
+title: ''
+labels: ''
+assignees: ''
+
+---
+
+**Describe the bug**
+A clear and concise description of what the bug is.
+
+**To Reproduce**
+Steps to reproduce the behavior:
+1. Go to '...'
+2. Click on '....'
+3. Scroll down to '....'
+4. See error
+
+**Expected behavior**
+A clear and concise description of what you expected to happen.
+
+**Screenshots**
+If applicable, add screenshots to help explain your problem.
+
+**Desktop (please complete the following information):**
+ - OS: [e.g. iOS]
+ - Browser [e.g. chrome, safari]
+ - Version [e.g. 22]
+
+**Smartphone (please complete the following information):**
+ - Device: [e.g. iPhone6]
+ - OS: [e.g. iOS8.1]
+ - Browser [e.g. stock browser, safari]
+ - Version [e.g. 22]
+
+**Additional context**
+Add any other context about the problem here.
diff --git a/.github/ISSUE_TEMPLATE/documentation-update.md b/.github/ISSUE_TEMPLATE/documentation-update.md
new file mode 100644
index 000000000..d9c891651
--- /dev/null
+++ b/.github/ISSUE_TEMPLATE/documentation-update.md
@@ -0,0 +1,10 @@
+---
+name: Documentation Update
+about: 'Update PMP documentation '
+title: ''
+labels: ''
+assignees: ''
+
+---
+
+
diff --git a/.github/ISSUE_TEMPLATE/feature_request.md b/.github/ISSUE_TEMPLATE/feature_request.md
new file mode 100644
index 000000000..0fb08a38f
--- /dev/null
+++ b/.github/ISSUE_TEMPLATE/feature_request.md
@@ -0,0 +1,20 @@
+---
+name: Feature request
+about: Suggest an idea for PMP
+title: ''
+labels: ''
+assignees: ''
+
+---
+
+**Is your feature request related to a problem? Please describe.**
+A clear and concise description of what the problem is. Ex. I'm always frustrated when [...]
+
+**Describe the solution you'd like**
+A clear and concise description of what you want to happen.
+
+**Describe alternatives you've considered**
+A clear and concise description of any alternative solutions or features you've considered.
+
+**Additional context**
+Add any other context or screenshots about the feature request here.
diff --git a/.github/ISSUE_TEMPLATE/questions.md b/.github/ISSUE_TEMPLATE/questions.md
new file mode 100644
index 000000000..e66828798
--- /dev/null
+++ b/.github/ISSUE_TEMPLATE/questions.md
@@ -0,0 +1,10 @@
+---
+name: Questions
+about: Ask questions and discuss with other PMP community members here.
+title: ''
+labels: ''
+assignees: ''
+
+---
+
+Ask questions and discuss with other PMP community members here. Please browse the PMP Discussions Forum or PMP documentation first before asking a question to make sure it is not already answered. If you can't find an answer, please include a self-contained reproducible example with your question if possible. Thanks!

From 5de3b82c8e980e369257fd17d54cff76fe88bb7d Mon Sep 17 00:00:00 2001
From: Jiwoo Lee <lee1043@llnl.gov>
Date: Tue, 3 Oct 2023 16:45:54 -0700
Subject: [PATCH 077/133] add templates

---
 .github/DISCUSSION_TEMPLATE/2-questions.yml   | 70 +++++++++++++++++++
 .github/ISSUE_TEMPLATE/bug_report.md          | 38 ----------
 .github/ISSUE_TEMPLATE/bug_report.yml         | 61 ++++++++++++++++
 .github/ISSUE_TEMPLATE/config.yml             | 11 +++
 .../ISSUE_TEMPLATE/documentation-update.md    | 10 ---
 .github/ISSUE_TEMPLATE/documentation.yml      | 15 ++++
 .github/ISSUE_TEMPLATE/feature_request.md     | 20 ------
 .github/ISSUE_TEMPLATE/feature_request.yml    | 42 +++++++++++
 .github/ISSUE_TEMPLATE/questions.md           | 10 ---
 9 files changed, 199 insertions(+), 78 deletions(-)
 create mode 100644 .github/DISCUSSION_TEMPLATE/2-questions.yml
 delete mode 100644 .github/ISSUE_TEMPLATE/bug_report.md
 create mode 100644 .github/ISSUE_TEMPLATE/bug_report.yml
 create mode 100644 .github/ISSUE_TEMPLATE/config.yml
 delete mode 100644 .github/ISSUE_TEMPLATE/documentation-update.md
 create mode 100644 .github/ISSUE_TEMPLATE/documentation.yml
 delete mode 100644 .github/ISSUE_TEMPLATE/feature_request.md
 create mode 100644 .github/ISSUE_TEMPLATE/feature_request.yml
 delete mode 100644 .github/ISSUE_TEMPLATE/questions.md

diff --git a/.github/DISCUSSION_TEMPLATE/2-questions.yml b/.github/DISCUSSION_TEMPLATE/2-questions.yml
new file mode 100644
index 000000000..1bb506f44
--- /dev/null
+++ b/.github/DISCUSSION_TEMPLATE/2-questions.yml
@@ -0,0 +1,70 @@
+labels: [question]
+body:
+    - type: markdown
+      attributes:
+          value: |
+              Thanks for your interest in PMP! Please follow the template below to ensure the development team and community can help you effectively.
+
+    - type: checkboxes
+      id: checks
+      attributes:
+          label: Question criteria
+          description: Please confirm and check all the following options.
+          options:
+              - label: I added a descriptive title here.
+                required: true
+              - label: I searched the [PMP GitHub Discussions](https://github.com/PCMDI/pcmdi_metrics/discussions) to find a similar question and didn't find it.
+                required: true
+              - label: I searched the [PMP documentation](http://pcmdi.github.io/pcmdi_metrics/).
+                required: true
+
+    - type: textarea
+      id: your-question
+      attributes:
+          label: Describe your question
+          description: |
+              Please help the community help you. The more specific you can be, the easier it will be to help.
+      validations:
+          required: true
+
+    - type: textarea
+      id: possible-answers
+      attributes:
+          label: Are there are any possible answers you came across?
+          description: |
+              This will help others determine if you're on the right track. Include links to pages you've researched (e.g., software docs, Stack Overflow posts).
+
+    - type: textarea
+      id: sample-code
+      attributes:
+          label: Minimal Complete Verifiable Example (MVCE)
+          description: |
+              Minimal, self-contained copy-pastable example that generates the question/issue if possible. Please be concise with code posted (e.g., module imports, publicly accessible files).
+              Examples that follow these guidelines are easier to parse. This section will be automatically formatted into code, so no need for markdown backticks.
+
+              See guidelines below on how to provide a good MCVE:
+
+                - [Minimal Complete Verifiable Examples](https://stackoverflow.com/help/mcve)
+                - [Craft Minimal Bug Reports](http://matthewrocklin.com/blog/work/2018/02/28/minimal-bug-reports)
+          render: python
+
+    - type: textarea
+      id: log-output
+      attributes:
+          label: Relevant log output
+          description: Please copy and paste any relevant output. This will be automatically formatted into code, so no need for markdown backticks.
+          render: python
+
+    - type: textarea
+      id: show-versions
+      attributes:
+          label: Environment
+          description: |
+              If an MVCE and log output was provided, share your PMP version and some other information if your environment here
+
+    - type: textarea
+      id: extra
+      attributes:
+          label: Anything else we need to know?
+          description: |
+              Please describe any other information you want to share.
diff --git a/.github/ISSUE_TEMPLATE/bug_report.md b/.github/ISSUE_TEMPLATE/bug_report.md
deleted file mode 100644
index a79b5cb8b..000000000
--- a/.github/ISSUE_TEMPLATE/bug_report.md
+++ /dev/null
@@ -1,38 +0,0 @@
----
-name: Bug report
-about: " File a bug report to help us improve PMP "
-title: ''
-labels: ''
-assignees: ''
-
----
-
-**Describe the bug**
-A clear and concise description of what the bug is.
-
-**To Reproduce**
-Steps to reproduce the behavior:
-1. Go to '...'
-2. Click on '....'
-3. Scroll down to '....'
-4. See error
-
-**Expected behavior**
-A clear and concise description of what you expected to happen.
-
-**Screenshots**
-If applicable, add screenshots to help explain your problem.
-
-**Desktop (please complete the following information):**
- - OS: [e.g. iOS]
- - Browser [e.g. chrome, safari]
- - Version [e.g. 22]
-
-**Smartphone (please complete the following information):**
- - Device: [e.g. iPhone6]
- - OS: [e.g. iOS8.1]
- - Browser [e.g. stock browser, safari]
- - Version [e.g. 22]
-
-**Additional context**
-Add any other context about the problem here.
diff --git a/.github/ISSUE_TEMPLATE/bug_report.yml b/.github/ISSUE_TEMPLATE/bug_report.yml
new file mode 100644
index 000000000..f72e03459
--- /dev/null
+++ b/.github/ISSUE_TEMPLATE/bug_report.yml
@@ -0,0 +1,61 @@
+name: Bug Report
+description: File a bug report to help us improve PMP
+title: "[Bug]: "
+labels: ["Type: Bug"]
+assignees: []
+body:
+    - type: textarea
+      id: what-happened
+      attributes:
+          label: What happened?
+          description: |
+              Thanks for reporting a bug! Please describe what you were trying to get done.
+              Tell us what happened, what went wrong.
+      validations:
+          required: true
+
+    - type: textarea
+      id: what-did-you-expect-to-happen
+      attributes:
+          label: What did you expect to happen? Are there are possible answers you came across?
+          description: |
+              Describe what you expected to happen. Include links to pages you've researched (e.g., software docs, Stack Overflow posts).
+      validations:
+          required: false
+
+    - type: textarea
+      id: sample-code
+      attributes:
+          label: Minimal Complete Verifiable Example (MVCE)
+          description: |
+              Minimal, self-contained copy-pastable example that generates the issue if possible. Please be concise with code posted (e.g., module imports, publicly accessible files).
+              Bug reports that follow these guidelines are easier to diagnose, and so are often handled much more quickly. This section will be automatically formatted into code, so no need for markdown backticks.
+
+              See guidelines below on how to provide a good MCVE:
+
+                - [Minimal Complete Verifiable Examples](https://stackoverflow.com/help/mcve)
+                - [Craft Minimal Bug Reports](http://matthewrocklin.com/blog/work/2018/02/28/minimal-bug-reports)
+          render: python
+
+    - type: textarea
+      id: log-output
+      attributes:
+          label: Relevant log output
+          description: Please copy and paste any relevant output. This will be automatically formatted into code, so no need for markdown backticks.
+          render: python
+
+    - type: textarea
+      id: extra
+      attributes:
+          label: Anything else we need to know?
+          description: |
+              Please describe any other information you want to share.
+
+    - type: textarea
+      id: show-versions
+      attributes:
+          label: Environment
+          description: |
+              Please share some information about your environment.
+      validations:
+          required: true
diff --git a/.github/ISSUE_TEMPLATE/config.yml b/.github/ISSUE_TEMPLATE/config.yml
new file mode 100644
index 000000000..c2816362f
--- /dev/null
+++ b/.github/ISSUE_TEMPLATE/config.yml
@@ -0,0 +1,11 @@
+blank_issues_enabled: true
+contact_links:
+    - name: Questions (PMP)
+      url: https://github.com/PCMDI/pcmdi_metrics/discussions
+      about: |
+          Ask questions and discuss with other PMP community members here. Please
+          browse the PMP Discussions Forum or PMP documentation first before asking a
+          question to make sure it is not already answered. If you can't find an
+          answer, please include a self-contained reproducible example with your
+          question if possible. Thanks!
+
diff --git a/.github/ISSUE_TEMPLATE/documentation-update.md b/.github/ISSUE_TEMPLATE/documentation-update.md
deleted file mode 100644
index d9c891651..000000000
--- a/.github/ISSUE_TEMPLATE/documentation-update.md
+++ /dev/null
@@ -1,10 +0,0 @@
----
-name: Documentation Update
-about: 'Update PMP documentation '
-title: ''
-labels: ''
-assignees: ''
-
----
-
-
diff --git a/.github/ISSUE_TEMPLATE/documentation.yml b/.github/ISSUE_TEMPLATE/documentation.yml
new file mode 100644
index 000000000..fd5b1e767
--- /dev/null
+++ b/.github/ISSUE_TEMPLATE/documentation.yml
@@ -0,0 +1,15 @@
+name: Documentation Update
+description: Update PMP documentation
+title: "[Doc]: "
+labels: ["Type: Documentation"]
+assignees: []
+body:
+    - type: textarea
+      id: description
+      attributes:
+          label: Describe your documentation update
+          description: |
+              Concise description of why the documentation is being updated (e.g., missing content for new feature, typo)
+              If this is related to an issue or PR, please mention it.
+      validations:
+          required: true
diff --git a/.github/ISSUE_TEMPLATE/feature_request.md b/.github/ISSUE_TEMPLATE/feature_request.md
deleted file mode 100644
index 0fb08a38f..000000000
--- a/.github/ISSUE_TEMPLATE/feature_request.md
+++ /dev/null
@@ -1,20 +0,0 @@
----
-name: Feature request
-about: Suggest an idea for PMP
-title: ''
-labels: ''
-assignees: ''
-
----
-
-**Is your feature request related to a problem? Please describe.**
-A clear and concise description of what the problem is. Ex. I'm always frustrated when [...]
-
-**Describe the solution you'd like**
-A clear and concise description of what you want to happen.
-
-**Describe alternatives you've considered**
-A clear and concise description of any alternative solutions or features you've considered.
-
-**Additional context**
-Add any other context or screenshots about the feature request here.
diff --git a/.github/ISSUE_TEMPLATE/feature_request.yml b/.github/ISSUE_TEMPLATE/feature_request.yml
new file mode 100644
index 000000000..70ef8982c
--- /dev/null
+++ b/.github/ISSUE_TEMPLATE/feature_request.yml
@@ -0,0 +1,42 @@
+name: Feature Request
+description: Suggest an idea for PMP
+title: "[Feature]: "
+labels: ["Type: Enhancement"]
+assignees: []
+body:
+    - type: textarea
+      id: description
+      attributes:
+          label: Is your feature request related to a problem?
+          description: |
+              Please do a quick search of existing issues to make sure that this has not been asked before.
+              Please provide a clear and concise description of what the problem is. Ex. I'm always frustrated when [...]
+      validations:
+          required: true
+
+    - type: textarea
+      id: solution
+      attributes:
+          label: Describe the solution you'd like
+          description: |
+              A clear and concise description of what you want to happen.
+      validations:
+        required: false
+
+    - type: textarea
+      id: alternatives
+      attributes:
+          label: Describe alternatives you've considered
+          description: |
+              A clear and concise description of any alternative solutions or features you've considered.
+      validations:
+          required: false
+
+    - type: textarea
+      id: additional-context
+      attributes:
+          label: Additional context
+          description: |
+              Add any other context about the feature request here.
+      validations:
+          required: false
diff --git a/.github/ISSUE_TEMPLATE/questions.md b/.github/ISSUE_TEMPLATE/questions.md
deleted file mode 100644
index e66828798..000000000
--- a/.github/ISSUE_TEMPLATE/questions.md
+++ /dev/null
@@ -1,10 +0,0 @@
----
-name: Questions
-about: Ask questions and discuss with other PMP community members here.
-title: ''
-labels: ''
-assignees: ''
-
----
-
-Ask questions and discuss with other PMP community members here. Please browse the PMP Discussions Forum or PMP documentation first before asking a question to make sure it is not already answered. If you can't find an answer, please include a self-contained reproducible example with your question if possible. Thanks!

From 99fdea443c1195c9ef729041ea099d272723f2f1 Mon Sep 17 00:00:00 2001
From: Jiwoo Lee <lee1043@llnl.gov>
Date: Tue, 3 Oct 2023 16:58:09 -0700
Subject: [PATCH 078/133] add vscode setup file

---
 .vscode/pcmdi_metrics.code-workspace | 53 ++++++++++++++++++++++++++++
 1 file changed, 53 insertions(+)
 create mode 100644 .vscode/pcmdi_metrics.code-workspace

diff --git a/.vscode/pcmdi_metrics.code-workspace b/.vscode/pcmdi_metrics.code-workspace
new file mode 100644
index 000000000..93362f2d5
--- /dev/null
+++ b/.vscode/pcmdi_metrics.code-workspace
@@ -0,0 +1,53 @@
+// This file stores the pcmdi_metrics repository's VS Code workspace settings.
+// Simply open up this file in VS Code and the editor will be automatically configured using this file.
+// Workspace settings take precedence over your user settings.
+{
+    "folders": [
+        {
+            "path": ".."
+        }
+    ],
+    "settings": {
+        // ===================
+        // Editor settings
+        // ===================
+        "editor.formatOnSave": true,
+        // ===================
+        // Python settings
+        // ===================
+        "[python]": {
+            // editor.rulers: [comments, max line length, wrap line length],
+            // Black does not wrap comments.
+            "editor.rulers": [80, 88, 120],
+            "editor.wordWrap": "wordWrapColumn",
+            "editor.wordWrapColumn": 120,
+            "editor.defaultFormatter": "ms-python.black-formatter"
+        },
+        "black-formatter.importStrategy": "fromEnvironment",
+        // Code Formatting and Linting
+        // ---------------------------
+        "flake8.args": ["--config=setup.cfg"],
+        "flake8.importStrategy": "fromEnvironment",
+        // Type checking
+        // ---------------------------
+        "mypy-type-checker.args": ["--config=pyproject.toml"],
+        "mypy-type-checker.importStrategy": "fromEnvironment",
+        // Testing
+        // ---------------------------
+        "python.testing.unittestEnabled": false,
+        "python.testing.pytestEnabled": true,
+        // NOTE: Debugger doesn't work if pytest-cov is enabled, so set "--no-cov"
+        // https://github.com/microsoft/vscode-python/issues/693
+        "python.testing.pytestArgs": ["--no-cov"],
+        // ===================
+        // Extension settings
+        // ===================
+        "jupyter.notebookFileRoot": "${workspaceFolder}",
+        "autoDocstring.docstringFormat": "numpy",
+        "[restructuredtext]": {
+            "editor.rulers": [88, 120],
+            "editor.wordWrap": "wordWrapColumn",
+            "editor.wordWrapColumn": 120
+        }
+    }
+}

From 08941dba732085e10f8ca63688b994dfe84714ee Mon Sep 17 00:00:00 2001
From: Jiwoo Lee <lee1043@llnl.gov>
Date: Tue, 3 Oct 2023 17:03:25 -0700
Subject: [PATCH 079/133] clean up

---
 docs/contributing.rst | 356 ++++++++++++++++++++++++++++++++++++++++++
 1 file changed, 356 insertions(+)
 create mode 100644 docs/contributing.rst

diff --git a/docs/contributing.rst b/docs/contributing.rst
new file mode 100644
index 000000000..e39aaa26a
--- /dev/null
+++ b/docs/contributing.rst
@@ -0,0 +1,356 @@
+.. highlight:: shell
+
+============
+Contributing
+============
+
+Contributions are welcome and greatly appreciated! Every little bit helps, and credit will always be given.
+
+Types of Contributions
+----------------------
+
+PCMDI Metrics Package (PMP) includes issue templates based on the contribution type: https://github.com/PCMDI/pcmdi_metrics/issues/new/choose.
+Note, new contributions must be made under the Apache-2.0 with LLVM exception license.
+
+Bug Report
+~~~~~~~~~~
+
+Look through the `GitHub Issues`_ for bugs to fix. Any unassigned issues tagged with "Type: Bug" is open for implementation.
+
+Feature Request
+~~~~~~~~~~~~~~~
+
+Look through the `GitHub Issues`_ for feature suggestions. Any unassigned issues tagged with "Type: Enhancement" is open for implementation.
+
+If you are proposing a feature:
+
+* Explain in detail how it would work.
+* Keep the scope as narrow as possible, to make it easier to implement.
+* Remember that this is a open-source project, and that contributions are welcome :)
+
+Features must meet the following criteria before they are considered for implementation:
+
+1. Feature is not implemented by ``xarray``
+2. Feature is not implemented in another actively developed xarray-based package
+
+   * For example,  `cf_xarray`_ already handles interpretation of `CF convention`_ attributes on xarray objects
+
+3. Feature is not limited to specific use cases (e.g., data quality issues)
+4. Feature is generally reusable
+5. Feature is relatively simple and lightweight to implement and use
+
+Documentation Update
+~~~~~~~~~~~~~~~~~~~~
+
+Help improve PMP's documentation, whether that be the Sphinx documentation or the API docstrings.
+
+Community Discussion
+~~~~~~~~~~~~~~~~~~~~
+
+Take a look at the `GitHub Discussions`_ page to get involved, share ideas, or ask questions.
+
+.. _cf_xarray: https://cf-xarray.readthedocs.io/en/latest/index.html
+.. _CF convention: http://cfconventions.org/
+.. _GitHub Issues: https://github.com/PCMDI/pcmdi_metrics/issues
+.. _GitHub Discussions: https://github.com/PCMDI/pcmdi_metrics/discussions
+
+Version Control
+---------------
+
+The repository uses branch-based (core team) and fork-based (external collaborators)
+Git workflows with tagged software releases.
+
+.. figure:: _static/git-flow.svg
+   :alt: Git Flow Diagram
+
+Guidelines
+~~~~~~~~~~
+
+1. ``main`` must always be deployable
+2. All changes are made through support branches
+3. Rebase with the latest ``main`` to avoid/resolve conflicts
+4. Make sure pre-commit quality assurance checks pass when committing (enforced in CI/CD build)
+5. Open a pull request early for discussion
+6. Once the CI/CD build passes and pull request is approved, squash and rebase your commits
+7. Merge pull request into ``main`` and delete the branch
+
+Things to Avoid
+~~~~~~~~~~~~~~~
+
+1. Don't merge in broken or commented out code
+2. Don't commit directly to ``main``
+
+   *  There are branch-protection rules for ``main``
+
+3. Don't merge with conflicts. Instead, handle conflicts upon rebasing
+
+Source: https://gist.github.com/jbenet/ee6c9ac48068889b0912
+
+Pre-commit
+~~~~~~~~~~
+The repository uses the pre-commit package to manage pre-commit hooks.
+These hooks help with quality assurance standards by identifying simple issues
+at the commit level before submitting code reviews.
+
+.. figure:: _static/pre-commit-flow.svg
+   :alt: Pre-commit Flow Diagram
+
+   pre-commit Flow
+
+
+Get Started
+------------
+
+Ready to contribute? Here's how to set up PMP for local development.
+
+VS Code, the editor of choice
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+We recommend using VS Code as your IDE because it is open-source and has great Python development support.
+
+Get VS Code here: https://code.visualstudio.com
+
+VS Code Setup
+^^^^^^^^^^^^^
+PMP includes a VS Code workspace file (``.vscode/pcmdi_metrics.code-setting``). This file automatically configures your IDE with the quality assurance tools, code line-length rulers, and more.
+
+Make sure to follow the :ref:`Local Development` section below.
+
+Recommended VS Code Extensions
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+    * `Python <https://marketplace.visualstudio.com/items?itemName=ms-python.python>`_
+    * `Pylance <https://marketplace.visualstudio.com/items?itemName=ms-python.vscode-pylance>`_
+    * `Python Docstring Generator <https://marketplace.visualstudio.com/items?itemName=njpwerner.autodocstring>`_
+    * `Python Type Hint <https://marketplace.visualstudio.com/items?itemName=njqdev.vscode-python-typehint>`_
+    * `Better Comments <https://marketplace.visualstudio.com/items?itemName=aaron-bond.better-comments>`_
+    * `Jupyter <https://marketplace.visualstudio.com/items?itemName=ms-toolsai.jupyter>`_
+    * `Visual Studio Intellicode <https://marketplace.visualstudio.com/items?itemName=VisualStudioExptTeam.vscodeintellicode>`_
+
+
+.. _Local Development:
+
+Local Development
+~~~~~~~~~~~~~~~~~
+
+1. Download and install Conda
+
+    Linux
+        ::
+
+            $ wget https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh
+            $ bash ./Miniconda3-latest-Linux-x86_64.sh
+            Do you wish the installer to initialize Miniconda3 by running conda init? [yes|no] yes
+
+
+    MacOS
+        ::
+
+            $ wget https://repo.anaconda.com/miniconda/Miniconda3-latest-MacOSX-x86_64.sh
+            $ bash ./Miniconda3-latest-MacOSX-x86_64.sh
+            Do you wish the installer to initialize Miniconda3 by running conda init? [yes|no] yes
+
+2. Fork the ``pcmdi_metrics`` repo on GitHub.
+
+     - If you are a maintainer, you can clone and branch directly from the root repository here: https://github.com/PCMDI/pcmdi_metrics
+
+3. Clone your fork locally::
+
+    $ git clone git@github.com:your_name_here/pcmdi_metrics.git
+
+4. <OPTIONAL> Open ``.vscode/pcmdi_metrics.code-settings`` in VS Code
+
+
+5. Create and activate Conda development environment::
+
+    $ cd pcmdi_metrics
+    $ conda env create -f conda-env/dev.yml
+    $ conda activate pcmdi_metrics_dev
+
+6. <OPTIONAL> Set VS Code Python interpretor to ``pcmdi_metrics_dev``
+
+7. Install pre-commit::
+
+    $ pre-commit install
+    pre-commit installed at .git/hooks/pre-commit
+
+8. Create a branch for local development and make changes::
+
+    $ git checkout -b <BRANCH-NAME>
+
+9. `<OPTIONAL>` During or after making changes, check for formatting or linting issues using pre-commit::
+
+    # Step 9 performs this automatically on staged files in a commit
+    $ pre-commit run --all-files
+
+    Trim Trailing Whitespace.................................................Passed
+    Fix End of Files.........................................................Passed
+    Check Yaml...............................................................Passed
+    black....................................................................Passed
+    isort....................................................................Passed
+    flake8...................................................................Passed
+    mypy.....................................................................Passed
+
+11. Commit your changes::
+
+     $ git add .
+     $ git commit -m <Your detailed description of your changes>
+
+     Trim Trailing Whitespace.................................................Passed
+     Fix End of Files.........................................................Passed
+     Check Yaml...............................................................Passed
+     black....................................................................Passed
+     isort....................................................................Passed
+     flake8...................................................................Passed
+
+12. Make sure pre-commit QA checks pass. Otherwise, fix any caught issues.
+
+    - Most of the tools fix issues automatically so you just need to re-stage the files.
+    - flake8 and mypy issues must be fixed automatically.
+
+13. Push changes::
+
+    $ git push origin <BRANCH-NAME>
+
+14. Submit a pull request through the GitHub website.
+
+
+Pull Request Guidelines
+-----------------------
+
+Before you submit a pull request, check that it meets these guidelines:
+
+1. The pull request should include tests for new or modified code.
+2. Link issues to pull requests.
+3. If the pull request adds functionality, the docs should be updated. Put
+   your new functionality into a function with a docstring, and add the
+   feature to the list in README.rst.
+4. Squash and rebase commits for a clean and navigable Git history.
+
+When you open a pull request on GitHub, there is a template available for use.
+
+
+Style Guide
+-----------
+
+PMP integrates the Black code formatter for code styling. If you want to learn more, please read about it `here <https://black.readthedocs.io/en/stable/the_black_code_style.html>`__.
+
+PMP also leverages `Python Type Annotations <https://docs.python.org/3.8/library/typing.html>`_ to help the project scale.
+`mypy <https://mypy.readthedocs.io/en/stable/introduction.html>`_ performs optional static type checking through pre-commit.
+
+Testing
+-------
+
+Testing your local changes are important to ensure long-term maintainability and extensibility of the project.
+Since PMP is an open source library, we aim to avoid as many bugs as possible from reaching the end-user.
+
+To get started, here are guides on how to write tests using pytest:
+
+- https://docs.pytest.org/en/latest/
+- https://docs.python-guide.org/writing/tests/#py-test
+
+In most cases, if a function is hard to test, it is usually a symptom of being too complex (high cyclomatic-complexity).
+
+DOs for Testing
+~~~~~~~~~~~~~~~
+
+*  *DO* write tests for new or refactored code
+*  *DO* try to follow test-driven-development
+*  *DO* use the Coverage reports to see lines of code that need to be tested
+*  *DO* focus on simplistic, small, reusable modules for unit testing
+*  *DO* cover as many edge cases as possible when testing
+
+DON'Ts for Testing
+~~~~~~~~~~~~~~~~~~
+
+*  *DON'T* push or merge untested code
+*  *DON'T* introduce tests that fail or produce warnings
+
+Documenting Code
+----------------
+
+If you are using VS code, the `Python Docstring Generator <https://marketplace.visualstudio.com/items?itemName=njpwerner.autodocstring>`_ extension can be used to auto-generate a docstring snippet once a function/class has been written.
+If you want the extension to generate docstrings in Sphinx format, you must set the ``"autoDocstring.docstringFormat": "sphinx"`` setting, under File > Preferences > Settings.
+
+Note that it is best to write the docstrings once you have fully defined the function/class, as then the extension will generate the full docstring.
+If you make any changes to the code once a docstring is generated, you will have to manually go and update the affected docstrings.
+
+More info on docstrings here: https://sphinx-rtd-tutorial.readthedocs.io/en/latest/docstrings.html
+
+DOs for Documenting Code
+~~~~~~~~~~~~~~~~~~~~~~~~
+
+*  *DO* explain **why** something is done, its purpose, and its goal. The code shows **how** it is done, so commenting on this can be redundant.
+*  *DO* explain ambiguity or complexities to avoid confusion
+*  *DO* embrace documentation as an integral part of the overall development process
+*  *DO* treat documenting as code and follow principles such as *Don't Repeat Yourself* and *Easier to Change*
+
+DON'Ts for Documenting Code
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+*  *DON'T* write comments as a crutch for poor code
+*  *DON'T* comment *every* function, data structure, type declaration
+
+Developer Tips
+--------------
+
+* flake8 will warn you if the cyclomatic complexity of a function is too high.
+
+    * https://github.com/PyCQA/mccabe
+
+
+FAQs
+----
+
+.. _Why squash and rebase?:
+
+Why squash and rebase commits?
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+Before you merge a support branch back into ``main``, the branch is typically squashed down to a single buildable commit, and then rebased on top of the main repo's ``main`` branch.
+
+Why?
+
+* Ensures build passes from the commit
+* Cleans up Git history for easy navigation
+* Makes collaboration and review process more efficient
+* Makes handling conflicts from rebasing simple since you only have to deal with conflicted commits
+
+
+How do I squash and rebase commits?
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+* Use GitHub's Squash and Merge feature in the pull request
+
+   * You still need to rebase on the latest ``main`` if ``main`` is ahead of your branch.
+
+* Manually squash and rebase
+
+   1. `<OPTIONAL if you are forking>` Sync your fork of ``main`` (aka ``origin``) with the root ``main`` (aka ``upstream``) ::
+
+        git checkout main
+        git rebase upstream/main
+        git push -f origin main
+
+   2. Get the SHA of the commit OR number of commits to rebase to ::
+
+        git checkout <branch-name>
+        git log --graph --decorate --pretty=oneline --abbrev-commit
+
+   3. Squash commits::
+
+        git rebase -i [SHA]
+
+        # OR
+
+        git rebase -i HEAD~[NUMBER OF COMMITS]
+
+   4. Rebase branch onto ``main`` ::
+
+        git rebase main
+        git push -f origin <BRANCH-NAME>
+
+   5. Make sure your squashed commit messages are refined
+
+   6. Force push to remote branch ::
+
+        git push -f origin <BRANCH-NAME>

From 1db112f1931d6e2bce5f916e56f3dda1eb059bcb Mon Sep 17 00:00:00 2001
From: Jiwoo Lee <lee1043@llnl.gov>
Date: Tue, 3 Oct 2023 17:07:07 -0700
Subject: [PATCH 080/133] clean up

---
 docs/contributing.rst | 11 -----------
 1 file changed, 11 deletions(-)

diff --git a/docs/contributing.rst b/docs/contributing.rst
index e39aaa26a..44c4c7fd7 100644
--- a/docs/contributing.rst
+++ b/docs/contributing.rst
@@ -28,17 +28,6 @@ If you are proposing a feature:
 * Keep the scope as narrow as possible, to make it easier to implement.
 * Remember that this is a open-source project, and that contributions are welcome :)
 
-Features must meet the following criteria before they are considered for implementation:
-
-1. Feature is not implemented by ``xarray``
-2. Feature is not implemented in another actively developed xarray-based package
-
-   * For example,  `cf_xarray`_ already handles interpretation of `CF convention`_ attributes on xarray objects
-
-3. Feature is not limited to specific use cases (e.g., data quality issues)
-4. Feature is generally reusable
-5. Feature is relatively simple and lightweight to implement and use
-
 Documentation Update
 ~~~~~~~~~~~~~~~~~~~~
 

From c5204da966b20589f273c9e29a57cdfc861cf50b Mon Sep 17 00:00:00 2001
From: Jiwoo Lee <lee1043@llnl.gov>
Date: Tue, 3 Oct 2023 17:20:17 -0700
Subject: [PATCH 081/133] clean up

---
 docs/contributing.rst | 6 +++---
 docs/index.rst        | 1 +
 2 files changed, 4 insertions(+), 3 deletions(-)

diff --git a/docs/contributing.rst b/docs/contributing.rst
index 44c4c7fd7..886921cd4 100644
--- a/docs/contributing.rst
+++ b/docs/contributing.rst
@@ -1,8 +1,8 @@
 .. highlight:: shell
 
-============
-Contributing
-============
+==================
+Contributing Guide
+==================
 
 Contributions are welcome and greatly appreciated! Every little bit helps, and credit will always be given.
 
diff --git a/docs/index.rst b/docs/index.rst
index 62ebe58cd..ed1adb90e 100644
--- a/docs/index.rst
+++ b/docs/index.rst
@@ -78,6 +78,7 @@ BSD 3-Clause License. See `LICENSE <https://github.com/PCMDI/pcmdi_metrics/blob/
    :hidden:
    :caption: For developers/contributors:
 
+   contributing
    resources
    team
    GitHub repository <https://github.com/PCMDI/pcmdi_metrics>

From b05da9d11c7ddeed80620f7c140a4987e056ade5 Mon Sep 17 00:00:00 2001
From: Jiwoo Lee <lee1043@llnl.gov>
Date: Tue, 3 Oct 2023 17:24:29 -0700
Subject: [PATCH 082/133] add image files

---
 docs/_static/git-flow.svg        | 1 +
 docs/_static/pre-commit-flow.svg | 1 +
 2 files changed, 2 insertions(+)
 create mode 100644 docs/_static/git-flow.svg
 create mode 100644 docs/_static/pre-commit-flow.svg

diff --git a/docs/_static/git-flow.svg b/docs/_static/git-flow.svg
new file mode 100644
index 000000000..593e5149f
--- /dev/null
+++ b/docs/_static/git-flow.svg
@@ -0,0 +1 @@
+<svg xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" version="1.1" width="581px" height="331px" viewBox="-0.5 -0.5 581 331" content="&lt;mxfile host=&quot;cda7c10d-3c44-4746-ae72-12ec7e4943bf&quot; modified=&quot;2021-03-02T19:44:51.045Z&quot; agent=&quot;5.0 (Macintosh; Intel Mac OS X 10_15_7) AppleWebKit/537.36 (KHTML, like Gecko) Code/1.53.2 Chrome/87.0.4280.141 Electron/11.2.1 Safari/537.36&quot; version=&quot;14.2.4&quot; etag=&quot;Tqn4QkiWGkscdADMXLiR&quot; type=&quot;embed&quot;&gt;&lt;diagram id=&quot;9Xwwk1sZy75J8h5TdrZL&quot; name=&quot;Page-1&quot;&gt;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&lt;/diagram&gt;&lt;/mxfile&gt;"><defs/><g><path d="M 120 100 L 193.63 100" fill="none" stroke="#000000" stroke-miterlimit="10" pointer-events="stroke"/><path d="M 198.88 100 L 191.88 103.5 L 193.63 100 L 191.88 96.5 Z" fill="#000000" stroke="#000000" stroke-miterlimit="10" pointer-events="all"/><rect x="0" y="70" width="120" height="60" rx="9" ry="9" fill="#99ccff" stroke="#000000" pointer-events="all"/><g transform="translate(-0.5 -0.5)"><switch><foreignObject style="overflow: visible; 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font-size: 12px; font-family: Helvetica; color: #000000; line-height: 1.2; pointer-events: all; font-weight: bold; white-space: normal; word-wrap: normal; ">workflow.yml</div></div></div></foreignObject><text x="745" y="214" fill="#000000" font-family="Helvetica" font-size="12px" text-anchor="middle" font-weight="bold">workflow.yml</text></switch></g></g><switch><g requiredFeatures="http://www.w3.org/TR/SVG11/feature#Extensibility"/><a transform="translate(0,-5)" xlink:href="https://www.diagrams.net/doc/faq/svg-export-text-problems" target="_blank"><text text-anchor="middle" font-size="10px" x="50%" y="100%">Viewer does not support full SVG 1.1</text></a></switch></svg>
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From bbbee3e7374471678c5adef9a315dc897937add0 Mon Sep 17 00:00:00 2001
From: Jiwoo Lee <lee1043@llnl.gov>
Date: Tue, 3 Oct 2023 17:34:05 -0700
Subject: [PATCH 083/133] typo fix

---
 docs/contributing.rst | 9 ++++-----
 1 file changed, 4 insertions(+), 5 deletions(-)

diff --git a/docs/contributing.rst b/docs/contributing.rst
index 886921cd4..329aa8435 100644
--- a/docs/contributing.rst
+++ b/docs/contributing.rst
@@ -177,9 +177,8 @@ Local Development
     black....................................................................Passed
     isort....................................................................Passed
     flake8...................................................................Passed
-    mypy.....................................................................Passed
 
-11. Commit your changes::
+10. Commit your changes::
 
      $ git add .
      $ git commit -m <Your detailed description of your changes>
@@ -191,16 +190,16 @@ Local Development
      isort....................................................................Passed
      flake8...................................................................Passed
 
-12. Make sure pre-commit QA checks pass. Otherwise, fix any caught issues.
+11. Make sure pre-commit QA checks pass. Otherwise, fix any caught issues.
 
     - Most of the tools fix issues automatically so you just need to re-stage the files.
     - flake8 and mypy issues must be fixed automatically.
 
-13. Push changes::
+12. Push changes::
 
     $ git push origin <BRANCH-NAME>
 
-14. Submit a pull request through the GitHub website.
+13. Submit a pull request through the GitHub website.
 
 
 Pull Request Guidelines

From 14eacbe866d74f41f4189b72d74a5094567377f8 Mon Sep 17 00:00:00 2001
From: lee1043 <lee1043@llnl.gov>
Date: Sun, 8 Oct 2023 15:25:01 -0700
Subject: [PATCH 084/133] update

---
 .../mjo/lib/post_process_plot_ensemble_mean.py       |  6 +++---
 .../variability_mode/param/myParam_demo_NAM.py       |  6 ++++--
 .../scripts_pcmdi/run_pmp_parallel.sh                | 12 ------------
 3 files changed, 7 insertions(+), 17 deletions(-)

diff --git a/pcmdi_metrics/mjo/lib/post_process_plot_ensemble_mean.py b/pcmdi_metrics/mjo/lib/post_process_plot_ensemble_mean.py
index 959fd570a..af1f862e8 100644
--- a/pcmdi_metrics/mjo/lib/post_process_plot_ensemble_mean.py
+++ b/pcmdi_metrics/mjo/lib/post_process_plot_ensemble_mean.py
@@ -8,10 +8,10 @@
 
 
 def main():
-    mip = "cmip5"
-    # mip = 'cmip6'
+    # mip = "cmip5"
+    mip = 'cmip6'
     exp = "historical"
-    version = "v20200807"
+    version = "v20230924"
     period = "1985-2004"
     datadir = (
         "/p/user_pub/pmp/pmp_results/pmp_v1.1.2/diagnostic_results/mjo/"
diff --git a/pcmdi_metrics/variability_mode/param/myParam_demo_NAM.py b/pcmdi_metrics/variability_mode/param/myParam_demo_NAM.py
index 521aac0b6..913099c19 100755
--- a/pcmdi_metrics/variability_mode/param/myParam_demo_NAM.py
+++ b/pcmdi_metrics/variability_mode/param/myParam_demo_NAM.py
@@ -29,8 +29,10 @@
 # -------------------------------------------------
 reference_data_name = "NOAA-CIRES_20CR"
 reference_data_path = os.path.join(
-    "/p/user_pub/PCMDIobs/PCMDIobs2/atmos/mon/psl/20CR/gn/v20200707",
-    "psl_mon_20CR_BE_gn_v20200707_187101-201212.nc",
+    "/p/user_pub/PCMDIobs/obs4MIPs/NOAA-ESRL-PSD/20CR/mon/psl/gn/latest",
+    "psl_mon_20CR_PCMDI_gn_187101-201212.nc"
+    #"/p/user_pub/PCMDIobs/PCMDIobs2/atmos/mon/psl/20CR/gn/v20200707",
+    #"psl_mon_20CR_BE_gn_v20200707_187101-201212.nc",
 )
 
 varOBS = "psl"
diff --git a/pcmdi_metrics/variability_mode/scripts_pcmdi/run_pmp_parallel.sh b/pcmdi_metrics/variability_mode/scripts_pcmdi/run_pmp_parallel.sh
index c54e3c31f..9abc6d7b7 100755
--- a/pcmdi_metrics/variability_mode/scripts_pcmdi/run_pmp_parallel.sh
+++ b/pcmdi_metrics/variability_mode/scripts_pcmdi/run_pmp_parallel.sh
@@ -9,7 +9,6 @@ export OMP_NUM_THREADS=1
 
 ver=`date +"%Y%m%d-%H%M"`
 case_id="v"`date +"%Y%m%d"`
-case_id="v20210119"
 
 #mips='cmip3 cmip5 cmip6'
 #mips='cmip5 cmip6'
@@ -24,8 +23,6 @@ exps='historical'
 #exps='amip'
 
 #modes='all'
-#modes='SAM'
-#modes='SAM NAM'
 modes='NAO NPO PNA'
 
 modnames='all'
@@ -37,15 +34,6 @@ param_dir='../../../sample_setups/pcmdi_parameter_files/variability_modes'
 
 for mip in $mips; do
     echo $mip
-    #if [ $mip == 'cmip5' ]; then
-    #    realization='r1i1p1'
-    #    #modnames="BNU-ESM CESM1-FASTCHEM CMCC-CM FGOALS-g2 HadCM3 HadGEM2-CC IPSL-CM5A-LR IPSL-CM5A-MR MIROC4h MIROC5 MPI-ESM-LR MPI-ESM-MR"
-    #    #modnames="HadGEM2-CC HadGEM2-ES INMCM4 IPSL-CM5A-LR IPSL-CM5A-MR IPSL-CM5B-LR MIROC-ESM MIROC-ESM-CHEM MIROC4h MIROC5 MPI-ESM-LR MPI-ESM-MR MPI-ESM-P MRI-CGCM3 MRI-ESM1 NorESM1-M NorESM1-ME"
-    #fi
-    if [ $mip == 'cmip6' ]; then
-    #    realization='r1i1p1f1'
-        modnames="CNRM-CM6-1-HR EC-Earth3 EC-Earth3-AerChem EC-Earth3-Veg HadGEM3-GC31-MM"
-    fi
     for exp in $exps; do
         if [ $modes == 'all' ]; then
             if [ $exp == 'historical' ] || [ $exp == '20c3m' ]; then

From 33754ef6a86f39e73d634052e6cf386c58313137 Mon Sep 17 00:00:00 2001
From: Jiwoo Lee <lee1043@users.noreply.github.com>
Date: Wed, 25 Oct 2023 10:35:08 -0700
Subject: [PATCH 085/133] Add PMP intro video screenshot image file

---
 docs/_static/PMP_intro_video.png | Bin 0 -> 118413 bytes
 1 file changed, 0 insertions(+), 0 deletions(-)
 create mode 100644 docs/_static/PMP_intro_video.png

diff --git a/docs/_static/PMP_intro_video.png b/docs/_static/PMP_intro_video.png
new file mode 100644
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GIT binary patch
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From d7ea881d935103c7072240aa0e8a907954daa167 Mon Sep 17 00:00:00 2001
From: Jiwoo Lee <lee1043@users.noreply.github.com>
Date: Wed, 25 Oct 2023 10:40:30 -0700
Subject: [PATCH 086/133] Update index.rst

Include link and image to PMP video
---
 docs/index.rst | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/docs/index.rst b/docs/index.rst
index ed1adb90e..21febda3e 100644
--- a/docs/index.rst
+++ b/docs/index.rst
@@ -16,6 +16,8 @@ regional monsoons, and high frequency characteristics of simulated precipitation
 
 `PCMDI`_ uses the PMP to produce `quick-look simulation summaries across generations of CMIP <https://pcmdi.llnl.gov/metrics>`_.
 
+[![PMP_Intro_Video](_static/PMP_intro_video.png)](https://youtu.be/STfCq5Biqf0?feature=shared "PMP Introduction")
+
 The PMP expects model data to be `CF-compliant <http://cfconventions.org/>`_, otherwise, 
 to successfully use the package may require some input data conditioning. 
 It is also strongly suggested to work with observation datasets following the `CF-compliant <http://cfconventions.org/>`_, 

From 7117ed21cc7854afe477916f415d8a1abf75e0e5 Mon Sep 17 00:00:00 2001
From: Jiwoo Lee <lee1043@users.noreply.github.com>
Date: Wed, 25 Oct 2023 10:43:32 -0700
Subject: [PATCH 087/133] Update index.rst

---
 docs/index.rst | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/docs/index.rst b/docs/index.rst
index 21febda3e..f502f83b5 100644
--- a/docs/index.rst
+++ b/docs/index.rst
@@ -16,7 +16,7 @@ regional monsoons, and high frequency characteristics of simulated precipitation
 
 `PCMDI`_ uses the PMP to produce `quick-look simulation summaries across generations of CMIP <https://pcmdi.llnl.gov/metrics>`_.
 
-[![PMP_Intro_Video](_static/PMP_intro_video.png)](https://youtu.be/STfCq5Biqf0?feature=shared "PMP Introduction")
+[![plot](./_static/PMP_intro_video.png)](https://youtu.be/STfCq5Biqf0?feature=shared "PMP Introduction")
 
 The PMP expects model data to be `CF-compliant <http://cfconventions.org/>`_, otherwise, 
 to successfully use the package may require some input data conditioning. 

From 5ea068c773dd5cb2e48a3992bbf38fc550afe65c Mon Sep 17 00:00:00 2001
From: Jiwoo Lee <lee1043@users.noreply.github.com>
Date: Wed, 25 Oct 2023 10:44:23 -0700
Subject: [PATCH 088/133] Update index.rst

---
 docs/index.rst | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/docs/index.rst b/docs/index.rst
index f502f83b5..b695df457 100644
--- a/docs/index.rst
+++ b/docs/index.rst
@@ -16,7 +16,7 @@ regional monsoons, and high frequency characteristics of simulated precipitation
 
 `PCMDI`_ uses the PMP to produce `quick-look simulation summaries across generations of CMIP <https://pcmdi.llnl.gov/metrics>`_.
 
-[![plot](./_static/PMP_intro_video.png)](https://youtu.be/STfCq5Biqf0?feature=shared "PMP Introduction")
+[![PMP_Intro_Video](./_static/PMP_intro_video.png)](https://youtu.be/STfCq5Biqf0?feature=shared "PMP Introduction")
 
 The PMP expects model data to be `CF-compliant <http://cfconventions.org/>`_, otherwise, 
 to successfully use the package may require some input data conditioning. 

From 8243842043b8f5edf247b995cefd7a4ec5120d8e Mon Sep 17 00:00:00 2001
From: Jiwoo Lee <lee1043@users.noreply.github.com>
Date: Wed, 25 Oct 2023 10:44:47 -0700
Subject: [PATCH 089/133] Update index.rst

---
 docs/index.rst | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/docs/index.rst b/docs/index.rst
index b695df457..21febda3e 100644
--- a/docs/index.rst
+++ b/docs/index.rst
@@ -16,7 +16,7 @@ regional monsoons, and high frequency characteristics of simulated precipitation
 
 `PCMDI`_ uses the PMP to produce `quick-look simulation summaries across generations of CMIP <https://pcmdi.llnl.gov/metrics>`_.
 
-[![PMP_Intro_Video](./_static/PMP_intro_video.png)](https://youtu.be/STfCq5Biqf0?feature=shared "PMP Introduction")
+[![PMP_Intro_Video](_static/PMP_intro_video.png)](https://youtu.be/STfCq5Biqf0?feature=shared "PMP Introduction")
 
 The PMP expects model data to be `CF-compliant <http://cfconventions.org/>`_, otherwise, 
 to successfully use the package may require some input data conditioning. 

From 098123719bf5be06948a6377e6312ee2225beb38 Mon Sep 17 00:00:00 2001
From: Jiwoo Lee <lee1043@users.noreply.github.com>
Date: Wed, 25 Oct 2023 10:46:39 -0700
Subject: [PATCH 090/133] Update index.rst

---
 docs/index.rst | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/docs/index.rst b/docs/index.rst
index 21febda3e..c3fd3dab5 100644
--- a/docs/index.rst
+++ b/docs/index.rst
@@ -16,7 +16,7 @@ regional monsoons, and high frequency characteristics of simulated precipitation
 
 `PCMDI`_ uses the PMP to produce `quick-look simulation summaries across generations of CMIP <https://pcmdi.llnl.gov/metrics>`_.
 
-[![PMP_Intro_Video](_static/PMP_intro_video.png)](https://youtu.be/STfCq5Biqf0?feature=shared "PMP Introduction")
+[![PMP_Intro_Video](_static/PMP_intro_video.png)](https://youtu.be/STfCq5Biqf0?feature=shared "PMP Introduction - Click to Watch!"")
 
 The PMP expects model data to be `CF-compliant <http://cfconventions.org/>`_, otherwise, 
 to successfully use the package may require some input data conditioning. 

From 8d17b83825ad46a5a70aed973f1613c87673f3f5 Mon Sep 17 00:00:00 2001
From: Jiwoo Lee <lee1043@llnl.gov>
Date: Wed, 25 Oct 2023 11:28:25 -0700
Subject: [PATCH 091/133] add PMP video

---
 docs/index.rst | 13 ++++++++-----
 1 file changed, 8 insertions(+), 5 deletions(-)

diff --git a/docs/index.rst b/docs/index.rst
index c3fd3dab5..c962e263f 100644
--- a/docs/index.rst
+++ b/docs/index.rst
@@ -16,12 +16,11 @@ regional monsoons, and high frequency characteristics of simulated precipitation
 
 `PCMDI`_ uses the PMP to produce `quick-look simulation summaries across generations of CMIP <https://pcmdi.llnl.gov/metrics>`_.
 
-[![PMP_Intro_Video](_static/PMP_intro_video.png)](https://youtu.be/STfCq5Biqf0?feature=shared "PMP Introduction - Click to Watch!"")
+.. raw:: html
 
-The PMP expects model data to be `CF-compliant <http://cfconventions.org/>`_, otherwise, 
-to successfully use the package may require some input data conditioning. 
-It is also strongly suggested to work with observation datasets following the `CF-compliant <http://cfconventions.org/>`_, 
-such as datasets from the `obs4MIPs`_ project. 
+    <div style="position: relative; padding-bottom: 56.25%; height: 0; overflow: hidden; max-width: 100%; height: auto;">
+        <iframe src="https://www.youtube.com/embed/STfCq5Biqf0" frameborder="0" allowfullscreen style="position: absolute; top: 0; left: 0; width: 100%; height: 100%;"></iframe>
+    </div>
 
 
 Getting Started
@@ -33,6 +32,10 @@ are summarized with interactive Jupyter notebooks in the :ref:`metrics` page
 
 Some installation support for CMIP participating modeling groups is available: pcmdi-metrics@llnl.gov
 
+The PMP expects model data to be `CF-compliant <http://cfconventions.org/>`_, otherwise, 
+to successfully use the package may require some input data conditioning. 
+It is also strongly suggested to work with observation datasets following the `CF-compliant <http://cfconventions.org/>`_, 
+such as datasets from the `obs4MIPs`_ project. 
 
 Acknowledgement
 ===============

From 17ff1dd12c541ba34e865c8b65f96417555de69b Mon Sep 17 00:00:00 2001
From: Jiwoo Lee <lee1043@llnl.gov>
Date: Wed, 25 Oct 2023 11:28:51 -0700
Subject: [PATCH 092/133] clean up

---
 docs/_static/PMP_intro_video.png | Bin 118413 -> 0 bytes
 1 file changed, 0 insertions(+), 0 deletions(-)
 delete mode 100644 docs/_static/PMP_intro_video.png

diff --git a/docs/_static/PMP_intro_video.png b/docs/_static/PMP_intro_video.png
deleted file mode 100644
index 172e3ace4729952f32f43a5277ac9304428f84b9..0000000000000000000000000000000000000000
GIT binary patch
literal 0
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G2~7Y;-GHY6


From d4fd8f81485c0e971745db736bbf8b54597790a9 Mon Sep 17 00:00:00 2001
From: Jiwoo Lee <lee1043@llnl.gov>
Date: Wed, 25 Oct 2023 11:45:24 -0700
Subject: [PATCH 093/133] update

---
 docs/index.rst    | 8 +++++++-
 docs/overview.rst | 6 ++++++
 docs/team.rst     | 2 +-
 3 files changed, 14 insertions(+), 2 deletions(-)

diff --git a/docs/index.rst b/docs/index.rst
index c962e263f..e34529908 100644
--- a/docs/index.rst
+++ b/docs/index.rst
@@ -18,7 +18,7 @@ regional monsoons, and high frequency characteristics of simulated precipitation
 
 .. raw:: html
 
-    <div style="position: relative; padding-bottom: 56.25%; height: 0; overflow: hidden; max-width: 100%; height: auto;">
+    <div style="position: relative; padding-bottom: 56.25%; height: 0; overflow: hidden; max-width: 80%; height: auto; margin-left: auto; margin-right: auto">
         <iframe src="https://www.youtube.com/embed/STfCq5Biqf0" frameborder="0" allowfullscreen style="position: absolute; top: 0; left: 0; width: 100%; height: 100%;"></iframe>
     </div>
 
@@ -37,6 +37,12 @@ to successfully use the package may require some input data conditioning.
 It is also strongly suggested to work with observation datasets following the `CF-compliant <http://cfconventions.org/>`_, 
 such as datasets from the `obs4MIPs`_ project. 
 
+References
+==========
+Lee et al. (in prep, to be submitted soon), Objective Evaluation of Earth System Models: PCMDI Metrics Package (PMP) version 3, Geoscientific Model Development
+
+Gleckler et al. (2016), A more powerful reality test for climate models, Eos, 97, `doi:10.1029/2016EO051663 <https://eos.org/science-updates/a-more-powerful-reality-test-for-climate-models>`_.
+
 Acknowledgement
 ===============
 
diff --git a/docs/overview.rst b/docs/overview.rst
index 834dcf874..4f1e537df 100644
--- a/docs/overview.rst
+++ b/docs/overview.rst
@@ -9,6 +9,12 @@ The primary application of the PMP is to evaluate simulations from the `Coupled
 It can also be used to provide objective performance summaries during the model development process as well as selected research purposes.  
 The notes below provide a brief summary of some of the key aspects of the PMP design.  
 
+.. raw:: html
+
+    <div style="position: relative; padding-bottom: 56.25%; height: 0; overflow: hidden; max-width: 80%; height: auto; margin-left: auto; margin-right: auto">
+        <iframe src="https://www.youtube.com/embed/STfCq5Biqf0" frameborder="0" allowfullscreen style="position: absolute; top: 0; left: 0; width: 100%; height: 100%;"></iframe>
+    </div>
+
 Software framework and dependencies
 -----------------------------------
 
diff --git a/docs/team.rst b/docs/team.rst
index a9b2fb7f7..b45b8dfe2 100644
--- a/docs/team.rst
+++ b/docs/team.rst
@@ -15,4 +15,4 @@ Core developers
 * Peter Gleckler (LLNL)
 * Min-Seop Ahn (NASA)
 
-pcmdi-metrics@llnl.gov
\ No newline at end of file
+Contact: pcmdi-metrics@llnl.gov

From d69c52ef4a11d296ae2b396a668f27437a6e6398 Mon Sep 17 00:00:00 2001
From: Jiwoo Lee <lee1043@users.noreply.github.com>
Date: Wed, 25 Oct 2023 09:39:53 -0700
Subject: [PATCH 094/133] Update .pre-commit-config.yaml

Update isort version and repo
---
 .pre-commit-config.yaml | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml
index 4f98bfd8e..c97cf9eba 100644
--- a/.pre-commit-config.yaml
+++ b/.pre-commit-config.yaml
@@ -20,11 +20,11 @@ repos:
     # Python code formatters
     # =======================
     - repo: https://github.com/psf/black
-      rev: 23.1.0
+      rev: 23.3.0
       hooks:
           - id: black
 
-    - repo: https://github.com/PyCQA/isort
+    - repo: https://github.com/timothycrosley/isort
       rev: 5.12.0
       hooks:
           - id: isort

From 44e4097cdd759e3edfe061c679b4ab84c14baff6 Mon Sep 17 00:00:00 2001
From: Jiwoo Lee <lee1043@users.noreply.github.com>
Date: Wed, 25 Oct 2023 09:59:36 -0700
Subject: [PATCH 095/133] Update build_workflow.yml

Remove node setup and node-version
---
 .github/workflows/build_workflow.yml | 17 ++---------------
 1 file changed, 2 insertions(+), 15 deletions(-)

diff --git a/.github/workflows/build_workflow.yml b/.github/workflows/build_workflow.yml
index 3488f5f64..56d67a4b8 100644
--- a/.github/workflows/build_workflow.yml
+++ b/.github/workflows/build_workflow.yml
@@ -27,25 +27,16 @@ jobs:
     runs-on: ubuntu-latest
     timeout-minutes: 10
     steps:
-      - uses: actions/setup-node@v3
-        with:
-          node-version: '16'
       - name: Checkout Code Repository
         uses: actions/checkout@v3
-        with:
-          node-version: '16'
+        
       - name: Set up Python
         uses: actions/setup-python@v4
         with:
-          node-version: '16'
           python-version: '3.10'
-      # Run all pre-commit hooks on all the files.
-      # Getting only staged files can be tricky in case a new PR is opened
-      # since the action is run on a branch in detached head state
+
       - name: Install and Run Pre-commit
         uses: pre-commit/action@v3.0.0
-        with:
-          node-version: '16'
 
   build:
     needs: check-jobs-to-skip
@@ -56,9 +47,6 @@ jobs:
         shell: bash -l {0}
     timeout-minutes: 10
     steps:
-      - uses: actions/setup-node@v3
-        with:
-          node-version: '16'
       - uses: actions/checkout@v3
 
       - name: Set up Conda Environment
@@ -82,7 +70,6 @@ jobs:
       - name: Cache Conda
         uses: actions/cache@v3
         with:
-          node-version: '16'
           path: ${{ env.CONDA }}/envs
           key:
             conda-${{ runner.os }}--${{ runner.arch }}--${{

From b89d75920277f6b0cd4de2e5df5aec749ae6ee05 Mon Sep 17 00:00:00 2001
From: Jiwoo Lee <lee1043@users.noreply.github.com>
Date: Wed, 25 Oct 2023 10:00:52 -0700
Subject: [PATCH 096/133] Update build_workflow.yml

Add env
---
 .github/workflows/build_workflow.yml | 4 ++++
 1 file changed, 4 insertions(+)

diff --git a/.github/workflows/build_workflow.yml b/.github/workflows/build_workflow.yml
index 56d67a4b8..192e22322 100644
--- a/.github/workflows/build_workflow.yml
+++ b/.github/workflows/build_workflow.yml
@@ -9,6 +9,10 @@ on:
 
   workflow_dispatch:
 
+env:
+    CANCEL_OTHERS: true
+    PATHS_IGNORE: '["**/README.rst", "**/docs/**", "**/ISSUE_TEMPLATE/**", "**/pull_request_template.md", "**/.vscode/**"]'
+
 jobs:
   check-jobs-to-skip:
     runs-on: ubuntu-latest

From b1f4e22b7cfb6415d19bd846a6a273d2755f1115 Mon Sep 17 00:00:00 2001
From: Tom Vo <tomvothecoder@gmail.com>
Date: Thu, 26 Oct 2023 09:58:39 -0700
Subject: [PATCH 097/133] Move most `setup.cfg` configs to `pyproject.toml`

---
 .pre-commit-config.yaml |  1 -
 pyproject.toml          | 26 +++++++++++++++++++++++++-
 setup.cfg               | 25 +------------------------
 3 files changed, 26 insertions(+), 26 deletions(-)

diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml
index c97cf9eba..9380c6d41 100644
--- a/.pre-commit-config.yaml
+++ b/.pre-commit-config.yaml
@@ -29,7 +29,6 @@ repos:
       hooks:
           - id: isort
 
-
     # =======================
     # Python linting
     # =======================
diff --git a/pyproject.toml b/pyproject.toml
index 7c50f1a5a..537fe11d0 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -1,6 +1,6 @@
 [tool.black]
 line-length = 88
-target-version = ['py36']
+target-version = ['py39']
 include = '\.pyi?$'
 exclude = '''
 (
@@ -21,3 +21,27 @@ exclude = '''
   )/
 )
 '''
+
+[tool.isort]
+# Docs: https://pycqa.github.io/isort/docs/configuration/options.html#example-pyprojecttoml_4
+multi_line_output = 3
+include_trailing_comma = true
+force_grid_wrap = 0
+use_parentheses = true
+line_length = 88
+
+[tool.pytest.ini_options]
+# Docs: https://docs.pytest.org/en/7.2.x/reference/customize.html#configuration
+junit_family = "xunit2"
+addopts = "--cov=pcmdi_metrics --cov-report term --cov-report html:tests_coverage_reports/htmlcov --cov-report xml:tests_coverage_reports/coverage.xml -s --ignore tests/deprecated"
+python_files = ["tests.py", "test_*.py"]
+
+
+[tool.mypy]
+# Docs: https://mypy.readthedocs.io/en/stable/config_file.html
+python_version = 3.10
+check_untyped_defs = true
+ignore_missing_imports = true
+warn_unused_ignores = true
+warn_redundant_casts = true
+warn_unused_configs = true
diff --git a/setup.cfg b/setup.cfg
index 3a6b56554..5645d6806 100644
--- a/setup.cfg
+++ b/setup.cfg
@@ -1,5 +1,6 @@
 [flake8]
 # https://pep8.readthedocs.io/en/latest/intro.html#error-codes
+# flake8 dooes not support pyproject.toml yet: https://github.com/PyCQA/flake8/issues/234
 ignore =
     # whitespace before ‘:’
     E203
@@ -31,31 +32,7 @@ exclude =
     *json
     *md
 
-[isort]
-multi_line_output=3
-include_trailing_comma=True
-force_grid_wrap=0
-use_parentheses=True
-line_length=88
 
-[pycodestyle]
-max-line-length = 119
-exclude =
-    .tox
-    .git
-    */migrations/*
-    */static/CACHE/*
-    docs
-    node_modules
-    .idea
-    .mypy_cache
-    .pytest_cache
-    *__init__.py
-    venv
-
-[aliases]
-# Define setup.py command aliases here
-test = pytest
 
 [tool:pytest]
 junit_family = xunit2

From 7e6bbe87582b1b5afffa62d4dc6fe00385fc670c Mon Sep 17 00:00:00 2001
From: Tom Vo <tomvothecoder@gmail.com>
Date: Thu, 26 Oct 2023 10:15:29 -0700
Subject: [PATCH 098/133] Fix `pre-commit` exclude skipping all files

---
 .pre-commit-config.yaml | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml
index 9380c6d41..c21f665b8 100644
--- a/.pre-commit-config.yaml
+++ b/.pre-commit-config.yaml
@@ -1,6 +1,6 @@
 # Configuration file for `pre-commit` tool.
 # Source: https://pre-commit.com/#pre-commit-configyaml---top-level
-exclude: "docs|node_modules|migrations|.git|.tox|.md|sample_setups/(external-setups|jsons)|tests/deprecated"
+exclude: "docs|node_modules|migrations|.git|.tox|README.md|sample_setups/(external-setups|jsons)|tests/deprecated"
 default_stages: [commit]
 fail_fast: true
 

From 1401dc28d4227b76ea4d3a2b3fae20faf9dba362 Mon Sep 17 00:00:00 2001
From: Tom Vo <tomvothecoder@gmail.com>
Date: Thu, 26 Oct 2023 10:27:53 -0700
Subject: [PATCH 099/133] Update pyproject.toml

---
 pyproject.toml | 9 ---------
 1 file changed, 9 deletions(-)

diff --git a/pyproject.toml b/pyproject.toml
index 537fe11d0..120918cf8 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -36,12 +36,3 @@ junit_family = "xunit2"
 addopts = "--cov=pcmdi_metrics --cov-report term --cov-report html:tests_coverage_reports/htmlcov --cov-report xml:tests_coverage_reports/coverage.xml -s --ignore tests/deprecated"
 python_files = ["tests.py", "test_*.py"]
 
-
-[tool.mypy]
-# Docs: https://mypy.readthedocs.io/en/stable/config_file.html
-python_version = 3.10
-check_untyped_defs = true
-ignore_missing_imports = true
-warn_unused_ignores = true
-warn_redundant_casts = true
-warn_unused_configs = true

From d29cb53fa3cf0ddb48d696a249c9422212b0047c Mon Sep 17 00:00:00 2001
From: Jiwoo Lee <lee1043@llnl.gov>
Date: Thu, 26 Oct 2023 12:57:31 -0700
Subject: [PATCH 100/133] pre-commit compliance

---
 pcmdi_metrics/__init__.py                     |   5 +-
 .../cloud_feedback/cloud_feedback_driver.py   |  41 +-
 .../cloud_feedback/lib/argparse_functions.py  |   1 -
 .../lib/cal_CloudRadKernel_xr.py              |   7 +-
 .../lib/cld_fbks_ecs_assessment_v3.py         |  41 +-
 .../cloud_feedback/lib/compute_ECS_xr.py      | 153 ++++----
 .../cloud_feedback/lib/organize_jsons.py      |   5 +-
 pcmdi_metrics/enso/enso_driver.py             |   1 -
 pcmdi_metrics/enso/lib/enso_lib.py            |   1 -
 .../graphics/bar_chart/lib/barChart.py        |   1 -
 .../parallel_coordinate_plot_lib.py           |  27 +-
 .../portrait_plot/portrait_plot_lib.py        |   1 -
 pcmdi_metrics/graphics/share/Metrics.py       |   5 -
 .../graphics/share/read_json_mean_clim.py     |  14 +-
 .../graphics/taylor_diagram/taylor_diagram.py |   1 -
 pcmdi_metrics/io/base.py                      |  37 +-
 pcmdi_metrics/io/default_regions_define.py    |  54 +--
 pcmdi_metrics/io/region_from_file.py          |   9 +-
 pcmdi_metrics/io/xcdat_openxml.py             |  10 +-
 .../mean_climate/deprecated/lib/dataset.py    |  26 +-
 .../mean_climate/deprecated/lib/io.py         |   1 -
 .../lib/mean_climate_metrics_driver.py        |  42 +-
 .../mean_climate/deprecated/lib/model.py      |  26 +-
 .../deprecated/lib/observation.py             |   3 +-
 .../deprecated/lib/outputmetrics.py           |  62 +--
 pcmdi_metrics/mean_climate/lib/__init__.py    |   2 +-
 .../mean_climate/lib/calculate_climatology.py |  93 +++--
 .../mean_climate/lib/compute_metrics.py       | 115 +++---
 .../mean_climate/lib/compute_statistics.py    |  71 ++--
 .../lib/create_mean_climate_parser.py         |  13 +-
 .../mean_climate/lib/load_and_regrid.py       |  99 +++--
 .../lib/mean_climate_metrics_to_json.py       |  18 +-
 .../mean_climate/mean_climate_driver.py       | 371 ++++++++++++------
 .../param/basic_annual_cycle_param.py         |  12 +-
 .../mean_climate/param/basic_param.py         |  32 +-
 .../param/pcmdi_MIP_EXP_pmp_parameterfile.py  | 185 ++++++---
 .../pcmdi_compute_climatologies.py            |  40 +-
 .../scripts/allvars_parallel_mod_clims.py     |  91 +++--
 .../scripts/get_all_MIP_mods_from_CLIMS.py    |  26 +-
 .../mean_climate/scripts/mk_CRF_clims.py      |  89 +++--
 .../pcmdi_compute_climatologies-CMOR.py       |   1 -
 .../scripts/post_process_merge_jsons.py       |  33 +-
 .../misc/scripts/parallel_submitter.py        |   2 +-
 pcmdi_metrics/mjo/lib/debug_chk_plot.py       |   1 -
 pcmdi_metrics/mjo/lib/mjo_metric_calc.py      |   3 +-
 .../lib/plot_wavenumber_frequency_power.py    |   2 -
 pcmdi_metrics/mjo/lib/post_process_plot.py    |   1 -
 pcmdi_metrics/mjo/mjo_metrics_driver.py       |  14 +-
 .../mjo/param/myParam_mjo_highresmip.py       |   2 +-
 .../monsoon_wang/graphics/SeabarChart_mpl.py  |   1 -
 .../monsoon_wang/monsoon_wang_driver.py       |   3 +-
 .../scripts/bar_chart_monsoon_precip_index.py |   2 +-
 .../param/precip_distribution_params_CFSR.py  |  18 +-
 .../precip_distribution_params_CMORPH.py      |  18 +-
 .../precip_distribution_params_ERA-Interim.py |  18 +-
 .../param/precip_distribution_params_ERA5.py  |  18 +-
 .../param/precip_distribution_params_GPCP.py  |  18 +-
 .../param/precip_distribution_params_IMERG.py |  18 +-
 .../precip_distribution_params_JRA-55.py      |  18 +-
 .../param/precip_distribution_params_MERRA.py |  18 +-
 .../precip_distribution_params_MERRA2.py      |  18 +-
 .../precip_distribution_params_PERSIANN.py    |  18 +-
 .../param/precip_distribution_params_TRMM.py  |  18 +-
 .../param/precip_distribution_params_cmip5.py |  19 +-
 .../param/precip_distribution_params_cmip6.py |  19 +-
 .../precip_distribution_driver.py             |   7 +-
 .../scripts_pcmdi/parallel_driver_cmip.py     |   4 +-
 .../lib/argparse_functions.py                 |   8 +-
 .../lib/lib_variability_across_timescales.py  | 142 ++++---
 ..._across_timescales_PS_3hr_params_CMORPH.py |   9 +-
 ...y_across_timescales_PS_3hr_params_IMERG.py |   9 +-
 ...ty_across_timescales_PS_3hr_params_TRMM.py |   9 +-
 ...y_across_timescales_PS_3hr_params_cmip5.py |  10 +-
 ...y_across_timescales_PS_3hr_params_cmip6.py |  10 +-
 ...ty_across_timescales_PS_day_params_TRMM.py |   9 +-
 .../scripts_pcmdi/parallel_driver_cmip.py     |  25 +-
 .../scripts_pcmdi/run_obs.bash                |   1 -
 .../scripts_pcmdi/run_parallel.wait.bash      |   2 +-
 ...variability_across_timescales_PS_driver.py |  23 +-
 .../variability_mode/lib/calc_stat.py         |   1 -
 .../lib/lib_variability_mode.py               |   1 -
 .../variability_mode/lib/plot_map.py          |  17 +-
 .../scripts_pcmdi/post_process_merge_jsons.py |   1 -
 .../variability_modes_driver.py               |   7 +-
 pcmdi_metrics/version.py                      |   6 +-
 pyproject.toml                                |   1 -
 .../pcmdi_MIP_EXP_pmp_parameterfile.py        |   1 -
 87 files changed, 1592 insertions(+), 843 deletions(-)

diff --git a/pcmdi_metrics/__init__.py b/pcmdi_metrics/__init__.py
index c9533b8d3..30c426647 100644
--- a/pcmdi_metrics/__init__.py
+++ b/pcmdi_metrics/__init__.py
@@ -15,6 +15,7 @@
 plog.addHandler(ch)
 plog.setLevel(LOG_LEVEL)
 from . import io  # noqa
-#from . import pcmdi  # noqa
-#from . import mean_climate  # noqa
+
+# from . import pcmdi  # noqa
+# from . import mean_climate  # noqa
 from .version import __git_sha1__, __git_tag_describe__, __version__  # noqa
diff --git a/pcmdi_metrics/cloud_feedback/cloud_feedback_driver.py b/pcmdi_metrics/cloud_feedback/cloud_feedback_driver.py
index 8e882dafd..13b25894d 100644
--- a/pcmdi_metrics/cloud_feedback/cloud_feedback_driver.py
+++ b/pcmdi_metrics/cloud_feedback/cloud_feedback_driver.py
@@ -61,7 +61,10 @@
     with open(input_files_json) as f:
         ncfiles = json.load(f)
 else:
-    print('Warning: input files were not explicitly given. They will be searched from ', path)
+    print(
+        "Warning: input files were not explicitly given. They will be searched from ",
+        path,
+    )
 
 for exp in exps:
     filenames[exp] = dict()
@@ -169,15 +172,33 @@
 output_dict["RESULTS"][model] = OrderedDict()
 output_dict["RESULTS"][model][variant] = OrderedDict()
 output_dict["RESULTS"][model][variant]["assessed_cloud_feedback"] = OrderedDict()
-output_dict["RESULTS"][model][variant]["assessed_cloud_feedback"]["high_cloud_altitude"] = assessed_cld_fbk[0]
-output_dict["RESULTS"][model][variant]["assessed_cloud_feedback"]["tropical_marine_low_cloud"] = assessed_cld_fbk[1]
-output_dict["RESULTS"][model][variant]["assessed_cloud_feedback"]["tropical_anvil_cloud_area"] = assessed_cld_fbk[2]
-output_dict["RESULTS"][model][variant]["assessed_cloud_feedback"]["land_cloud_amount"] = assessed_cld_fbk[3]
-output_dict["RESULTS"][model][variant]["assessed_cloud_feedback"]["middle_latitude_marine_low_cloud_amount"] = assessed_cld_fbk[4]
-output_dict["RESULTS"][model][variant]["assessed_cloud_feedback"]["high_latitude_low_cloud_optical_depth"] = assessed_cld_fbk[5]
-output_dict["RESULTS"][model][variant]["assessed_cloud_feedback"]["implied_unassessed"] = assessed_cld_fbk[6]
-output_dict["RESULTS"][model][variant]["assessed_cloud_feedback"]["sum_of_assessed"] = assessed_cld_fbk[7]
-output_dict["RESULTS"][model][variant]["assessed_cloud_feedback"]["total_cloud_feedback"] = assessed_cld_fbk[8]
+output_dict["RESULTS"][model][variant]["assessed_cloud_feedback"][
+    "high_cloud_altitude"
+] = assessed_cld_fbk[0]
+output_dict["RESULTS"][model][variant]["assessed_cloud_feedback"][
+    "tropical_marine_low_cloud"
+] = assessed_cld_fbk[1]
+output_dict["RESULTS"][model][variant]["assessed_cloud_feedback"][
+    "tropical_anvil_cloud_area"
+] = assessed_cld_fbk[2]
+output_dict["RESULTS"][model][variant]["assessed_cloud_feedback"][
+    "land_cloud_amount"
+] = assessed_cld_fbk[3]
+output_dict["RESULTS"][model][variant]["assessed_cloud_feedback"][
+    "middle_latitude_marine_low_cloud_amount"
+] = assessed_cld_fbk[4]
+output_dict["RESULTS"][model][variant]["assessed_cloud_feedback"][
+    "high_latitude_low_cloud_optical_depth"
+] = assessed_cld_fbk[5]
+output_dict["RESULTS"][model][variant]["assessed_cloud_feedback"][
+    "implied_unassessed"
+] = assessed_cld_fbk[6]
+output_dict["RESULTS"][model][variant]["assessed_cloud_feedback"][
+    "sum_of_assessed"
+] = assessed_cld_fbk[7]
+output_dict["RESULTS"][model][variant]["assessed_cloud_feedback"][
+    "total_cloud_feedback"
+] = assessed_cld_fbk[8]
 output_dict["RESULTS"][model][variant]["clim_cloud_rmse"] = climo_cld_rmse
 output_dict["RESULTS"][model][variant]["cloud_feedback_rmse"] = cld_fbk_rmse
 output_dict["RESULTS"][model][variant]["equilibrium_climate_sensitivity"] = ecs
diff --git a/pcmdi_metrics/cloud_feedback/lib/argparse_functions.py b/pcmdi_metrics/cloud_feedback/lib/argparse_functions.py
index a8f749acb..a35d20989 100644
--- a/pcmdi_metrics/cloud_feedback/lib/argparse_functions.py
+++ b/pcmdi_metrics/cloud_feedback/lib/argparse_functions.py
@@ -4,7 +4,6 @@
 
 
 def AddParserArgument():
-
     P = CDPParser(
         default_args_file=[],
         description="Cloud feedback metrics",
diff --git a/pcmdi_metrics/cloud_feedback/lib/cal_CloudRadKernel_xr.py b/pcmdi_metrics/cloud_feedback/lib/cal_CloudRadKernel_xr.py
index 03b82248a..c676c7283 100644
--- a/pcmdi_metrics/cloud_feedback/lib/cal_CloudRadKernel_xr.py
+++ b/pcmdi_metrics/cloud_feedback/lib/cal_CloudRadKernel_xr.py
@@ -7,13 +7,14 @@
 # to expert-assessed values from Sherwood et al (2020)
 # =============================================
 
+from datetime import date
+
 # IMPORT STUFF:
 import cftime
+import numpy as np
 import xarray as xr
 import xcdat as xc
-import numpy as np
 from global_land_mask import globe
-from datetime import date
 
 # =============================================
 # define necessary information
@@ -190,7 +191,7 @@ def get_CRK_data(filepath):
 ###########################################################################
 def get_kernel_regrid(ctl):
     # Read in data and map kernels to lat/lon
-    
+
     f = xc.open_mfdataset(datadir + "cloud_kernels2.nc", decode_times=False)
     f = f.rename({"mo": "time", "tau_midpt": "tau", "p_midpt": "plev"})
     f["time"] = ctl["time"].copy()
diff --git a/pcmdi_metrics/cloud_feedback/lib/cld_fbks_ecs_assessment_v3.py b/pcmdi_metrics/cloud_feedback/lib/cld_fbks_ecs_assessment_v3.py
index 815e49fdb..587343293 100755
--- a/pcmdi_metrics/cloud_feedback/lib/cld_fbks_ecs_assessment_v3.py
+++ b/pcmdi_metrics/cloud_feedback/lib/cld_fbks_ecs_assessment_v3.py
@@ -4,8 +4,8 @@
 """
 
 import json
-import string
 import os
+import string
 from datetime import date
 
 import matplotlib as mpl
@@ -17,7 +17,7 @@
 HEIGHT = 0.45
 
 # ######################################################
-# DEFINE COLORS FOR ECS COLORBAR 
+# DEFINE COLORS FOR ECS COLORBAR
 # ######################################################
 cmap = plt.cm.RdBu_r  # define the colormap
 # extract all colors from the .jet map
@@ -75,7 +75,6 @@
 
 # ######################################################
 def get_expert_assessed_fbks():
-
     # ######################################################
     # ############# WCRP ASSESSMENT VALUES #################
     # ######################################################
@@ -272,7 +271,6 @@ def get_fbks(cld_fbks, obsc_cld_fbks, cld_errs, ecs_dict):
 
 # ######################################################
 def get_gcm_assessed_fbks(fbk_dict, obsc_fbk_dict):
-
     # dictionary is structured: [region][sfc][sec][name]
 
     assessed = []
@@ -364,7 +362,6 @@ def get_gcm_assessed_fbks(fbk_dict, obsc_fbk_dict):
 
 # ######################################################
 def get_gcm_cld_errs(err_dict, name):
-
     # dictionary is structured: [region][sfc][sec][name]
 
     DATA = []
@@ -403,7 +400,6 @@ def get_gcm_cld_errs(err_dict, name):
 
 # ######################################################
 def get_unassessed_fbks(fbk_dict, obsc_fbk_dict):
-
     # dictionary is structured: [region][sfc][sec][name]
 
     fbk_names = []
@@ -651,7 +647,6 @@ def label_models(ax, models5, models6):
 
 # ######################################################
 def plot_expert():
-
     (expert_cld_fbks, err_expert_cld_fbks, fbk_names) = get_expert_assessed_fbks()
     LN = len(fbk_names)
 
@@ -880,10 +875,15 @@ def static_plot(assessed, ecs, models, fbk_names, gen, fig, gs):
 
 # ######################################################
 def make_all_figs(
-    cld_fbks6, obsc_cld_fbks6, cld_errs6, ecs_dict56, newmod, 
-    figdir=None, datadir=None,
-    debug=False):
-
+    cld_fbks6,
+    obsc_cld_fbks6,
+    cld_errs6,
+    ecs_dict56,
+    newmod,
+    figdir=None,
+    datadir=None,
+    debug=False,
+):
     # Set a unique marker for your new model
     MARK[newmod] = "<"
 
@@ -998,7 +998,9 @@ def make_all_figs(
     # new axis for labeling all models
     ax = plt.subplot(gs[:10, 10:12])
     label_models(ax, models5, models6)
-    plt.savefig(os.path.join(figdir, "WCRP_assessed_cld_fbks_amip-p4K.png"), bbox_inches="tight")
+    plt.savefig(
+        os.path.join(figdir, "WCRP_assessed_cld_fbks_amip-p4K.png"), bbox_inches="tight"
+    )
     if debug:
         print("make_all_figs: WCRP_assessed_cld_fbks_amip-p4K.png done")
 
@@ -1030,7 +1032,10 @@ def make_all_figs(
     # new axis for labeling all models
     ax = plt.subplot(gs[:10, 10:12])
     label_models(ax, models5, models6)
-    plt.savefig(os.path.join(figdir, "WCRP_unassessed_cld_fbks_amip-p4K.png"), bbox_inches="tight")
+    plt.savefig(
+        os.path.join(figdir, "WCRP_unassessed_cld_fbks_amip-p4K.png"),
+        bbox_inches="tight",
+    )
     if debug:
         print("make_all_figs: WCRP_unassessed_cld_fbks_amip-p4K.png done")
 
@@ -1277,8 +1282,9 @@ def make_all_figs(
     )
     cb.ax.tick_params(labelsize=14)
     ax2.set_ylabel(r"$\mathrm{E_{NET}}$", size=14)
-    plt.savefig(os.path.join(
-        figdir, "WCRP_assessed_RMSE_v_cldfbk2_amip-p4K.png"), bbox_inches="tight"
+    plt.savefig(
+        os.path.join(figdir, "WCRP_assessed_RMSE_v_cldfbk2_amip-p4K.png"),
+        bbox_inches="tight",
     )
 
     # #####################################################
@@ -1402,7 +1408,10 @@ def make_all_figs(
     plt.title("b", fontsize=16, loc="left")
     plt.ylim(0.04, 0.15)
     plt.xlim(0.60, 1.65)
-    plt.savefig(os.path.join(figdir, "WCRP_totcldfbks2_v_E_NET_amip-p4K.png"), bbox_inches="tight")
+    plt.savefig(
+        os.path.join(figdir, "WCRP_totcldfbks2_v_E_NET_amip-p4K.png"),
+        bbox_inches="tight",
+    )
 
     # ######################################################
     # PRINT OUT THE TABLE OF EVERYTHING:
diff --git a/pcmdi_metrics/cloud_feedback/lib/compute_ECS_xr.py b/pcmdi_metrics/cloud_feedback/lib/compute_ECS_xr.py
index d257af846..9d7bb83ff 100644
--- a/pcmdi_metrics/cloud_feedback/lib/compute_ECS_xr.py
+++ b/pcmdi_metrics/cloud_feedback/lib/compute_ECS_xr.py
@@ -1,23 +1,25 @@
 # Compute ECS, ERF, and lambda from abrupt-4xCO2 experiments via Gregory approach
 
+import cftime
+import numpy as np
 import xarray as xr
 import xcdat
-import numpy as np
 from scipy import stats
-import cftime
+
 
 def dict_to_dataset(DICT):
     # Convert a dictionary of Datasets to a single Dataset
-    data_vars={}
+    data_vars = {}
     for var in list(DICT.keys()):
         data_vars[var] = (["year"], DICT[var][var].data)
     ds = xr.Dataset(
         data_vars,
         coords=dict(year=DICT[var].year),
     )
-    return(ds)
+    return ds
+
 
-def get_branch_time(pi,ab):
+def get_branch_time(pi, ab):
     # Determine when abrupt overlaps with piControl
     # returns the following:
     # 1) st: the branch time in cftime.datetime format
@@ -25,99 +27,111 @@ def get_branch_time(pi,ab):
     # 3) extensions beyond the ~150-year overlap period to allow for 21-year rolling mean:
     #    3a) extend_st -- the date 10 years prior to st
     #    3b) extend_fn -- the date 10 years after fn
-    
+
     var = ab.variable_id
     source = ab.source_id
     experiment = ab.experiment_id
-    absub = ab.isel(time=slice(0,12*150))
-    lenab = int(absub[var].shape[0]/12)-1
-    btip = int(absub.attrs['branch_time_in_parent']) # timestep, not a date
+    absub = ab.isel(time=slice(0, 12 * 150))
+    lenab = int(absub[var].shape[0] / 12) - 1
+    btip = int(absub.attrs["branch_time_in_parent"])  # timestep, not a date
 
-    if source=='GFDL-CM4' and (experiment=='1pctCO2' or experiment=='abrupt-4xCO2' or experiment=='historical'): 
-        btip = 91250 # https://errata.es-doc.org/static/view.html?uid=2f6b5963-f87e-b2df-a5b0-2f12b6b68d32    
-    ptu = absub.attrs['parent_time_units']
+    if source == "GFDL-CM4" and (
+        experiment == "1pctCO2"
+        or experiment == "abrupt-4xCO2"
+        or experiment == "historical"
+    ):
+        btip = 91250  # https://errata.es-doc.org/static/view.html?uid=2f6b5963-f87e-b2df-a5b0-2f12b6b68d32
+    ptu = absub.attrs["parent_time_units"]
     # print('  branch time in parent:   '+str(btip)+' '+ptu)
     # print('  parent times:   '+str(pi.time.dt.year[0].values)+' - '+str(pi.time.dt.year[-1].values))
-    start = int(ptu.split(' ')[2][:4])
+    start = int(ptu.split(" ")[2][:4])
     cal = pi.time.dt.calendar
     # see http://cfconventions.org/cf-conventions/cf-conventions.html#calendar
-    if cal=='360_day':
-        dpy=360
-    elif cal=='noleap':
-        dpy=365
-    elif cal=='proleptic_gregorian' or cal=='standard' or cal=='julian':
-        dpy=365.25
+    if cal == "360_day":
+        dpy = 360
+    elif cal == "noleap":
+        dpy = 365
+    elif cal == "proleptic_gregorian" or cal == "standard" or cal == "julian":
+        dpy = 365.25
     else:
-        raise(Exception('Not sure how to handle '+cal+' calendar'))
-    if source=='CIESM' and (experiment=='abrupt-4xCO2' or experiment=='1pctCO2'):
+        raise (Exception("Not sure how to handle " + cal + " calendar"))
+    if source == "CIESM" and (experiment == "abrupt-4xCO2" or experiment == "1pctCO2"):
         # info from Yi Qin 10/1/20:
-        # "The branch_time for 4xCO2 should be the 101-yr of these published 500-yr piControl data, 
+        # "The branch_time for 4xCO2 should be the 101-yr of these published 500-yr piControl data,
         # but I wrongly regarded the “raw” piControl with piControl-spinup as the parent_case, which is a 1000-yr long run.
-        year0 = 101 
-    elif source=='KACE-1-0-G' and (experiment=='abrupt-4xCO2' or experiment=='1pctCO2'):
+        year0 = 101
+    elif source == "KACE-1-0-G" and (
+        experiment == "abrupt-4xCO2" or experiment == "1pctCO2"
+    ):
         # info from Jae-Hee Lee (via Gavin Schmidt on 1/26/22):
-        # "We used year 2150 of piControl as the initial condition for the KACE-1-0-G 1pctCO2 runs. 
+        # "We used year 2150 of piControl as the initial condition for the KACE-1-0-G 1pctCO2 runs.
         # so, you can assume that 1850 in the 1pctCO2 and 2150 in the piControl are the same
-        year0 = 2150         
+        year0 = 2150
     else:
-        year0 = int((btip/dpy)+start)
-    year150 = int(year0+lenab)
-    st=cftime.datetime(year0, 1, 1).strftime("%Y-%m-%dT%H:%M:%S")
-    fn=cftime.datetime(year150, 12, 31,23,59,59).strftime("%Y-%m-%dT%H:%M:%S")
+        year0 = int((btip / dpy) + start)
+    year150 = int(year0 + lenab)
+    st = cftime.datetime(year0, 1, 1).strftime("%Y-%m-%dT%H:%M:%S")
+    fn = cftime.datetime(year150, 12, 31, 23, 59, 59).strftime("%Y-%m-%dT%H:%M:%S")
     # Add on 10 years before st and after fn to assist in computing 21-year rolling means
-    extend_st=cftime.datetime(np.max((year0-10,1)), 1, 1).strftime("%Y-%m-%dT%H:%M:%S")
-    extend_fn=cftime.datetime(year150+10, 12, pi.time.dt.day[-1],23,59,59).strftime("%Y-%m-%dT%H:%M:%S") 
+    extend_st = cftime.datetime(np.max((year0 - 10, 1)), 1, 1).strftime(
+        "%Y-%m-%dT%H:%M:%S"
+    )
+    extend_fn = cftime.datetime(
+        year150 + 10, 12, pi.time.dt.day[-1], 23, 59, 59
+    ).strftime("%Y-%m-%dT%H:%M:%S")
 
-    return(extend_st,extend_fn,st,fn)#,year0,year150)
+    return (extend_st, extend_fn, st, fn)  # ,year0,year150)
 
-def get_data(pi,ab):  
+
+def get_data(pi, ab):
     # Return the piControl running mean and the abrupt deviation from this
 
-    var=ab.variable_id
-    extend_st,extend_fn,st,fn = get_branch_time(pi,ab)
-    absub = ab.isel(time=slice(0,12*150))
-    pisub = pi.sel(time=slice(extend_st,extend_fn))
-    if len(pisub.time)<1200:
-        print('len(time)<1200...skipping')
+    var = ab.variable_id
+    extend_st, extend_fn, st, fn = get_branch_time(pi, ab)
+    absub = ab.isel(time=slice(0, 12 * 150))
+    pisub = pi.sel(time=slice(extend_st, extend_fn))
+    if len(pisub.time) < 1200:
+        print("len(time)<1200...skipping")
         return None
 
-    bdate = pisub.time.dt.year.values 
+    bdate = pisub.time.dt.year.values
     # compute global means:
     GLcntl = pisub.spatial.average(var)
     GLpert = absub.spatial.average(var)
     # compute annual means:
-    annpert = GLpert.groupby('time.year').mean('time')
-    anncntl = GLcntl.groupby('time.year').mean('time')
+    annpert = GLpert.groupby("time.year").mean("time")
+    anncntl = GLcntl.groupby("time.year").mean("time")
     # compute 21-year centered rolling mean:
-    runcntl = anncntl.rolling(year = 21, min_periods = 1, center = True).mean()
+    runcntl = anncntl.rolling(year=21, min_periods=1, center=True).mean()
     # subset the rolling mean for the actual overlapping time:
-    runcntlsub = runcntl.sel(year=slice(int(st[:4]),int(fn[:4])))
+    runcntlsub = runcntl.sel(year=slice(int(st[:4]), int(fn[:4])))
     # set the abrupt run's year coord to match the overlapping piCon's year coord
-    annpert=annpert.assign_coords(year=runcntlsub.coords['year'])
+    annpert = annpert.assign_coords(year=runcntlsub.coords["year"])
     # compute abrupt anomalies from piControl running mean:
     annanom = annpert - runcntlsub
-    
-    return(runcntl,annanom)
+
+    return (runcntl, annanom)
+
 
 def compute_ECS(filepath):
     # compute annual- anad global-mean tas and EEI anomalies in abrupt4xCO2 w.r.t. piControl climo, then compute ERF, lambda, and ECS via Gregory
-    cntl={}
-    anom={}
-    skip=False
+    cntl = {}
+    anom = {}
+    skip = False
     variables = list(filepath["piControl"].keys())
     for var in variables:
-        pi=xcdat.open_mfdataset(filepath["piControl"][var], use_cftime = True)
-        ab=xcdat.open_mfdataset(filepath["abrupt-4xCO2"][var], use_cftime = True)
-        
-        if len(ab.time)<140*12:
-            print('   len(ab.time)<140*12')
-            skip=True
+        pi = xcdat.open_mfdataset(filepath["piControl"][var], use_cftime=True)
+        ab = xcdat.open_mfdataset(filepath["abrupt-4xCO2"][var], use_cftime=True)
+
+        if len(ab.time) < 140 * 12:
+            print("   len(ab.time)<140*12")
+            skip = True
             break
-        output = get_data(pi,ab)
+        output = get_data(pi, ab)
         if output:
-            cntl[var],anom[var] = output
+            cntl[var], anom[var] = output
         else:
-            skip=True
+            skip = True
             break
     if skip:
         # print('Skipping this model...')
@@ -126,16 +140,15 @@ def compute_ECS(filepath):
     CNTL = dict_to_dataset(cntl)
     ANOM = dict_to_dataset(anom)
 
-    CNTL['eei'] = CNTL['rsdt']-CNTL['rsut']-CNTL['rlut']
-    ANOM['eei'] = ANOM['rsdt']-ANOM['rsut']-ANOM['rlut']
+    CNTL["eei"] = CNTL["rsdt"] - CNTL["rsut"] - CNTL["rlut"]
+    ANOM["eei"] = ANOM["rsdt"] - ANOM["rsut"] - ANOM["rlut"]
+
+    x = ANOM["tas"].load()
+    y = ANOM["eei"].load()
 
-    x = ANOM['tas'].load()
-    y = ANOM['eei'].load()
-    
-    m, b, r, p, std_err = stats.linregress(x,y)
-    ERF = b/2
+    m, b, r, p, std_err = stats.linregress(x, y)
+    ERF = b / 2
     lam = m
-    ecs = -ERF/lam
-    
-    return ecs
+    ecs = -ERF / lam
 
+    return ecs
diff --git a/pcmdi_metrics/cloud_feedback/lib/organize_jsons.py b/pcmdi_metrics/cloud_feedback/lib/organize_jsons.py
index e09af655f..b810bf3f1 100644
--- a/pcmdi_metrics/cloud_feedback/lib/organize_jsons.py
+++ b/pcmdi_metrics/cloud_feedback/lib/organize_jsons.py
@@ -2,10 +2,11 @@
 # Compute NET = LW+SW feedbacks
 # Append this dictionary to the existing json containing feedbacks and error metrics
 
-from datetime import date
+import json
 import urllib.request
+from datetime import date
+
 import numpy as np
-import json
 
 meta = {
     "date_modified": str(date.today()),
diff --git a/pcmdi_metrics/enso/enso_driver.py b/pcmdi_metrics/enso/enso_driver.py
index c648c5479..ef4491ba6 100755
--- a/pcmdi_metrics/enso/enso_driver.py
+++ b/pcmdi_metrics/enso/enso_driver.py
@@ -362,7 +362,6 @@
     # Loop for Realizations
     # -------------------------------------------------
     for run in runs_list:
-
         print(" --- run: ", run, " ---")
         mod_run = "_".join([mod, run])
         dict_mod = {mod_run: {}}
diff --git a/pcmdi_metrics/enso/lib/enso_lib.py b/pcmdi_metrics/enso/lib/enso_lib.py
index 09b331f8a..221024402 100755
--- a/pcmdi_metrics/enso/lib/enso_lib.py
+++ b/pcmdi_metrics/enso/lib/enso_lib.py
@@ -13,7 +13,6 @@
 
 
 def AddParserArgument():
-
     P = PMPParser()  # Includes all default options
 
     P.add_argument(
diff --git a/pcmdi_metrics/graphics/bar_chart/lib/barChart.py b/pcmdi_metrics/graphics/bar_chart/lib/barChart.py
index a045239b5..19b981ce1 100644
--- a/pcmdi_metrics/graphics/bar_chart/lib/barChart.py
+++ b/pcmdi_metrics/graphics/bar_chart/lib/barChart.py
@@ -4,7 +4,6 @@
 
 class BarChart(object):
     def __init__(self, mods, data, stat, unit=None, unit_adjust=1, fig=None, rect=111):
-
         # Canvas setup
         if fig is None:
             fig = plt.figure
diff --git a/pcmdi_metrics/graphics/parallel_coordinate_plot/parallel_coordinate_plot_lib.py b/pcmdi_metrics/graphics/parallel_coordinate_plot/parallel_coordinate_plot_lib.py
index a629c602a..490ba7214 100644
--- a/pcmdi_metrics/graphics/parallel_coordinate_plot/parallel_coordinate_plot_lib.py
+++ b/pcmdi_metrics/graphics/parallel_coordinate_plot/parallel_coordinate_plot_lib.py
@@ -89,7 +89,7 @@ def parallel_coordinate_plot(
     - `ax`: matplotlib component for axis
 
     Author: Jiwoo Lee @ LLNL (2021. 7)
-    Update history: 
+    Update history:
     2021-07 Plotting code created. Inspired by https://stackoverflow.com/questions/8230638/parallel-coordinates-plot-in-matplotlib
     2022-09 violin plots added
     2023-03 median centered option added
@@ -185,7 +185,9 @@ def parallel_coordinate_plot(
                     showextrema=False,
                 )
                 for pc in violin["bodies"]:
-                    if isinstance(violin_colors, tuple) or isinstance(violin_colors, list):
+                    if isinstance(violin_colors, tuple) or isinstance(
+                        violin_colors, list
+                    ):
                         violin_color = violin_colors[0]
                     else:
                         violin_color = violin_colors
@@ -219,17 +221,16 @@ def parallel_coordinate_plot(
     for j, model in enumerate(model_names):
         # to just draw straight lines between the axes:
         if model in models_to_highlight:
-            
             if models_to_highlight_colors is not None:
                 color = models_to_highlight_colors[mh_index]
             else:
                 color = colors[j]
-                
+
             if models_to_highlight_labels is not None:
                 label = models_to_highlight_labels[mh_index]
             else:
                 label = model
-                
+
             ax.plot(range(N), zs[j, :], "-", c=color, label=label, lw=3)
             mh_index += 1
         else:
@@ -242,12 +243,12 @@ def parallel_coordinate_plot(
                     label=model,
                     clip_on=False,
                 )
-    
+
     if vertical_center_line:
         if vertical_center_line_label is None:
             vertical_center_line_label = str(vertical_center)
         elif vertical_center_line_label == "off":
-             vertical_center_line_label = None     
+            vertical_center_line_label = None
         ax.plot(range(N), zs_middle, "-", c="k", label=vertical_center_line_label, lw=1)
 
     # Fill between lines
@@ -341,15 +342,15 @@ def _data_transform(
         ymaxs = np.nanmax(ys, axis=0)  # maximum (ignore nan value)
     else:
         ymaxs = np.repeat(ymax, N)
-        
+
     if ymin is None:
         ymins = np.nanmin(ys, axis=0)  # minimum (ignore nan value)
     else:
         ymins = np.repeat(ymin, N)
-    
+
     ymeds = np.nanmedian(ys, axis=0)  # median
     ymean = np.nanmean(ys, axis=0)  # mean
-    
+
     if vertical_center is not None:
         if vertical_center == "median":
             ymids = ymeds
@@ -358,7 +359,9 @@ def _data_transform(
         else:
             ymids = np.repeat(vertical_center, N)
         for i in range(0, N):
-            max_distance_from_middle = max(abs(ymaxs[i] - ymids[i]), abs(ymids[i] - ymins[i]))
+            max_distance_from_middle = max(
+                abs(ymaxs[i] - ymids[i]), abs(ymids[i] - ymins[i])
+            )
             ymaxs[i] = ymids[i] + max_distance_from_middle
             ymins[i] = ymids[i] - max_distance_from_middle
 
@@ -373,7 +376,7 @@ def _data_transform(
     zs = np.zeros_like(ys)
     zs[:, 0] = ys[:, 0]
     zs[:, 1:] = (ys[:, 1:] - ymins[1:]) / dys[1:] * dys[0] + ymins[0]
-    
+
     if vertical_center is not None:
         zs_middle = (ymids[:] - ymins[:]) / dys[:] * dys[0] + ymins[0]
     else:
diff --git a/pcmdi_metrics/graphics/portrait_plot/portrait_plot_lib.py b/pcmdi_metrics/graphics/portrait_plot/portrait_plot_lib.py
index 52dbc2310..5914e47c0 100644
--- a/pcmdi_metrics/graphics/portrait_plot/portrait_plot_lib.py
+++ b/pcmdi_metrics/graphics/portrait_plot/portrait_plot_lib.py
@@ -520,7 +520,6 @@ def triamatrix_wrap_up(
     inner_line_color="k",
     inner_line_width=0.5,
 ):
-
     # Colorbar range
     if norm is None:
         norm = matplotlib.colors.Normalize(vmin=vmin, vmax=vmax)
diff --git a/pcmdi_metrics/graphics/share/Metrics.py b/pcmdi_metrics/graphics/share/Metrics.py
index 45b0f045f..7ea8f1e4c 100644
--- a/pcmdi_metrics/graphics/share/Metrics.py
+++ b/pcmdi_metrics/graphics/share/Metrics.py
@@ -33,7 +33,6 @@ def __init__(self, files, mip=None):
         # or if it is a directory containing json files
 
         if isinstance(files, str):
-
             assert os.path.exists(files), "Specified path does not exist."
 
             if os.path.isfile(files):
@@ -42,7 +41,6 @@ def __init__(self, files, mip=None):
                 files = glob.glob(f"{files}/*.json")
 
         else:
-
             assert isinstance(
                 files, list
             ), "Input must either be a single file, directory, or list of files."
@@ -92,19 +90,16 @@ def merge(self, metrics_obj):
         # loop over superset of `stats`
         stats = set(self.df_dict.keys()).union(metrics_obj.df_dict.keys())
         for stat in sorted(stats):
-
             # loop over superset of seasons
             seasons = set(self.df_dict[stat].keys()).union(
                 metrics_obj.df_dict[stat].keys()
             )
             for season in seasons:
-
                 # loop over superset of regions
                 regions = set(self.df_dict[stat][season].keys()).union(
                     metrics_obj.df_dict[stat][season].keys()
                 )
                 for region in regions:
-
                     # consider both the current Metrics instance and
                     # candidate `metrics_obj` instance and determine if the
                     # [stat][season][region] nesting contains a pd.DataFrame.
diff --git a/pcmdi_metrics/graphics/share/read_json_mean_clim.py b/pcmdi_metrics/graphics/share/read_json_mean_clim.py
index 0db0be805..873147dc9 100644
--- a/pcmdi_metrics/graphics/share/read_json_mean_clim.py
+++ b/pcmdi_metrics/graphics/share/read_json_mean_clim.py
@@ -153,17 +153,19 @@ def extract_data(results_dict, var_list, region, stat, season, mip, debug=False)
     if "rlut" in list(results_dict.keys()):
         if "rlut" in list(results_dict["rlut"]["RESULTS"].keys()):
             model_list = sorted(list(results_dict["rlut"]["RESULTS"].keys()))
-            
-    print('extract_data:: model_list: ', model_list)
-    
+
+    print("extract_data:: model_list: ", model_list)
+
     data_list = []
     for model in model_list:
-        run_list = sort_human(list(
-            results_dict[var_list[0]]["RESULTS"][model]["default"].keys())
+        run_list = sort_human(
+            list(results_dict[var_list[0]]["RESULTS"][model]["default"].keys())
         )
         if "rlut" in list(results_dict.keys()):
             if "rlut" in list(results_dict["rlut"]["RESULTS"].keys()):
-                run_list = sort_human(list(results_dict["rlut"]["RESULTS"][model]["default"].keys()))
+                run_list = sort_human(
+                    list(results_dict["rlut"]["RESULTS"][model]["default"].keys())
+                )
 
         if debug:
             print("model, run_list:", model, run_list)
diff --git a/pcmdi_metrics/graphics/taylor_diagram/taylor_diagram.py b/pcmdi_metrics/graphics/taylor_diagram/taylor_diagram.py
index b8651acd5..f391ef320 100644
--- a/pcmdi_metrics/graphics/taylor_diagram/taylor_diagram.py
+++ b/pcmdi_metrics/graphics/taylor_diagram/taylor_diagram.py
@@ -28,7 +28,6 @@ def TaylorDiagram(
     grid=True,
     debug=False,
 ):
-
     """Plot a Taylor diagram
 
     Jiwoo Lee (PCMDI LLNL) - last update: 2022. 10
diff --git a/pcmdi_metrics/io/base.py b/pcmdi_metrics/io/base.py
index 15f30dd20..003f2313f 100755
--- a/pcmdi_metrics/io/base.py
+++ b/pcmdi_metrics/io/base.py
@@ -15,12 +15,11 @@
 import MV2
 import numpy
 import xcdat
+import xcdat as xc
 
 import pcmdi_metrics
 from pcmdi_metrics import LOG_LEVEL
 
-import xcdat as xc
-
 value = 0
 cdms2.setNetcdfShuffleFlag(value)  # where value is either 0 or 1
 cdms2.setNetcdfDeflateFlag(value)  # where value is either 0 or 1
@@ -83,7 +82,12 @@ def update_dict(d, u):
 
 
 def generateProvenance():
-    extra_pairs = {"matplotlib": "matplotlib ", "scipy": "scipy", "xcdat": "xcdat", "xarray": "xarray"}
+    extra_pairs = {
+        "matplotlib": "matplotlib ",
+        "scipy": "scipy",
+        "xcdat": "xcdat",
+        "xarray": "xarray",
+    }
     prov = cdat_info.generateProvenance(extra_pairs=extra_pairs)
     prov["packages"]["PMP"] = pcmdi_metrics.version.__git_tag_describe__
     prov["packages"][
@@ -361,7 +365,9 @@ def get(self, var, var_in_file=None, region={}, *args, **kwargs):
         if self.is_masking():
             self.var_from_file = self.mask_var(self.var_from_file)
 
-        self.var_from_file = self.set_target_grid_and_mask_in_var(self.var_from_file, var)
+        self.var_from_file = self.set_target_grid_and_mask_in_var(
+            self.var_from_file, var
+        )
 
         self.var_from_file = self.set_domain_in_var(self.var_from_file, self.region)
 
@@ -374,10 +380,12 @@ def extract_var_from_file(self, var, var_in_file, *args, **kwargs):
         try:
             ds = xc.open_mfdataset(self(), data_var=var_in_file, decode_times=True)
         except Exception:
-            ds = xc.open_mfdataset(self(), data_var=var_in_file, decode_times=False)  # Temporary part to read in cdms written obs4MIP AC files
+            ds = xc.open_mfdataset(
+                self(), data_var=var_in_file, decode_times=False
+            )  # Temporary part to read in cdms written obs4MIP AC files
 
-        if 'level' in list(kwargs.keys()):
-            level = kwargs['level']
+        if "level" in list(kwargs.keys()):
+            level = kwargs["level"]
             ds = ds.sel(plev=level)
 
         extracted_var = ds
@@ -395,7 +403,7 @@ def mask_var(self, var):
         self: <pcmdi_metrics.io.base.Base object at 0x7f24a0768a60>
         var: <xarray.Dataset>
         """
-        var_shape = tuple(var.dims[d] for d in ['lat', 'lon'])
+        var_shape = tuple(var.dims[d] for d in ["lat", "lon"])
 
         if self.mask is None:
             self.set_file_mask_template()
@@ -414,7 +422,9 @@ def set_target_grid_and_mask_in_var(self, var, var_in_file):
         self(): string, path to input file
         """
         if self.target_grid is not None:
-            var = var.regridder.horizontal(var_in_file, self.target_grid, tool=self.regrid_tool)
+            var = var.regridder.horizontal(
+                var_in_file, self.target_grid, tool=self.regrid_tool
+            )
             if self.target_mask is not None:
                 # if self.target_mask.shape != var.shape:
                 if self.target_mask.shape != var[var_in_file].shape:
@@ -430,10 +440,11 @@ def set_domain_in_var(self, var, region):
         var: <xarray.Dataset>
         region: <class 'dict'>, e.g., {'domain': Selector(<cdutil.region.DomainComponent object at 0x7fdbe2b70760>), 'id': 'NHEX'}
         """
-        region_id = region['id']
+        region_id = region["id"]
         from pcmdi_metrics.io import load_regions_specs, region_subset
+
         regions_specs = load_regions_specs()
-        if region_id not in ['global', 'land', 'ocean']:
+        if region_id not in ["global", "land", "ocean"]:
             var = region_subset(var, regions_specs, region=region_id)
 
         return var
@@ -465,7 +476,9 @@ def set_target_grid(self, target, regrid_tool="esmf", regrid_method="linear"):
         self.regrid_method = regrid_method
         if target == "2.5x2.5":
             # self.target_grid = cdms2.createUniformGrid(-88.875, 72, 2.5, 0, 144, 2.5)
-            self.target_grid = xcdat.create_uniform_grid(-88.875, 88.625, 2.5, 0, 357.5, 2.5)
+            self.target_grid = xcdat.create_uniform_grid(
+                -88.875, 88.625, 2.5, 0, 357.5, 2.5
+            )
             self.target_grid_name = target
         elif cdms2.isGrid(target):
             self.target_grid = target
diff --git a/pcmdi_metrics/io/default_regions_define.py b/pcmdi_metrics/io/default_regions_define.py
index b5362a2a7..11a238994 100755
--- a/pcmdi_metrics/io/default_regions_define.py
+++ b/pcmdi_metrics/io/default_regions_define.py
@@ -2,7 +2,6 @@
 
 
 def load_regions_specs():
-
     regions_specs = {
         # Mean Climate
         "global": {},
@@ -19,15 +18,18 @@ def load_regions_specs():
         "land_NHEX": {"value": 100, "domain": {"latitude": (30.0, 90)}},
         "land_SHEX": {"value": 100, "domain": {"latitude": (-90.0, -30)}},
         "land_TROPICS": {"value": 100, "domain": {"latitude": (-30.0, 30)}},
-        "land_CONUS": {"value": 100, "domain": {"latitude": (24.7, 49.4), "longitude": (-124.78, -66.92)}},
+        "land_CONUS": {
+            "value": 100,
+            "domain": {"latitude": (24.7, 49.4), "longitude": (-124.78, -66.92)},
+        },
         "ocean": {"value": 0},
         "ocean_NHEX": {"value": 0, "domain": {"latitude": (30.0, 90)}},
         "ocean_SHEX": {"value": 0, "domain": {"latitude": (-90.0, -30)}},
         "ocean_TROPICS": {"value": 0, "domain": {"latitude": (30.0, 30)}},
-        "ocean_50S50N" : {"value":0.,'domain': {"latitude": (-50., 50)}},
-        "ocean_50S20S" : {"value":0.,'domain': {"latitude": (-50., -20)}},
-        "ocean_20S20N": {"value":0.,'domain': {"latitude": (-20., 20)}},
-        "ocean_20N50N" : {"value":0.,'domain': {"latitude": (20., 50)}},
+        "ocean_50S50N": {"value": 0.0, "domain": {"latitude": (-50.0, 50)}},
+        "ocean_50S20S": {"value": 0.0, "domain": {"latitude": (-50.0, -20)}},
+        "ocean_20S20N": {"value": 0.0, "domain": {"latitude": (-20.0, 20)}},
+        "ocean_20N50N": {"value": 0.0, "domain": {"latitude": (20.0, 50)}},
         # Modes of variability
         "NAM": {"domain": {"latitude": (20.0, 90), "longitude": (-180, 180)}},
         "NAO": {"domain": {"latitude": (20.0, 80), "longitude": (-90, 40)}},
@@ -75,42 +77,48 @@ def region_subset(ds, regions_specs, region=None):
     region: string
     """
 
-    if ((region is None) or ((region is not None) and (region not in list(regions_specs.keys())))):
-        print('Error: region not defined')
+    if (region is None) or (
+        (region is not None) and (region not in list(regions_specs.keys()))
+    ):
+        print("Error: region not defined")
     else:
-        if 'domain' in list(regions_specs[region].keys()):
-            if 'latitude' in list(regions_specs[region]['domain'].keys()):
-                lat0 = regions_specs[region]['domain']['latitude'][0]
-                lat1 = regions_specs[region]['domain']['latitude'][1]
+        if "domain" in list(regions_specs[region].keys()):
+            if "latitude" in list(regions_specs[region]["domain"].keys()):
+                lat0 = regions_specs[region]["domain"]["latitude"][0]
+                lat1 = regions_specs[region]["domain"]["latitude"][1]
                 # proceed subset
-                if 'latitude' in (ds.coords.dims):
+                if "latitude" in (ds.coords.dims):
                     ds = ds.sel(latitude=slice(lat0, lat1))
-                elif 'lat' in (ds.coords.dims):
+                elif "lat" in (ds.coords.dims):
                     ds = ds.sel(lat=slice(lat0, lat1))
 
-            if 'longitude' in list(regions_specs[region]['domain'].keys()):
-                lon0 = regions_specs[region]['domain']['longitude'][0]
-                lon1 = regions_specs[region]['domain']['longitude'][1]
+            if "longitude" in list(regions_specs[region]["domain"].keys()):
+                lon0 = regions_specs[region]["domain"]["longitude"][0]
+                lon1 = regions_specs[region]["domain"]["longitude"][1]
 
                 # check original dataset longitude range
-                if 'longitude' in (ds.coords.dims):
+                if "longitude" in (ds.coords.dims):
                     lon_min = ds.longitude.min()
                     lon_max = ds.longitude.max()
-                elif 'lon' in (ds.coords.dims):
+                elif "lon" in (ds.coords.dims):
                     lon_min = ds.lon.min()
                     lon_max = ds.lon.max()
 
                 # longitude range swap if needed
-                if min(lon0, lon1) < 0:  # when subset region lon is defined in (-180, 180) range
-                    if min(lon_min, lon_max) < 0:  # if original data lon range is (-180, 180) no treatment needed
+                if (
+                    min(lon0, lon1) < 0
+                ):  # when subset region lon is defined in (-180, 180) range
+                    if (
+                        min(lon_min, lon_max) < 0
+                    ):  # if original data lon range is (-180, 180) no treatment needed
                         pass
                     else:  # if original data lon range is (0, 360), convert swap lon
                         ds = xc.swap_lon_axis(ds, to=(-180, 180))
 
                 # proceed subset
-                if 'longitude' in (ds.coords.dims):
+                if "longitude" in (ds.coords.dims):
                     ds = ds.sel(longitude=slice(lon0, lon1))
-                elif 'lon' in (ds.coords.dims):
+                elif "lon" in (ds.coords.dims):
                     ds = ds.sel(lon=slice(lon0, lon1))
 
     return ds
diff --git a/pcmdi_metrics/io/region_from_file.py b/pcmdi_metrics/io/region_from_file.py
index ce43aebd9..8e315b2c5 100644
--- a/pcmdi_metrics/io/region_from_file.py
+++ b/pcmdi_metrics/io/region_from_file.py
@@ -3,7 +3,8 @@
 import xarray as xr
 import xcdat
 
-def region_from_file(data,rgn_path,attr,feature):
+
+def region_from_file(data, rgn_path, attr, feature):
     # Return data masked from a feature in input file.
     # Arguments:
     #    data: xcdat dataset
@@ -17,18 +18,18 @@ def region_from_file(data,rgn_path,attr,feature):
     print("Reading region from file.")
     try:
         regions_df = gpd.read_file(rgn_path)
-        regions = regionmask.from_geopandas(regions_df,names=attr)
+        regions = regionmask.from_geopandas(regions_df, names=attr)
         mask = regions.mask(lon, lat)
         # Can't match mask by name, rather index of name
         val = list(regions_df[attr]).index(feature)
     except Exception as e:
         print("Error in creating region subset from file:")
         raise e
-    
+
     try:
         masked_data = data.where(mask == val)
     except Exception as e:
         print("Error: Region selection failed.")
         raise e
 
-    return  masked_data
+    return masked_data
diff --git a/pcmdi_metrics/io/xcdat_openxml.py b/pcmdi_metrics/io/xcdat_openxml.py
index 4dfc451f9..fe245eac8 100644
--- a/pcmdi_metrics/io/xcdat_openxml.py
+++ b/pcmdi_metrics/io/xcdat_openxml.py
@@ -24,10 +24,12 @@ def xcdat_open(infile, data_var=None, decode_times=True):
     if isinstance(infile, list):
         ds = xcdat.open_mfdataset(infile, data_var=data_var, decode_times=decode_times)
     else:
-        if infile.split('.')[-1].lower() == 'xml':
+        if infile.split(".")[-1].lower() == "xml":
             ds = xcdat_openxml(infile, data_var=data_var, decode_times=decode_times)
         else:
-            ds = xcdat.open_mfdataset(infile, data_var=data_var, decode_times=decode_times)
+            ds = xcdat.open_mfdataset(
+                infile, data_var=data_var, decode_times=decode_times
+            )
 
     return ds
 
@@ -47,12 +49,12 @@ def xcdat_openxml(xmlfile, data_var=None, decode_times=True):
         xcdat dataset
     """
     if not os.path.exists(xmlfile):
-        sys.exit('ERROR: File not exist: {}'.format(xmlfile))
+        sys.exit("ERROR: File not exist: {}".format(xmlfile))
 
     with open(xmlfile) as fd:
         doc = xmltodict.parse(fd.read())
 
-    ncfile_list = glob.glob(os.path.join(doc['dataset']['@directory'], '*.nc'))
+    ncfile_list = glob.glob(os.path.join(doc["dataset"]["@directory"], "*.nc"))
     ds = xcdat.open_mfdataset(ncfile_list, data_var=data_var, decode_times=decode_times)
 
     return ds
diff --git a/pcmdi_metrics/mean_climate/deprecated/lib/dataset.py b/pcmdi_metrics/mean_climate/deprecated/lib/dataset.py
index 42090a48f..1e6a24ae9 100644
--- a/pcmdi_metrics/mean_climate/deprecated/lib/dataset.py
+++ b/pcmdi_metrics/mean_climate/deprecated/lib/dataset.py
@@ -73,12 +73,14 @@ def create_sftlf(parameter):
         """Create the sftlf file from the parameter."""
         sftlf = {}
 
-        print('jwlee-test_create_sftlf, parameter.test_data_set:', parameter.test_data_set)
+        print(
+            "jwlee-test_create_sftlf, parameter.test_data_set:", parameter.test_data_set
+        )
         for test in parameter.test_data_set:
             tmp_name = getattr(parameter, "sftlf_filename_template")
             if tmp_name is None:  # Not defined from commandline or param file
                 tmp_name = parameter.filename_template
-            print('jwlee-test_create_sftlf, tmp_name:', tmp_name)
+            print("jwlee-test_create_sftlf, tmp_name:", tmp_name)
             sft = Base(parameter.test_data_path, tmp_name)
             sft.model_version = test
             sft.table = "fx"
@@ -90,34 +92,34 @@ def create_sftlf(parameter):
             sft.realization = "r0i0p0"
             DataSet.apply_custom_keys(sft, parameter.custom_keys, "sftlf")
             try:
-                print('jwlee-test_create_sftlf, chk1')
+                print("jwlee-test_create_sftlf, chk1")
                 sftlf[test] = {"raw": sft.get("sftlf")}
-                print('jwlee-test_create_sftlf, chk1-2')
+                print("jwlee-test_create_sftlf, chk1-2")
                 sftlf[test]["filename"] = os.path.basename(sft())
-                print('jwlee-test_create_sftlf, chk1-3')
+                print("jwlee-test_create_sftlf, chk1-3")
                 sftlf[test]["md5"] = sft.hash()
-                print('jwlee-test_create_sftlf, chk1-4')
+                print("jwlee-test_create_sftlf, chk1-4")
             except Exception:
-                print('jwlee-test_create_sftlf, chk2')
+                print("jwlee-test_create_sftlf, chk2")
                 sftlf[test] = {"raw": None}
                 sftlf[test]["filename"] = None
                 sftlf[test]["md5"] = None
-        print('jwlee-test-target_grid-create')
+        print("jwlee-test-target_grid-create")
         if parameter.target_grid == "2.5x2.5":
             t_grid_cdms2 = cdms2.createUniformGrid(-88.875, 72, 2.5, 0, 144, 2.5)
             t_grid = xcdat.create_uniform_grid(-88.875, 88.625, 2.5, 0, 357.5, 2.5)
         else:
             t_grid = parameter.target_grid
-        print('jwlee-test-target_grid-create done')
-        print('jwlee-test-target_grid-create t_grid:', t_grid)
+        print("jwlee-test-target_grid-create done")
+        print("jwlee-test-target_grid-create t_grid:", t_grid)
 
         # sft = cdutil.generateLandSeaMask(t_grid)
         sft = cdutil.generateLandSeaMask(t_grid_cdms2)
         sft[:] = sft.filled(1.0) * 100.0
         sftlf["target_grid"] = sft
-        print('jwlee-test-target_grid, type(sft), sft.shape:', type(sft), sft.shape)
+        print("jwlee-test-target_grid, type(sft), sft.shape:", type(sft), sft.shape)
 
-        print("jwlee-test_create_sftlf, sftlf[test]['raw']:", sftlf[test]['raw'])
+        print("jwlee-test_create_sftlf, sftlf[test]['raw']:", sftlf[test]["raw"])
 
         return sftlf
 
diff --git a/pcmdi_metrics/mean_climate/deprecated/lib/io.py b/pcmdi_metrics/mean_climate/deprecated/lib/io.py
index 19078d2ec..e2ed14399 100644
--- a/pcmdi_metrics/mean_climate/deprecated/lib/io.py
+++ b/pcmdi_metrics/mean_climate/deprecated/lib/io.py
@@ -8,7 +8,6 @@ class OBS(pcmdi_metrics.io.base.Base):
     def __init__(
         self, root, var, obs_dic, reference="default", file_mask_template=None
     ):
-
         template = (
             "%(realm)/%(frequency)/%(variable)/" + "%(reference)/%(ac)/%(filename)"
         )
diff --git a/pcmdi_metrics/mean_climate/deprecated/lib/mean_climate_metrics_driver.py b/pcmdi_metrics/mean_climate/deprecated/lib/mean_climate_metrics_driver.py
index c7dea489f..c7c5ddc18 100644
--- a/pcmdi_metrics/mean_climate/deprecated/lib/mean_climate_metrics_driver.py
+++ b/pcmdi_metrics/mean_climate/deprecated/lib/mean_climate_metrics_driver.py
@@ -69,9 +69,7 @@ def load_obs_dict(self):
         """Loads obs_info_dictionary.json and appends
         custom_observations from the parameter file if needed."""
         obs_file_name = "obs_info_dictionary.json"
-        obs_json_file = DataSet.load_path_as_file_obj(
-            obs_file_name
-        )
+        obs_json_file = DataSet.load_path_as_file_obj(obs_file_name)
         obs_dict = json.loads(obs_json_file.read())
         obs_json_file.close()
 
@@ -104,11 +102,9 @@ def create_regions_dict(self):
     def load_default_regions_and_regions_specs(self):
         """Gets the default_regions dict and regions_specs dict
         from default_regions.py and stores them as attributes."""
-        default_regions_file = (
-            DataSet.load_path_as_file_obj(
-                "default_regions.py"
-                # "default_regions_xcdat.py"
-            )
+        default_regions_file = DataSet.load_path_as_file_obj(
+            "default_regions.py"
+            # "default_regions_xcdat.py"
         )
         exec(
             compile(
@@ -158,7 +154,7 @@ def run_reference_and_test_comparison(self):
         reference_data_set = self.parameter.reference_data_set
         test_data_set = self.parameter.test_data_set
 
-        print('jwlee-test-0, test_data_set:', test_data_set)
+        print("jwlee-test-0, test_data_set:", test_data_set)
 
         reference_data_set_is_obs = self.is_data_set_obs(reference_data_set)
         test_data_set_is_obs = self.is_data_set_obs(test_data_set)
@@ -180,8 +176,8 @@ def run_reference_and_test_comparison(self):
                 test_data_set, self.obs_dict, self.var
             )
 
-        print('jwlee-test-1, test_data_set:', test_data_set)
-        print('jwlee-test-1, test_data_set_is_obs:', test_data_set_is_obs)
+        print("jwlee-test-1, test_data_set:", test_data_set)
+        print("jwlee-test-1, test_data_set_is_obs:", test_data_set_is_obs)
 
         if len(reference_data_set) == 0:  # We did not find any ref!!!
             raise RuntimeError("No reference dataset found!")
@@ -206,7 +202,12 @@ def run_reference_and_test_comparison(self):
                 )
                 self.output_metric.add_region(self.region)
                 try:
-                    print('jwlee-test-1.5, test_data_set_is_obs, test, self.parameter.test_data_path:', test_data_set_is_obs, test, self.parameter.test_data_path)
+                    print(
+                        "jwlee-test-1.5, test_data_set_is_obs, test, self.parameter.test_data_path:",
+                        test_data_set_is_obs,
+                        test,
+                        self.parameter.test_data_path,
+                    )
                     tst = self.determine_obs_or_model(
                         test_data_set_is_obs, test, self.parameter.test_data_path
                     )
@@ -222,9 +223,13 @@ def run_reference_and_test_comparison(self):
                     break
 
                 try:
-                    print('jwlee-test-2: type(self), ref, tst:', type(self), ref, tst)
-                    print('jwlee-test-2: self.var, self.var_name_long:', self.var, self.var_name_long)
-                    print('jwlee-test-2: tst()[self.var].shape:', tst()[self.var].shape)
+                    print("jwlee-test-2: type(self), ref, tst:", type(self), ref, tst)
+                    print(
+                        "jwlee-test-2: self.var, self.var_name_long:",
+                        self.var,
+                        self.var_name_long,
+                    )
+                    print("jwlee-test-2: tst()[self.var].shape:", tst()[self.var].shape)
                     self.output_metric.calculate_and_output_metrics(ref, tst)
                 except RuntimeError:
                     continue
@@ -251,7 +256,12 @@ def is_data_set_obs(self, data_set):
         return data_set_is_obs
 
     def determine_obs_or_model(self, is_obs, ref_or_test, data_path):
-        print('jwlee-test-1.5-1: is_obs, ref_or_test, data_path:', is_obs, ref_or_test, data_path)
+        print(
+            "jwlee-test-1.5-1: is_obs, ref_or_test, data_path:",
+            is_obs,
+            ref_or_test,
+            data_path,
+        )
         """Actually create Observation or Module object
         based on if ref_or_test is an obs or model."""
         if is_obs:
diff --git a/pcmdi_metrics/mean_climate/deprecated/lib/model.py b/pcmdi_metrics/mean_climate/deprecated/lib/model.py
index 8a052d128..4b718f012 100644
--- a/pcmdi_metrics/mean_climate/deprecated/lib/model.py
+++ b/pcmdi_metrics/mean_climate/deprecated/lib/model.py
@@ -44,17 +44,23 @@ def setup_target_mask(self):
         """Sets the mask and target_mask attribute of self._model_file"""
         self.var_in_file = self.get_var_in_file()
 
-        print('jwlee-test-setup_target_mask, self.var_in_file:', self.var_in_file)
-        print('jwlee-test-setup_target_mask, self.region:', self.region)
-        print('jwlee-test-setup_target_mask, self.obs_or_model:', self.obs_or_model)
+        print("jwlee-test-setup_target_mask, self.var_in_file:", self.var_in_file)
+        print("jwlee-test-setup_target_mask, self.region:", self.region)
+        print("jwlee-test-setup_target_mask, self.obs_or_model:", self.obs_or_model)
 
         if self.region is not None:
             region_value = self.region.get("value", None)
-            print('jwlee-test-setup_target_mask, region_value:', region_value)
+            print("jwlee-test-setup_target_mask, region_value:", region_value)
             if region_value is not None:
-                print('jwlee-test-setup_target_mask, self.sftlf:', self.sftlf)
-                print('jwlee-test-setup_target_mask, self.sftlf[self.obs_or_model]:', self.sftlf[self.obs_or_model])
-                print('jwlee-test-setup_target_mask, self.sftlf[self.obs_or_model]["raw"]:', self.sftlf[self.obs_or_model]["raw"])
+                print("jwlee-test-setup_target_mask, self.sftlf:", self.sftlf)
+                print(
+                    "jwlee-test-setup_target_mask, self.sftlf[self.obs_or_model]:",
+                    self.sftlf[self.obs_or_model],
+                )
+                print(
+                    'jwlee-test-setup_target_mask, self.sftlf[self.obs_or_model]["raw"]:',
+                    self.sftlf[self.obs_or_model]["raw"],
+                )
                 if self.sftlf[self.obs_or_model]["raw"] is None:
                     self.create_sftlf_model_raw(self.var_in_file)
 
@@ -66,7 +72,11 @@ def setup_target_mask(self):
     def get(self):
         """Gets the variable based on the region and level (if given) for
         the file from data_path, which is defined in the initalizer."""
-        print('jwlee-test-get: self.var_in_file, self.region:', self.var_in_file, self.region)
+        print(
+            "jwlee-test-get: self.var_in_file, self.region:",
+            self.var_in_file,
+            self.region,
+        )
         try:
             if self.level is None:
                 data_model = self._model_file.get(
diff --git a/pcmdi_metrics/mean_climate/deprecated/lib/observation.py b/pcmdi_metrics/mean_climate/deprecated/lib/observation.py
index 2d7faea1c..6668fbbda 100644
--- a/pcmdi_metrics/mean_climate/deprecated/lib/observation.py
+++ b/pcmdi_metrics/mean_climate/deprecated/lib/observation.py
@@ -16,7 +16,6 @@ class OBS(Base):
     """Creates an output the netCDF file for an observation."""
 
     def __init__(self, root, var, obs_dict, obs="default", file_mask_template=None):
-
         logging.getLogger("pcmdi_metrics").setLevel(LOG_LEVEL)
 
         if obs not in obs_dict[var]:
@@ -132,7 +131,7 @@ def get(self):
         """Gets the variable based on the region and level (if given) for
         the file from data_path, which is defined in the initializer."""
         try:
-            print('jwlee-test-observation-get, self.level:', self.level)
+            print("jwlee-test-observation-get, self.level:", self.level)
             if self.level is not None:
                 data_obs = self._obs_file.get(
                     self.var, level=self.level, region=self.region
diff --git a/pcmdi_metrics/mean_climate/deprecated/lib/outputmetrics.py b/pcmdi_metrics/mean_climate/deprecated/lib/outputmetrics.py
index 05a6fe434..f7ee6d156 100644
--- a/pcmdi_metrics/mean_climate/deprecated/lib/outputmetrics.py
+++ b/pcmdi_metrics/mean_climate/deprecated/lib/outputmetrics.py
@@ -120,7 +120,10 @@ def calculate_and_output_metrics(self, ref, test):
 
         self.metrics_dictionary["References"][ref.obs_or_model] = self.obs_var_ref
 
-        print('jwlee-test-calculate_and_output_metrics, self.obs_var_ref:', self.obs_var_ref)
+        print(
+            "jwlee-test-calculate_and_output_metrics, self.obs_var_ref:",
+            self.obs_var_ref,
+        )
 
         ref_data = None
 
@@ -143,29 +146,34 @@ def calculate_and_output_metrics(self, ref, test):
             raise RuntimeError("Need to skip model: %s" % test.obs_or_model)
 
         # Todo: Make this a fcn
-        print('jwlee-test-calculate_and_output_metrics, grid_in_metrics_dict start')
+        print("jwlee-test-calculate_and_output_metrics, grid_in_metrics_dict start")
         self.set_grid_in_metrics_dictionary(test_data, self.var)
-        print('jwlee-test-calculate_and_output_metrics, grid_in_metrics_dict done')
-        print('jwlee-test type(ref_data), type(test_data):', type(ref_data), type(test_data))
-        print('jwlee-test ref_data:', ref_data)
-        print('jwlee-test test_data:', test_data)
-        print('jwlee-test ref_data[self.var]:', ref_data[self.var])
-        print('jwlee-test test_data[self.var]:', test_data[self.var])
-        print('jwlee-test ref_data[self.var].shape:', ref_data[self.var].shape)
-        print('jwlee-test test_data[self.var].shape:', test_data[self.var].shape)
+        print("jwlee-test-calculate_and_output_metrics, grid_in_metrics_dict done")
+        print(
+            "jwlee-test type(ref_data), type(test_data):",
+            type(ref_data),
+            type(test_data),
+        )
+        print("jwlee-test ref_data:", ref_data)
+        print("jwlee-test test_data:", test_data)
+        print("jwlee-test ref_data[self.var]:", ref_data[self.var])
+        print("jwlee-test test_data[self.var]:", test_data[self.var])
+        print("jwlee-test ref_data[self.var].shape:", ref_data[self.var].shape)
+        print("jwlee-test test_data[self.var].shape:", test_data[self.var].shape)
 
         # if ref_data.shape != test_data.shape:
         if ref_data[self.var].shape != test_data[self.var].shape:
-            print('jwlee-test raise runtime error')
+            print("jwlee-test raise runtime error")
             raise RuntimeError(
                 "Two data sets have different shapes. {} vs {}".format(
-                    str(ref_data[self.var].shape), str(test_data[self.var].shape))
+                    str(ref_data[self.var].shape), str(test_data[self.var].shape)
+                )
                 # % (ref_data.shape, test_data.shape)
             )
 
-        print('jwlee-test-calculate_and_output_metrics, set_simulation_desc start')
+        print("jwlee-test-calculate_and_output_metrics, set_simulation_desc start")
         self.set_simulation_desc(test, test_data)
-        print('jwlee-test-calculate_and_output_metrics, set_simulation_desc done')
+        print("jwlee-test-calculate_and_output_metrics, set_simulation_desc done")
 
         if (
             ref.obs_or_model
@@ -180,13 +188,14 @@ def calculate_and_output_metrics(self, ref, test):
         ].get(self.parameter.realization, {})
 
         if not self.parameter.dry_run:
-            print('jwlee-test-calculate_and_output_metrics, compute_metrics start')
-            print('jwlee-test-calculate_and_output_metrics, self.var_name_long:', self.var_name_long)
-
-            pr_rgn = compute_metrics(
-                self.var_name_long, test_data, ref_data
+            print("jwlee-test-calculate_and_output_metrics, compute_metrics start")
+            print(
+                "jwlee-test-calculate_and_output_metrics, self.var_name_long:",
+                self.var_name_long,
             )
-            print('jwlee-test-calculate_and_output_metrics, compute_metrics done')
+
+            pr_rgn = compute_metrics(self.var_name_long, test_data, ref_data)
+            print("jwlee-test-calculate_and_output_metrics, compute_metrics done")
 
             # Calling compute_metrics with None for the model and obs returns
             # the definitions.
@@ -228,18 +237,18 @@ def calculate_and_output_metrics(self, ref, test):
 
     def set_grid_in_metrics_dictionary(self, test_data, var):
         """Set the grid in metrics_dictionary."""
-        print('jwlee-test set_grid_in_metrics_dictionary start')
+        print("jwlee-test set_grid_in_metrics_dictionary start")
         grid = {}
         grid["RegridMethod"] = self.regrid_method
         grid["RegridTool"] = self.regrid_tool
         grid["GridName"] = self.parameter.target_grid
-        print('jwlee-test set_grid_in_metrics_dictionary middle')
-        print('jwlee-test var:', var)
+        print("jwlee-test set_grid_in_metrics_dictionary middle")
+        print("jwlee-test var:", var)
         # print('jwlee-test dir(test_data):', dir(test_data))
         # grid["GridResolution"] = test_data.shape[1:]
         grid["GridResolution"] = test_data[var].shape[1:]
         self.metrics_dictionary["GridInfo"] = grid
-        print('jwlee-test set_grid_in_metrics_dictionary done')
+        print("jwlee-test set_grid_in_metrics_dictionary done")
 
     def set_simulation_desc(self, test, test_data):
         """Fillout information for the output .json and .txt files."""
@@ -250,7 +259,6 @@ def set_simulation_desc(self, test, test_data):
             "SimulationDescription"
             not in self.metrics_dictionary["RESULTS"][test.obs_or_model]
         ):
-
             descr = {
                 "MIPTable": self.obs_var_ref["CMIP_CMOR_TABLE"],
                 "Model": test.obs_or_model,
@@ -336,8 +344,8 @@ def output_interpolated_model_climatologies(self, test, test_data):
         clim_file.region = region_name
         clim_file.realization = self.parameter.realization
         DataSet.apply_custom_keys(clim_file, self.parameter.custom_keys, self.var)
-        print('jwlee-test outputmetrics clim_file.write')
-        print('type(test_data):', type(test_data))
+        print("jwlee-test outputmetrics clim_file.write")
+        print("type(test_data):", type(test_data))
         clim_file.write(test_data, type="nc", id=self.var)
 
     def get_region_name_from_region(self, region):
diff --git a/pcmdi_metrics/mean_climate/lib/__init__.py b/pcmdi_metrics/mean_climate/lib/__init__.py
index d62544d27..dc6352aa5 100644
--- a/pcmdi_metrics/mean_climate/lib/__init__.py
+++ b/pcmdi_metrics/mean_climate/lib/__init__.py
@@ -1,3 +1,4 @@
+from .calculate_climatology import calculate_climatology  # noqa
 from .compute_metrics import compute_metrics  # noqa
 from .compute_statistics import (  # noqa
     annual_mean,
@@ -17,4 +18,3 @@
 from .create_mean_climate_parser import create_mean_climate_parser  # noqa
 from .load_and_regrid import load_and_regrid  # noqa
 from .mean_climate_metrics_to_json import mean_climate_metrics_to_json  # noqa
-from .calculate_climatology import calculate_climatology  # noqa
diff --git a/pcmdi_metrics/mean_climate/lib/calculate_climatology.py b/pcmdi_metrics/mean_climate/lib/calculate_climatology.py
index d4ca28afe..b3f78a831 100644
--- a/pcmdi_metrics/mean_climate/lib/calculate_climatology.py
+++ b/pcmdi_metrics/mean_climate/lib/calculate_climatology.py
@@ -2,18 +2,24 @@
 import os
 
 import dask
-from genutil import StringConstructor
-
 import xcdat as xc
+from genutil import StringConstructor
 
 
 def calculate_climatology(
-    var, infile,
-    outfile=None, outpath=None, outfilename=None,
-    start=None, end=None, ver=None, periodinname=None, climlist=None):
-
+    var,
+    infile,
+    outfile=None,
+    outpath=None,
+    outfilename=None,
+    start=None,
+    end=None,
+    ver=None,
+    periodinname=None,
+    climlist=None,
+):
     if ver is None:
-        ver=datetime.datetime.now().strftime("v%Y%m%d")
+        ver = datetime.datetime.now().strftime("v%Y%m%d")
 
     print("ver:", ver)
 
@@ -53,7 +59,9 @@ def calculate_climatology(
         start_da = 1
         end_yr = int(end.split("-")[0])
         end_mo = int(end.split("-")[1])
-        end_da = int(d.time.dt.days_in_month.sel(time=(d.time.dt.year == end_yr))[end_mo - 1])
+        end_da = int(
+            d.time.dt.days_in_month.sel(time=(d.time.dt.year == end_yr))[end_mo - 1]
+        )
 
     start_yr_str = str(start_yr).zfill(4)
     start_mo_str = str(start_mo).zfill(2)
@@ -63,8 +71,12 @@ def calculate_climatology(
     end_da_str = str(end_da).zfill(2)
 
     # Subset given time period
-    d = d.sel(time=slice(start_yr_str + '-' + start_mo_str + '-' + start_da_str,
-                         end_yr_str + '-' + end_mo_str + '-' + end_da_str))
+    d = d.sel(
+        time=slice(
+            start_yr_str + "-" + start_mo_str + "-" + start_da_str,
+            end_yr_str + "-" + end_mo_str + "-" + end_da_str,
+        )
+    )
 
     print("start_yr_str is ", start_yr_str)
     print("start_mo_str is ", start_mo_str)
@@ -72,48 +84,51 @@ def calculate_climatology(
     print("end_mo_str is ", end_mo_str)
 
     # Calculate climatology
-    dask.config.set(**{'array.slicing.split_large_chunks': True})
-    d_clim = d.temporal.climatology(var, freq="season", weighted=True, season_config={"dec_mode": "DJF", "drop_incomplete_djf": True},)
+    dask.config.set(**{"array.slicing.split_large_chunks": True})
+    d_clim = d.temporal.climatology(
+        var,
+        freq="season",
+        weighted=True,
+        season_config={"dec_mode": "DJF", "drop_incomplete_djf": True},
+    )
     d_ac = d.temporal.climatology(var, freq="month", weighted=True)
 
     d_clim_dict = dict()
 
-    d_clim_dict['DJF'] = d_clim.isel(time=0)
-    d_clim_dict['MAM'] = d_clim.isel(time=1)
-    d_clim_dict['JJA'] = d_clim.isel(time=2)
-    d_clim_dict['SON'] = d_clim.isel(time=3)
-    d_clim_dict['AC'] = d_ac
+    d_clim_dict["DJF"] = d_clim.isel(time=0)
+    d_clim_dict["MAM"] = d_clim.isel(time=1)
+    d_clim_dict["JJA"] = d_clim.isel(time=2)
+    d_clim_dict["SON"] = d_clim.isel(time=3)
+    d_clim_dict["AC"] = d_ac
 
-    if climlist is None: 
+    if climlist is None:
         clims = ["AC", "DJF", "MAM", "JJA", "SON"]
     else:
-        clims = climlist 
+        clims = climlist
 
     for s in clims:
-        if periodinname is None: 
-         addf = (
-            "."
-            + start_yr_str
-            + start_mo_str
-            + "-"
-            + end_yr_str
-            + end_mo_str
-            + "."
-            + s
-            + "."
-            + ver
-            + ".nc")
+        if periodinname is None:
+            addf = (
+                "."
+                + start_yr_str
+                + start_mo_str
+                + "-"
+                + end_yr_str
+                + end_mo_str
+                + "."
+                + s
+                + "."
+                + ver
+                + ".nc"
+            )
         if periodinname is not None:
-         addf = (
-            "."
-            + s
-            + "."
-            + ver
-            + ".nc")
+            addf = "." + s + "." + ver + ".nc"
 
         if outfilename is not None:
             out = os.path.join(outdir, outfilename)
         out_season = out.replace(".nc", addf)
 
         print("output file is", out_season)
-        d_clim_dict[s].to_netcdf(out_season)  # global attributes are automatically saved as well
+        d_clim_dict[s].to_netcdf(
+            out_season
+        )  # global attributes are automatically saved as well
diff --git a/pcmdi_metrics/mean_climate/lib/compute_metrics.py b/pcmdi_metrics/mean_climate/lib/compute_metrics.py
index 7d1e842f3..d45ea1994 100644
--- a/pcmdi_metrics/mean_climate/lib/compute_metrics.py
+++ b/pcmdi_metrics/mean_climate/lib/compute_metrics.py
@@ -25,28 +25,30 @@ def compute_metrics(Var, dm, do, debug=False):
         metrics_defs["annual_mean"] = pcmdi_metrics.mean_climate.lib.annual_mean(
             None, None
         )
-        metrics_defs["zonal_mean"] = pcmdi_metrics.mean_climate.lib.zonal_mean(None, None)
+        metrics_defs["zonal_mean"] = pcmdi_metrics.mean_climate.lib.zonal_mean(
+            None, None
+        )
         return metrics_defs
 
     # cdms.setAutoBounds("on")
-    print('var: ', var)
+    print("var: ", var)
 
     # unify time and time bounds between observation and model
     if debug:
-        print('before time and time bounds unifying')
-        print('dm.time: ', dm['time'])
-        print('do.time: ', do['time'])
-        
+        print("before time and time bounds unifying")
+        print("dm.time: ", dm["time"])
+        print("do.time: ", do["time"])
+
     # Below is temporary...
-    dm['time'] = do['time']
-    dm[dm.time.attrs['bounds']] = do[do.time.attrs['bounds']]
-    
+    dm["time"] = do["time"]
+    dm[dm.time.attrs["bounds"]] = do[do.time.attrs["bounds"]]
+
     if debug:
-        print('after time and time bounds unifying')
-        print('dm.time: ', dm['time'])
-        print('do.time: ', do['time'])
+        print("after time and time bounds unifying")
+        print("dm.time: ", dm["time"])
+        print("do.time: ", do["time"])
 
-    #if debug:
+    # if debug:
     #    dm.to_netcdf('dm.nc')
     #    do.to_netcdf('do.nc')
 
@@ -64,85 +66,93 @@ def compute_metrics(Var, dm, do, debug=False):
         sig_digits = ".3f"
 
     # CALCULATE ANNUAL CYCLE SPACE-TIME RMS, CORRELATIONS and STD
-    print('compute_metrics-CALCULATE ANNUAL CYCLE SPACE-TIME RMS, CORRELATIONS and STD')
-    print('compute_metrics, rms_xyt')
+    print("compute_metrics-CALCULATE ANNUAL CYCLE SPACE-TIME RMS, CORRELATIONS and STD")
+    print("compute_metrics, rms_xyt")
     rms_xyt = pcmdi_metrics.mean_climate.lib.rms_xyt(dm, do, var)
-    print('compute_metrics, stdObs_xyt')
+    print("compute_metrics, stdObs_xyt")
     stdObs_xyt = pcmdi_metrics.mean_climate.lib.std_xyt(do, var)
-    print('compute_metrics, std_xyt')
+    print("compute_metrics, std_xyt")
     std_xyt = pcmdi_metrics.mean_climate.lib.std_xyt(dm, var)
 
     # CALCULATE ANNUAL MEANS
-    print('compute_metrics-CALCULATE ANNUAL MEANS')
+    print("compute_metrics-CALCULATE ANNUAL MEANS")
     dm_am, do_am = pcmdi_metrics.mean_climate.lib.annual_mean(dm, do, var)
 
     # CALCULATE ANNUAL MEAN BIAS
-    print('compute_metrics-CALCULATE ANNUAL MEAN BIAS')
+    print("compute_metrics-CALCULATE ANNUAL MEAN BIAS")
     bias_xy = pcmdi_metrics.mean_climate.lib.bias_xy(dm_am, do_am, var)
 
     # CALCULATE MEAN ABSOLUTE ERROR
-    print('compute_metrics-CALCULATE MSE')
+    print("compute_metrics-CALCULATE MSE")
     mae_xy = pcmdi_metrics.mean_climate.lib.meanabs_xy(dm_am, do_am, var)
 
     # CALCULATE ANNUAL MEAN RMS (centered and uncentered)
-    print('compute_metrics-CALCULATE MEAN RMS')
+    print("compute_metrics-CALCULATE MEAN RMS")
     rms_xy = pcmdi_metrics.mean_climate.lib.rms_xy(dm_am, do_am, var)
     rmsc_xy = pcmdi_metrics.mean_climate.lib.rmsc_xy(dm_am, do_am, var)
 
     # CALCULATE ANNUAL MEAN CORRELATION
-    print('compute_metrics-CALCULATE MEAN CORR')
+    print("compute_metrics-CALCULATE MEAN CORR")
     cor_xy = pcmdi_metrics.mean_climate.lib.cor_xy(dm_am, do_am, var)
 
     # CALCULATE ANNUAL OBS and MOD STD
-    print('compute_metrics-CALCULATE ANNUAL OBS AND MOD STD')
+    print("compute_metrics-CALCULATE ANNUAL OBS AND MOD STD")
     stdObs_xy = pcmdi_metrics.mean_climate.lib.std_xy(do_am, var)
     std_xy = pcmdi_metrics.mean_climate.lib.std_xy(dm_am, var)
 
     # CALCULATE ANNUAL OBS and MOD MEAN
-    print('compute_metrics-CALCULATE ANNUAL OBS AND MOD MEAN')
+    print("compute_metrics-CALCULATE ANNUAL OBS AND MOD MEAN")
     meanObs_xy = pcmdi_metrics.mean_climate.lib.mean_xy(do_am, var)
     mean_xy = pcmdi_metrics.mean_climate.lib.mean_xy(dm_am, var)
 
     # ZONAL MEANS ######
     # CALCULATE ANNUAL MEANS
-    print('compute_metrics-CALCULATE ANNUAL MEANS')
+    print("compute_metrics-CALCULATE ANNUAL MEANS")
     dm_amzm, do_amzm = pcmdi_metrics.mean_climate.lib.zonal_mean(dm_am, do_am, var)
 
     # CALCULATE ANNUAL AND ZONAL MEAN RMS
-    print('compute_metrics-CALCULATE ANNUAL AND ZONAL MEAN RMS')
+    print("compute_metrics-CALCULATE ANNUAL AND ZONAL MEAN RMS")
     rms_y = pcmdi_metrics.mean_climate.lib.rms_0(dm_amzm, do_amzm, var)
 
     # CALCULATE ANNUAL MEAN DEVIATION FROM ZONAL MEAN RMS
-    print('compute_metrics-CALCULATE ANNUAL MEAN DEVIATION FROM ZONAL MEAN RMS')
+    print("compute_metrics-CALCULATE ANNUAL MEAN DEVIATION FROM ZONAL MEAN RMS")
     dm_am_devzm = dm_am - dm_amzm
     do_am_devzm = do_am - do_amzm
-    rms_xy_devzm = pcmdi_metrics.mean_climate.lib.rms_xy(dm_am_devzm, do_am_devzm, var, weights=dm.spatial.get_weights(axis=['X', 'Y']))
+    rms_xy_devzm = pcmdi_metrics.mean_climate.lib.rms_xy(
+        dm_am_devzm, do_am_devzm, var, weights=dm.spatial.get_weights(axis=["X", "Y"])
+    )
 
     # CALCULATE ANNUAL AND ZONAL MEAN STD
 
     # CALCULATE ANNUAL MEAN DEVIATION FROM ZONAL MEAN STD
-    print('compute_metrics-CALCULATE ANNUAL MEAN DEVIATION FROM ZONAL MEAN STD')
-    stdObs_xy_devzm = pcmdi_metrics.mean_climate.lib.std_xy(do_am_devzm, var, weights=do.spatial.get_weights(axis=['X', 'Y']))
-    std_xy_devzm = pcmdi_metrics.mean_climate.lib.std_xy(dm_am_devzm, var, weights=dm.spatial.get_weights(axis=['X', 'Y']))
-
-    for stat in sorted([
-        "std-obs_xy",
-        "std_xy",
-        "std-obs_xyt",
-        "std_xyt",
-        "std-obs_xy_devzm",
-        "mean_xy",
-        "mean-obs_xy",
-        "std_xy_devzm",
-        "rms_xyt",
-        "rms_xy",
-        "rmsc_xy",
-        "cor_xy",
-        "bias_xy",
-        "mae_xy",
-        "rms_y",
-        "rms_devzm",
-    ]):
+    print("compute_metrics-CALCULATE ANNUAL MEAN DEVIATION FROM ZONAL MEAN STD")
+    stdObs_xy_devzm = pcmdi_metrics.mean_climate.lib.std_xy(
+        do_am_devzm, var, weights=do.spatial.get_weights(axis=["X", "Y"])
+    )
+    std_xy_devzm = pcmdi_metrics.mean_climate.lib.std_xy(
+        dm_am_devzm, var, weights=dm.spatial.get_weights(axis=["X", "Y"])
+    )
+
+    for stat in sorted(
+        [
+            "std-obs_xy",
+            "std_xy",
+            "std-obs_xyt",
+            "std_xyt",
+            "std-obs_xy_devzm",
+            "mean_xy",
+            "mean-obs_xy",
+            "std_xy_devzm",
+            "rms_xyt",
+            "rms_xy",
+            "rmsc_xy",
+            "cor_xy",
+            "bias_xy",
+            "mae_xy",
+            "rms_y",
+            "rms_devzm",
+        ]
+    ):
         metrics_dictionary[stat] = OrderedDict()
 
     metrics_dictionary["mean-obs_xy"]["ann"] = format(meanObs_xy * conv, sig_digits)
@@ -167,7 +177,6 @@ def compute_metrics(Var, dm, do, debug=False):
 
     # CALCULATE SEASONAL MEANS
     for sea in ["djf", "mam", "jja", "son"]:
-
         dm_sea = pcmdi_metrics.mean_climate.lib.seasonal_mean(dm, sea, var)
         do_sea = pcmdi_metrics.mean_climate.lib.seasonal_mean(do, sea, var)
 
@@ -193,7 +202,9 @@ def compute_metrics(Var, dm, do, debug=False):
         metrics_dictionary["mae_xy"][sea] = format(mae_sea * conv, sig_digits)
         metrics_dictionary["std-obs_xy"][sea] = format(stdObs_xy_sea * conv, sig_digits)
         metrics_dictionary["std_xy"][sea] = format(std_xy_sea * conv, sig_digits)
-        metrics_dictionary["mean-obs_xy"][sea] = format(meanObs_xy_sea * conv, sig_digits)
+        metrics_dictionary["mean-obs_xy"][sea] = format(
+            meanObs_xy_sea * conv, sig_digits
+        )
         metrics_dictionary["mean_xy"][sea] = format(mean_xy_sea * conv, sig_digits)
 
     rms_mo_l = []
diff --git a/pcmdi_metrics/mean_climate/lib/compute_statistics.py b/pcmdi_metrics/mean_climate/lib/compute_statistics.py
index a5abdfcaa..4ef809b1f 100644
--- a/pcmdi_metrics/mean_climate/lib/compute_statistics.py
+++ b/pcmdi_metrics/mean_climate/lib/compute_statistics.py
@@ -65,7 +65,7 @@ def bias_xy(dm, do, var=None, weights=None):
         }
     dif = dm[var] - do[var]
     if weights is None:
-        weights = dm.spatial.get_weights(axis=['X', 'Y'])
+        weights = dm.spatial.get_weights(axis=["X", "Y"])
     stat = float(dif.weighted(weights).mean(("lon", "lat")))
     return float(stat)
 
@@ -79,8 +79,10 @@ def bias_xyt(dm, do, var=None):
             "Contact": "pcmdi-metrics@llnl.gov",
         }
     ds = dm.copy(deep=True)
-    ds['dif'] = dm[var] - do[var]
-    stat = ds.spatial.average('dif', axis=['X', 'Y']).temporal.average('dif')['dif'].values
+    ds["dif"] = dm[var] - do[var]
+    stat = (
+        ds.spatial.average("dif", axis=["X", "Y"]).temporal.average("dif")["dif"].values
+    )
     return float(stat)
 
 
@@ -93,12 +95,17 @@ def cor_xy(dm, do, var=None, weights=None):
             "Contact": "pcmdi-metrics@llnl.gov",
         }
     if weights is None:
-        weights = dm.spatial.get_weights(axis=['X', 'Y'])
+        weights = dm.spatial.get_weights(axis=["X", "Y"])
 
-    dm_avg = dm.spatial.average(var, axis=['X', 'Y'], weights=weights)[var].values
-    do_avg = do.spatial.average(var, axis=['X', 'Y'], weights=weights)[var].values
+    dm_avg = dm.spatial.average(var, axis=["X", "Y"], weights=weights)[var].values
+    do_avg = do.spatial.average(var, axis=["X", "Y"], weights=weights)[var].values
 
-    covariance = ((dm[var] - dm_avg) * (do[var] - do_avg)).weighted(weights).mean(dim=['lon', 'lat']).values
+    covariance = (
+        ((dm[var] - dm_avg) * (do[var] - do_avg))
+        .weighted(weights)
+        .mean(dim=["lon", "lat"])
+        .values
+    )
     std_dm = std_xy(dm, var)
     std_do = std_xy(do, var)
     stat = covariance / (std_dm * std_do)
@@ -116,7 +123,7 @@ def mean_xy(d, var=None, weights=None):
         }
 
     if weights is None:
-        weights = d.spatial.get_weights(axis=['X', 'Y'])
+        weights = d.spatial.get_weights(axis=["X", "Y"])
     stat = float(d[var].weighted(weights).mean(("lon", "lat")))
     return float(stat)
 
@@ -131,7 +138,7 @@ def meanabs_xy(dm, do, var=None, weights=None):
             "Contact": "pcmdi-metrics@llnl.gov",
         }
     if weights is None:
-        weights = dm.spatial.get_weights(axis=['X', 'Y'])
+        weights = dm.spatial.get_weights(axis=["X", "Y"])
     dif = abs(dm[var] - do[var])
     stat = dif.weighted(weights).mean(("lon", "lat"))
     return float(stat)
@@ -147,8 +154,12 @@ def meanabs_xyt(dm, do, var=None):
             "Contact": "pcmdi-metrics@llnl.gov",
         }
     ds = dm.copy(deep=True)
-    ds['absdif'] = abs(dm[var] - do[var])
-    stat = ds.spatial.average('absdif', axis=['X', 'Y']).temporal.average('absdif')['absdif'].values
+    ds["absdif"] = abs(dm[var] - do[var])
+    stat = (
+        ds.spatial.average("absdif", axis=["X", "Y"])
+        .temporal.average("absdif")["absdif"]
+        .values
+    )
     return float(stat)
 
 
@@ -160,9 +171,9 @@ def rms_0(dm, do, var=None, weighted=True):
             "Abstract": "Compute Root Mean Square over the first axis",
             "Contact": "pcmdi-metrics@llnl.gov",
         }
-    dif_square = (dm[var] - do[var])**2
+    dif_square = (dm[var] - do[var]) ** 2
     if weighted:
-        weights = dm.spatial.get_weights(axis=['Y'])
+        weights = dm.spatial.get_weights(axis=["Y"])
         stat = math.sqrt(dif_square.weighted(weights).mean(("lat")))
     else:
         stat = math.sqrt(dif_square.mean(("lat")))
@@ -177,9 +188,9 @@ def rms_xy(dm, do, var=None, weights=None):
             "Abstract": "Compute Spatial Root Mean Square",
             "Contact": "pcmdi-metrics@llnl.gov",
         }
-    dif_square = (dm[var] - do[var])**2
+    dif_square = (dm[var] - do[var]) ** 2
     if weights is None:
-        weights = dm.spatial.get_weights(axis=['X', 'Y'])
+        weights = dm.spatial.get_weights(axis=["X", "Y"])
     stat = math.sqrt(dif_square.weighted(weights).mean(("lon", "lat")))
     return float(stat)
 
@@ -193,9 +204,11 @@ def rms_xyt(dm, do, var=None):
             "Contact": "pcmdi-metrics@llnl.gov",
         }
     ds = dm.copy(deep=True)
-    ds['diff_square'] = (dm[var] - do[var])**2
-    ds['diff_square_sqrt'] = np.sqrt(ds.spatial.average('diff_square', axis=['X', 'Y'])['diff_square'])
-    stat = ds.temporal.average('diff_square_sqrt')['diff_square_sqrt'].values
+    ds["diff_square"] = (dm[var] - do[var]) ** 2
+    ds["diff_square_sqrt"] = np.sqrt(
+        ds.spatial.average("diff_square", axis=["X", "Y"])["diff_square"]
+    )
+    stat = ds.temporal.average("diff_square_sqrt")["diff_square_sqrt"].values
     return float(stat)
 
 
@@ -208,11 +221,11 @@ def rmsc_xy(dm, do, var=None, weights=None):
             "Contact": "pcmdi-metrics@llnl.gov",
         }
     if weights is None:
-        weights = dm.spatial.get_weights(axis=['X', 'Y'])
+        weights = dm.spatial.get_weights(axis=["X", "Y"])
 
     dm_anomaly = dm[var] - dm[var].weighted(weights).mean(("lon", "lat"))
     do_anomaly = do[var] - do[var].weighted(weights).mean(("lon", "lat"))
-    diff_square = (dm_anomaly - do_anomaly)**2
+    diff_square = (dm_anomaly - do_anomaly) ** 2
 
     stat = math.sqrt(diff_square.weighted(weights).mean(("lon", "lat")))
     return float(stat)
@@ -227,9 +240,9 @@ def std_xy(d, var=None, weights=None):
             "Contact": "pcmdi-metrics@llnl.gov",
         }
     if weights is None:
-        weights = d.spatial.get_weights(axis=['X', 'Y'])
+        weights = d.spatial.get_weights(axis=["X", "Y"])
     average = float(d[var].weighted(weights).mean(("lon", "lat")))
-    anomaly = (d[var] - average)**2
+    anomaly = (d[var] - average) ** 2
     variance = float(anomaly.weighted(weights).mean(("lon", "lat")))
     std = math.sqrt(variance)
     return float(std)
@@ -244,11 +257,13 @@ def std_xyt(d, var=None):
             "Contact": "pcmdi-metrics@llnl.gov",
         }
     ds = d.copy(deep=True)
-    average = d.spatial.average(var, axis=['X', 'Y']).temporal.average(var)[var]
-    ds['anomaly'] = (d[var] - average)**2
-    variance = ds.spatial.average('anomaly').temporal.average('anomaly')['anomaly'].values
+    average = d.spatial.average(var, axis=["X", "Y"]).temporal.average(var)[var]
+    ds["anomaly"] = (d[var] - average) ** 2
+    variance = (
+        ds.spatial.average("anomaly").temporal.average("anomaly")["anomaly"].values
+    )
     std = math.sqrt(variance)
-    return(std)
+    return std
 
 
 def zonal_mean(dm, do, var=None):
@@ -260,6 +275,6 @@ def zonal_mean(dm, do, var=None):
             "Contact": "pcmdi-metrics@llnl.gov",
             "Comments": "",
         }
-    dm_zm = dm.spatial.average(var, axis=['X'])
-    do_zm = do.spatial.average(var, axis=['X'])
+    dm_zm = dm.spatial.average(var, axis=["X"])
+    do_zm = do.spatial.average(var, axis=["X"])
     return dm_zm, do_zm  # DataSets
diff --git a/pcmdi_metrics/mean_climate/lib/create_mean_climate_parser.py b/pcmdi_metrics/mean_climate/lib/create_mean_climate_parser.py
index ba542bdf1..4f514e828 100644
--- a/pcmdi_metrics/mean_climate/lib/create_mean_climate_parser.py
+++ b/pcmdi_metrics/mean_climate/lib/create_mean_climate_parser.py
@@ -47,7 +47,7 @@ def create_mean_climate_parser():
         help="Users can customize regions values names",
         required=False,
     )
-    
+
     parser.add_argument(
         "--regions_specs",
         type=ast.literal_eval,
@@ -98,7 +98,7 @@ def create_mean_climate_parser():
         dest="target_grid",
         help='Options are "2.5x2.5" or an actual cdms2 grid object',
         required=False,
-        default="2.5x2.5"
+        default="2.5x2.5",
     )
 
     parser.add_argument(
@@ -153,10 +153,7 @@ def create_mean_climate_parser():
     )
 
     parser.add_argument(
-        "--ext", 
-        dest="ext", 
-        help="Extension for the output files?", 
-        required=False
+        "--ext", dest="ext", help="Extension for the output files?", required=False
     )
 
     parser.add_argument(
@@ -187,7 +184,7 @@ def create_mean_climate_parser():
         dest="custom_observations",
         help="Path to an alternative, custom observation file",
         required=False,
-        default=""
+        default="",
     )
 
     parser.add_argument(
@@ -196,7 +193,7 @@ def create_mean_climate_parser():
         help="Directory of where to put the results",
         required=False,
     )
-    
+
     parser.add_argument(
         "--diagnostics_output_path",
         dest="diagnostics_output_path",
diff --git a/pcmdi_metrics/mean_climate/lib/load_and_regrid.py b/pcmdi_metrics/mean_climate/lib/load_and_regrid.py
index b09c963c2..1bad706c4 100644
--- a/pcmdi_metrics/mean_climate/lib/load_and_regrid.py
+++ b/pcmdi_metrics/mean_climate/lib/load_and_regrid.py
@@ -1,16 +1,26 @@
 import cftime
-import xcdat as xc
 import numpy as np
+import xcdat as xc
+
 from pcmdi_metrics.io import xcdat_open
 
 
-def load_and_regrid(data_path, varname, varname_in_file=None, level=None, t_grid=None, decode_times=True, regrid_tool='regrid2', debug=False):
+def load_and_regrid(
+    data_path,
+    varname,
+    varname_in_file=None,
+    level=None,
+    t_grid=None,
+    decode_times=True,
+    regrid_tool="regrid2",
+    debug=False,
+):
     """Load data and regrid to target grid
 
     Args:
         data_path (str): full data path for nc or xml file
         varname (str): variable name
-        varname_in_file (str): variable name if data array named differently 
+        varname_in_file (str): variable name if data array named differently
         level (float): level to extract (unit in hPa)
         t_grid (xarray.core.dataset.Dataset): target grid to regrid
         decode_times (bool): Default is True. decode_times=False will be removed once obs4MIP written using xcdat
@@ -18,35 +28,41 @@ def load_and_regrid(data_path, varname, varname_in_file=None, level=None, t_grid
         debug (bool): Default is False. If True, print more info to help debugging process
     """
     if debug:
-        print('load_and_regrid start')
+        print("load_and_regrid start")
 
     if varname_in_file is None:
         varname_in_file = varname
 
     # load data
-    ds = xcdat_open(data_path, data_var=varname_in_file, decode_times=decode_times)  # NOTE: decode_times=False will be removed once obs4MIP written using xcdat
+    ds = xcdat_open(
+        data_path, data_var=varname_in_file, decode_times=decode_times
+    )  # NOTE: decode_times=False will be removed once obs4MIP written using xcdat
 
     # calendar quality check
     if "calendar" in list(ds.time.attrs.keys()):
         if debug:
             print('ds.time.attrs["calendar"]:', ds.time.attrs["calendar"])
-        if 'calendar' in ds.attrs.keys():
+        if "calendar" in ds.attrs.keys():
             if debug:
-                print('ds.calendar:', ds.calendar)
+                print("ds.calendar:", ds.calendar)
             if ds.calendar != ds.time.attrs["calendar"]:
-                print('[WARNING]: calendar info mismatch. ds.time.attrs["calendar"] is adjusted to ds.calendar')
+                print(
+                    '[WARNING]: calendar info mismatch. ds.time.attrs["calendar"] is adjusted to ds.calendar'
+                )
                 ds.time.attrs["calendar"] = ds.calendar
     else:
-        if 'calendar' in ds.attrs.keys():
+        if "calendar" in ds.attrs.keys():
             ds.time.attrs["calendar"] = ds.calendar
-                
+
     # time bound check -- add proper time bound info if cdms-generated annual cycle is loaded
-    if isinstance(ds.time.values[0], np.float64):  # and "units" not in list(ds.time.attrs.keys()):
-        ds.time.attrs['units'] = "days since 0001-01-01"
+    if isinstance(
+        ds.time.values[0], np.float64
+    ):  # and "units" not in list(ds.time.attrs.keys()):
+        ds.time.attrs["units"] = "days since 0001-01-01"
         ds = xc.decode_time(ds)
         if debug:
-            print('decode_time done')
-    
+            print("decode_time done")
+
     # level - extract a specific level if needed
     if level is not None:
         if isinstance(level, int) or isinstance(level, float):
@@ -55,43 +71,47 @@ def load_and_regrid(data_path, varname, varname_in_file=None, level=None, t_grid
             level = float(level)
 
         # check vertical coordinate first
-        if 'plev' in list(ds.coords.keys()):
-            if ds.plev.units == 'Pa':
+        if "plev" in list(ds.coords.keys()):
+            if ds.plev.units == "Pa":
                 level = level * 100  # hPa to Pa
             try:
                 ds = ds.sel(plev=level)
             except Exception as ex:
-                print('WARNING: ', ex)
+                print("WARNING: ", ex)
 
                 nearest_level = find_nearest(ds.plev.values, level)
 
-                print('  Given level', level)
-                print('  Selected nearest level from dataset:', nearest_level)
+                print("  Given level", level)
+                print("  Selected nearest level from dataset:", nearest_level)
 
                 diff_percentage = abs(nearest_level - level) / level * 100
                 if diff_percentage < 0.1:  # acceptable if differance is less than 0.1%
-                    ds = ds.sel(plev=level, method='nearest')
-                    print('  Difference is in acceptable range.')
+                    ds = ds.sel(plev=level, method="nearest")
+                    print("  Difference is in acceptable range.")
                     pass
                 else:
-                    print('ERROR: Difference between two levels are too big!')
+                    print("ERROR: Difference between two levels are too big!")
                     return
             if debug:
-                print('ds:', ds)
-                print('ds.plev.units:', ds.plev.units)
+                print("ds:", ds)
+                print("ds.plev.units:", ds.plev.units)
         else:
-            print('ERROR: plev is not in the nc file. Check vertical coordinate.')
-            print('  Coordinates keys in the nc file:', list(ds.coords.keys()))
-            print('ERROR: load and regrid can not complete')
+            print("ERROR: plev is not in the nc file. Check vertical coordinate.")
+            print("  Coordinates keys in the nc file:", list(ds.coords.keys()))
+            print("ERROR: load and regrid can not complete")
             return
-    
+
     # regrid
-    if regrid_tool == 'regrid2':
-        ds_regridded = ds.regridder.horizontal(varname_in_file, t_grid, tool=regrid_tool)
-    elif regrid_tool in ['esmf', 'xesmf']:
-        regrid_tool = 'xesmf'
-        regrid_method = 'bilinear'
-        ds_regridded = ds.regridder.horizontal(varname_in_file, t_grid, tool=regrid_tool, method=regrid_method)
+    if regrid_tool == "regrid2":
+        ds_regridded = ds.regridder.horizontal(
+            varname_in_file, t_grid, tool=regrid_tool
+        )
+    elif regrid_tool in ["esmf", "xesmf"]:
+        regrid_tool = "xesmf"
+        regrid_method = "bilinear"
+        ds_regridded = ds.regridder.horizontal(
+            varname_in_file, t_grid, tool=regrid_tool, method=regrid_method
+        )
 
     if varname != varname_in_file:
         ds_regridded[varname] = ds_regridded[varname_in_file]
@@ -101,13 +121,13 @@ def load_and_regrid(data_path, varname, varname_in_file=None, level=None, t_grid
         units = ds[varname].units
     except Exception as e:
         print(e)
-        units = "" 
-    print('units:', units)
+        units = ""
+    print("units:", units)
+
+    ds_regridded[varname] = ds_regridded[varname].assign_attrs({"units": units})
 
-    ds_regridded[varname] = ds_regridded[varname].assign_attrs({'units': units})
-        
     if debug:
-        print('ds_regridded:', ds_regridded)
+        print("ds_regridded:", ds_regridded)
     return ds_regridded
 
 
@@ -115,4 +135,3 @@ def find_nearest(array, value):
     array = np.asarray(array)
     idx = (np.abs(array - value)).argmin()
     return array[idx]
-
diff --git a/pcmdi_metrics/mean_climate/lib/mean_climate_metrics_to_json.py b/pcmdi_metrics/mean_climate/lib/mean_climate_metrics_to_json.py
index 0e42f7ec7..da0b01666 100644
--- a/pcmdi_metrics/mean_climate/lib/mean_climate_metrics_to_json.py
+++ b/pcmdi_metrics/mean_climate/lib/mean_climate_metrics_to_json.py
@@ -5,14 +5,16 @@
 
 
 def mean_climate_metrics_to_json(
-    outdir, json_filename, result_dict,
-    model=None, run=None,
-    cmec_flag=False, debug=False
+    outdir,
+    json_filename,
+    result_dict,
+    model=None,
+    run=None,
+    cmec_flag=False,
+    debug=False,
 ):
     # Open JSON
-    JSON = Base(
-        outdir, json_filename
-    )
+    JSON = Base(outdir, json_filename)
     # Dict for JSON
     json_dict = deepcopy(result_dict)
     if model is not None or run is not None:
@@ -46,8 +48,8 @@ def mean_climate_metrics_to_json(
     )
 
     if debug:
-        print('in mean_climate_metrics_to_json, model, run:', model, run)
-        print('json_dict:', json.dumps(json_dict, sort_keys=True, indent=4))
+        print("in mean_climate_metrics_to_json, model, run:", model, run)
+        print("json_dict:", json.dumps(json_dict, sort_keys=True, indent=4))
 
     if cmec_flag:
         print("Writing cmec file")
diff --git a/pcmdi_metrics/mean_climate/mean_climate_driver.py b/pcmdi_metrics/mean_climate/mean_climate_driver.py
index 1a9fd6c62..203c7ec58 100755
--- a/pcmdi_metrics/mean_climate/mean_climate_driver.py
+++ b/pcmdi_metrics/mean_climate/mean_climate_driver.py
@@ -3,8 +3,8 @@
 import glob
 import json
 import os
-from re import split
 from collections import OrderedDict
+from re import split
 
 import cdms2
 import cdutil
@@ -19,9 +19,7 @@
     load_and_regrid,
     mean_climate_metrics_to_json,
 )
-from pcmdi_metrics.variability_mode.lib import tree
-from pcmdi_metrics.variability_mode.lib import sort_human
-
+from pcmdi_metrics.variability_mode.lib import sort_human, tree
 
 parser = create_mean_climate_parser()
 parameter = parser.get_parameter(argparse_vals_only=False)
@@ -53,12 +51,14 @@
 parallel = parameter.parallel
 
 if metrics_output_path is not None:
-    metrics_output_path = parameter.metrics_output_path.replace('%(case_id)', case_id)
+    metrics_output_path = parameter.metrics_output_path.replace("%(case_id)", case_id)
 
 if diagnostics_output_path is None:
-    diagnostics_output_path = metrics_output_path.replace('metrics_results', 'diagnostic_results')    
-    
-diagnostics_output_path = diagnostics_output_path.replace('%(case_id)', case_id)
+    diagnostics_output_path = metrics_output_path.replace(
+        "metrics_results", "diagnostic_results"
+    )
+
+diagnostics_output_path = diagnostics_output_path.replace("%(case_id)", case_id)
 
 find_all_realizations = False
 first_realization_only = False
@@ -72,63 +72,112 @@
 realizations = [realization]
 
 if debug:
-    print('regions_specs (before loading internally defined):', regions_specs)
+    print("regions_specs (before loading internally defined):", regions_specs)
 
 if regions_specs is None or not bool(regions_specs):
     regions_specs = load_regions_specs()
 
-default_regions = ['global', 'NHEX', 'SHEX', 'TROPICS']
+default_regions = ["global", "NHEX", "SHEX", "TROPICS"]
 print(
-    'case_id: ', case_id, '\n',
-    'test_data_set:', test_data_set, '\n',
-    'realization:', realization, '\n',
-    'vars:', vars, '\n',
-    'varname_in_test_data:', varname_in_test_data, '\n',
-    'reference_data_set:', reference_data_set, '\n',
-    'target_grid:', target_grid, '\n',
-    'regrid_tool:', regrid_tool, '\n',
-    'regrid_tool_ocn:', regrid_tool_ocn, '\n',
-    'save_test_clims:', save_test_clims, '\n',
-    'test_clims_interpolated_output:', test_clims_interpolated_output, '\n',
-    'filename_template:', filename_template, '\n',
-    'sftlf_filename_template:', sftlf_filename_template, '\n',
-    'generate_sftlf:', generate_sftlf, '\n',
-    'regions_specs:', regions_specs, '\n',
-    'regions:', regions, '\n',
-    'test_data_path:', test_data_path, '\n',
-    'reference_data_path:', reference_data_path, '\n',
-    'metrics_output_path:', metrics_output_path, '\n',
-    'diagnostics_output_path:', diagnostics_output_path, '\n',
-    'debug:', debug, '\n')
-
-print('--- prepare mean climate metrics calculation ---')
+    "case_id: ",
+    case_id,
+    "\n",
+    "test_data_set:",
+    test_data_set,
+    "\n",
+    "realization:",
+    realization,
+    "\n",
+    "vars:",
+    vars,
+    "\n",
+    "varname_in_test_data:",
+    varname_in_test_data,
+    "\n",
+    "reference_data_set:",
+    reference_data_set,
+    "\n",
+    "target_grid:",
+    target_grid,
+    "\n",
+    "regrid_tool:",
+    regrid_tool,
+    "\n",
+    "regrid_tool_ocn:",
+    regrid_tool_ocn,
+    "\n",
+    "save_test_clims:",
+    save_test_clims,
+    "\n",
+    "test_clims_interpolated_output:",
+    test_clims_interpolated_output,
+    "\n",
+    "filename_template:",
+    filename_template,
+    "\n",
+    "sftlf_filename_template:",
+    sftlf_filename_template,
+    "\n",
+    "generate_sftlf:",
+    generate_sftlf,
+    "\n",
+    "regions_specs:",
+    regions_specs,
+    "\n",
+    "regions:",
+    regions,
+    "\n",
+    "test_data_path:",
+    test_data_path,
+    "\n",
+    "reference_data_path:",
+    reference_data_path,
+    "\n",
+    "metrics_output_path:",
+    metrics_output_path,
+    "\n",
+    "diagnostics_output_path:",
+    diagnostics_output_path,
+    "\n",
+    "debug:",
+    debug,
+    "\n",
+)
+
+print("--- prepare mean climate metrics calculation ---")
 
 # generate target grid
-res=target_grid.split("x")
-lat_res=float(res[0])
-lon_res=float(res[1])
-start_lat=-90.+lat_res/2
-start_lon=0.
-end_lat = 90.-lat_res/2
-end_lon = 360.-lon_res
-nlat = ((end_lat - start_lat) * 1./lat_res) + 1
-nlon = ((end_lon - start_lon) * 1./lon_res) + 1
-t_grid=xc.create_uniform_grid(start_lat,end_lat,lat_res,start_lon,end_lon,lon_res)
+res = target_grid.split("x")
+lat_res = float(res[0])
+lon_res = float(res[1])
+start_lat = -90.0 + lat_res / 2
+start_lon = 0.0
+end_lat = 90.0 - lat_res / 2
+end_lon = 360.0 - lon_res
+nlat = ((end_lat - start_lat) * 1.0 / lat_res) + 1
+nlon = ((end_lon - start_lon) * 1.0 / lon_res) + 1
+t_grid = xc.create_uniform_grid(
+    start_lat, end_lat, lat_res, start_lon, end_lon, lon_res
+)
 if debug:
-    print('type(t_grid):', type(t_grid))  # Expected type is 'xarray.core.dataset.Dataset'
-    print('t_grid:', t_grid)
+    print(
+        "type(t_grid):", type(t_grid)
+    )  # Expected type is 'xarray.core.dataset.Dataset'
+    print("t_grid:", t_grid)
 # identical target grid in cdms2 to use generateLandSeaMask function that is yet to exist in xcdat
-t_grid_cdms2 = cdms2.createUniformGrid(start_lat,nlat,lat_res,start_lon,nlon,lon_res)
+t_grid_cdms2 = cdms2.createUniformGrid(
+    start_lat, nlat, lat_res, start_lon, nlon, lon_res
+)
 # generate land sea mask for the target grid
 sft = cdutil.generateLandSeaMask(t_grid_cdms2)
 if debug:
-    print('sft:', sft)
-    print('sft.getAxisList():', sft.getAxisList())
+    print("sft:", sft)
+    print("sft.getAxisList():", sft.getAxisList())
 # add sft to target grid dataset
-t_grid['sftlf'] = (['lat', 'lon'], np.array(sft))
+t_grid["sftlf"] = (["lat", "lon"], np.array(sft))
 if debug:
-    print('t_grid (after sftlf added):', t_grid)
-    t_grid.to_netcdf('target_grid.nc')
+    print("t_grid (after sftlf added):", t_grid)
+    t_grid.to_netcdf("target_grid.nc")
 
 # load obs catalogue json
 egg_pth = resources.resource_path()
@@ -140,9 +189,9 @@
 with open(obs_file_path) as fo:
     obs_dict = json.loads(fo.read())
 # if debug:
-    # print('obs_dict:', json.dumps(obs_dict, indent=4, sort_keys=True))
+# print('obs_dict:', json.dumps(obs_dict, indent=4, sort_keys=True))
 
-print('--- start mean climate metrics calculation ---')
+print("--- start mean climate metrics calculation ---")
 
 # -------------
 # variable loop
@@ -151,10 +200,9 @@
     vars = [vars]
 
 for var in vars:
-
-    if '_' in var or '-' in var:
-        varname = split('_|-', var)[0]
-        level = float(split('_|-', var)[1])
+    if "_" in var or "-" in var:
+        varname = split("_|-", var)[0]
+        level = float(split("_|-", var)[1])
     else:
         varname = var
         level = None
@@ -162,8 +210,8 @@
     if varname not in list(regions.keys()):
         regions[varname] = default_regions
 
-    print('varname:', varname)
-    print('level:', level)
+    print("varname:", varname)
+    print("level:", level)
 
     if varname_in_test_data is not None:
         varname_testdata = varname_in_test_data[varname]
@@ -176,128 +224,225 @@
     result_dict["Variable"] = dict()
     result_dict["Variable"]["id"] = varname
     if level is not None:
-        result_dict["Variable"]["level"] = level*100  # hPa to Pa
+        result_dict["Variable"]["level"] = level * 100  # hPa to Pa
 
-    result_dict['References'] = dict()
+    result_dict["References"] = dict()
 
     # ----------------
     # observation loop
     # ----------------
     if "all" in reference_data_set:
-        reference_data_set = [x for x in list(obs_dict[varname].keys()) if (x == "default" or "alternate" in x)]
+        reference_data_set = [
+            x
+            for x in list(obs_dict[varname].keys())
+            if (x == "default" or "alternate" in x)
+        ]
         print("reference_data_set (all): ", reference_data_set)
 
     for ref in reference_data_set:
-        print('ref:', ref)
+        print("ref:", ref)
         # identify data to load (annual cycle (AC) data is loading in)
         ref_dataset_name = obs_dict[varname][ref]
         ref_data_full_path = os.path.join(
-            reference_data_path,
-            obs_dict[varname][ref_dataset_name]["template"])
-        print('ref_data_full_path:', ref_data_full_path)
+            reference_data_path, obs_dict[varname][ref_dataset_name]["template"]
+        )
+        print("ref_data_full_path:", ref_data_full_path)
         # load data and regrid
-        ds_ref = load_and_regrid(data_path=ref_data_full_path, varname=varname, level=level, t_grid=t_grid, decode_times=False, regrid_tool=regrid_tool, debug=debug)
+        ds_ref = load_and_regrid(
+            data_path=ref_data_full_path,
+            varname=varname,
+            level=level,
+            t_grid=t_grid,
+            decode_times=False,
+            regrid_tool=regrid_tool,
+            debug=debug,
+        )
         ds_ref_dict = OrderedDict()
         # for record in output json
-        result_dict['References'][ref] = obs_dict[varname][ref_dataset_name]
+        result_dict["References"][ref] = obs_dict[varname][ref_dataset_name]
 
         # ----------
         # model loop
         # ----------
         for model in test_data_set:
+            print("=================================")
+            print(
+                "model, runs, find_all_realizations:",
+                model,
+                realizations,
+                find_all_realizations,
+            )
 
-            print('=================================')
-            print('model, runs, find_all_realizations:', model, realizations, find_all_realizations)
-
-            result_dict["RESULTS"][model][ref]["source"] = ref_dataset_name 
+            result_dict["RESULTS"][model][ref]["source"] = ref_dataset_name
 
             if find_all_realizations or first_realization_only:
-                test_data_full_path = os.path.join(
-                    test_data_path,
-                    filename_template).replace('%(variable)', varname).replace('%(model)', model).replace('%(model_version)', model).replace('%(realization)', '*')
-                print('test_data_full_path: ', test_data_full_path)
+                test_data_full_path = (
+                    os.path.join(test_data_path, filename_template)
+                    .replace("%(variable)", varname)
+                    .replace("%(model)", model)
+                    .replace("%(model_version)", model)
+                    .replace("%(realization)", "*")
+                )
+                print("test_data_full_path: ", test_data_full_path)
                 ncfiles = sorted(glob.glob(test_data_full_path))
                 realizations = []
                 for ncfile in ncfiles:
-                    realizations.append(ncfile.split('/')[-1].split('.')[3])
+                    realizations.append(ncfile.split("/")[-1].split(".")[3])
                 realizations = sort_human(realizations)
                 if first_realization_only:
                     realizations = realizations[0:1]
-                print('realizations (after search): ', realizations)
+                print("realizations (after search): ", realizations)
 
             for run in realizations:
                 # identify data to load (annual cycle (AC) data is loading in)
-                test_data_full_path = os.path.join(
-                    test_data_path,
-                    filename_template).replace('%(variable)', varname).replace('%(model)', model).replace('%(model_version)', model).replace('%(realization)', run)
+                test_data_full_path = (
+                    os.path.join(test_data_path, filename_template)
+                    .replace("%(variable)", varname)
+                    .replace("%(model)", model)
+                    .replace("%(model_version)", model)
+                    .replace("%(realization)", run)
+                )
                 if os.path.exists(test_data_full_path):
-                    print('-----------------------')
-                    print('model, run:', model, run)
-                    print('test_data (model in this case) full_path:', test_data_full_path)
+                    print("-----------------------")
+                    print("model, run:", model, run)
+                    print(
+                        "test_data (model in this case) full_path:", test_data_full_path
+                    )
                     try:
                         ds_test_dict = OrderedDict()
 
                         # load data and regrid
-                        ds_test = load_and_regrid(data_path=test_data_full_path, varname=varname, varname_in_file=varname_testdata, level=level, t_grid=t_grid, decode_times=True, regrid_tool=regrid_tool, debug=debug)
-                        print('load and regrid done')
+                        ds_test = load_and_regrid(
+                            data_path=test_data_full_path,
+                            varname=varname,
+                            varname_in_file=varname_testdata,
+                            level=level,
+                            t_grid=t_grid,
+                            decode_times=True,
+                            regrid_tool=regrid_tool,
+                            debug=debug,
+                        )
+                        print("load and regrid done")
                         result_dict["RESULTS"][model]["units"] = ds_test[varname].units
-                        result_dict["RESULTS"][model][ref][run]["InputClimatologyFileName"] = test_data_full_path.split('/')[-1] 
+                        result_dict["RESULTS"][model][ref][run][
+                            "InputClimatologyFileName"
+                        ] = test_data_full_path.split("/")[-1]
 
                         # -----------
                         # region loop
                         # -----------
                         for region in regions[varname]:
-                            print('region:', region)
+                            print("region:", region)
 
                             # land/sea mask -- conduct masking only for variable data array, not entire data
-                            if ('land' in region.split('_')) or ('ocean' in region.split('_')):
+                            if ("land" in region.split("_")) or (
+                                "ocean" in region.split("_")
+                            ):
                                 ds_test_tmp = ds_test.copy(deep=True)
                                 ds_ref_tmp = ds_ref.copy(deep=True)
-                                if 'land' in region.split('_'):
-                                    ds_test_tmp[varname] = ds_test[varname].where(t_grid['sftlf'] != 0.)
-                                    ds_ref_tmp[varname] = ds_ref[varname].where(t_grid['sftlf'] != 0.)
-                                elif 'ocean' in region.split('_'):
-                                    ds_test_tmp[varname] = ds_test[varname].where(t_grid['sftlf'] == 0.)
-                                    ds_ref_tmp[varname] = ds_ref[varname].where(t_grid['sftlf'] == 0.)
-                                    print('mask done')
+                                if "land" in region.split("_"):
+                                    ds_test_tmp[varname] = ds_test[varname].where(
+                                        t_grid["sftlf"] != 0.0
+                                    )
+                                    ds_ref_tmp[varname] = ds_ref[varname].where(
+                                        t_grid["sftlf"] != 0.0
+                                    )
+                                elif "ocean" in region.split("_"):
+                                    ds_test_tmp[varname] = ds_test[varname].where(
+                                        t_grid["sftlf"] == 0.0
+                                    )
+                                    ds_ref_tmp[varname] = ds_ref[varname].where(
+                                        t_grid["sftlf"] == 0.0
+                                    )
+                                    print("mask done")
                             else:
                                 ds_test_tmp = ds_test
                                 ds_ref_tmp = ds_ref
 
                             # spatial subset
-                            if region.lower() in ['global', 'land', 'ocean']:
+                            if region.lower() in ["global", "land", "ocean"]:
                                 ds_test_dict[region] = ds_test_tmp
                                 if region not in list(ds_ref_dict.keys()):
                                     ds_ref_dict[region] = ds_ref_tmp
                             else:
-                                ds_test_tmp = region_subset(ds_test_tmp, regions_specs, region=region)
+                                ds_test_tmp = region_subset(
+                                    ds_test_tmp, regions_specs, region=region
+                                )
                                 ds_test_dict[region] = ds_test_tmp
                                 if region not in list(ds_ref_dict.keys()):
-                                    ds_ref_dict[region] = region_subset(ds_ref_tmp, regions_specs, region=region)
-                                print('spatial subset done')
-                                
+                                    ds_ref_dict[region] = region_subset(
+                                        ds_ref_tmp, regions_specs, region=region
+                                    )
+                                print("spatial subset done")
+
                             if save_test_clims and ref == reference_data_set[0]:
                                 test_clims_dir = os.path.join(
-                                    diagnostics_output_path, var, 'interpolated_model_clims')
+                                    diagnostics_output_path,
+                                    var,
+                                    "interpolated_model_clims",
+                                )
                                 os.makedirs(test_clims_dir, exist_ok=True)
                                 test_clims_file = os.path.join(
                                     test_clims_dir,
-                                    '_'.join([var, model, run, 'interpolated', regrid_tool, region, 'AC', case_id + '.nc']))
+                                    "_".join(
+                                        [
+                                            var,
+                                            model,
+                                            run,
+                                            "interpolated",
+                                            regrid_tool,
+                                            region,
+                                            "AC",
+                                            case_id + ".nc",
+                                        ]
+                                    ),
+                                )
                                 ds_test_dict[region].to_netcdf(test_clims_file)
 
                             if debug:
-                                print('ds_test_tmp:', ds_test_tmp)
-                                ds_test_dict[region].to_netcdf('_'.join([var, 'model', model, run, region, case_id + '.nc']))
+                                print("ds_test_tmp:", ds_test_tmp)
+                                ds_test_dict[region].to_netcdf(
+                                    "_".join(
+                                        [
+                                            var,
+                                            "model",
+                                            model,
+                                            run,
+                                            region,
+                                            case_id + ".nc",
+                                        ]
+                                    )
+                                )
                                 if model == test_data_set[0] and run == realizations[0]:
-                                    ds_ref_dict[region].to_netcdf('_'.join([var, 'ref', region + '.nc']))
+                                    ds_ref_dict[region].to_netcdf(
+                                        "_".join([var, "ref", region + ".nc"])
+                                    )
 
                             # compute metrics
-                            print('compute metrics start')
-                            result_dict["RESULTS"][model][ref][run][region] = compute_metrics(varname, ds_test_dict[region], ds_ref_dict[region], debug=debug)
+                            print("compute metrics start")
+                            result_dict["RESULTS"][model][ref][run][
+                                region
+                            ] = compute_metrics(
+                                varname,
+                                ds_test_dict[region],
+                                ds_ref_dict[region],
+                                debug=debug,
+                            )
 
                             # write individual JSON
                             # --- single simulation, obs (need to accumulate later) / single variable
-                            json_filename_tmp = "_".join([var, model, run, target_grid, regrid_tool, "metrics", ref, case_id])
+                            json_filename_tmp = "_".join(
+                                [
+                                    var,
+                                    model,
+                                    run,
+                                    target_grid,
+                                    regrid_tool,
+                                    "metrics",
+                                    ref,
+                                    case_id,
+                                ]
+                            )
                             mean_climate_metrics_to_json(
                                 os.path.join(metrics_output_path, var),
                                 json_filename_tmp,
@@ -305,13 +450,13 @@
                                 model=model,
                                 run=run,
                                 cmec_flag=cmec,
-                                debug=debug
+                                debug=debug,
                             )
- 
+
                     except Exception as e:
                         if debug:
                             raise
-                        print('error occured for ', model, run)
+                        print("error occured for ", model, run)
                         print(e)
 
     # ========================================================================
@@ -327,4 +472,4 @@
             cmec_flag=cmec,
         )
 
-print('pmp mean clim driver completed')
+print("pmp mean clim driver completed")
diff --git a/pcmdi_metrics/mean_climate/param/basic_annual_cycle_param.py b/pcmdi_metrics/mean_climate/param/basic_annual_cycle_param.py
index 29d40c1ea..ad580dcee 100644
--- a/pcmdi_metrics/mean_climate/param/basic_annual_cycle_param.py
+++ b/pcmdi_metrics/mean_climate/param/basic_annual_cycle_param.py
@@ -1,15 +1,15 @@
 # VARIABLES TO USE
-vars = ['pr']
+vars = ["pr"]
 # vars = ['ua', 'ta']
-vars = ['pr', 'ua', 'ta']
+vars = ["pr", "ua", "ta"]
 
 # START AND END DATES FOR CLIMATOLOGY
-start = '1981-01'
+start = "1981-01"
 # end = '1983-12'
-end = '2005-12'
+end = "2005-12"
 
 # INPUT DATASET - CAN BE MODEL OR OBSERVATIONS
-infile = '/work/lee1043/ESGF/E3SMv2/atmos/mon/cmip6.E3SMv2.historical.r1i1p1f1.mon.%(variable).xml'
+infile = "/work/lee1043/ESGF/E3SMv2/atmos/mon/cmip6.E3SMv2.historical.r1i1p1f1.mon.%(variable).xml"
 
 # DIRECTORY WHERE TO PUT RESULTS
-outfile = 'clim/cmip6.historical.E3SMv2.r1i1p1.mon.%(variable).nc'
+outfile = "clim/cmip6.historical.E3SMv2.r1i1p1.mon.%(variable).nc"
diff --git a/pcmdi_metrics/mean_climate/param/basic_param.py b/pcmdi_metrics/mean_climate/param/basic_param.py
index c2d68dae1..cee846ef5 100644
--- a/pcmdi_metrics/mean_climate/param/basic_param.py
+++ b/pcmdi_metrics/mean_climate/param/basic_param.py
@@ -8,44 +8,46 @@
 # RUN IDENTIFICATION
 # DEFINES A SUBDIRECTORY TO METRICS OUTPUT RESULTS SO MULTIPLE CASES CAN
 # BE COMPARED
-case_id = 'v20221130'
+case_id = "v20221130"
 
 # LIST OF MODEL VERSIONS TO BE TESTED - WHICH ARE EXPECTED TO BE PART OF
 # CLIMATOLOGY FILENAME
-test_data_set = ['E3SMv2']
+test_data_set = ["E3SMv2"]
 
 
 # VARIABLES TO USE
 # vars = ['pr', 'ua_850']
 # vars = ['pr']
 # vars = ['ta-850']
-vars = ['ua-850']
+vars = ["ua-850"]
 
 
 # Observations to use at the moment "default" or "alternate"
 # reference_data_set = ['all']
-reference_data_set = ['default']
+reference_data_set = ["default"]
 # ext = '.nc'
 
 # INTERPOLATION OPTIONS
-target_grid = '2.5x2.5'  # OPTIONS: '2.5x2.5' or an actual cdms2 grid object
-regrid_tool = 'regrid2'  # 'regrid2' # OPTIONS: 'regrid2','esmf'
+target_grid = "2.5x2.5"  # OPTIONS: '2.5x2.5' or an actual cdms2 grid object
+regrid_tool = "regrid2"  # 'regrid2' # OPTIONS: 'regrid2','esmf'
 # OPTIONS: 'linear','conservative', only if tool is esmf
-regrid_method = 'linear'
-regrid_tool_ocn = 'esmf'  # OPTIONS: "regrid2","esmf"
+regrid_method = "linear"
+regrid_tool_ocn = "esmf"  # OPTIONS: "regrid2","esmf"
 # OPTIONS: 'linear','conservative', only if tool is esmf
-regrid_method_ocn = 'linear'
+regrid_method_ocn = "linear"
 
 # SAVE INTERPOLATED MODEL CLIMATOLOGIES?
 save_test_clims = True  # True or False
 
 # DIRECTORY WHERE TO PUT INTERPOLATED MODELS' CLIMATOLOGIES
-test_clims_interpolated_output = './interpolated_model_clims'
+test_clims_interpolated_output = "./interpolated_model_clims"
 
 
 # Templates for climatology files
 # %(param) will subsitute param with values in this file
-filename_template = "cmip6.historical.E3SMv2.r1i1p1.mon.%(variable).198101-200512.AC.v20221027.nc"
+filename_template = (
+    "cmip6.historical.E3SMv2.r1i1p1.mon.%(variable).198101-200512.AC.v20221027.nc"
+)
 
 # filename template for landsea masks ('sftlf')
 # sftlf_filename_template = "sftlf_fx_E3SM-1-0_historical_r1i1p1f1_gr.nc"
@@ -70,12 +72,10 @@
 
 # ROOT PATH FOR MODELS CLIMATOLOGIES
 # test_data_path = '/work/lee1043/ESGF/E3SMv2/atmos/mon'
-test_data_path = './clim'
+test_data_path = "./clim"
 # ROOT PATH FOR OBSERVATIONS
 # Note that atm/mo/%(variable)/ac will be added to this
-reference_data_path = '/p/user_pub/PCMDIobs/obs4MIPs_clims'
+reference_data_path = "/p/user_pub/PCMDIobs/obs4MIPs_clims"
 
 # DIRECTORY WHERE TO PUT RESULTS
-metrics_output_path = os.path.join(
-    'output',
-    "%(case_id)")
+metrics_output_path = os.path.join("output", "%(case_id)")
diff --git a/pcmdi_metrics/mean_climate/param/pcmdi_MIP_EXP_pmp_parameterfile.py b/pcmdi_metrics/mean_climate/param/pcmdi_MIP_EXP_pmp_parameterfile.py
index 72e579fe1..df1aa73d3 100755
--- a/pcmdi_metrics/mean_climate/param/pcmdi_MIP_EXP_pmp_parameterfile.py
+++ b/pcmdi_metrics/mean_climate/param/pcmdi_MIP_EXP_pmp_parameterfile.py
@@ -3,36 +3,36 @@
 import os
 import sys
 
-ver = datetime.datetime.now().strftime('v%Y%m%d')
+ver = datetime.datetime.now().strftime("v%Y%m%d")
 
 # ###############################################################################
 #  OPTIONS ARE SET BY USER IN THIS FILE AS INDICATED BELOW BY:
 # ###############################################################################
 case_id = ver
 
-MIP = 'cmip6'  # 'CMIP6'
+MIP = "cmip6"  # 'CMIP6'
 # MIP = 'cmip5'  # 'CMIP6'
 # exp = 'historical'
-exp = 'amip'
+exp = "amip"
 # exp = 'picontrol'
 
 user_notes = "Provenance and results"
-metrics_in_single_file = 'y'  # 'y' or 'n'
+metrics_in_single_file = "y"  # 'y' or 'n'
 
 cmec = False  # True
 
 # ################################################################
 
-if MIP == 'cmip6':
+if MIP == "cmip6":
     # modver = 'v20230202'
-    modver = 'v20230324'
-if MIP == 'cmip5':
+    modver = "v20230324"
+if MIP == "cmip5":
     # modver = 'v20230208'
-    modver = 'v20230324'
+    modver = "v20230324"
 
 # LIST OF MODEL VERSIONS TO BE TESTED - WHICH ARE EXPECTED TO BE PART OF CLIMATOLOGY FILENAME
 
-all_mods_dic = json.load(open('all_mip_mods-' + modver + '.json'))
+all_mods_dic = json.load(open("all_mip_mods-" + modver + ".json"))
 # all_mods_dic = ['E3SM-1-0', 'ACCESS-CM2']
 
 # test_data_set = all_mods_dic
@@ -40,17 +40,20 @@
 test_data_set.sort()
 # test_data_set = ['ACCESS-CM2']
 
-print(len(test_data_set), ' ', test_data_set)
-print('----------------------------------------------------------------')
+print(len(test_data_set), " ", test_data_set)
+print("----------------------------------------------------------------")
 
-simulation_description_mapping = {"creation_date": "creation_date", "tracking_id": 'tracking_id', }
+simulation_description_mapping = {
+    "creation_date": "creation_date",
+    "tracking_id": "tracking_id",
+}
 
 # VARIABLES AND OBSERVATIONS TO USE
 
-realm = 'Amon'
+realm = "Amon"
 # realm = 'Omon'
 
-vars = ['ts', 'pr']
+vars = ["ts", "pr"]
 
 # MODEL SPECIFIC PARAMETERS
 model_tweaks = {
@@ -79,29 +82,33 @@
 # regions_values = {"land": 100., "ocean": 0.}
 
 # Observations to use at the moment "default" or "alternate"
-ref = 'all'
-reference_data_set = ['default']  # ['default']  #, 'alternate1']  #, 'alternate', 'ref3']
-ext = '.xml'  #'.nc'
-ext = '.nc'
+ref = "all"
+reference_data_set = [
+    "default"
+]  # ['default']  #, 'alternate1']  #, 'alternate', 'ref3']
+ext = ".xml"  #'.nc'
+ext = ".nc"
 
 # INTERPOLATION OPTIONS
 
-target_grid = '2.5x2.5'  # OPTIONS: '2.5x2.5' or an actual cdms2 grid object
+target_grid = "2.5x2.5"  # OPTIONS: '2.5x2.5' or an actual cdms2 grid object
 targetGrid = target_grid
-target_grid_string = '2p5x2p5'
-regrid_tool = 'regrid2'  # 'esmf' #'regrid2' # OPTIONS: 'regrid2', 'esmf'
-regrid_method = 'regrid2'  # 'conservative'  #'linear'  # OPTIONS: 'linear', 'conservative', only if tool is esmf
-regrid_tool_ocn = 'esmf'     # OPTIONS: "regrid2", "esmf"
-regrid_method_ocn = 'conservative'  # OPTIONS: 'linear', 'conservative', only if tool is esmf
+target_grid_string = "2p5x2p5"
+regrid_tool = "regrid2"  # 'esmf' #'regrid2' # OPTIONS: 'regrid2', 'esmf'
+regrid_method = "regrid2"  # 'conservative'  #'linear'  # OPTIONS: 'linear', 'conservative', only if tool is esmf
+regrid_tool_ocn = "esmf"  # OPTIONS: "regrid2", "esmf"
+regrid_method_ocn = (
+    "conservative"  # OPTIONS: 'linear', 'conservative', only if tool is esmf
+)
 
 # regrid_tool       = 'esmf' #'esmf' #'regrid2' # OPTIONS: 'regrid2', 'esmf'
 # regrid_method     = 'linear'  #'conservative'  #'linear'  # OPTIONS: 'linear', 'conservative', only if tool is esmf
 
 # SIMULATION PARAMETERg
-period = '1981-2005'
+period = "1981-2005"
 
 # realization = 'r1i1p1f1'
-realization = 'all'
+realization = "all"
 
 # SAVE INTERPOLATED MODEL CLIMATOLOGIES ?
 save_test_clims = True  # True or False
@@ -110,54 +117,130 @@
 # ################################################
 # Templates for climatology files
 
-verd = '*'
-if exp == 'historical' and MIP == 'cmip5':
-    filename_template = MIP + '.historical.%(model).r1i1p1.mon.%(variable).198101-200512.AC.' + modver + '.nc'
-if exp == 'amip' and MIP == 'cmip5':
-    filename_template = MIP + '.amip.%(model).r1i1p1.mon.%(variable).198101-200512.AC.' + modver + '.nc'
-if exp == 'historical' and MIP == 'cmip6':
-    filename_template = MIP + '.historical.%(model).r1i1p1f1.mon.%(variable).198101-200512.AC.' + modver + '.nc'
-if exp == 'amip' and MIP == 'cmip6':
-    filename_template = MIP + '.amip.%(model).r1i1p1f1.mon.%(variable).198101-200512.AC.' + modver + '.nc'
-if exp == 'picontrol':
-    filename_template = "%(variable)_%(model)_%(table)_picontrol_%(exp)_r1i1p1_01-12-clim.nc"
+verd = "*"
+if exp == "historical" and MIP == "cmip5":
+    filename_template = (
+        MIP
+        + ".historical.%(model).r1i1p1.mon.%(variable).198101-200512.AC."
+        + modver
+        + ".nc"
+    )
+if exp == "amip" and MIP == "cmip5":
+    filename_template = (
+        MIP + ".amip.%(model).r1i1p1.mon.%(variable).198101-200512.AC." + modver + ".nc"
+    )
+if exp == "historical" and MIP == "cmip6":
+    filename_template = (
+        MIP
+        + ".historical.%(model).r1i1p1f1.mon.%(variable).198101-200512.AC."
+        + modver
+        + ".nc"
+    )
+if exp == "amip" and MIP == "cmip6":
+    filename_template = (
+        MIP
+        + ".amip.%(model).r1i1p1f1.mon.%(variable).198101-200512.AC."
+        + modver
+        + ".nc"
+    )
+if exp == "picontrol":
+    filename_template = (
+        "%(variable)_%(model)_%(table)_picontrol_%(exp)_r1i1p1_01-12-clim.nc"
+    )
 
 # Templates for MODEL land/sea mask (sftlf)
 # filename template for landsea masks ('sftlf')
 # sftlf_filename_template = "/work/gleckler1/processed_data/cmip5_fixed_fields/sftlf/sftlf_%(model).nc"
 
-generate_sftlf = True    # ESTIMATE LAND SEA MASK IF NOT FOUND
+generate_sftlf = True  # ESTIMATE LAND SEA MASK IF NOT FOUND
 
-sftlf_filename_template = "cmip6.historical.%(model).sftlf.nc"   # "sftlf_%(model).nc"
+sftlf_filename_template = "cmip6.historical.%(model).sftlf.nc"  # "sftlf_%(model).nc"
 
 # ROOT PATH FOR MODELS CLIMATOLOGIES
-test_data_path = '/p/user_pub/pmp/pmp_results/pmp_v1.1.2/diagnostic_results/CMIP_CLIMS/' + MIP + '/' + exp + '/' + modver + '/%(variable)/'
+test_data_path = (
+    "/p/user_pub/pmp/pmp_results/pmp_v1.1.2/diagnostic_results/CMIP_CLIMS/"
+    + MIP
+    + "/"
+    + exp
+    + "/"
+    + modver
+    + "/%(variable)/"
+)
 
 # ROOT PATH FOR OBSERVATIONS
-reference_data_path = '/p/user_pub/PCMDIobs/obs4MIPs_clims/'
-custom_observations = os.path.abspath('/p/user_pub/PCMDIobs/catalogue/obs4MIPs_PCMDI_clims_byVar_catalogue_v20210816.json')
+reference_data_path = "/p/user_pub/PCMDIobs/obs4MIPs_clims/"
+custom_observations = os.path.abspath(
+    "/p/user_pub/PCMDIobs/catalogue/obs4MIPs_PCMDI_clims_byVar_catalogue_v20210816.json"
+)
 # custom_observations = './obs4MIPs_PCMDI_clims_byVar_catalogue_v20210805_ljw.json'
 
-print('CUSTOM OBS ARE ', custom_observations)
+print("CUSTOM OBS ARE ", custom_observations)
 if not os.path.exists(custom_observations):
     sys.exit()
 
 # ######################################
 # DIRECTORY AND FILENAME FOR OUTPUTING METRICS RESULTS
 # BY INDIVIDUAL MODELS
-if metrics_in_single_file != 'y':
-    metrics_output_path = '/p/user_pub/pmp/pmp_results/pmp_v1.1.2/metrics_results/mean_climate/' + MIP + '/' + exp + '/%(case_id)/%(variable)%(level)/'  # INDIVIDUAL MOD FILES
-    output_json_template = '%(model).%(variable)%(level).' + MIP + '.' + exp + '.%(regrid_method).' + target_grid_string + '.' + case_id  # INDIVIDUAL MOD FILES
+if metrics_in_single_file != "y":
+    metrics_output_path = (
+        "/p/user_pub/pmp/pmp_results/pmp_v1.1.2/metrics_results/mean_climate/"
+        + MIP
+        + "/"
+        + exp
+        + "/%(case_id)/%(variable)%(level)/"
+    )  # INDIVIDUAL MOD FILES
+    output_json_template = (
+        "%(model).%(variable)%(level)."
+        + MIP
+        + "."
+        + exp
+        + ".%(regrid_method)."
+        + target_grid_string
+        + "."
+        + case_id
+    )  # INDIVIDUAL MOD FILES
 # ALL MODELS IN ONE FILE
-if metrics_in_single_file == 'y':
-    metrics_output_path = '/p/user_pub/pmp/pmp_results/pmp_v1.1.2/metrics_results/mean_climate/' + MIP + '/' + exp + '/%(case_id)/'  # All SAME FILE
-    output_json_template = '%(variable)%(level).' + MIP + '.' + exp + '.%(regrid_method).' + target_grid_string + '.' + case_id  # ALL SAME FILE
+if metrics_in_single_file == "y":
+    metrics_output_path = (
+        "/p/user_pub/pmp/pmp_results/pmp_v1.1.2/metrics_results/mean_climate/"
+        + MIP
+        + "/"
+        + exp
+        + "/%(case_id)/"
+    )  # All SAME FILE
+    output_json_template = (
+        "%(variable)%(level)."
+        + MIP
+        + "."
+        + exp
+        + ".%(regrid_method)."
+        + target_grid_string
+        + "."
+        + case_id
+    )  # ALL SAME FILE
 # #######################################
 
 # DIRECTORY WHERE TO PUT INTERPOLATED MODELS' CLIMATOLOGIES
-test_clims_interpolated_output = '/p/user_pub/pmp/pmp_results/pmp_v1.1.2/diagnostic_results' + '/interpolated_model_clims/' + MIP + '/' + exp + '/' + case_id
+test_clims_interpolated_output = (
+    "/p/user_pub/pmp/pmp_results/pmp_v1.1.2/diagnostic_results"
+    + "/interpolated_model_clims/"
+    + MIP
+    + "/"
+    + exp
+    + "/"
+    + case_id
+)
 
 # FILENAME FOR INTERPOLATED CLIMATOLGIES OUTPUT
-filename_output_template = MIP + ".%(model)." + exp + "." + realization + ".mo.%(variable)%(level).%(period).interpolated.%(regrid_method).%(region).AC." + case_id + "%(ext)"
+filename_output_template = (
+    MIP
+    + ".%(model)."
+    + exp
+    + "."
+    + realization
+    + ".mo.%(variable)%(level).%(period).interpolated.%(regrid_method).%(region).AC."
+    + case_id
+    + "%(ext)"
+)
 
 debug = False
diff --git a/pcmdi_metrics/mean_climate/pcmdi_compute_climatologies.py b/pcmdi_metrics/mean_climate/pcmdi_compute_climatologies.py
index 2cb1285ca..65fa04f4e 100755
--- a/pcmdi_metrics/mean_climate/pcmdi_compute_climatologies.py
+++ b/pcmdi_metrics/mean_climate/pcmdi_compute_climatologies.py
@@ -4,9 +4,8 @@
 
 from genutil import StringConstructor
 
-from pcmdi_metrics.mean_climate.lib.pmp_parser import PMPMetricsParser
 from pcmdi_metrics.mean_climate.lib import calculate_climatology
-
+from pcmdi_metrics.mean_climate.lib.pmp_parser import PMPMetricsParser
 
 ver = datetime.datetime.now().strftime("v%Y%m%d")
 
@@ -31,9 +30,19 @@
 )
 P.add_argument("--end", dest="end", help="Defines end year and month", required=False)
 
-P.add_argument("--periodinname", dest="periodinname", help="Include clim period in name (default yes) or not", required=False)
+P.add_argument(
+    "--periodinname",
+    dest="periodinname",
+    help="Include clim period in name (default yes) or not",
+    required=False,
+)
 
-P.add_argument("--climlist", dest="climlist", help="Defines list of clim seasons to output (default='all')", required=False)
+P.add_argument(
+    "--climlist",
+    dest="climlist",
+    help="Defines list of clim seasons to output (default='all')",
+    required=False,
+)
 
 args = P.get_parameter()
 
@@ -67,11 +76,22 @@
     outfilename = OutFileName()
     outpath = OutPath()
 
-    print('var:', var)
-    print('infile:', infile)
-    print('outfile:', outfile)
-    print('outfilename:', outfilename)
-    print('outpath:', outpath)
+    print("var:", var)
+    print("infile:", infile)
+    print("outfile:", outfile)
+    print("outfilename:", outfilename)
+    print("outpath:", outpath)
 
     # calculate climatologies for this variable
-    calculate_climatology(var, infile, outfile, outpath, outfilename, start, end, ver, periodinname, climlist)
+    calculate_climatology(
+        var,
+        infile,
+        outfile,
+        outpath,
+        outfilename,
+        start,
+        end,
+        ver,
+        periodinname,
+        climlist,
+    )
diff --git a/pcmdi_metrics/mean_climate/scripts/allvars_parallel_mod_clims.py b/pcmdi_metrics/mean_climate/scripts/allvars_parallel_mod_clims.py
index 2e40d3643..97e2d645f 100644
--- a/pcmdi_metrics/mean_climate/scripts/allvars_parallel_mod_clims.py
+++ b/pcmdi_metrics/mean_climate/scripts/allvars_parallel_mod_clims.py
@@ -10,47 +10,90 @@ def find_latest(path):
     return sorted(dir_list)[-1]
 
 
-mip = 'cmip5'
+mip = "cmip5"
 # mip = 'cmip6'
 # exp = 'historical'
-exp = 'amip'
+exp = "amip"
 
 data_path = find_latest("/p/user_pub/pmp/pmp_results/pmp_v1.1.2/additional_xmls/latest")
-pathout_base = '/p/user_pub/pmp/pmp_results/pmp_v1.1.2/diagnostic_results/CMIP_CLIMS/' + mip + '/' + exp + '/'
-start = '1981-01'
-end = '2005-12'
+pathout_base = (
+    "/p/user_pub/pmp/pmp_results/pmp_v1.1.2/diagnostic_results/CMIP_CLIMS/"
+    + mip
+    + "/"
+    + exp
+    + "/"
+)
+start = "1981-01"
+end = "2005-12"
 numw = 20  # number of workers in parallel processing
-verout = datetime.datetime.now().strftime('v%Y%m%d')
-
-vars = ['hur', 'hurs', 'huss', 'pr', 'prw', 'psl', 'rlds', 'rldscs', 'rlus', 'rlut', 'rlutcs', 'rsds', 'rsdscs', 'rsdt', 'rsus', 'rsut', 'rsutcs', 'sfcWind', 'ta', 'tas', 'tauu', 'tauv', 'ts', 'ua', 'uas', 'va', 'vas', 'wap', 'zg']
+verout = datetime.datetime.now().strftime("v%Y%m%d")
+
+vars = [
+    "hur",
+    "hurs",
+    "huss",
+    "pr",
+    "prw",
+    "psl",
+    "rlds",
+    "rldscs",
+    "rlus",
+    "rlut",
+    "rlutcs",
+    "rsds",
+    "rsdscs",
+    "rsdt",
+    "rsus",
+    "rsut",
+    "rsutcs",
+    "sfcWind",
+    "ta",
+    "tas",
+    "tauu",
+    "tauv",
+    "ts",
+    "ua",
+    "uas",
+    "va",
+    "vas",
+    "wap",
+    "zg",
+]
 
 lst1 = []
 listlog = []
 
 for var in vars:
-    pin = os.path.join(data_path, mip, exp,'atmos', 'mon', var)
-    lst = sorted(glob.glob(os.path.join(pin, '*r1i1p1*.xml')))
+    pin = os.path.join(data_path, mip, exp, "atmos", "mon", var)
+    lst = sorted(glob.glob(os.path.join(pin, "*r1i1p1*.xml")))
 
     pathoutdir = os.path.join(pathout_base, verout, var)
     os.makedirs(pathoutdir, exist_ok=True)
 
     for li in lst:
-
-        print(li.split('.'))
-        mod = li.split('.')[4]  # model
-        rn = li.split('.')[5]  # realization
-
-        outfilename = mip + '.' + exp + '.' + mod + '.' + rn + '.mon.' + var + '.nc'
-        cmd0 = "pcmdi_compute_climatologies.py --start " + start + " --end " + end + " --infile "
-
-        pathout = pathoutdir + '/' + outfilename
-        cmd = cmd0 + li + ' --outfile ' + pathout + ' --var ' + var
+        print(li.split("."))
+        mod = li.split(".")[4]  # model
+        rn = li.split(".")[5]  # realization
+
+        outfilename = mip + "." + exp + "." + mod + "." + rn + ".mon." + var + ".nc"
+        cmd0 = (
+            "pcmdi_compute_climatologies.py --start "
+            + start
+            + " --end "
+            + end
+            + " --infile "
+        )
+
+        pathout = pathoutdir + "/" + outfilename
+        cmd = cmd0 + li + " --outfile " + pathout + " --var " + var
 
         lst1.append(cmd)
-        logf = mod + '.' + rn + '.' + var
+        logf = mod + "." + rn + "." + var
         listlog.append(logf)
         print(logf)
 
-print('Number of jobs starting is ', str(len(lst1)))
-parallel_submitter(lst1, log_dir='./logs/' + verout, logfilename_list=listlog, num_workers=numw)
-print('done submitting')
+print("Number of jobs starting is ", str(len(lst1)))
+parallel_submitter(
+    lst1, log_dir="./logs/" + verout, logfilename_list=listlog, num_workers=numw
+)
+print("done submitting")
diff --git a/pcmdi_metrics/mean_climate/scripts/get_all_MIP_mods_from_CLIMS.py b/pcmdi_metrics/mean_climate/scripts/get_all_MIP_mods_from_CLIMS.py
index d913f5b70..32a98dfde 100755
--- a/pcmdi_metrics/mean_climate/scripts/get_all_MIP_mods_from_CLIMS.py
+++ b/pcmdi_metrics/mean_climate/scripts/get_all_MIP_mods_from_CLIMS.py
@@ -1,32 +1,36 @@
 import glob
 import json
 
-ver = 'v20230324'
+ver = "v20230324"
 
-pin = '/p/user_pub/pmp/pmp_results/pmp_v1.1.2/diagnostic_results/CMIP_CLIMS/%(MIP)/%(EXP)/' + ver + '/ts/'
+pin = (
+    "/p/user_pub/pmp/pmp_results/pmp_v1.1.2/diagnostic_results/CMIP_CLIMS/%(MIP)/%(EXP)/"
+    + ver
+    + "/ts/"
+)
 
-MIPS = ['cmip6', 'cmip5']
+MIPS = ["cmip6", "cmip5"]
 # exps = ['historical', 'amip']
-exps = ['amip']
+exps = ["amip"]
 
 mod_dic = {}
 
 for mip in MIPS:
     mod_dic[mip] = {}
     for exp in exps:
-        ptmp = pin.replace('%(MIP)', mip).replace('%(EXP)', exp)
-        print('MIP: ', mip)
-        print('exp: ', exp)
-        print('dir: ', ptmp)
+        ptmp = pin.replace("%(MIP)", mip).replace("%(EXP)", exp)
+        print("MIP: ", mip)
+        print("exp: ", exp)
+        print("dir: ", ptmp)
 
-        lst = sorted(glob.glob(ptmp + '*.r1*.AC.' + ver + '.nc'))
+        lst = sorted(glob.glob(ptmp + "*.r1*.AC." + ver + ".nc"))
         mods = []
         for li in lst:
-            mod = li.split('.')[4]
+            mod = li.split(".")[4]
             if mod not in mods:
                 mods.append(mod)
 
         print(mods)
         mod_dic[mip][exp] = sorted(mods)
 
-json.dump(mod_dic, open('all_mip_mods-' + ver + '.json', 'w'), indent=4, sort_keys=True)
+json.dump(mod_dic, open("all_mip_mods-" + ver + ".json", "w"), indent=4, sort_keys=True)
diff --git a/pcmdi_metrics/mean_climate/scripts/mk_CRF_clims.py b/pcmdi_metrics/mean_climate/scripts/mk_CRF_clims.py
index 6237ebe29..47c554a76 100755
--- a/pcmdi_metrics/mean_climate/scripts/mk_CRF_clims.py
+++ b/pcmdi_metrics/mean_climate/scripts/mk_CRF_clims.py
@@ -6,50 +6,57 @@
 import cdms2 as cdms
 import MV2 as MV
 
-cdms.setAutoBounds('on')
+cdms.setAutoBounds("on")
 
 cdms.setNetcdfShuffleFlag(0)
 cdms.setNetcdfDeflateFlag(0)
 cdms.setNetcdfDeflateLevelFlag(0)
 
-#exp = 'historical'
-exp = 'amip'
+# exp = 'historical'
+exp = "amip"
 
-MIP = 'cmip6'   # 'CMIP6'
+MIP = "cmip6"  # 'CMIP6'
 # MIP = 'cmip5'   # 'CMIP5'
 
-if MIP == 'cmip6':
-    ver = 'v20230324'
-if MIP == 'cmip5':
-    ver = 'v20230324'
+if MIP == "cmip6":
+    ver = "v20230324"
+if MIP == "cmip5":
+    ver = "v20230324"
 
 # NEED TO RUN SEPERATELY FOR LW AND SW (i.e., rsut and rlut)
 # radvar = 'rsut'
-radvar = 'rlut'
+radvar = "rlut"
 
-pit = '/p/user_pub/pmp/pmp_results/pmp_v1.1.2/diagnostic_results/CMIP_CLIMS/' + MIP + '/' + exp + '/' + ver + '/'
-pi = pit + radvar + 'cs/'
+pit = (
+    "/p/user_pub/pmp/pmp_results/pmp_v1.1.2/diagnostic_results/CMIP_CLIMS/"
+    + MIP
+    + "/"
+    + exp
+    + "/"
+    + ver
+    + "/"
+)
+pi = pit + radvar + "cs/"
 
-lst = sorted(glob.glob(pi + '*' + radvar + 'cs' '*.nc'))
+lst = sorted(glob.glob(pi + "*" + radvar + "cs" "*.nc"))
 
 for lc in lst:
     try:
-        li = lc.replace(radvar + 'cs', radvar)
+        li = lc.replace(radvar + "cs", radvar)
 
         if os.path.isfile(li):
+            if radvar == "rsut":
+                fixname = "rstcre"
+            elif radvar == "rlut":
+                fixname = "rltcre"
 
-            if radvar == 'rsut':
-                fixname = 'rstcre'
-            elif radvar == 'rlut':
-                fixname = 'rltcre'
-
-            os.makedirs(pi.replace(radvar + 'cs', fixname), exist_ok=True)
+            os.makedirs(pi.replace(radvar + "cs", fixname), exist_ok=True)
 
             f = cdms.open(li)
             d = f(radvar)
             fc = cdms.open(lc)
             att_keys = fc.attributes.keys()
-            dc = fc(radvar + 'cs')
+            dc = fc(radvar + "cs")
             f.close()
             fc.close()
 
@@ -64,33 +71,33 @@
 
             lo = li.replace(radvar, fixname)
 
-            g = cdms.open(lo, 'w+')
+            g = cdms.open(lo, "w+")
             for att in f.attributes.keys():
                 setattr(g, att, f.attributes[att])
             g.write(cre)
             g.close()
 
-            print('done with ', lo)
+            print("done with ", lo)
 
-            if radvar == 'rsut':
-                l1 = lc.replace('rsutcs', 'rsdt')
+            if radvar == "rsut":
+                l1 = lc.replace("rsutcs", "rsdt")
 
             try:
                 f1 = cdms.open(l1)
-                d1 = f1('rsdt')
+                d1 = f1("rsdt")
                 # dif = -1.*d1
                 dif = MV.subtract(d1, d)
 
-                dif.units = 'W m-2'
-                dif.id = 'rst'
+                dif.units = "W m-2"
+                dif.id = "rst"
 
-                l2 = l1.replace('rsdt', 'rst')
+                l2 = l1.replace("rsdt", "rst")
 
-                os.makedirs(pit + '/rst', exist_ok=True)
+                os.makedirs(pit + "/rst", exist_ok=True)
 
-                print('starting ', l2)
+                print("starting ", l2)
 
-                g = cdms.open(l2, 'w+')
+                g = cdms.open(l2, "w+")
 
                 for att in f1.attributes.keys():
                     setattr(g, att, f1.attributes[att])
@@ -102,31 +109,31 @@
                 f1.close()
 
             except Exception:
-                print('no rsdt ')  # for ', l1
+                print("no rsdt ")  # for ', l1
 
             # ### AND FINALLY, THE NET
             try:
-                lw = l2.replace('rst', 'rlut')
+                lw = l2.replace("rst", "rlut")
                 f3 = cdms.open(lw)
-                d3 = f3('rlut')
+                d3 = f3("rlut")
 
                 net = MV.subtract(dif, d3)
-                net.id = 'rt'
+                net.id = "rt"
 
-                os.makedirs(pit + '/rt', exist_ok=True)
+                os.makedirs(pit + "/rt", exist_ok=True)
 
-                ln = lw.replace('rlut', 'rt')
+                ln = lw.replace("rlut", "rt")
 
-                g3 = cdms.open(ln, 'w+')
+                g3 = cdms.open(ln, "w+")
                 for att in f3.attributes.keys():
                     setattr(g3, att, f3.attributes[att])
 
                 g3.write(net)
-                print('done with ', ln)
+                print("done with ", ln)
                 f3.close()
                 g3.close()
             except Exception:
-                print('not working for ', lc)
+                print("not working for ", lc)
     except Exception:
-        print('not working for -----', lc)
+        print("not working for -----", lc)
     pass
diff --git a/pcmdi_metrics/mean_climate/scripts/pcmdi_compute_climatologies-CMOR.py b/pcmdi_metrics/mean_climate/scripts/pcmdi_compute_climatologies-CMOR.py
index 24c6d9bfc..11e81c7f7 100644
--- a/pcmdi_metrics/mean_climate/scripts/pcmdi_compute_climatologies-CMOR.py
+++ b/pcmdi_metrics/mean_climate/scripts/pcmdi_compute_climatologies-CMOR.py
@@ -448,7 +448,6 @@ def store_attributes(var):
 
 
 def runClim(A):
-
     print("OK SO START IS:", A.start)
     # season dictionary
     season_function = {
diff --git a/pcmdi_metrics/mean_climate/scripts/post_process_merge_jsons.py b/pcmdi_metrics/mean_climate/scripts/post_process_merge_jsons.py
index 4ff3286f9..c1af7aed1 100755
--- a/pcmdi_metrics/mean_climate/scripts/post_process_merge_jsons.py
+++ b/pcmdi_metrics/mean_climate/scripts/post_process_merge_jsons.py
@@ -17,7 +17,7 @@ def main():
     # mips = ['cmip3']
 
     # exps = ['historical', 'amip']
-    exps = ['historical']
+    exps = ["historical"]
     # exps = ["amip"]
     # exps = ['20c3m', 'amip']
     # exps = ['20c3m']
@@ -35,19 +35,36 @@ def main():
 
     for mip in mips:
         for exp in exps:
-            variables = [s.split('/')[-1] for s in glob.glob(os.path.join(pmprdir, "metrics_results", "mean_climate", mip, exp, case_id, "*")) if os.path.isdir(s)]
+            variables = [
+                s.split("/")[-1]
+                for s in glob.glob(
+                    os.path.join(
+                        pmprdir,
+                        "metrics_results",
+                        "mean_climate",
+                        mip,
+                        exp,
+                        case_id,
+                        "*",
+                    )
+                )
+                if os.path.isdir(s)
+            ]
             print("variables:", variables)
             for var in variables:
                 # json merge
-                #try:
+                # try:
                 if 1:
-                    merge_json(mip, exp, case_id, var, obs_selection, syear, eyear, pmprdir)
+                    merge_json(
+                        mip, exp, case_id, var, obs_selection, syear, eyear, pmprdir
+                    )
                 """
                 except Exception as err:
                     print("ERROR: ", mip, exp, var, err)
                     pass
                 """
 
+
 def merge_json(mip, exp, case_id, var, obs, syear, eyear, pmprdir):
     json_file_dir_template = (
         "metrics_results/mean_climate/%(mip)/%(exp)/%(case_id)/%(var)"
@@ -58,7 +75,7 @@ def merge_json(mip, exp, case_id, var, obs, syear, eyear, pmprdir):
         json_file_dir_template(mip=mip, exp=exp, case_id=case_id, var=var),
     )
 
-    print('json_file_dir:', json_file_dir)
+    print("json_file_dir:", json_file_dir)
 
     json_file_template = "%(model)_%(var)_*_%(obs).json"
     json_file_template = StringConstructor(json_file_template)
@@ -80,7 +97,7 @@ def merge_json(mip, exp, case_id, var, obs, syear, eyear, pmprdir):
         )
     )
 
-    print('json_files:', json_files)
+    print("json_files:", json_files)
 
     # Remove diveDown JSONs and previously generated merged JSONs if included
     json_files_revised = copy.copy(json_files)
@@ -103,7 +120,9 @@ def merge_json(mip, exp, case_id, var, obs, syear, eyear, pmprdir):
         f.close()
 
     # Dump final dictionary to JSON
-    final_json_filename = StringConstructor("%(var)_%(mip)_%(exp)_%(case_id).json")(var=var, mip=mip, exp=exp, case_id=case_id)
+    final_json_filename = StringConstructor("%(var)_%(mip)_%(exp)_%(case_id).json")(
+        var=var, mip=mip, exp=exp, case_id=case_id
+    )
     final_json_file = os.path.join(json_file_dir, "..", final_json_filename)
 
     with open(final_json_file, "w") as fp:
diff --git a/pcmdi_metrics/misc/scripts/parallel_submitter.py b/pcmdi_metrics/misc/scripts/parallel_submitter.py
index 090f11d8a..9b981e1f3 100644
--- a/pcmdi_metrics/misc/scripts/parallel_submitter.py
+++ b/pcmdi_metrics/misc/scripts/parallel_submitter.py
@@ -108,7 +108,7 @@ def check_for_done(processes):
 def main():
     cmd_list = ["expr 1 + " + str(r) for r in range(1, 10)]
     logfilename_list = ["log_" + str(r) for r in range(1, 10)]
-    for (process, log_file) in zip(cmd_list, logfilename_list):
+    for process, log_file in zip(cmd_list, logfilename_list):
         print(process, "\t", log_file)
     num_workers = 2
     parallel_submitter(
diff --git a/pcmdi_metrics/mjo/lib/debug_chk_plot.py b/pcmdi_metrics/mjo/lib/debug_chk_plot.py
index eb30129a2..656f545e9 100644
--- a/pcmdi_metrics/mjo/lib/debug_chk_plot.py
+++ b/pcmdi_metrics/mjo/lib/debug_chk_plot.py
@@ -6,7 +6,6 @@
 
 
 def debug_chk_plot(d_seg_x_ano, Power, OEE, segment_year, daSeaCyc, segment_ano_year):
-
     os.makedirs("debug", exist_ok=True)
 
     """ FIX ME ---
diff --git a/pcmdi_metrics/mjo/lib/mjo_metric_calc.py b/pcmdi_metrics/mjo/lib/mjo_metric_calc.py
index 49f9acc58..a7a1c87c8 100644
--- a/pcmdi_metrics/mjo/lib/mjo_metric_calc.py
+++ b/pcmdi_metrics/mjo/lib/mjo_metric_calc.py
@@ -41,7 +41,6 @@ def mjo_metric_ewr_calculation(
     outdir,
     season="NDJFMA",
 ):
-
     # Open file to read daily dataset
     if debug:
         print("debug: open file")
@@ -167,7 +166,7 @@ def mjo_metric_ewr_calculation(
     if plot:
         os.makedirs(outdir(output_type="graphics"), exist_ok=True)
         fout = os.path.join(outdir(output_type="graphics"), output_filename)
-        if model == 'obs':
+        if model == "obs":
             title = (
                 " OBS ("
                 + run
diff --git a/pcmdi_metrics/mjo/lib/plot_wavenumber_frequency_power.py b/pcmdi_metrics/mjo/lib/plot_wavenumber_frequency_power.py
index e9126998c..d60683fd3 100644
--- a/pcmdi_metrics/mjo/lib/plot_wavenumber_frequency_power.py
+++ b/pcmdi_metrics/mjo/lib/plot_wavenumber_frequency_power.py
@@ -8,7 +8,6 @@
 
 
 def plot_power(d, title, fout, ewr=None):
-
     y = d.getAxis(0)[:]
     x = d.getAxis(1)[:]
 
@@ -119,7 +118,6 @@ def plot_power(d, title, fout, ewr=None):
 
 
 if __name__ == "__main__":
-
     datadir = "/p/user_pub/pmp/pmp_results/pmp_v1.1.2/diagnostic_results/mjo/cmip5/historical/v20190628"
     ncfile = "cmip5_obs_historical_obs_mjo_1997-2010_cmmGrid.nc"
     title = "OBS (GPCP-1-3) \n Pr, NDJFMA, 1997-2010, common grid (2.5x2.5deg)"
diff --git a/pcmdi_metrics/mjo/lib/post_process_plot.py b/pcmdi_metrics/mjo/lib/post_process_plot.py
index b92a8f697..bea7ca873 100644
--- a/pcmdi_metrics/mjo/lib/post_process_plot.py
+++ b/pcmdi_metrics/mjo/lib/post_process_plot.py
@@ -7,7 +7,6 @@
 
 
 def main():
-
     # mip = 'cmip5'
     mip = "cmip6"
     exp = "historical"
diff --git a/pcmdi_metrics/mjo/mjo_metrics_driver.py b/pcmdi_metrics/mjo/mjo_metrics_driver.py
index 9f6277e9c..5e6028612 100755
--- a/pcmdi_metrics/mjo/mjo_metrics_driver.py
+++ b/pcmdi_metrics/mjo/mjo_metrics_driver.py
@@ -45,6 +45,7 @@
 from genutil import StringConstructor
 
 import pcmdi_metrics
+from pcmdi_metrics.mean_climate.lib import pmp_parser
 from pcmdi_metrics.mjo.lib import (
     AddParserArgument,
     YearCheck,
@@ -52,9 +53,6 @@
     mjo_metrics_to_json,
 )
 
-from pcmdi_metrics.mean_climate.lib import pmp_parser
-
-
 # To avoid below error
 # OpenBLAS blas_thread_init: pthread_create failed for thread XX of 96: Resource temporarily unavailable
 # os.environ['OPENBLAS_NUM_THREADS'] = '1'
@@ -109,9 +107,9 @@
 
 # Include all models if conditioned
 if ("all" in [m.lower() for m in models]) or (models == "all"):
-    model_index_path = re.split('. |_', param.modpath.split("/")[-1]).index("%(model)")
+    model_index_path = re.split(". |_", param.modpath.split("/")[-1]).index("%(model)")
     models = [
-        re.split('. |_', p.split("/")[-1])[model_index_path]
+        re.split(". |_", p.split("/")[-1])[model_index_path]
         for p in glob.glob(
             modpath(mip=mip, exp=exp, model="*", realization="*", variable=varModel)
         )
@@ -264,8 +262,10 @@ def tree():
                     run = reference_data_name
                 else:
                     if realization in ["all", "All", "ALL", "*"]:
-                        run_index = re.split('. |_', param.modpath.split("/")[-1]).index("%(realization)")
-                        run = re.split('. |_', model_path.split("/")[-1])[run_index]
+                        run_index = re.split(
+                            ". |_", param.modpath.split("/")[-1]
+                        ).index("%(realization)")
+                        run = re.split(". |_", model_path.split("/")[-1])[run_index]
                     else:
                         run = realization
                     # dict
diff --git a/pcmdi_metrics/mjo/param/myParam_mjo_highresmip.py b/pcmdi_metrics/mjo/param/myParam_mjo_highresmip.py
index 4cbf63f8d..898f0f15b 100644
--- a/pcmdi_metrics/mjo/param/myParam_mjo_highresmip.py
+++ b/pcmdi_metrics/mjo/param/myParam_mjo_highresmip.py
@@ -30,7 +30,7 @@ def find_latest(path):
 # Observation
 # -------------------------------------------------
 reference_data_name = "GPCP-1-3"
-#reference_data_path = "/p/user_pub/PCMDIobs/PCMDIobs2/atmos/day/pr/GPCP-1-3/gn/v20200707/pr_day_GPCP-1-3_BE_gn_v20200707_19961002-20170101.nc"  # noqa
+# reference_data_path = "/p/user_pub/PCMDIobs/PCMDIobs2/atmos/day/pr/GPCP-1-3/gn/v20200707/pr_day_GPCP-1-3_BE_gn_v20200707_19961002-20170101.nc"  # noqa
 reference_data_path = "/global/homes/l/lee1043/DATA/pr_day_GPCP-1-3_BE_gn_v20200707_19961002-20170101.nc"  # noqa
 
 varOBS = "pr"
diff --git a/pcmdi_metrics/monsoon_wang/graphics/SeabarChart_mpl.py b/pcmdi_metrics/monsoon_wang/graphics/SeabarChart_mpl.py
index a1cf3b9a4..0e187360e 100644
--- a/pcmdi_metrics/monsoon_wang/graphics/SeabarChart_mpl.py
+++ b/pcmdi_metrics/monsoon_wang/graphics/SeabarChart_mpl.py
@@ -4,7 +4,6 @@
 
 class BarChart(object):
     def __init__(self, mods, data, uni, fig=None, rect=111, **kwargs):
-
         print("IN FCN..... ")
 
         # Canvas setup
diff --git a/pcmdi_metrics/monsoon_wang/monsoon_wang_driver.py b/pcmdi_metrics/monsoon_wang/monsoon_wang_driver.py
index 581bd80aa..688cb763c 100644
--- a/pcmdi_metrics/monsoon_wang/monsoon_wang_driver.py
+++ b/pcmdi_metrics/monsoon_wang/monsoon_wang_driver.py
@@ -10,8 +10,8 @@
 
 import pcmdi_metrics
 from pcmdi_metrics import resources
-from pcmdi_metrics.monsoon_wang import mpd, mpi_skill_scores
 from pcmdi_metrics.mean_climate.lib.pmp_parser import PMPParser
+from pcmdi_metrics.monsoon_wang import mpd, mpi_skill_scores
 
 
 def create_monsoon_wang_parser():
@@ -198,7 +198,6 @@ def monsoon_wang_runner(args):
         )
 
         for dom in doms:
-
             mpi_stats_dic[mod][dom] = {}
 
             reg_sel = regions_specs[dom]["domain"]
diff --git a/pcmdi_metrics/monsoon_wang/scripts/bar_chart_monsoon_precip_index.py b/pcmdi_metrics/monsoon_wang/scripts/bar_chart_monsoon_precip_index.py
index 4e5e02b48..c5dc61ecc 100644
--- a/pcmdi_metrics/monsoon_wang/scripts/bar_chart_monsoon_precip_index.py
+++ b/pcmdi_metrics/monsoon_wang/scripts/bar_chart_monsoon_precip_index.py
@@ -6,8 +6,8 @@
 import MV2
 from SeabarChart_mpl import BarChart
 
-from pcmdi_metrics.mean_climate.lib import pmp_parser
 from pcmdi_metrics.io.base import JSONs
+from pcmdi_metrics.mean_climate.lib import pmp_parser
 
 test = False
 # test = True
diff --git a/pcmdi_metrics/precip_distribution/param/precip_distribution_params_CFSR.py b/pcmdi_metrics/precip_distribution/param/precip_distribution_params_CFSR.py
index 9fbe16599..da3a3427d 100644
--- a/pcmdi_metrics/precip_distribution/param/precip_distribution_params_CFSR.py
+++ b/pcmdi_metrics/precip_distribution/param/precip_distribution_params_CFSR.py
@@ -22,7 +22,21 @@
 case_id = "{:v%Y%m%d}".format(datetime.datetime.now())
 # case_id = ver
 pmpdir = "/p/user_pub/pmp/pmp_results/pmp_v1.1.2"
-results_dir = os.path.join(pmpdir, "%(output_type)", "precip", "%(mip)", "%(case_id)", "intensity.frequency_distribution")
+results_dir = os.path.join(
+    pmpdir,
+    "%(output_type)",
+    "precip",
+    "%(mip)",
+    "%(case_id)",
+    "intensity.frequency_distribution",
+)
 
 ref = "IMERG-V06-FU"  # For Perkins socre, P10, and P90
-ref_dir = os.path.join(pmpdir, "%(output_type)", "precip", "obs", "%(case_id)", "intensity.frequency_distribution")
+ref_dir = os.path.join(
+    pmpdir,
+    "%(output_type)",
+    "precip",
+    "obs",
+    "%(case_id)",
+    "intensity.frequency_distribution",
+)
diff --git a/pcmdi_metrics/precip_distribution/param/precip_distribution_params_CMORPH.py b/pcmdi_metrics/precip_distribution/param/precip_distribution_params_CMORPH.py
index 0a4227bf6..fa3b990bc 100644
--- a/pcmdi_metrics/precip_distribution/param/precip_distribution_params_CMORPH.py
+++ b/pcmdi_metrics/precip_distribution/param/precip_distribution_params_CMORPH.py
@@ -22,7 +22,21 @@
 case_id = "{:v%Y%m%d}".format(datetime.datetime.now())
 # case_id = ver
 pmpdir = "/p/user_pub/pmp/pmp_results/pmp_v1.1.2"
-results_dir = os.path.join(pmpdir, "%(output_type)", "precip", "%(mip)", "%(case_id)", "intensity.frequency_distribution")
+results_dir = os.path.join(
+    pmpdir,
+    "%(output_type)",
+    "precip",
+    "%(mip)",
+    "%(case_id)",
+    "intensity.frequency_distribution",
+)
 
 ref = "IMERG-V06-FU"  # For Perkins socre, P10, and P90
-ref_dir = os.path.join(pmpdir, "%(output_type)", "precip", "obs", "%(case_id)", "intensity.frequency_distribution")
+ref_dir = os.path.join(
+    pmpdir,
+    "%(output_type)",
+    "precip",
+    "obs",
+    "%(case_id)",
+    "intensity.frequency_distribution",
+)
diff --git a/pcmdi_metrics/precip_distribution/param/precip_distribution_params_ERA-Interim.py b/pcmdi_metrics/precip_distribution/param/precip_distribution_params_ERA-Interim.py
index a88a88e5a..7eba324cf 100644
--- a/pcmdi_metrics/precip_distribution/param/precip_distribution_params_ERA-Interim.py
+++ b/pcmdi_metrics/precip_distribution/param/precip_distribution_params_ERA-Interim.py
@@ -23,7 +23,21 @@
 case_id = "{:v%Y%m%d}".format(datetime.datetime.now())
 # case_id = ver
 pmpdir = "/p/user_pub/pmp/pmp_results/pmp_v1.1.2"
-results_dir = os.path.join(pmpdir, "%(output_type)", "precip", "%(mip)", "%(case_id)", "intensity.frequency_distribution")
+results_dir = os.path.join(
+    pmpdir,
+    "%(output_type)",
+    "precip",
+    "%(mip)",
+    "%(case_id)",
+    "intensity.frequency_distribution",
+)
 
 ref = "IMERG-V06-FU"  # For Perkins socre, P10, and P90
-ref_dir = os.path.join(pmpdir, "%(output_type)", "precip", "obs", "%(case_id)", "intensity.frequency_distribution")
+ref_dir = os.path.join(
+    pmpdir,
+    "%(output_type)",
+    "precip",
+    "obs",
+    "%(case_id)",
+    "intensity.frequency_distribution",
+)
diff --git a/pcmdi_metrics/precip_distribution/param/precip_distribution_params_ERA5.py b/pcmdi_metrics/precip_distribution/param/precip_distribution_params_ERA5.py
index fb0b737d6..83c1a9ba4 100644
--- a/pcmdi_metrics/precip_distribution/param/precip_distribution_params_ERA5.py
+++ b/pcmdi_metrics/precip_distribution/param/precip_distribution_params_ERA5.py
@@ -22,7 +22,21 @@
 case_id = "{:v%Y%m%d}".format(datetime.datetime.now())
 # case_id = ver
 pmpdir = "/p/user_pub/pmp/pmp_results/pmp_v1.1.2"
-results_dir = os.path.join(pmpdir, "%(output_type)", "precip", "%(mip)", "%(case_id)", "intensity.frequency_distribution")
+results_dir = os.path.join(
+    pmpdir,
+    "%(output_type)",
+    "precip",
+    "%(mip)",
+    "%(case_id)",
+    "intensity.frequency_distribution",
+)
 
 ref = "IMERG-V06-FU"  # For Perkins socre, P10, and P90
-ref_dir = os.path.join(pmpdir, "%(output_type)", "precip", "obs", "%(case_id)", "intensity.frequency_distribution")
+ref_dir = os.path.join(
+    pmpdir,
+    "%(output_type)",
+    "precip",
+    "obs",
+    "%(case_id)",
+    "intensity.frequency_distribution",
+)
diff --git a/pcmdi_metrics/precip_distribution/param/precip_distribution_params_GPCP.py b/pcmdi_metrics/precip_distribution/param/precip_distribution_params_GPCP.py
index 0bb152fd8..c9d11ead6 100644
--- a/pcmdi_metrics/precip_distribution/param/precip_distribution_params_GPCP.py
+++ b/pcmdi_metrics/precip_distribution/param/precip_distribution_params_GPCP.py
@@ -22,7 +22,21 @@
 case_id = "{:v%Y%m%d}".format(datetime.datetime.now())
 # case_id = ver
 pmpdir = "/p/user_pub/pmp/pmp_results/pmp_v1.1.2"
-results_dir = os.path.join(pmpdir, "%(output_type)", "precip", "%(mip)", "%(case_id)", "intensity.frequency_distribution")
+results_dir = os.path.join(
+    pmpdir,
+    "%(output_type)",
+    "precip",
+    "%(mip)",
+    "%(case_id)",
+    "intensity.frequency_distribution",
+)
 
 ref = "IMERG-V06-FU"  # For Perkins socre, P10, and P90
-ref_dir = os.path.join(pmpdir, "%(output_type)", "precip", "obs", "%(case_id)", "intensity.frequency_distribution")
+ref_dir = os.path.join(
+    pmpdir,
+    "%(output_type)",
+    "precip",
+    "obs",
+    "%(case_id)",
+    "intensity.frequency_distribution",
+)
diff --git a/pcmdi_metrics/precip_distribution/param/precip_distribution_params_IMERG.py b/pcmdi_metrics/precip_distribution/param/precip_distribution_params_IMERG.py
index e7e6f21d7..73cd7ef9e 100644
--- a/pcmdi_metrics/precip_distribution/param/precip_distribution_params_IMERG.py
+++ b/pcmdi_metrics/precip_distribution/param/precip_distribution_params_IMERG.py
@@ -22,7 +22,21 @@
 case_id = "{:v%Y%m%d}".format(datetime.datetime.now())
 # case_id = ver
 pmpdir = "/p/user_pub/pmp/pmp_results/pmp_v1.1.2"
-results_dir = os.path.join(pmpdir, "%(output_type)", "precip", "%(mip)", "%(case_id)", "intensity.frequency_distribution")
+results_dir = os.path.join(
+    pmpdir,
+    "%(output_type)",
+    "precip",
+    "%(mip)",
+    "%(case_id)",
+    "intensity.frequency_distribution",
+)
 
 ref = "IMERG-V06-FU"  # For Perkins socre, P10, and P90
-ref_dir = os.path.join(pmpdir, "%(output_type)", "precip", "obs", "%(case_id)", "intensity.frequency_distribution")
+ref_dir = os.path.join(
+    pmpdir,
+    "%(output_type)",
+    "precip",
+    "obs",
+    "%(case_id)",
+    "intensity.frequency_distribution",
+)
diff --git a/pcmdi_metrics/precip_distribution/param/precip_distribution_params_JRA-55.py b/pcmdi_metrics/precip_distribution/param/precip_distribution_params_JRA-55.py
index 5e762e38d..ad8b6ded7 100644
--- a/pcmdi_metrics/precip_distribution/param/precip_distribution_params_JRA-55.py
+++ b/pcmdi_metrics/precip_distribution/param/precip_distribution_params_JRA-55.py
@@ -22,7 +22,21 @@
 case_id = "{:v%Y%m%d}".format(datetime.datetime.now())
 # case_id = ver
 pmpdir = "/p/user_pub/pmp/pmp_results/pmp_v1.1.2"
-results_dir = os.path.join(pmpdir, "%(output_type)", "precip", "%(mip)", "%(case_id)", "intensity.frequency_distribution")
+results_dir = os.path.join(
+    pmpdir,
+    "%(output_type)",
+    "precip",
+    "%(mip)",
+    "%(case_id)",
+    "intensity.frequency_distribution",
+)
 
 ref = "IMERG-V06-FU"  # For Perkins socre, P10, and P90
-ref_dir = os.path.join(pmpdir, "%(output_type)", "precip", "obs", "%(case_id)", "intensity.frequency_distribution")
+ref_dir = os.path.join(
+    pmpdir,
+    "%(output_type)",
+    "precip",
+    "obs",
+    "%(case_id)",
+    "intensity.frequency_distribution",
+)
diff --git a/pcmdi_metrics/precip_distribution/param/precip_distribution_params_MERRA.py b/pcmdi_metrics/precip_distribution/param/precip_distribution_params_MERRA.py
index c0924564b..3a94aaa89 100644
--- a/pcmdi_metrics/precip_distribution/param/precip_distribution_params_MERRA.py
+++ b/pcmdi_metrics/precip_distribution/param/precip_distribution_params_MERRA.py
@@ -23,7 +23,21 @@
 case_id = "{:v%Y%m%d}".format(datetime.datetime.now())
 # case_id = ver
 pmpdir = "/p/user_pub/pmp/pmp_results/pmp_v1.1.2"
-results_dir = os.path.join(pmpdir, "%(output_type)", "precip", "%(mip)", "%(case_id)", "intensity.frequency_distribution")
+results_dir = os.path.join(
+    pmpdir,
+    "%(output_type)",
+    "precip",
+    "%(mip)",
+    "%(case_id)",
+    "intensity.frequency_distribution",
+)
 
 ref = "IMERG-V06-FU"  # For Perkins socre, P10, and P90
-ref_dir = os.path.join(pmpdir, "%(output_type)", "precip", "obs", "%(case_id)", "intensity.frequency_distribution")
+ref_dir = os.path.join(
+    pmpdir,
+    "%(output_type)",
+    "precip",
+    "obs",
+    "%(case_id)",
+    "intensity.frequency_distribution",
+)
diff --git a/pcmdi_metrics/precip_distribution/param/precip_distribution_params_MERRA2.py b/pcmdi_metrics/precip_distribution/param/precip_distribution_params_MERRA2.py
index b4369a3c8..c931aae10 100644
--- a/pcmdi_metrics/precip_distribution/param/precip_distribution_params_MERRA2.py
+++ b/pcmdi_metrics/precip_distribution/param/precip_distribution_params_MERRA2.py
@@ -23,7 +23,21 @@
 case_id = "{:v%Y%m%d}".format(datetime.datetime.now())
 # case_id = ver
 pmpdir = "/p/user_pub/pmp/pmp_results/pmp_v1.1.2"
-results_dir = os.path.join(pmpdir, "%(output_type)", "precip", "%(mip)", "%(case_id)", "intensity.frequency_distribution")
+results_dir = os.path.join(
+    pmpdir,
+    "%(output_type)",
+    "precip",
+    "%(mip)",
+    "%(case_id)",
+    "intensity.frequency_distribution",
+)
 
 ref = "IMERG-V06-FU"  # For Perkins socre, P10, and P90
-ref_dir = os.path.join(pmpdir, "%(output_type)", "precip", "obs", "%(case_id)", "intensity.frequency_distribution")
+ref_dir = os.path.join(
+    pmpdir,
+    "%(output_type)",
+    "precip",
+    "obs",
+    "%(case_id)",
+    "intensity.frequency_distribution",
+)
diff --git a/pcmdi_metrics/precip_distribution/param/precip_distribution_params_PERSIANN.py b/pcmdi_metrics/precip_distribution/param/precip_distribution_params_PERSIANN.py
index 3e14c91a0..8919b2529 100644
--- a/pcmdi_metrics/precip_distribution/param/precip_distribution_params_PERSIANN.py
+++ b/pcmdi_metrics/precip_distribution/param/precip_distribution_params_PERSIANN.py
@@ -22,7 +22,21 @@
 case_id = "{:v%Y%m%d}".format(datetime.datetime.now())
 # case_id = ver
 pmpdir = "/p/user_pub/pmp/pmp_results/pmp_v1.1.2"
-results_dir = os.path.join(pmpdir, "%(output_type)", "precip", "%(mip)", "%(case_id)", "intensity.frequency_distribution")
+results_dir = os.path.join(
+    pmpdir,
+    "%(output_type)",
+    "precip",
+    "%(mip)",
+    "%(case_id)",
+    "intensity.frequency_distribution",
+)
 
 ref = "IMERG-V06-FU"  # For Perkins socre, P10, and P90
-ref_dir = os.path.join(pmpdir, "%(output_type)", "precip", "obs", "%(case_id)", "intensity.frequency_distribution")
+ref_dir = os.path.join(
+    pmpdir,
+    "%(output_type)",
+    "precip",
+    "obs",
+    "%(case_id)",
+    "intensity.frequency_distribution",
+)
diff --git a/pcmdi_metrics/precip_distribution/param/precip_distribution_params_TRMM.py b/pcmdi_metrics/precip_distribution/param/precip_distribution_params_TRMM.py
index 8361626ba..39f3f2e0e 100644
--- a/pcmdi_metrics/precip_distribution/param/precip_distribution_params_TRMM.py
+++ b/pcmdi_metrics/precip_distribution/param/precip_distribution_params_TRMM.py
@@ -22,7 +22,21 @@
 case_id = "{:v%Y%m%d}".format(datetime.datetime.now())
 # case_id = ver
 pmpdir = "/p/user_pub/pmp/pmp_results/pmp_v1.1.2"
-results_dir = os.path.join(pmpdir, "%(output_type)", "precip", "%(mip)", "%(case_id)", "intensity.frequency_distribution")
+results_dir = os.path.join(
+    pmpdir,
+    "%(output_type)",
+    "precip",
+    "%(mip)",
+    "%(case_id)",
+    "intensity.frequency_distribution",
+)
 
 ref = "IMERG-V06-FU"  # For Perkins socre, P10, and P90
-ref_dir = os.path.join(pmpdir, "%(output_type)", "precip", "obs", "%(case_id)", "intensity.frequency_distribution")
+ref_dir = os.path.join(
+    pmpdir,
+    "%(output_type)",
+    "precip",
+    "obs",
+    "%(case_id)",
+    "intensity.frequency_distribution",
+)
diff --git a/pcmdi_metrics/precip_distribution/param/precip_distribution_params_cmip5.py b/pcmdi_metrics/precip_distribution/param/precip_distribution_params_cmip5.py
index 5414ec54a..81d52cc3c 100644
--- a/pcmdi_metrics/precip_distribution/param/precip_distribution_params_cmip5.py
+++ b/pcmdi_metrics/precip_distribution/param/precip_distribution_params_cmip5.py
@@ -18,7 +18,22 @@
 case_id = "{:v%Y%m%d}".format(datetime.datetime.now())
 # case_id = ver
 pmpdir = "/p/user_pub/pmp/pmp_results/pmp_v1.1.2"
-results_dir = os.path.join(pmpdir, "%(output_type)", "precip", "%(mip)", exp, "%(case_id)", "intensity.frequency_distribution")
+results_dir = os.path.join(
+    pmpdir,
+    "%(output_type)",
+    "precip",
+    "%(mip)",
+    exp,
+    "%(case_id)",
+    "intensity.frequency_distribution",
+)
 
 ref = "IMERG-V06-FU"  # For Perkins socre, P10, and P90
-ref_dir = os.path.join(pmpdir, "%(output_type)", "precip", "obs", "%(case_id)", "intensity.frequency_distribution")
+ref_dir = os.path.join(
+    pmpdir,
+    "%(output_type)",
+    "precip",
+    "obs",
+    "%(case_id)",
+    "intensity.frequency_distribution",
+)
diff --git a/pcmdi_metrics/precip_distribution/param/precip_distribution_params_cmip6.py b/pcmdi_metrics/precip_distribution/param/precip_distribution_params_cmip6.py
index 585bac87a..e372f4e7f 100644
--- a/pcmdi_metrics/precip_distribution/param/precip_distribution_params_cmip6.py
+++ b/pcmdi_metrics/precip_distribution/param/precip_distribution_params_cmip6.py
@@ -18,7 +18,22 @@
 case_id = "{:v%Y%m%d}".format(datetime.datetime.now())
 # case_id = ver
 pmpdir = "/p/user_pub/pmp/pmp_results/pmp_v1.1.2"
-results_dir = os.path.join(pmpdir, "%(output_type)", "precip", "%(mip)", exp, "%(case_id)", "intensity.frequency_distribution")
+results_dir = os.path.join(
+    pmpdir,
+    "%(output_type)",
+    "precip",
+    "%(mip)",
+    exp,
+    "%(case_id)",
+    "intensity.frequency_distribution",
+)
 
 ref = "IMERG-V06-FU"  # For Perkins socre, P10, and P90
-ref_dir = os.path.join(pmpdir, "%(output_type)", "precip", "obs", "%(case_id)", "intensity.frequency_distribution")
+ref_dir = os.path.join(
+    pmpdir,
+    "%(output_type)",
+    "precip",
+    "obs",
+    "%(case_id)",
+    "intensity.frequency_distribution",
+)
diff --git a/pcmdi_metrics/precip_distribution/precip_distribution_driver.py b/pcmdi_metrics/precip_distribution/precip_distribution_driver.py
index 5d0fc95f3..45b3bc91b 100644
--- a/pcmdi_metrics/precip_distribution/precip_distribution_driver.py
+++ b/pcmdi_metrics/precip_distribution/precip_distribution_driver.py
@@ -5,19 +5,18 @@
 import os
 
 import MV2 as MV
+import xarray as xr
+import xcdat
 from genutil import StringConstructor
 
 from pcmdi_metrics.mean_climate.lib.pmp_parser import PMPParser
-from pcmdi_metrics.precip_distribution.lib import ( 
+from pcmdi_metrics.precip_distribution.lib import (
     AddParserArgument,
     Regrid,
     precip_distribution_cum,
     precip_distribution_frq_amt,
 )
 
-import xarray as xr
-import xcdat
-
 # Read parameters
 P = PMPParser()
 P = AddParserArgument(P)
diff --git a/pcmdi_metrics/precip_distribution/scripts_pcmdi/parallel_driver_cmip.py b/pcmdi_metrics/precip_distribution/scripts_pcmdi/parallel_driver_cmip.py
index f767a8b0e..3d876604e 100644
--- a/pcmdi_metrics/precip_distribution/scripts_pcmdi/parallel_driver_cmip.py
+++ b/pcmdi_metrics/precip_distribution/scripts_pcmdi/parallel_driver_cmip.py
@@ -23,7 +23,9 @@
 if mod is None:
     pathDict = xs.findPaths(exp, var, frq, cmipTable=frq, mip_era=mip.upper())
 else:
-    pathDict = xs.findPaths(exp, var, frq, cmipTable=frq, mip_era=mip.upper(), model=mod)
+    pathDict = xs.findPaths(
+        exp, var, frq, cmipTable=frq, mip_era=mip.upper(), model=mod
+    )
 path_list = sorted(list(pathDict.keys()))
 print("Number of datasets:", len(path_list))
 print(path_list)
diff --git a/pcmdi_metrics/precip_variability/lib/argparse_functions.py b/pcmdi_metrics/precip_variability/lib/argparse_functions.py
index 98ce33225..b447f02a0 100644
--- a/pcmdi_metrics/precip_variability/lib/argparse_functions.py
+++ b/pcmdi_metrics/precip_variability/lib/argparse_functions.py
@@ -2,11 +2,9 @@ def AddParserArgument(P):
     P.add_argument(
         "--mip", type=str, dest="mip", default=None, help="cmip5, cmip6 or other mip"
     )
-    P.add_argument("--exp",
-               type=str,
-               dest='exp',
-               default=None,
-               help="amip, cmip or others")
+    P.add_argument(
+        "--exp", type=str, dest="exp", default=None, help="amip, cmip or others"
+    )
     P.add_argument("--mod", type=str, dest="mod", default=None, help="model")
     P.add_argument(
         "--var", type=str, dest="var", default=None, help="pr or other variable"
diff --git a/pcmdi_metrics/precip_variability/lib/lib_variability_across_timescales.py b/pcmdi_metrics/precip_variability/lib/lib_variability_across_timescales.py
index 74352ec9a..8964ec40e 100644
--- a/pcmdi_metrics/precip_variability/lib/lib_variability_across_timescales.py
+++ b/pcmdi_metrics/precip_variability/lib/lib_variability_across_timescales.py
@@ -6,14 +6,14 @@
 import cdutil
 import numpy as np
 import pandas as pd
+import xarray as xr
+import xcdat
 from scipy import signal
 from scipy.stats import chi2
+from xcdat.regridder import grid
 
 import pcmdi_metrics
 
-import xarray as xr
-import xcdat
-from xcdat.regridder import grid
 
 # ==================================================================================
 def precip_variability_across_timescale(
@@ -26,7 +26,7 @@ def precip_variability_across_timescale(
     psdmfm = {"RESULTS": {}}
 
     f = xcdat.open_mfdataset(file)
-    cal = f.time.encoding["calendar"]   
+    cal = f.time.encoding["calendar"]
     if "360" in cal:
         ldy = 30
     else:
@@ -35,16 +35,20 @@ def precip_variability_across_timescale(
     print("syr, eyr:", syr, eyr)
     for iyr in range(syr, eyr + 1):
         print(iyr)
-        do = f.sel(time=slice(str(iyr) + "-01-01 00:00:00",str(iyr) + "-12-" + str(ldy) + " 23:59:59"))
-        
+        do = f.sel(
+            time=slice(
+                str(iyr) + "-01-01 00:00:00", str(iyr) + "-12-" + str(ldy) + " 23:59:59"
+            )
+        )
+
         # Regridding
-        rgtmp = Regrid2deg(do, var)*float(fac)
+        rgtmp = Regrid2deg(do, var) * float(fac)
         if iyr == syr:
             drg = copy.deepcopy(rgtmp)
         else:
-            drg = xr.concat([drg, rgtmp], dim='time')
-        print(iyr, drg.shape)      
-        
+            drg = xr.concat([drg, rgtmp], dim="time")
+        print(iyr, drg.shape)
+
     f.close()
 
     # Anomaly
@@ -63,8 +67,12 @@ def precip_variability_across_timescale(
     # Write data (nc file)
     outfilename = "PS_pr." + str(dfrq) + "_regrid.180x90_" + dat + ".nc"
     custom_dataset = xr.merge([freqs, ps, rn, sig95])
-    custom_dataset.to_netcdf(path=os.path.join(outdir.replace("%(output_type)","diagnostic_results"), outfilename))
-        
+    custom_dataset.to_netcdf(
+        path=os.path.join(
+            outdir.replace("%(output_type)", "diagnostic_results"), outfilename
+        )
+    )
+
     # Power spectum of anomaly
     freqs, ps, rn, sig95 = Powerspectrum(anom, nperseg, noverlap)
     # Domain & Frequency average
@@ -72,8 +80,12 @@ def precip_variability_across_timescale(
     # Write data (nc file)
     outfilename = "PS_pr." + str(dfrq) + "_regrid.180x90_" + dat + "_unforced.nc"
     custom_dataset = xr.merge([freqs, ps, rn, sig95])
-    custom_dataset.to_netcdf(path=os.path.join(outdir.replace("%(output_type)","diagnostic_results"), outfilename))
-        
+    custom_dataset.to_netcdf(
+        path=os.path.join(
+            outdir.replace("%(output_type)", "diagnostic_results"), outfilename
+        )
+    )
+
     # Write data (json file)
     psdmfm["RESULTS"][dat] = {}
     psdmfm["RESULTS"][dat]["forced"] = psdmfm_forced
@@ -83,7 +95,7 @@ def precip_variability_across_timescale(
         "PS_pr." + str(dfrq) + "_regrid.180x90_area.freq.mean_" + dat + ".json"
     )
     JSON = pcmdi_metrics.io.base.Base(
-        outdir.replace("%(output_type)","metrics_results"), outfilename
+        outdir.replace("%(output_type)", "metrics_results"), outfilename
     )
     JSON.write(
         psdmfm,
@@ -106,9 +118,11 @@ def Regrid2deg(d, var):
     Output
     - drg: xCDAT variable with 2deg horizontal resolution
     """
-    tgrid = grid.create_uniform_grid(-89, 89, 2.0, 0.0, 358., 2.0)
-    drg = d.regridder.horizontal(var, tgrid, tool="xesmf", method="conservative_normed",periodic=True)[var]
-    
+    tgrid = grid.create_uniform_grid(-89, 89, 2.0, 0.0, 358.0, 2.0)
+    drg = d.regridder.horizontal(
+        var, tgrid, tool="xesmf", method="conservative_normed", periodic=True
+    )[var]
+
     print("Complete regridding from", d[var].shape, "to", drg.shape)
     return drg
 
@@ -127,7 +141,7 @@ def ClimAnom(d, ntd, syr, eyr, cal):
     - clim: climatology (climatological diurnal and annual cycles)
     - anom: anomaly departure from the climatological diurnal and annual cycles
     """
-        
+
     # Year segment
     nyr = eyr - syr + 1
     if "gregorian" in cal:
@@ -135,13 +149,19 @@ def ClimAnom(d, ntd, syr, eyr, cal):
         ldy = 31
         dseg = np.zeros((nyr, ndy, ntd, d.shape[1], d.shape[2]), dtype=float)
         for iyr, year in enumerate(range(syr, eyr + 1)):
-            yrtmp = d.sel(time=slice(str(year) + "-01-01 00:00:00",str(year) + "-12-" + str(ldy) + " 23:59:59"))
-                        
-            expanded = yrtmp.expand_dims("ndy")          
+            yrtmp = d.sel(
+                time=slice(
+                    str(year) + "-01-01 00:00:00",
+                    str(year) + "-12-" + str(ldy) + " 23:59:59",
+                )
+            )
+
+            expanded = yrtmp.expand_dims("ndy")
             yrtmpseg = np.reshape(
-                expanded, (int(yrtmp.shape[0] / ntd), ntd, yrtmp.shape[1], yrtmp.shape[2])
-            )            
-            
+                expanded,
+                (int(yrtmp.shape[0] / ntd), ntd, yrtmp.shape[1], yrtmp.shape[2]),
+            )
+
             if yrtmpseg.shape[0] == 365:
                 dseg[iyr, 0:59] = yrtmpseg[0:59]
                 dseg[iyr, 60:366] = yrtmpseg[59:365]
@@ -155,17 +175,22 @@ def ClimAnom(d, ntd, syr, eyr, cal):
         else:  # 365-canlendar
             ndy = 365
             ldy = 31
-        dseg = np.zeros((nyr, ndy, ntd, d.shape[1], d.shape[2]), dtype=float)        
+        dseg = np.zeros((nyr, ndy, ntd, d.shape[1], d.shape[2]), dtype=float)
         for iyr, year in enumerate(range(syr, eyr + 1)):
-            yrtmp = d.sel(time=slice(str(year) + "-01-01 00:00:00",str(year) + "-12-" + str(ldy) + " 23:59:59"))            
-            
-            expanded = yrtmp.expand_dims("ndy")          
+            yrtmp = d.sel(
+                time=slice(
+                    str(year) + "-01-01 00:00:00",
+                    str(year) + "-12-" + str(ldy) + " 23:59:59",
+                )
+            )
+
+            expanded = yrtmp.expand_dims("ndy")
             yrtmpseg = np.reshape(
-                expanded, (int(yrtmp.shape[0] / ntd), ntd, yrtmp.shape[1], yrtmp.shape[2])
-            )            
+                expanded,
+                (int(yrtmp.shape[0] / ntd), ntd, yrtmp.shape[1], yrtmp.shape[2]),
+            )
             dseg[iyr] = yrtmpseg
 
-
     # Climatology
     clim = np.nanmean(dseg, axis=0)
 
@@ -173,8 +198,13 @@ def ClimAnom(d, ntd, syr, eyr, cal):
     anom = np.array([])
     if "gregorian" in cal:
         for iyr, year in enumerate(range(syr, eyr + 1)):
-            yrtmp = d.sel(time=slice(str(year) + "-01-01 00:00:00",str(year) + "-12-" + str(ldy) + " 23:59:59"))            
-                        
+            yrtmp = d.sel(
+                time=slice(
+                    str(year) + "-01-01 00:00:00",
+                    str(year) + "-12-" + str(ldy) + " 23:59:59",
+                )
+            )
+
             if yrtmp.shape[0] == 365 * ntd:
                 anom = np.append(
                     anom,
@@ -189,11 +219,15 @@ def ClimAnom(d, ntd, syr, eyr, cal):
     # Reahape and Dimension information
     clim = np.reshape(clim, (ndy * ntd, d.shape[1], d.shape[2]))
     anom = np.reshape(anom, (d.shape[0], d.shape[1], d.shape[2]))
-    
-    climtime = pd.period_range(start="0001-01-01", periods=clim.shape[0], freq=str(24/ntd)+"H")
-    clim = xr.DataArray(clim, coords=[climtime, d.coords[d.dims[1]], d.coords[d.dims[2]]], dims=d.dims)
+
+    climtime = pd.period_range(
+        start="0001-01-01", periods=clim.shape[0], freq=str(24 / ntd) + "H"
+    )
+    clim = xr.DataArray(
+        clim, coords=[climtime, d.coords[d.dims[1]], d.coords[d.dims[2]]], dims=d.dims
+    )
     anom = xr.DataArray(anom, coords=d.coords, dims=d.dims)
-    
+
     print("Complete calculating climatology and anomaly for calendar of", cal)
     return clim, anom
 
@@ -242,13 +276,13 @@ def Powerspectrum(d, nperseg, noverlap):
     # Decorate arrays with dimensions
     # axisfrq = np.arange(len(freqs))
     axisfrq = range(len(freqs))
-    coords=[axisfrq, d.coords[d.dims[1]], d.coords[d.dims[2]]]
-    dims=["frequency", d.dims[1], d.dims[2]]   
+    coords = [axisfrq, d.coords[d.dims[1]], d.coords[d.dims[2]]]
+    dims = ["frequency", d.dims[1], d.dims[2]]
     freqs = xr.DataArray(freqs, coords=[axisfrq], dims=["frequency"], name="freqs")
     psd = xr.DataArray(psd, coords=coords, dims=dims, name="power")
     rn = xr.DataArray(rn, coords=coords, dims=dims, name="rednoise")
     sig95 = xr.DataArray(sig95, coords=coords, dims=dims, name="sig95")
-    
+
     print("Complete power spectra (segment: ", nperseg, " nps:", nps, ")")
     return freqs, psd, rn, sig95
 
@@ -256,7 +290,7 @@ def Powerspectrum(d, nperseg, noverlap):
 # ==================================================================================
 def lag1_autocorrelation(x):
     lag = 1
-    result = float(np.corrcoef(x[:-lag], x[lag:])[0,1])
+    result = float(np.corrcoef(x[:-lag], x[lag:])[0, 1])
     return result
 
 
@@ -358,30 +392,38 @@ def Avg_PS_DomFrq(d, frequency, ntd, dat, mip, frc):
     d_cdms = xr.DataArray.to_cdms2(d[0])
     mask = cdutil.generateLandSeaMask(d_cdms)
     mask = xr.DataArray.from_cdms2(mask)
-    
+
     psdmfm = {}
     for dom in domains:
         psdmfm[dom] = {}
 
         if "Ocean" in dom:
-            dmask = d.where(mask==0)
+            dmask = d.where(mask == 0)
         elif "Land" in dom:
-            dmask = d.where(mask==1)
+            dmask = d.where(mask == 1)
         else:
             dmask = d
 
         dmask = dmask.to_dataset(name="ps")
         dmask = dmask.bounds.add_bounds(axis="X", width=0.5)
         dmask = dmask.bounds.add_bounds(axis="Y", width=0.5)
-            
+
         if "50S50N" in dom:
-            am = dmask.sel(lat=slice(-50, 50)).spatial.average("ps", axis=["X", "Y"], weights='generate')["ps"]
+            am = dmask.sel(lat=slice(-50, 50)).spatial.average(
+                "ps", axis=["X", "Y"], weights="generate"
+            )["ps"]
         if "30N50N" in dom:
-            am = dmask.sel(lat=slice(30, 50)).spatial.average("ps", axis=["X", "Y"], weights='generate')["ps"]
+            am = dmask.sel(lat=slice(30, 50)).spatial.average(
+                "ps", axis=["X", "Y"], weights="generate"
+            )["ps"]
         if "30S30N" in dom:
-            am = dmask.sel(lat=slice(-30, 30)).spatial.average("ps", axis=["X", "Y"], weights='generate')["ps"]
+            am = dmask.sel(lat=slice(-30, 30)).spatial.average(
+                "ps", axis=["X", "Y"], weights="generate"
+            )["ps"]
         if "50S30S" in dom:
-            am = dmask.sel(lat=slice(-50, -30)).spatial.average("ps", axis=["X", "Y"], weights='generate')["ps"]
+            am = dmask.sel(lat=slice(-50, -30)).spatial.average(
+                "ps", axis=["X", "Y"], weights="generate"
+            )["ps"]
         am = np.array(am)
 
         for frq in frqs:
diff --git a/pcmdi_metrics/precip_variability/param/variability_across_timescales_PS_3hr_params_CMORPH.py b/pcmdi_metrics/precip_variability/param/variability_across_timescales_PS_3hr_params_CMORPH.py
index bb9de85af..9ddfe9854 100644
--- a/pcmdi_metrics/precip_variability/param/variability_across_timescales_PS_3hr_params_CMORPH.py
+++ b/pcmdi_metrics/precip_variability/param/variability_across_timescales_PS_3hr_params_CMORPH.py
@@ -13,7 +13,14 @@
 case_id = "{:v%Y%m%d}".format(datetime.datetime.now())
 # case_id = ver
 pmpdir = "/p/user_pub/pmp/pmp_results/pmp_v1.1.2"
-results_dir = os.path.join(pmpdir, "%(output_type)", "precip", "%(mip)", "%(case_id)", "variability_across_timescales")
+results_dir = os.path.join(
+    pmpdir,
+    "%(output_type)",
+    "precip",
+    "%(mip)",
+    "%(case_id)",
+    "variability_across_timescales",
+)
 
 prd = [2001, 2019]  # analysis period
 fac = 86400  # factor to make unit of [mm/day]
diff --git a/pcmdi_metrics/precip_variability/param/variability_across_timescales_PS_3hr_params_IMERG.py b/pcmdi_metrics/precip_variability/param/variability_across_timescales_PS_3hr_params_IMERG.py
index bb78ee192..1aa78dc20 100644
--- a/pcmdi_metrics/precip_variability/param/variability_across_timescales_PS_3hr_params_IMERG.py
+++ b/pcmdi_metrics/precip_variability/param/variability_across_timescales_PS_3hr_params_IMERG.py
@@ -13,7 +13,14 @@
 case_id = "{:v%Y%m%d}".format(datetime.datetime.now())
 # case_id = ver
 pmpdir = "/p/user_pub/pmp/pmp_results/pmp_v1.1.2"
-results_dir = os.path.join(pmpdir, "%(output_type)", "precip", "%(mip)", "%(case_id)", "variability_across_timescales")
+results_dir = os.path.join(
+    pmpdir,
+    "%(output_type)",
+    "precip",
+    "%(mip)",
+    "%(case_id)",
+    "variability_across_timescales",
+)
 
 prd = [2001, 2019]  # analysis period
 fac = 86400  # factor to make unit of [mm/day]
diff --git a/pcmdi_metrics/precip_variability/param/variability_across_timescales_PS_3hr_params_TRMM.py b/pcmdi_metrics/precip_variability/param/variability_across_timescales_PS_3hr_params_TRMM.py
index 2a0bcb469..80d4d2218 100644
--- a/pcmdi_metrics/precip_variability/param/variability_across_timescales_PS_3hr_params_TRMM.py
+++ b/pcmdi_metrics/precip_variability/param/variability_across_timescales_PS_3hr_params_TRMM.py
@@ -13,7 +13,14 @@
 case_id = "{:v%Y%m%d}".format(datetime.datetime.now())
 # case_id = ver
 pmpdir = "/p/user_pub/pmp/pmp_results/pmp_v1.1.2"
-results_dir = os.path.join(pmpdir, "%(output_type)", "precip", "%(mip)", "%(case_id)", "variability_across_timescales")
+results_dir = os.path.join(
+    pmpdir,
+    "%(output_type)",
+    "precip",
+    "%(mip)",
+    "%(case_id)",
+    "variability_across_timescales",
+)
 
 prd = [2001, 2019]  # analysis period
 fac = 86400  # factor to make unit of [mm/day]
diff --git a/pcmdi_metrics/precip_variability/param/variability_across_timescales_PS_3hr_params_cmip5.py b/pcmdi_metrics/precip_variability/param/variability_across_timescales_PS_3hr_params_cmip5.py
index afb957264..549f6266b 100644
--- a/pcmdi_metrics/precip_variability/param/variability_across_timescales_PS_3hr_params_cmip5.py
+++ b/pcmdi_metrics/precip_variability/param/variability_across_timescales_PS_3hr_params_cmip5.py
@@ -10,7 +10,15 @@
 case_id = "{:v%Y%m%d}".format(datetime.datetime.now())
 # case_id = ver
 pmpdir = "/p/user_pub/pmp/pmp_results/pmp_v1.1.2"
-results_dir = os.path.join(pmpdir, "%(output_type)", "precip", "%(mip)", exp, "%(case_id)", "variability_across_timescales")
+results_dir = os.path.join(
+    pmpdir,
+    "%(output_type)",
+    "precip",
+    "%(mip)",
+    exp,
+    "%(case_id)",
+    "variability_across_timescales",
+)
 
 prd = [1985, 2004]  # analysis period
 fac = 86400  # factor to make unit of [mm/day]
diff --git a/pcmdi_metrics/precip_variability/param/variability_across_timescales_PS_3hr_params_cmip6.py b/pcmdi_metrics/precip_variability/param/variability_across_timescales_PS_3hr_params_cmip6.py
index 27c634594..b17db8331 100644
--- a/pcmdi_metrics/precip_variability/param/variability_across_timescales_PS_3hr_params_cmip6.py
+++ b/pcmdi_metrics/precip_variability/param/variability_across_timescales_PS_3hr_params_cmip6.py
@@ -10,7 +10,15 @@
 case_id = "{:v%Y%m%d}".format(datetime.datetime.now())
 # case_id = ver
 pmpdir = "/p/user_pub/pmp/pmp_results/pmp_v1.1.2"
-results_dir = os.path.join(pmpdir, "%(output_type)", "precip", "%(mip)", exp, "%(case_id)", "variability_across_timescales")
+results_dir = os.path.join(
+    pmpdir,
+    "%(output_type)",
+    "precip",
+    "%(mip)",
+    exp,
+    "%(case_id)",
+    "variability_across_timescales",
+)
 
 prd = [1985, 2004]  # analysis period
 fac = 86400  # factor to make unit of [mm/day]
diff --git a/pcmdi_metrics/precip_variability/param/variability_across_timescales_PS_day_params_TRMM.py b/pcmdi_metrics/precip_variability/param/variability_across_timescales_PS_day_params_TRMM.py
index 17d603ff1..b95d29697 100644
--- a/pcmdi_metrics/precip_variability/param/variability_across_timescales_PS_day_params_TRMM.py
+++ b/pcmdi_metrics/precip_variability/param/variability_across_timescales_PS_day_params_TRMM.py
@@ -13,7 +13,14 @@
 case_id = "{:v%Y%m%d}".format(datetime.datetime.now())
 # case_id = ver
 pmpdir = "/p/user_pub/pmp/pmp_results/pmp_v1.1.2"
-results_dir = os.path.join(pmpdir, "%(output_type)", "precip", "%(mip)", "%(case_id)", "variability_across_timescales")
+results_dir = os.path.join(
+    pmpdir,
+    "%(output_type)",
+    "precip",
+    "%(mip)",
+    "%(case_id)",
+    "variability_across_timescales",
+)
 
 prd = [2001, 2019]  # analysis period
 fac = 86400  # factor to make unit of [mm/day]
diff --git a/pcmdi_metrics/precip_variability/scripts_pcmdi/parallel_driver_cmip.py b/pcmdi_metrics/precip_variability/scripts_pcmdi/parallel_driver_cmip.py
index 717b5c3de..af338f44c 100644
--- a/pcmdi_metrics/precip_variability/scripts_pcmdi/parallel_driver_cmip.py
+++ b/pcmdi_metrics/precip_variability/scripts_pcmdi/parallel_driver_cmip.py
@@ -1,5 +1,6 @@
-import os
 import glob
+import os
+
 import xsearch as xs
 
 from pcmdi_metrics.mean_climate.lib.pmp_parser import PMPParser
@@ -21,24 +22,32 @@
 if mod is None:
     pathDict = xs.findPaths(exp, var, frq, cmipTable=frq, mip_era=mip.upper())
 else:
-    pathDict = xs.findPaths(exp, var, frq, cmipTable=frq, mip_era=mip.upper(), model=mod)
+    pathDict = xs.findPaths(
+        exp, var, frq, cmipTable=frq, mip_era=mip.upper(), model=mod
+    )
 path_list = sorted(list(pathDict.keys()))
 print("Number of datasets:", len(path_list))
 print(path_list)
 
-cmd_list=[]
-log_list=[]
+cmd_list = []
+log_list = []
 for path in path_list:
-    fl = sorted(glob.glob(os.path.join(path, '*')))
+    fl = sorted(glob.glob(os.path.join(path, "*")))
     model = fl[0].split("/")[-1].split("_")[2]
     ens = fl[0].split("/")[-1].split("_")[4]
     dat = model + "." + ens
-    cmd_list.append('python -u ../variability_across_timescales_PS_driver.py -p ../param/variability_across_timescales_PS_3hr_params_'+mip+'.py --modpath '+path+' --mod *')
-    log_list.append('log_'+mip+'_'+var+'_'+dat)
+    cmd_list.append(
+        "python -u ../variability_across_timescales_PS_driver.py -p ../param/variability_across_timescales_PS_3hr_params_"
+        + mip
+        + ".py --modpath "
+        + path
+        + " --mod *"
+    )
+    log_list.append("log_" + mip + "_" + var + "_" + dat)
 
 parallel_submitter(
     cmd_list,
-    log_dir='./log',
+    log_dir="./log",
     logfilename_list=log_list,
     num_workers=num_cpus,
 )
diff --git a/pcmdi_metrics/precip_variability/scripts_pcmdi/run_obs.bash b/pcmdi_metrics/precip_variability/scripts_pcmdi/run_obs.bash
index 554a4d01a..83fb5c804 100755
--- a/pcmdi_metrics/precip_variability/scripts_pcmdi/run_obs.bash
+++ b/pcmdi_metrics/precip_variability/scripts_pcmdi/run_obs.bash
@@ -4,4 +4,3 @@ nohup python -u ../variability_across_timescales_PS_driver.py -p ../param/variab
 nohup python -u ../variability_across_timescales_PS_driver.py -p ../param/variability_across_timescales_PS_3hr_params_CMORPH.py  > ./log/log_PS_3hr_CMORPH &
 
 #nohup python -u ../variability_across_timescales_PS_driver.py -p ../param/variability_across_timescales_PS_day_params_TRMM.py  > ./log/log_PS_day_TRMM &
-
diff --git a/pcmdi_metrics/precip_variability/scripts_pcmdi/run_parallel.wait.bash b/pcmdi_metrics/precip_variability/scripts_pcmdi/run_parallel.wait.bash
index d08699eec..38cca06b3 100755
--- a/pcmdi_metrics/precip_variability/scripts_pcmdi/run_parallel.wait.bash
+++ b/pcmdi_metrics/precip_variability/scripts_pcmdi/run_parallel.wait.bash
@@ -3,4 +3,4 @@ nohup python -u parallel_driver_cmip.py -p ../param/variability_across_timescale
 wait
 nohup python -u parallel_driver_cmip.py -p ../param/variability_across_timescales_PS_3hr_params_cmip6.py > ./log/log_parallel.wait_cmip6 &
 
-# nohup python -u parallel_driver_cmip.py -p ../param/variability_across_timescales_PS_3hr_params_cmip6.py --mod 'EC-Earth3' > ./log/log_parallel.wait_cmip6 &
\ No newline at end of file
+# nohup python -u parallel_driver_cmip.py -p ../param/variability_across_timescales_PS_3hr_params_cmip6.py --mod 'EC-Earth3' > ./log/log_parallel.wait_cmip6 &
diff --git a/pcmdi_metrics/precip_variability/variability_across_timescales_PS_driver.py b/pcmdi_metrics/precip_variability/variability_across_timescales_PS_driver.py
index 45d4e4ee1..3b7374ae0 100644
--- a/pcmdi_metrics/precip_variability/variability_across_timescales_PS_driver.py
+++ b/pcmdi_metrics/precip_variability/variability_across_timescales_PS_driver.py
@@ -33,9 +33,11 @@
 # Create output directory
 case_id = param.case_id
 outdir_template = param.results_dir
-outdir_template = outdir_template.replace("%(mip)",str(mip)).replace("%(case_id)",str(case_id))
+outdir_template = outdir_template.replace("%(mip)", str(mip)).replace(
+    "%(case_id)", str(case_id)
+)
 for output_type in ["graphics", "diagnostic_results", "metrics_results"]:
-    outdir = outdir_template.replace("%(output_type)",output_type)
+    outdir = outdir_template.replace("%(output_type)", output_type)
     os.makedirs(outdir, exist_ok=True)
     print(outdir)
 
@@ -48,11 +50,22 @@
     ens = file_list[0].split("/")[-1].split("_")[4]
     dat = model + "." + ens
 print(dat)
-print(file_list)   
+print(file_list)
 
 # Regridding -> Anomaly -> Power spectra -> Domain&Frequency average -> Write
 syr = prd[0]
 eyr = prd[1]
 precip_variability_across_timescale(
-        file_list, syr, eyr, dfrq, mip, dat, var, fac, nperseg, noverlap, outdir_template, cmec
-    )
+    file_list,
+    syr,
+    eyr,
+    dfrq,
+    mip,
+    dat,
+    var,
+    fac,
+    nperseg,
+    noverlap,
+    outdir_template,
+    cmec,
+)
diff --git a/pcmdi_metrics/variability_mode/lib/calc_stat.py b/pcmdi_metrics/variability_mode/lib/calc_stat.py
index 59f4cc82d..6031c65a6 100644
--- a/pcmdi_metrics/variability_mode/lib/calc_stat.py
+++ b/pcmdi_metrics/variability_mode/lib/calc_stat.py
@@ -58,7 +58,6 @@ def calc_stats_save_dict(
     # Note: '_glo' indicates statistics calculated over global domain
     # . . . . . . . . . . . . . . . . . . . . . . . . .
     if obs_compare:
-
         if method in ["eof", "cbf"]:
             ref_grid_global = eof_lr_obs.getGrid()
             # Regrid (interpolation, model grid to ref grid)
diff --git a/pcmdi_metrics/variability_mode/lib/lib_variability_mode.py b/pcmdi_metrics/variability_mode/lib/lib_variability_mode.py
index caa9b707b..1c44c7a95 100644
--- a/pcmdi_metrics/variability_mode/lib/lib_variability_mode.py
+++ b/pcmdi_metrics/variability_mode/lib/lib_variability_mode.py
@@ -56,7 +56,6 @@ def read_data_in(
     LandMask,
     debug=False,
 ):
-
     f = cdms2.open(path)
     data_timeseries = f(var_in_data, time=(start_time, end_time), latitude=(-90, 90))
     cdutil.setTimeBoundsMonthly(data_timeseries)
diff --git a/pcmdi_metrics/variability_mode/lib/plot_map.py b/pcmdi_metrics/variability_mode/lib/plot_map.py
index 69a872ae1..230af64f8 100644
--- a/pcmdi_metrics/variability_mode/lib/plot_map.py
+++ b/pcmdi_metrics/variability_mode/lib/plot_map.py
@@ -150,7 +150,7 @@ def plot_map_cartopy(
 
     # map types example:
     # https://github.com/SciTools/cartopy-tutorial/blob/master/tutorial/projections_crs_and_terms.ipynb
-    
+
     if proj == "PlateCarree":
         projection = ccrs.PlateCarree(central_longitude=center_lon_global)
     elif proj == "Robinson":
@@ -237,15 +237,20 @@ def plot_map_cartopy(
         boundary = mpath.Path(vertices)
         ax.set_boundary(boundary, transform=ccrs.PlateCarree(central_longitude=180))
         ax.set_extent([min_lon, max_lon, min_lat, max_lat], crs=ccrs.PlateCarree())
-        if gridline:     
-            gl = ax.gridlines(draw_labels=True, alpha=0.8, linestyle="--", crs=cartopy.crs.PlateCarree())
-            gl.xformatter = LONGITUDE_FORMATTER 
+        if gridline:
+            gl = ax.gridlines(
+                draw_labels=True,
+                alpha=0.8,
+                linestyle="--",
+                crs=cartopy.crs.PlateCarree(),
+            )
+            gl.xformatter = LONGITUDE_FORMATTER
             gl.yformatter = LATITUDE_FORMATTER
             gl.ylocator = mticker.FixedLocator([30, 60])
-            gl.xlocator = mticker.FixedLocator([120, 160, 200-360, 240-360])
+            gl.xlocator = mticker.FixedLocator([120, 160, 200 - 360, 240 - 360])
             gl.top_labels = False  # suppress top labels
             # suppress right labels
-            # gl.right_labels = False  
+            # gl.right_labels = False
             for ea in gl.ylabel_artists:
                 right_label = ea.get_position()[0] > 0
                 if right_label:
diff --git a/pcmdi_metrics/variability_mode/scripts_pcmdi/post_process_merge_jsons.py b/pcmdi_metrics/variability_mode/scripts_pcmdi/post_process_merge_jsons.py
index 80de80b97..85ba1bb1b 100755
--- a/pcmdi_metrics/variability_mode/scripts_pcmdi/post_process_merge_jsons.py
+++ b/pcmdi_metrics/variability_mode/scripts_pcmdi/post_process_merge_jsons.py
@@ -36,7 +36,6 @@ def main():
 
     for mip in mips:
         for exp in exps:
-
             if exp == "amip":
                 modes = ["NAM", "NAO", "PNA", "SAM", "NPO"]
                 # modes = ['SAM']
diff --git a/pcmdi_metrics/variability_mode/variability_modes_driver.py b/pcmdi_metrics/variability_mode/variability_modes_driver.py
index 2d4741541..22de70d07 100755
--- a/pcmdi_metrics/variability_mode/variability_modes_driver.py
+++ b/pcmdi_metrics/variability_mode/variability_modes_driver.py
@@ -62,6 +62,7 @@
 
 import pcmdi_metrics
 from pcmdi_metrics import resources
+from pcmdi_metrics.mean_climate.lib import pmp_parser
 from pcmdi_metrics.variability_mode.lib import (
     AddParserArgument,
     VariabilityModeCheck,
@@ -83,8 +84,6 @@
     variability_metrics_to_json,
     write_nc_output,
 )
-from pcmdi_metrics.mean_climate.lib import pmp_parser
-
 
 # To avoid below error
 # OpenBLAS blas_thread_init: pthread_create failed for thread XX of 96: Resource temporarily unavailable
@@ -316,7 +315,6 @@
 # Observation
 # -------------------------------------------------
 if obs_compare:
-
     obs_lf_path = None
 
     # read data in
@@ -533,7 +531,6 @@
     # Run
     # -------------------------------------------------
     for model_path in model_path_list:
-
         try:
             if realization == "*":
                 run = (model_path.split("/")[-1]).split(".")[run_in_modpath]
@@ -609,7 +606,6 @@
                 # Common Basis Function Approach
                 # - - - - - - - - - - - - - - - - - - - - - - - - -
                 if CBF and obs_compare:
-
                     if "cbf" not in list(
                         result_dict["RESULTS"][model][run]["defaultReference"][mode][
                             season
@@ -779,7 +775,6 @@
                 # Conventional EOF approach as supplementary
                 # - - - - - - - - - - - - - - - - - - - - - - - - -
                 if ConvEOF:
-
                     eofn_mod_max = 3
 
                     # EOF analysis
diff --git a/pcmdi_metrics/version.py b/pcmdi_metrics/version.py
index ff38ca34a..0dad08242 100644
--- a/pcmdi_metrics/version.py
+++ b/pcmdi_metrics/version.py
@@ -1,3 +1,3 @@
-__version__ = 'v3.0.2'
-__git_tag_describe__ = 'v3.0.2-11-g06b151f'
-__git_sha1__ = '06b151f46b182259277ddc6b9e25ea9ff08b1f50'
+__version__ = "v3.0.2"
+__git_tag_describe__ = "v3.0.2-11-g06b151f"
+__git_sha1__ = "06b151f46b182259277ddc6b9e25ea9ff08b1f50"
diff --git a/pyproject.toml b/pyproject.toml
index 120918cf8..ac2c1de46 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -35,4 +35,3 @@ line_length = 88
 junit_family = "xunit2"
 addopts = "--cov=pcmdi_metrics --cov-report term --cov-report html:tests_coverage_reports/htmlcov --cov-report xml:tests_coverage_reports/coverage.xml -s --ignore tests/deprecated"
 python_files = ["tests.py", "test_*.py"]
-
diff --git a/sample_setups/pcmdi_parameter_files/mean_climate/mean_climate_metrics/pcmdi_MIP_EXP_pmp_parameterfile.py b/sample_setups/pcmdi_parameter_files/mean_climate/mean_climate_metrics/pcmdi_MIP_EXP_pmp_parameterfile.py
index 2b2616dcc..c4b4a278b 100644
--- a/sample_setups/pcmdi_parameter_files/mean_climate/mean_climate_metrics/pcmdi_MIP_EXP_pmp_parameterfile.py
+++ b/sample_setups/pcmdi_parameter_files/mean_climate/mean_climate_metrics/pcmdi_MIP_EXP_pmp_parameterfile.py
@@ -192,7 +192,6 @@
 
 # USER CUSTOMIZED REGIONS
 if regional == "y":
-
     regions_specs = {
         "Nino34": {
             "value": 0.0,

From 47fd62229622ddc925582147a30c5108c0ed3d5f Mon Sep 17 00:00:00 2001
From: Jiwoo Lee <lee1043@llnl.gov>
Date: Thu, 26 Oct 2023 13:11:19 -0700
Subject: [PATCH 101/133] catch up with pre-commit

---
 pcmdi_metrics/__init__.py                                | 5 +----
 .../cloud_feedback/lib/cal_CloudRadKernel_xr.py          | 9 ++++-----
 pcmdi_metrics/cloud_feedback/lib/compute_ECS_xr.py       | 2 +-
 pcmdi_metrics/cloud_feedback/lib/organize_jsons.py       | 4 ++--
 pcmdi_metrics/graphics/share/read_json_mean_clim.py      | 4 ++--
 pcmdi_metrics/io/region_from_file.py                     | 2 --
 pcmdi_metrics/mean_climate/lib/calculate_climatology.py  | 1 -
 pcmdi_metrics/mean_climate/lib/load_and_regrid.py        | 1 -
 .../mean_climate/scripts/post_process_merge_jsons.py     | 1 -
 pcmdi_metrics/mjo/scripts/parallel_driver.py             | 1 -
 .../precip_distribution/precip_distribution_driver.py    | 1 -
 .../variability_mode/scripts_pcmdi/parallel_driver.py    | 1 -
 .../variability_mode/variability_modes_driver.py         | 1 -
 13 files changed, 10 insertions(+), 23 deletions(-)

diff --git a/pcmdi_metrics/__init__.py b/pcmdi_metrics/__init__.py
index 30c426647..bcbb8a8e0 100644
--- a/pcmdi_metrics/__init__.py
+++ b/pcmdi_metrics/__init__.py
@@ -1,5 +1,5 @@
 #  put here the import calls to expose whatever we want to the user
-import logging
+import logging  # noqa
 
 LOG_LEVEL = logging.DEBUG
 plog = logging.getLogger("pcmdi_metrics")
@@ -15,7 +15,4 @@
 plog.addHandler(ch)
 plog.setLevel(LOG_LEVEL)
 from . import io  # noqa
-
-# from . import pcmdi  # noqa
-# from . import mean_climate  # noqa
 from .version import __git_sha1__, __git_tag_describe__, __version__  # noqa
diff --git a/pcmdi_metrics/cloud_feedback/lib/cal_CloudRadKernel_xr.py b/pcmdi_metrics/cloud_feedback/lib/cal_CloudRadKernel_xr.py
index c676c7283..ba88177a8 100644
--- a/pcmdi_metrics/cloud_feedback/lib/cal_CloudRadKernel_xr.py
+++ b/pcmdi_metrics/cloud_feedback/lib/cal_CloudRadKernel_xr.py
@@ -10,7 +10,6 @@
 from datetime import date
 
 # IMPORT STUFF:
-import cftime
 import numpy as np
 import xarray as xr
 import xcdat as xc
@@ -371,7 +370,7 @@ def bony_sorting_part2(OKwaps, data, OKland, WTS, binedges):
     # Ensure that the area matrix has zeros rather than masked points
     CNTS = CNTS.where(~np.isnan(CNTS), 0.0)
 
-    if np.allclose(0.5, np.sum(CNTS.values, 1)) == False:
+    if np.allclose(0.5, np.sum(CNTS.values, 1)) is False:
         print(
             "sum of fractional counts over all wapbins does not equal 0.5 (tropical fraction)"
         )
@@ -386,7 +385,7 @@ def bony_sorting_part2(OKwaps, data, OKland, WTS, binedges):
     v2b = (data * WTS).sum(dim=["lat", "lon"]).transpose("time", "tau", "plev")
 
     # if np.allclose(v1.values,v2a.values)==False or np.allclose(v1.values,v2b.values)==False:
-    if np.allclose(v1.values, v2b.values) == False:
+    if np.allclose(v1.values, v2b.values) is False:
         print("Cannot reconstruct tropical average via summing regimes")
 
     return DATA, CNTS  # [time,wapbin]
@@ -603,7 +602,7 @@ def obscuration_terms3(c1, c2):
     L_R_bar = L_R_bar.where(L_R_bar >= 0, 0.0)
     F_bar = F_bar.where(F_bar >= 0, 0.0)
 
-    rep_L_bar = tile_uneven(L_bar, L12)
+    # rep_L_bar = tile_uneven(L_bar, L12)
     rep_L_R_bar = tile_uneven(L_R_bar, L_R12)
     rep_F_bar = tile_uneven(F_bar, F12b)
 
@@ -874,7 +873,7 @@ def klein_metrics(obs_clisccp, gcm_clisccp, LWkern, SWkern, WTS):
     # take only clouds with tau>3.6
     clisccp_bias_CPM = clisccp_bias[:, 2:, :]  # 4 tau bins
     obs_clisccp_CPM = obs_clisccp[:, 2:, :]  # 4 tau bins
-    gcm_clisccp_CPM = gcm_clisccp[:, 2:, :]  # 4 tau bins
+    # gcm_clisccp_CPM = gcm_clisccp[:, 2:, :]  # 4 tau bins
     LWkernel_CPM = LWkern[:, 2:, :]  # 4 tau bins
     SWkernel_CPM = SWkern[:, 2:, :]  # 4 tau bins
     NETkernel_CPM = SWkernel_CPM + LWkernel_CPM
diff --git a/pcmdi_metrics/cloud_feedback/lib/compute_ECS_xr.py b/pcmdi_metrics/cloud_feedback/lib/compute_ECS_xr.py
index 9d7bb83ff..dcfb56575 100644
--- a/pcmdi_metrics/cloud_feedback/lib/compute_ECS_xr.py
+++ b/pcmdi_metrics/cloud_feedback/lib/compute_ECS_xr.py
@@ -94,7 +94,7 @@ def get_data(pi, ab):
         print("len(time)<1200...skipping")
         return None
 
-    bdate = pisub.time.dt.year.values
+    # bdate = pisub.time.dt.year.values
     # compute global means:
     GLcntl = pisub.spatial.average(var)
     GLpert = absub.spatial.average(var)
diff --git a/pcmdi_metrics/cloud_feedback/lib/organize_jsons.py b/pcmdi_metrics/cloud_feedback/lib/organize_jsons.py
index b810bf3f1..4a01c7abf 100644
--- a/pcmdi_metrics/cloud_feedback/lib/organize_jsons.py
+++ b/pcmdi_metrics/cloud_feedback/lib/organize_jsons.py
@@ -65,7 +65,7 @@ def organize_err_jsons(new_dict, mo, ripf):
                         old_dict[mo][ripf][region][sfc][sec][name] = new_dict[sec][
                             region
                         ][sfc][name]
-                    except:
+                    except Exception:
                         old_dict[mo][ripf][region][sfc][sec][name] = np.nan
                 # end name loop
             # end section loop:
@@ -85,7 +85,7 @@ def organize_ecs_jsons(new_ecs, mo, ripf):
     ) as url:
         old_dict = json.load(url)
 
-    if new_ecs != None:
+    if new_ecs is not None:
         old_dict["CMIP6"][mo][ripf]["ECS"] = new_ecs
 
     return old_dict  # now updated to include info from input dictionary
diff --git a/pcmdi_metrics/graphics/share/read_json_mean_clim.py b/pcmdi_metrics/graphics/share/read_json_mean_clim.py
index 873147dc9..a67195c31 100644
--- a/pcmdi_metrics/graphics/share/read_json_mean_clim.py
+++ b/pcmdi_metrics/graphics/share/read_json_mean_clim.py
@@ -97,7 +97,7 @@ def extract_unit(var, results_dict_var):
     model_list = sorted(list(results_dict_var["RESULTS"].keys()))
     try:
         units = results_dict_var["RESULTS"][model_list[0]]["units"]
-    except Exception as e:
+    except Exception:
         units = None
     return units
 
@@ -106,7 +106,7 @@ def extract_ref(var, results_dict_var):
     model_list = sorted(list(results_dict_var["RESULTS"].keys()))
     try:
         ref = results_dict_var["RESULTS"][model_list[0]]["default"]["source"]
-    except Exception as e:
+    except Exception:
         ref = None
     return ref
 
diff --git a/pcmdi_metrics/io/region_from_file.py b/pcmdi_metrics/io/region_from_file.py
index 8e315b2c5..bcb5144fe 100644
--- a/pcmdi_metrics/io/region_from_file.py
+++ b/pcmdi_metrics/io/region_from_file.py
@@ -1,7 +1,5 @@
 import geopandas as gpd
 import regionmask
-import xarray as xr
-import xcdat
 
 
 def region_from_file(data, rgn_path, attr, feature):
diff --git a/pcmdi_metrics/mean_climate/lib/calculate_climatology.py b/pcmdi_metrics/mean_climate/lib/calculate_climatology.py
index b3f78a831..d074b62cd 100644
--- a/pcmdi_metrics/mean_climate/lib/calculate_climatology.py
+++ b/pcmdi_metrics/mean_climate/lib/calculate_climatology.py
@@ -3,7 +3,6 @@
 
 import dask
 import xcdat as xc
-from genutil import StringConstructor
 
 
 def calculate_climatology(
diff --git a/pcmdi_metrics/mean_climate/lib/load_and_regrid.py b/pcmdi_metrics/mean_climate/lib/load_and_regrid.py
index 1bad706c4..35ded42df 100644
--- a/pcmdi_metrics/mean_climate/lib/load_and_regrid.py
+++ b/pcmdi_metrics/mean_climate/lib/load_and_regrid.py
@@ -1,4 +1,3 @@
-import cftime
 import numpy as np
 import xcdat as xc
 
diff --git a/pcmdi_metrics/mean_climate/scripts/post_process_merge_jsons.py b/pcmdi_metrics/mean_climate/scripts/post_process_merge_jsons.py
index c1af7aed1..02653f8a1 100755
--- a/pcmdi_metrics/mean_climate/scripts/post_process_merge_jsons.py
+++ b/pcmdi_metrics/mean_climate/scripts/post_process_merge_jsons.py
@@ -4,7 +4,6 @@
 import glob
 import json
 import os
-import sys
 
 from genutil import StringConstructor
 
diff --git a/pcmdi_metrics/mjo/scripts/parallel_driver.py b/pcmdi_metrics/mjo/scripts/parallel_driver.py
index f8179a838..a0548614d 100755
--- a/pcmdi_metrics/mjo/scripts/parallel_driver.py
+++ b/pcmdi_metrics/mjo/scripts/parallel_driver.py
@@ -8,7 +8,6 @@
 
 from genutil import StringConstructor
 
-import pcmdi_metrics
 from pcmdi_metrics.mean_climate.lib.pmp_parser import PMPParser
 from pcmdi_metrics.misc.scripts import parallel_submitter
 from pcmdi_metrics.mjo.lib import AddParserArgument
diff --git a/pcmdi_metrics/precip_distribution/precip_distribution_driver.py b/pcmdi_metrics/precip_distribution/precip_distribution_driver.py
index 45b3bc91b..2f2ba2175 100644
--- a/pcmdi_metrics/precip_distribution/precip_distribution_driver.py
+++ b/pcmdi_metrics/precip_distribution/precip_distribution_driver.py
@@ -6,7 +6,6 @@
 
 import MV2 as MV
 import xarray as xr
-import xcdat
 from genutil import StringConstructor
 
 from pcmdi_metrics.mean_climate.lib.pmp_parser import PMPParser
diff --git a/pcmdi_metrics/variability_mode/scripts_pcmdi/parallel_driver.py b/pcmdi_metrics/variability_mode/scripts_pcmdi/parallel_driver.py
index eed0ce129..c8bad0ae7 100755
--- a/pcmdi_metrics/variability_mode/scripts_pcmdi/parallel_driver.py
+++ b/pcmdi_metrics/variability_mode/scripts_pcmdi/parallel_driver.py
@@ -8,7 +8,6 @@
 
 from genutil import StringConstructor
 
-import pcmdi_metrics
 from pcmdi_metrics.mean_climate.lib.pmp_parser import PMPParser
 from pcmdi_metrics.misc.scripts import parallel_submitter
 from pcmdi_metrics.variability_mode.lib import (
diff --git a/pcmdi_metrics/variability_mode/variability_modes_driver.py b/pcmdi_metrics/variability_mode/variability_modes_driver.py
index 22de70d07..0b202edf0 100755
--- a/pcmdi_metrics/variability_mode/variability_modes_driver.py
+++ b/pcmdi_metrics/variability_mode/variability_modes_driver.py
@@ -60,7 +60,6 @@
 import MV2
 from genutil import StringConstructor
 
-import pcmdi_metrics
 from pcmdi_metrics import resources
 from pcmdi_metrics.mean_climate.lib import pmp_parser
 from pcmdi_metrics.variability_mode.lib import (

From 7c5c8f18c5ba93b59eab87fce42e795122486521 Mon Sep 17 00:00:00 2001
From: lee1043 <lee1043@llnl.gov>
Date: Thu, 2 Nov 2023 16:21:11 -0700
Subject: [PATCH 102/133] update

---
 conda-env/dev.yml                             |   4 +-
 .../lib/argparse_functions.py                 |  16 ++-
 .../variability_mode/lib/plot_map.py          |  45 +++++-
 .../parallel_submitter_all_modes_models.py    | 131 ++++++++++++++++++
 .../variability_modes_driver.py               |  46 +++---
 5 files changed, 208 insertions(+), 34 deletions(-)
 create mode 100644 pcmdi_metrics/variability_mode/scripts_pcmdi/parallel_submitter_all_modes_models.py

diff --git a/conda-env/dev.yml b/conda-env/dev.yml
index 597d568a8..8a632cb78 100644
--- a/conda-env/dev.yml
+++ b/conda-env/dev.yml
@@ -11,7 +11,7 @@ dependencies:
     - python=3.10.10
     - pip=23.1.2
     - numpy=1.23.5
-    - cartopy=0.21.1
+    - cartopy=0.22.0
     - matplotlib=3.7.1
     - cdat_info=8.2.1
     - cdms2=3.1.5
@@ -27,7 +27,7 @@ dependencies:
     - netcdf4=1.6.3
     - regionmask=0.9.0
     - rasterio=1.3.6
-    - shapely=2.0.1
+    - shapely=2.0.2
     # ==================
     # Testing
     # ==================
diff --git a/pcmdi_metrics/variability_mode/lib/argparse_functions.py b/pcmdi_metrics/variability_mode/lib/argparse_functions.py
index 2654659de..1d0c96075 100644
--- a/pcmdi_metrics/variability_mode/lib/argparse_functions.py
+++ b/pcmdi_metrics/variability_mode/lib/argparse_functions.py
@@ -150,13 +150,25 @@ def AddParserArgument(P):
         "--nc_out",
         type=bool,
         default=True,
-        help="Option for generate netCDF file output: True (default) / False",
+        help="Option for generate netCDF file output for models: True (default) / False",
     )
     P.add_argument(
         "--plot",
         type=bool,
         default=True,
-        help="Option for generate individual plots: True (default) / False",
+        help="Option for generate individual plots for models: True (default) / False",
+    )
+    P.add_argument(
+        "--nc_out_obs",
+        type=bool,
+        default=True,
+        help="Option for generate netCDF file output for obs: True (default) / False",
+    )
+    P.add_argument(
+        "--plot_obs",
+        type=bool,
+        default=True,
+        help="Option for generate individual plots for obs: True (default) / False",
     )
     P.add_argument(
         "--parallel",
diff --git a/pcmdi_metrics/variability_mode/lib/plot_map.py b/pcmdi_metrics/variability_mode/lib/plot_map.py
index 69a872ae1..73123647e 100644
--- a/pcmdi_metrics/variability_mode/lib/plot_map.py
+++ b/pcmdi_metrics/variability_mode/lib/plot_map.py
@@ -1,6 +1,6 @@
 import sys
 
-import cartopy
+#import cartopy
 import cartopy.crs as ccrs
 import matplotlib.path as mpath
 import matplotlib.pyplot as plt
@@ -8,9 +8,16 @@
 import numpy as np
 from cartopy.mpl.gridliner import LATITUDE_FORMATTER, LONGITUDE_FORMATTER
 from cartopy.mpl.ticker import LatitudeFormatter, LongitudeFormatter
+from pcmdi_metrics.variability_mode.lib import debug_print
+from cartopy.feature import LAND as cartopy_land
+from cartopy.feature import OCEAN as cartopy_ocean
 
+import faulthandler
 
-def plot_map(mode, model, syear, eyear, season, eof_Nth, frac_Nth, output_file_name):
+faulthandler.enable()
+
+
+def plot_map(mode, model, syear, eyear, season, eof_Nth, frac_Nth, output_file_name, debug=False):
     """Plot dive down map and save
 
     Parameters
@@ -67,6 +74,8 @@ def plot_map(mode, model, syear, eyear, season, eof_Nth, frac_Nth, output_file_n
         + percentage
     )
 
+    debug_print('plot_map: projection, plot_title:' + projection +', '+ plot_title, debug)
+
     gridline = True
 
     if mode in [
@@ -97,6 +106,7 @@ def plot_map(mode, model, syear, eyear, season, eof_Nth, frac_Nth, output_file_n
         levels=levels,
         maskout=maskout,
         center_lon_global=center_lon_global,
+        debug=debug,
     )
 
 
@@ -138,6 +148,8 @@ def plot_map_cartopy(
         Switch for debugging print statements (default is False)
     """
 
+    debug_print('plot_map_cartopy starts', debug)
+
     lons = data.getLongitude()
     lats = data.getLatitude()
 
@@ -148,6 +160,8 @@ def plot_map_cartopy(
     if debug:
         print(min_lon, max_lon, min_lat, max_lat)
 
+    debug_print('Central longitude setup starts', debug)
+    debug_print('proj: '+proj, debug)
     # map types example:
     # https://github.com/SciTools/cartopy-tutorial/blob/master/tutorial/projections_crs_and_terms.ipynb
     
@@ -170,22 +184,35 @@ def plot_map_cartopy(
             central_latitude=central_latitude,
             standard_parallels=(20, max_lat),
         )
+    else:
+        print('Error: projection not defined!')
+
+    if debug:
+        debug_print('Central longitude setup completes', debug)
+        print('projection:', projection)
 
     # Generate plot
-    fig = plt.figure(figsize=(8, 6))
-    ax = plt.axes(projection=projection)
+    debug_print('Generate plot starts', debug)
+    #fig = plt.figure(figsize=(8, 6))
+    debug_print('fig done', debug)
+    #ax = plt.axes(projection=projection)
+    ax = plt.axes(projection=ccrs.NorthPolarStereo())
+    debug_print('ax done', debug)
     im = ax.contourf(
         lons,
         lats,
-        data,
+        np.array(data),
         transform=ccrs.PlateCarree(),
         cmap=cmap,
         levels=levels,
         extend="both",
     )
+    debug_print('contourf done', debug)
     ax.coastlines()
+    debug_print('Generate plot completed', debug)
 
     # Grid Lines and tick labels
+    debug_print('projection starts', debug)
     if proj == "PlateCarree":
         if data_area == "global":
             if gridline:
@@ -203,6 +230,7 @@ def plot_map_cartopy(
         if gridline:
             gl = ax.gridlines(alpha=0.5, linestyle="--")
     elif "Stereo" in proj:
+        debug_print(proj + ' start', debug)
         if gridline:
             gl = ax.gridlines(draw_labels=True, alpha=0.5, linestyle="--")
             gl.xlocator = mticker.FixedLocator(
@@ -226,6 +254,7 @@ def plot_map_cartopy(
         verts = np.vstack([np.sin(theta), np.cos(theta)]).T
         circle = mpath.Path(verts * radius + center)
         ax.set_boundary(circle, transform=ax.transAxes)
+        debug_print(proj + ' plotted', debug)
     elif proj == "Lambert":
         # Make a boundary path in PlateCarree projection, I choose to start in
         # the bottom left and go round anticlockwise, creating a boundary point
@@ -250,6 +279,7 @@ def plot_map_cartopy(
                 right_label = ea.get_position()[0] > 0
                 if right_label:
                     ea.set_visible(False)
+    debug_print('projection completed', debug)
 
     # Add title
     plt.title(title, pad=15, fontsize=15)
@@ -268,13 +298,14 @@ def plot_map_cartopy(
     if maskout is not None:
         if maskout == "land":
             ax.add_feature(
-                cartopy.feature.LAND, zorder=100, edgecolor="k", facecolor="lightgrey"
+                cartopy_land, zorder=100, edgecolor="k", facecolor="lightgrey"
             )
         if maskout == "ocean":
             ax.add_feature(
-                cartopy.feature.OCEAN, zorder=100, edgecolor="k", facecolor="lightgrey"
+                cartopy_ocean, zorder=100, edgecolor="k", facecolor="lightgrey"
             )
 
     # Done, save figure
+    debug_print('plot done, save figure as ' + filename, debug)
     fig.savefig(filename)
     plt.close("all")
diff --git a/pcmdi_metrics/variability_mode/scripts_pcmdi/parallel_submitter_all_modes_models.py b/pcmdi_metrics/variability_mode/scripts_pcmdi/parallel_submitter_all_modes_models.py
new file mode 100644
index 000000000..76cf89cc9
--- /dev/null
+++ b/pcmdi_metrics/variability_mode/scripts_pcmdi/parallel_submitter_all_modes_models.py
@@ -0,0 +1,131 @@
+import datetime
+import glob
+import os
+
+from pcmdi_metrics.misc.scripts import parallel_submitter
+from pcmdi_metrics.variability_mode.lib import sort_human
+
+
+def find_latest(path):
+    dir_list = [p for p in glob.glob(path + "/v????????")]
+    return sorted(dir_list)[-1]
+
+# ---------------------------------------------------------------
+
+mip = "cmip6"
+exp = "historical"
+
+datadir = '/p/user_pub/pmp/pmp_results/pmp_v1.1.2/additional_xmls/latest'
+param_dir = "../param"
+
+modes = ["NAM", "SAM", "NAO", "PNA", "NPO", "PDO", "NPGO"]
+
+case_id = "{:v%Y%m%d}".format(datetime.datetime.now())
+
+num_workers = 40
+debug = True 
+
+# ---------------------------------------------------------------
+
+cmds_list = list()
+logfilename_list = list()
+
+for mode in modes:
+    if mode in ["PDO", "NPGO", "AMO"]:
+        var = "ts"
+        param_file = "myParam_demo_PDO.py"
+    else:
+        var = "psl"
+        param_file = "myParam_demo_NAM.py"
+
+    if mode in ["SAM"]:
+        osyear = 1955
+    else:
+        osyear = 1900
+
+    if mode in ["NPO", "NPGO"]:
+        eofn_obs = 2
+        eofn_mod = 2
+    else:
+        eofn_obs = 1
+        eofn_mod = 1
+    
+    datadir_ver = find_latest(datadir)
+    datadir_final = os.path.join(datadir_ver, mip, exp, 'atmos', 'mon', var)
+    
+    xml_list = glob.glob(os.path.join(datadir_final, '*.xml'))
+    
+    # get list of models
+    models_list = sort_human(
+        [r.split("/")[-1].split(".")[2] for r in xml_list]
+    )
+    # remove repeat
+    models_list = sort_human(list(dict.fromkeys(models_list)))
+
+    if debug:
+        models_list = models_list[0:3]
+    
+    print(models_list)
+    print(len(models_list))
+    
+    for model in models_list:
+        file_list_model = glob.glob(os.path.join(datadir_final, '*.' + model + '.*.xml'))
+        runs_list = sort_human(
+            [
+                r.split("/")[-1].split(".")[3]
+                for r in file_list_model
+            ]
+        )
+
+        if debug:
+            runs_list = runs_list[0:1]
+    
+        print(model, runs_list)
+
+        for run in runs_list:
+
+            cmd_content = [
+                'variability_modes_driver.py', 
+                '-p', os.path.join(param_dir, param_file), 
+                '--variability_mode', mode,
+                '--osyear', str(osyear),
+                '--eofn_obs', str(eofn_obs),
+                '--eofn_mod', str(eofn_mod),
+                '--mip', mip, '--exp', exp,
+                '--modnames', model,
+                '--realization', run,
+            ]
+
+            if model != models_list[0] or run != runs_list[0]:
+                cmd_content.extend(["--no_plot_obs", "--no_nc_out_obs"])
+
+            if debug:
+                cmd_content.append('--debug True')
+
+            cmd = ' '.join(cmd_content)
+
+            log_file = '_'.join(['variability_modes', mode, mip, exp, model, run]) + '.txt'
+    
+            print(cmd) 
+            cmds_list.append(cmd)
+            logfilename_list.append(log_file)
+
+# =================================================
+# Run subprocesses in parallel
+# -------------------------------------------------
+# log dir
+log_dir = os.path.join(
+    "/p/user_pub/pmp/pmp_results/pmp_v1.1.2",
+    "log",
+    "variability_modes",
+    mip, exp, case_id)
+ 
+os.makedirs(log_dir, exist_ok=True)
+
+parallel_submitter(
+    cmds_list,
+    log_dir=log_dir,
+    logfilename_list=logfilename_list,
+    num_workers=num_workers,
+)
+
diff --git a/pcmdi_metrics/variability_mode/variability_modes_driver.py b/pcmdi_metrics/variability_mode/variability_modes_driver.py
index 2d4741541..77e20e89a 100755
--- a/pcmdi_metrics/variability_mode/variability_modes_driver.py
+++ b/pcmdi_metrics/variability_mode/variability_modes_driver.py
@@ -439,28 +439,12 @@
         if EofScaling:
             output_filename_obs += "_EOFscaled"
 
-        # Save global map, pc timeseries, and fraction in NetCDF output
-        if nc_out_obs:
-            output_nc_file_obs = os.path.join(
-                outdir(output_type="diagnostic_results"), output_filename_obs
-            )
-            write_nc_output(
-                output_nc_file_obs,
-                eof_lr_obs[season],
-                pc_obs[season],
-                frac_obs[season],
-                slope_obs,
-                intercept_obs,
-            )
-
-        # Plotting
+        # Plot
         if plot_obs:
+            debug_print("plot obs", debug)
             output_img_file_obs = os.path.join(
                 outdir(output_type="graphics"), output_filename_obs
             )
-            # plot_map(mode, '[REF] '+obs_name, osyear, oeyear, season,
-            #          eof_obs[season], frac_obs[season],
-            #          output_img_file_obs+'_org_eof')
             plot_map(
                 mode,
                 "[REF] " + obs_name,
@@ -470,6 +454,7 @@
                 eof_lr_obs[season](region_subdomain),
                 frac_obs[season],
                 output_img_file_obs,
+                debug=debug
             )
             plot_map(
                 mode + "_teleconnection",
@@ -480,9 +465,25 @@
                 eof_lr_obs[season](longitude=(lon1g, lon2g)),
                 frac_obs[season],
                 output_img_file_obs + "_teleconnection",
+                debug=debug
             )
             debug_print("obs plotting end", debug)
 
+        # NetCDF: Save global map, pc timeseries, and fraction in NetCDF output
+        if nc_out_obs:
+            debug_print("write obs nc", debug)
+            output_nc_file_obs = os.path.join(
+                outdir(output_type="diagnostic_results"), output_filename_obs
+            )
+            write_nc_output(
+                output_nc_file_obs,
+                eof_lr_obs[season],
+                pc_obs[season],
+                frac_obs[season],
+                slope_obs,
+                intercept_obs,
+            )
+
         # Save stdv of PC time series in dictionary
         dict_head_obs["stdv_pc"] = stdv_pc_obs[season]
         dict_head_obs["frac"] = float(frac_obs[season])
@@ -761,6 +762,7 @@
                             eof_lr_cbf(region_subdomain),
                             frac_cbf,
                             output_img_file + "_cbf",
+                            debug=debug
                         )
                         plot_map(
                             mode + "_teleconnection",
@@ -771,6 +773,7 @@
                             eof_lr_cbf(longitude=(lon1g, lon2g)),
                             frac_cbf,
                             output_img_file + "_cbf_teleconnection",
+                            debug=debug
                         )
 
                     debug_print("cbf pcs end", debug)
@@ -920,11 +923,6 @@
                             outdir(output_type="graphics"), output_filename
                         )
                         if plot_model:
-                            # plot_map(mode,
-                            #          mip.upper()+' '+model+' ('+run+')',
-                            #          msyear, meyear, season,
-                            #          eof, frac,
-                            #          output_img_file+'_org_eof')
                             plot_map(
                                 mode,
                                 mip.upper()
@@ -940,6 +938,7 @@
                                 eof_lr(region_subdomain),
                                 frac,
                                 output_img_file,
+                                debug=debug,
                             )
                             plot_map(
                                 mode + "_teleconnection",
@@ -956,6 +955,7 @@
                                 eof_lr(longitude=(lon1g, lon2g)),
                                 frac,
                                 output_img_file + "_teleconnection",
+                                debug=debug,
                             )
 
                         # - - - - - - - - - - - - - - - - - - - - - - - - -

From 5e29b8aec3a32dbd07bf1dc80f5f1133e336b515 Mon Sep 17 00:00:00 2001
From: lee1043 <lee1043@llnl.gov>
Date: Thu, 2 Nov 2023 16:29:01 -0700
Subject: [PATCH 103/133] update

---
 .../scripts_pcmdi/parallel_driver.py                | 13 +++++--------
 1 file changed, 5 insertions(+), 8 deletions(-)

diff --git a/pcmdi_metrics/variability_mode/scripts_pcmdi/parallel_driver.py b/pcmdi_metrics/variability_mode/scripts_pcmdi/parallel_driver.py
index eed0ce129..2c1e336f8 100755
--- a/pcmdi_metrics/variability_mode/scripts_pcmdi/parallel_driver.py
+++ b/pcmdi_metrics/variability_mode/scripts_pcmdi/parallel_driver.py
@@ -95,6 +95,10 @@
 debug = param.debug
 print("debug:", debug)
 
+# number of tasks to submit at the same time
+# num_workers = 20
+num_workers = param.num_workers
+
 # =================================================
 # Create output directories
 # -------------------------------------------------
@@ -154,8 +158,7 @@
     for r, run in enumerate(runs_list):
         # command line for queue
         cmd = [
-            "python",
-            "../variability_modes_driver.py",
+            "variability_modes_driver.py",
             "-p",
             param_file,
             "--case_id",
@@ -191,12 +194,6 @@
 log_dir = outdir(output_type="log")
 os.makedirs(log_dir, exist_ok=True)
 
-# number of tasks to submit at the same time
-num_workers = 3
-# num_workers = 5
-# num_workers = 10
-# num_workers = 30
-
 parallel_submitter(
     cmds_list,
     log_dir=log_dir,

From 33376263826fc1eb7e238ed1fd54b74aa14282d6 Mon Sep 17 00:00:00 2001
From: lee1043 <lee1043@llnl.gov>
Date: Thu, 2 Nov 2023 16:32:56 -0700
Subject: [PATCH 104/133] update;

---
 .../param/myParam_demo_NAM.py                 | 16 ++++--
 .../param/myParam_demo_PDO.py                 | 13 +++--
 .../scripts_pcmdi/run_all_modes_mips.sh       |  4 --
 .../scripts_pcmdi/run_pmp_parallel.sh         | 14 ++++-
 .../run_pmp_parallel_example_setup.sh         | 57 +++++++++++++++++++
 5 files changed, 89 insertions(+), 15 deletions(-)
 create mode 100755 pcmdi_metrics/variability_mode/scripts_pcmdi/run_pmp_parallel_example_setup.sh

diff --git a/pcmdi_metrics/variability_mode/param/myParam_demo_NAM.py b/pcmdi_metrics/variability_mode/param/myParam_demo_NAM.py
index 913099c19..ba5deb940 100755
--- a/pcmdi_metrics/variability_mode/param/myParam_demo_NAM.py
+++ b/pcmdi_metrics/variability_mode/param/myParam_demo_NAM.py
@@ -13,7 +13,7 @@
 # Analysis Options
 # -------------------------------------------------
 variability_mode = "NAM"  # Available domains: NAM, NAO, SAM, PNA, PDO
-seasons = ["DJF"]  # Available seasons: DJF, MAM, JJA, SON, monthly, yearly
+seasons = ["DJF", "MAM", "JJA", "SON"]  # Available seasons: DJF, MAM, JJA, SON, monthly, yearly
 
 ConvEOF = True  # Calculate conventioanl EOF for model
 CBF = True  # Calculate Common Basis Function (CBF) for model
@@ -21,8 +21,8 @@
 # =================================================
 # Miscellaneous
 # -------------------------------------------------
-update_json = True  # False
-debug = True  # False
+update_json = False
+debug = False
 
 # =================================================
 # Observation
@@ -81,5 +81,11 @@
     "%(reference_data_name)",
 )
 
-nc_out = True  # Write output in NetCDF
-plot = True  # Create map graphics
+# Output for obs
+plot_obs = True  # Create map graphics
+nc_out_obs = True  # Write output in NetCDF
+
+# Output for models
+nc_out = True
+plot = True
+
diff --git a/pcmdi_metrics/variability_mode/param/myParam_demo_PDO.py b/pcmdi_metrics/variability_mode/param/myParam_demo_PDO.py
index 986041f56..efc99dbdd 100755
--- a/pcmdi_metrics/variability_mode/param/myParam_demo_PDO.py
+++ b/pcmdi_metrics/variability_mode/param/myParam_demo_PDO.py
@@ -23,8 +23,8 @@
 # =================================================
 # Miscellaneous
 # -------------------------------------------------
-update_json = True  # False
-debug = True  # False
+update_json = False  # False
+debug = False  # False
 
 # =================================================
 # Observation
@@ -88,5 +88,10 @@
     "%(reference_data_name)",
 )
 
-nc_out = True  # Write output in NetCDF
-plot = True  # Create map graphics
+# Output for obs
+plot_obs = True  # Create map graphics
+nc_out_obs = True  # Write output in NetCDF
+
+# Output for models
+nc_out = True
+plot = True  
diff --git a/pcmdi_metrics/variability_mode/scripts_pcmdi/run_all_modes_mips.sh b/pcmdi_metrics/variability_mode/scripts_pcmdi/run_all_modes_mips.sh
index 31bc65951..55aa5d0e7 100755
--- a/pcmdi_metrics/variability_mode/scripts_pcmdi/run_all_modes_mips.sh
+++ b/pcmdi_metrics/variability_mode/scripts_pcmdi/run_all_modes_mips.sh
@@ -1,10 +1,6 @@
 #!/bin/sh
 set -ax
 
-# Working conda env
-# - Crunchy: pmp_nightly_20181128, pmp_nightly_20190617
-# - Grim: pmp_nightly_20190522
-
 ver=`date +"%Y%m%d"`
 mips='cmip3 cmip5 cmip6'
 mips='cmip6'
diff --git a/pcmdi_metrics/variability_mode/scripts_pcmdi/run_pmp_parallel.sh b/pcmdi_metrics/variability_mode/scripts_pcmdi/run_pmp_parallel.sh
index 9abc6d7b7..df696abcc 100755
--- a/pcmdi_metrics/variability_mode/scripts_pcmdi/run_pmp_parallel.sh
+++ b/pcmdi_metrics/variability_mode/scripts_pcmdi/run_pmp_parallel.sh
@@ -29,8 +29,11 @@ modnames='all'
 
 realization='all'
 
-param_dir='../../../sample_setups/pcmdi_parameter_files/variability_modes'
+num_workers=5
+
+#param_dir='../../../sample_setups/pcmdi_parameter_files/variability_modes'
 #param_dir='../../../sample_setups/pcmdi_parameter_files/variability_modes/alternative_obs'
+param_dir='../param'
 
 for mip in $mips; do
     echo $mip
@@ -48,8 +51,15 @@ for mip in $mips; do
         mkdir -p ./log/$mip/$exp/$case_id
         # Run
         for mode in $modes_list; do
+
+            if [ $mode == 'PDO' ] || [ $mode == 'NPGO' ] || [ $mode == 'AMO' ]; then
+                mode_o='PDO'
+            else
+                mode_o='NAM'
+            fi
+
             echo $mip $exp $mode $case_id
-            python ./parallel_driver.py -p ${param_dir}/myParam_${mode}_${mip}.py --param_dir $param_dir --mip $mip --exp $exp --case_id $case_id --modnames $modnames --realization $realization --variability_mode $mode >& ./log/$mip/$exp/$case_id/log.${mip}.${exp}.${mode}.all.v${ver}.txt &
+            python ./parallel_driver.py -p ${param_dir}/myParam_${mode_o}_${mip}.py --param_dir $param_dir --mip $mip --exp $exp --case_id $case_id --modnames $modnames --realization $realization --variability_mode $mode --num_workers $num_workers >& ./log/$mip/$exp/$case_id/log.${mip}.${exp}.${mode}.all.v${ver}.txt &
             disown
             sleep 1
         done
diff --git a/pcmdi_metrics/variability_mode/scripts_pcmdi/run_pmp_parallel_example_setup.sh b/pcmdi_metrics/variability_mode/scripts_pcmdi/run_pmp_parallel_example_setup.sh
new file mode 100755
index 000000000..9abc6d7b7
--- /dev/null
+++ b/pcmdi_metrics/variability_mode/scripts_pcmdi/run_pmp_parallel_example_setup.sh
@@ -0,0 +1,57 @@
+#!/bin/sh
+set -a
+
+# To avoid below error
+# OpenBLAS blas_thread_init: pthread_create failed for thread XX of 96: Resource temporarily unavailable
+export OMP_NUM_THREADS=1
+
+# Working conda env in gates: cdat82_20191107_py27
+
+ver=`date +"%Y%m%d-%H%M"`
+case_id="v"`date +"%Y%m%d"`
+
+#mips='cmip3 cmip5 cmip6'
+#mips='cmip5 cmip6'
+#mips='cmip5'
+mips='cmip6'
+#mips='cmip3'
+
+#exps='20c3m amip'
+#exps='20c3m'
+#exps='historical amip'
+exps='historical'
+#exps='amip'
+
+#modes='all'
+modes='NAO NPO PNA'
+
+modnames='all'
+
+realization='all'
+
+param_dir='../../../sample_setups/pcmdi_parameter_files/variability_modes'
+#param_dir='../../../sample_setups/pcmdi_parameter_files/variability_modes/alternative_obs'
+
+for mip in $mips; do
+    echo $mip
+    for exp in $exps; do
+        if [ $modes == 'all' ]; then
+            if [ $exp == 'historical' ] || [ $exp == '20c3m' ]; then
+                modes_list='NAM NAO PNA SAM PDO NPO NPGO'
+            elif [ $exp == 'amip' ]; then
+                modes_list='NAM NAO PNA SAM NPO'
+            fi
+        else
+            modes_list=$modes
+        fi
+        # Log dir
+        mkdir -p ./log/$mip/$exp/$case_id
+        # Run
+        for mode in $modes_list; do
+            echo $mip $exp $mode $case_id
+            python ./parallel_driver.py -p ${param_dir}/myParam_${mode}_${mip}.py --param_dir $param_dir --mip $mip --exp $exp --case_id $case_id --modnames $modnames --realization $realization --variability_mode $mode >& ./log/$mip/$exp/$case_id/log.${mip}.${exp}.${mode}.all.v${ver}.txt &
+            disown
+            sleep 1
+        done
+    done
+done

From 6f05b680d9483a62bd55b89098b8dddba8056c61 Mon Sep 17 00:00:00 2001
From: lee1043 <lee1043@llnl.gov>
Date: Thu, 2 Nov 2023 16:51:31 -0700
Subject: [PATCH 105/133] pre-commit check

---
 .../lib/post_process_plot_ensemble_mean.py    |  2 +-
 .../variability_mode/lib/plot_map.py          | 56 +++++++------
 .../param/myParam_demo_NAM.py                 | 12 ++-
 .../param/myParam_demo_PDO.py                 |  2 +-
 .../parallel_submitter_all_modes_models.py    | 83 +++++++++++--------
 .../variability_modes_driver.py               |  8 +-
 6 files changed, 91 insertions(+), 72 deletions(-)

diff --git a/pcmdi_metrics/mjo/lib/post_process_plot_ensemble_mean.py b/pcmdi_metrics/mjo/lib/post_process_plot_ensemble_mean.py
index af1f862e8..83f9012c0 100644
--- a/pcmdi_metrics/mjo/lib/post_process_plot_ensemble_mean.py
+++ b/pcmdi_metrics/mjo/lib/post_process_plot_ensemble_mean.py
@@ -9,7 +9,7 @@
 
 def main():
     # mip = "cmip5"
-    mip = 'cmip6'
+    mip = "cmip6"
     exp = "historical"
     version = "v20230924"
     period = "1985-2004"
diff --git a/pcmdi_metrics/variability_mode/lib/plot_map.py b/pcmdi_metrics/variability_mode/lib/plot_map.py
index dd044e115..71fac7b45 100644
--- a/pcmdi_metrics/variability_mode/lib/plot_map.py
+++ b/pcmdi_metrics/variability_mode/lib/plot_map.py
@@ -1,23 +1,25 @@
+import faulthandler
 import sys
 
-#import cartopy
+# import cartopy
 import cartopy.crs as ccrs
 import matplotlib.path as mpath
 import matplotlib.pyplot as plt
 import matplotlib.ticker as mticker
 import numpy as np
-from cartopy.mpl.gridliner import LATITUDE_FORMATTER, LONGITUDE_FORMATTER
-from cartopy.mpl.ticker import LatitudeFormatter, LongitudeFormatter
-from pcmdi_metrics.variability_mode.lib import debug_print
 from cartopy.feature import LAND as cartopy_land
 from cartopy.feature import OCEAN as cartopy_ocean
+from cartopy.mpl.gridliner import LATITUDE_FORMATTER, LONGITUDE_FORMATTER
+from cartopy.mpl.ticker import LatitudeFormatter, LongitudeFormatter
 
-import faulthandler
+from pcmdi_metrics.variability_mode.lib import debug_print
 
 faulthandler.enable()
 
 
-def plot_map(mode, model, syear, eyear, season, eof_Nth, frac_Nth, output_file_name, debug=False):
+def plot_map(
+    mode, model, syear, eyear, season, eof_Nth, frac_Nth, output_file_name, debug=False
+):
     """Plot dive down map and save
 
     Parameters
@@ -74,7 +76,9 @@ def plot_map(mode, model, syear, eyear, season, eof_Nth, frac_Nth, output_file_n
         + percentage
     )
 
-    debug_print('plot_map: projection, plot_title:' + projection +', '+ plot_title, debug)
+    debug_print(
+        "plot_map: projection, plot_title:" + projection + ", " + plot_title, debug
+    )
 
     gridline = True
 
@@ -148,7 +152,7 @@ def plot_map_cartopy(
         Switch for debugging print statements (default is False)
     """
 
-    debug_print('plot_map_cartopy starts', debug)
+    debug_print("plot_map_cartopy starts", debug)
 
     lons = data.getLongitude()
     lats = data.getLatitude()
@@ -160,8 +164,8 @@ def plot_map_cartopy(
     if debug:
         print(min_lon, max_lon, min_lat, max_lat)
 
-    debug_print('Central longitude setup starts', debug)
-    debug_print('proj: '+proj, debug)
+    debug_print("Central longitude setup starts", debug)
+    debug_print("proj: " + proj, debug)
     # map types example:
     # https://github.com/SciTools/cartopy-tutorial/blob/master/tutorial/projections_crs_and_terms.ipynb
 
@@ -185,19 +189,19 @@ def plot_map_cartopy(
             standard_parallels=(20, max_lat),
         )
     else:
-        print('Error: projection not defined!')
+        print("Error: projection not defined!")
 
     if debug:
-        debug_print('Central longitude setup completes', debug)
-        print('projection:', projection)
+        debug_print("Central longitude setup completes", debug)
+        print("projection:", projection)
 
     # Generate plot
-    debug_print('Generate plot starts', debug)
-    #fig = plt.figure(figsize=(8, 6))
-    debug_print('fig done', debug)
-    #ax = plt.axes(projection=projection)
+    debug_print("Generate plot starts", debug)
+    fig = plt.figure(figsize=(8, 6))
+    debug_print("fig done", debug)
+    # ax = plt.axes(projection=projection)
     ax = plt.axes(projection=ccrs.NorthPolarStereo())
-    debug_print('ax done', debug)
+    debug_print("ax done", debug)
     im = ax.contourf(
         lons,
         lats,
@@ -207,12 +211,12 @@ def plot_map_cartopy(
         levels=levels,
         extend="both",
     )
-    debug_print('contourf done', debug)
+    debug_print("contourf done", debug)
     ax.coastlines()
-    debug_print('Generate plot completed', debug)
+    debug_print("Generate plot completed", debug)
 
     # Grid Lines and tick labels
-    debug_print('projection starts', debug)
+    debug_print("projection starts", debug)
     if proj == "PlateCarree":
         if data_area == "global":
             if gridline:
@@ -230,7 +234,7 @@ def plot_map_cartopy(
         if gridline:
             gl = ax.gridlines(alpha=0.5, linestyle="--")
     elif "Stereo" in proj:
-        debug_print(proj + ' start', debug)
+        debug_print(proj + " start", debug)
         if gridline:
             gl = ax.gridlines(draw_labels=True, alpha=0.5, linestyle="--")
             gl.xlocator = mticker.FixedLocator(
@@ -254,7 +258,7 @@ def plot_map_cartopy(
         verts = np.vstack([np.sin(theta), np.cos(theta)]).T
         circle = mpath.Path(verts * radius + center)
         ax.set_boundary(circle, transform=ax.transAxes)
-        debug_print(proj + ' plotted', debug)
+        debug_print(proj + " plotted", debug)
     elif proj == "Lambert":
         # Make a boundary path in PlateCarree projection, I choose to start in
         # the bottom left and go round anticlockwise, creating a boundary point
@@ -271,7 +275,7 @@ def plot_map_cartopy(
                 draw_labels=True,
                 alpha=0.8,
                 linestyle="--",
-                crs=cartopy.crs.PlateCarree(),
+                crs=ccrs.PlateCarree(),
             )
             gl.xformatter = LONGITUDE_FORMATTER
             gl.yformatter = LATITUDE_FORMATTER
@@ -284,7 +288,7 @@ def plot_map_cartopy(
                 right_label = ea.get_position()[0] > 0
                 if right_label:
                     ea.set_visible(False)
-    debug_print('projection completed', debug)
+    debug_print("projection completed", debug)
 
     # Add title
     plt.title(title, pad=15, fontsize=15)
@@ -311,6 +315,6 @@ def plot_map_cartopy(
             )
 
     # Done, save figure
-    debug_print('plot done, save figure as ' + filename, debug)
+    debug_print("plot done, save figure as " + filename, debug)
     fig.savefig(filename)
     plt.close("all")
diff --git a/pcmdi_metrics/variability_mode/param/myParam_demo_NAM.py b/pcmdi_metrics/variability_mode/param/myParam_demo_NAM.py
index ba5deb940..6412f85e1 100755
--- a/pcmdi_metrics/variability_mode/param/myParam_demo_NAM.py
+++ b/pcmdi_metrics/variability_mode/param/myParam_demo_NAM.py
@@ -13,7 +13,12 @@
 # Analysis Options
 # -------------------------------------------------
 variability_mode = "NAM"  # Available domains: NAM, NAO, SAM, PNA, PDO
-seasons = ["DJF", "MAM", "JJA", "SON"]  # Available seasons: DJF, MAM, JJA, SON, monthly, yearly
+seasons = [
+    "DJF",
+    "MAM",
+    "JJA",
+    "SON",
+]  # Available seasons: DJF, MAM, JJA, SON, monthly, yearly
 
 ConvEOF = True  # Calculate conventioanl EOF for model
 CBF = True  # Calculate Common Basis Function (CBF) for model
@@ -31,8 +36,8 @@
 reference_data_path = os.path.join(
     "/p/user_pub/PCMDIobs/obs4MIPs/NOAA-ESRL-PSD/20CR/mon/psl/gn/latest",
     "psl_mon_20CR_PCMDI_gn_187101-201212.nc"
-    #"/p/user_pub/PCMDIobs/PCMDIobs2/atmos/mon/psl/20CR/gn/v20200707",
-    #"psl_mon_20CR_BE_gn_v20200707_187101-201212.nc",
+    # "/p/user_pub/PCMDIobs/PCMDIobs2/atmos/mon/psl/20CR/gn/v20200707",
+    # "psl_mon_20CR_BE_gn_v20200707_187101-201212.nc",
 )
 
 varOBS = "psl"
@@ -88,4 +93,3 @@
 # Output for models
 nc_out = True
 plot = True
-
diff --git a/pcmdi_metrics/variability_mode/param/myParam_demo_PDO.py b/pcmdi_metrics/variability_mode/param/myParam_demo_PDO.py
index efc99dbdd..a67075840 100755
--- a/pcmdi_metrics/variability_mode/param/myParam_demo_PDO.py
+++ b/pcmdi_metrics/variability_mode/param/myParam_demo_PDO.py
@@ -94,4 +94,4 @@
 
 # Output for models
 nc_out = True
-plot = True  
+plot = True
diff --git a/pcmdi_metrics/variability_mode/scripts_pcmdi/parallel_submitter_all_modes_models.py b/pcmdi_metrics/variability_mode/scripts_pcmdi/parallel_submitter_all_modes_models.py
index 76cf89cc9..ddead83b3 100644
--- a/pcmdi_metrics/variability_mode/scripts_pcmdi/parallel_submitter_all_modes_models.py
+++ b/pcmdi_metrics/variability_mode/scripts_pcmdi/parallel_submitter_all_modes_models.py
@@ -10,12 +10,13 @@ def find_latest(path):
     dir_list = [p for p in glob.glob(path + "/v????????")]
     return sorted(dir_list)[-1]
 
+
 # ---------------------------------------------------------------
 
 mip = "cmip6"
 exp = "historical"
 
-datadir = '/p/user_pub/pmp/pmp_results/pmp_v1.1.2/additional_xmls/latest'
+datadir = "/p/user_pub/pmp/pmp_results/pmp_v1.1.2/additional_xmls/latest"
 param_dir = "../param"
 
 modes = ["NAM", "SAM", "NAO", "PNA", "NPO", "PDO", "NPGO"]
@@ -23,7 +24,7 @@ def find_latest(path):
 case_id = "{:v%Y%m%d}".format(datetime.datetime.now())
 
 num_workers = 40
-debug = True 
+debug = True
 
 # ---------------------------------------------------------------
 
@@ -49,64 +50,72 @@ def find_latest(path):
     else:
         eofn_obs = 1
         eofn_mod = 1
-    
+
     datadir_ver = find_latest(datadir)
-    datadir_final = os.path.join(datadir_ver, mip, exp, 'atmos', 'mon', var)
-    
-    xml_list = glob.glob(os.path.join(datadir_final, '*.xml'))
-    
+    datadir_final = os.path.join(datadir_ver, mip, exp, "atmos", "mon", var)
+
+    xml_list = glob.glob(os.path.join(datadir_final, "*.xml"))
+
     # get list of models
-    models_list = sort_human(
-        [r.split("/")[-1].split(".")[2] for r in xml_list]
-    )
+    models_list = sort_human([r.split("/")[-1].split(".")[2] for r in xml_list])
     # remove repeat
     models_list = sort_human(list(dict.fromkeys(models_list)))
 
     if debug:
         models_list = models_list[0:3]
-    
+
     print(models_list)
     print(len(models_list))
-    
+
     for model in models_list:
-        file_list_model = glob.glob(os.path.join(datadir_final, '*.' + model + '.*.xml'))
+        file_list_model = glob.glob(
+            os.path.join(datadir_final, "*." + model + ".*.xml")
+        )
         runs_list = sort_human(
-            [
-                r.split("/")[-1].split(".")[3]
-                for r in file_list_model
-            ]
+            [r.split("/")[-1].split(".")[3] for r in file_list_model]
         )
 
         if debug:
             runs_list = runs_list[0:1]
-    
+
         print(model, runs_list)
 
         for run in runs_list:
-
             cmd_content = [
-                'variability_modes_driver.py', 
-                '-p', os.path.join(param_dir, param_file), 
-                '--variability_mode', mode,
-                '--osyear', str(osyear),
-                '--eofn_obs', str(eofn_obs),
-                '--eofn_mod', str(eofn_mod),
-                '--mip', mip, '--exp', exp,
-                '--modnames', model,
-                '--realization', run,
+                "variability_modes_driver.py",
+                "-p",
+                os.path.join(param_dir, param_file),
+                "--variability_mode",
+                mode,
+                "--osyear",
+                str(osyear),
+                "--eofn_obs",
+                str(eofn_obs),
+                "--eofn_mod",
+                str(eofn_mod),
+                "--mip",
+                mip,
+                "--exp",
+                exp,
+                "--modnames",
+                model,
+                "--realization",
+                run,
             ]
 
             if model != models_list[0] or run != runs_list[0]:
                 cmd_content.extend(["--no_plot_obs", "--no_nc_out_obs"])
 
             if debug:
-                cmd_content.append('--debug True')
+                cmd_content.append("--debug True")
+
+            cmd = " ".join(cmd_content)
 
-            cmd = ' '.join(cmd_content)
+            log_file = (
+                "_".join(["variability_modes", mode, mip, exp, model, run]) + ".txt"
+            )
 
-            log_file = '_'.join(['variability_modes', mode, mip, exp, model, run]) + '.txt'
-    
-            print(cmd) 
+            print(cmd)
             cmds_list.append(cmd)
             logfilename_list.append(log_file)
 
@@ -118,8 +127,11 @@ def find_latest(path):
     "/p/user_pub/pmp/pmp_results/pmp_v1.1.2",
     "log",
     "variability_modes",
-    mip, exp, case_id)
- 
+    mip,
+    exp,
+    case_id,
+)
+
 os.makedirs(log_dir, exist_ok=True)
 
 parallel_submitter(
@@ -128,4 +140,3 @@ def find_latest(path):
     logfilename_list=logfilename_list,
     num_workers=num_workers,
 )
-
diff --git a/pcmdi_metrics/variability_mode/variability_modes_driver.py b/pcmdi_metrics/variability_mode/variability_modes_driver.py
index 2920b6311..16e5244b6 100755
--- a/pcmdi_metrics/variability_mode/variability_modes_driver.py
+++ b/pcmdi_metrics/variability_mode/variability_modes_driver.py
@@ -451,7 +451,7 @@
                 eof_lr_obs[season](region_subdomain),
                 frac_obs[season],
                 output_img_file_obs,
-                debug=debug
+                debug=debug,
             )
             plot_map(
                 mode + "_teleconnection",
@@ -462,7 +462,7 @@
                 eof_lr_obs[season](longitude=(lon1g, lon2g)),
                 frac_obs[season],
                 output_img_file_obs + "_teleconnection",
-                debug=debug
+                debug=debug,
             )
             debug_print("obs plotting end", debug)
 
@@ -757,7 +757,7 @@
                             eof_lr_cbf(region_subdomain),
                             frac_cbf,
                             output_img_file + "_cbf",
-                            debug=debug
+                            debug=debug,
                         )
                         plot_map(
                             mode + "_teleconnection",
@@ -768,7 +768,7 @@
                             eof_lr_cbf(longitude=(lon1g, lon2g)),
                             frac_cbf,
                             output_img_file + "_cbf_teleconnection",
-                            debug=debug
+                            debug=debug,
                         )
 
                     debug_print("cbf pcs end", debug)

From 75c54e1897ab00f6c0329870a877c445b0557e8b Mon Sep 17 00:00:00 2001
From: lee1043 <lee1043@llnl.gov>
Date: Thu, 2 Nov 2023 17:08:12 -0700
Subject: [PATCH 106/133] keep shapely version to 2.0.1 because of python
 version limit for cdat

---
 conda-env/dev.yml | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/conda-env/dev.yml b/conda-env/dev.yml
index 8a632cb78..31788ad64 100644
--- a/conda-env/dev.yml
+++ b/conda-env/dev.yml
@@ -27,7 +27,7 @@ dependencies:
     - netcdf4=1.6.3
     - regionmask=0.9.0
     - rasterio=1.3.6
-    - shapely=2.0.2
+    - shapely=2.0.1
     # ==================
     # Testing
     # ==================

From f99d5e3abeaa9a56c46a72c583df3622478d2ff8 Mon Sep 17 00:00:00 2001
From: Jiwoo Lee <lee1043@llnl.gov>
Date: Mon, 6 Nov 2023 21:25:58 -0800
Subject: [PATCH 107/133] new option: use arrow to contrast two highlighted
 models

---
 .../parallel_coordinate_plot_lib.py           | 85 +++++++++++++------
 1 file changed, 60 insertions(+), 25 deletions(-)

diff --git a/pcmdi_metrics/graphics/parallel_coordinate_plot/parallel_coordinate_plot_lib.py b/pcmdi_metrics/graphics/parallel_coordinate_plot/parallel_coordinate_plot_lib.py
index 490ba7214..c59450b62 100644
--- a/pcmdi_metrics/graphics/parallel_coordinate_plot/parallel_coordinate_plot_lib.py
+++ b/pcmdi_metrics/graphics/parallel_coordinate_plot/parallel_coordinate_plot_lib.py
@@ -15,8 +15,11 @@ def parallel_coordinate_plot(
     metric_names,
     model_names,
     models_to_highlight=list(),
+    models_to_highlight_by_line=True,
     models_to_highlight_colors=None,
     models_to_highlight_labels=None,
+    models_to_highlight_markers=['s', 'o', '^', '*'],
+    models_to_highlight_markers_size=10,
     fig=None,
     ax=None,
     figsize=(15, 5),
@@ -37,7 +40,10 @@ def parallel_coordinate_plot(
     group2_name="group2",
     comparing_models=None,
     fill_between_lines=False,
-    fill_between_lines_colors=("green", "red"),
+    fill_between_lines_colors=("red", "green"),
+    arrow_between_lines=False,
+    arrow_between_lines_colors=("red", "green"),
+    arrow_alpha=1,
     vertical_center=None,
     vertical_center_line=False,
     vertical_center_line_label=None,
@@ -50,9 +56,12 @@ def parallel_coordinate_plot(
     - `data`: 2-d numpy array for metrics
     - `metric_names`: list, names of metrics for individual vertical axes (axis=1)
     - `model_names`: list, name of models for markers/lines (axis=0)
-    - `models_to_highlight`: list, default=None, List of models to highlight as lines
+    - `models_to_highlight`: list, default=None, List of models to highlight as lines or marker 
+    - `models_to_highlight_by_line`: bool, default=True, highlight as lines. If False, as marker
     - `models_to_highlight_colors`: list, default=None, List of colors for models to highlight as lines
     - `models_to_highlight_labels`: list, default=None, List of string labels for models to highlight as lines
+    - `models_to_highlight_markers`: list, matplotlib markers for models to highlight if as marker
+    - `models_to_highlight_markers_size`: float, size of matplotlib markers for models to highlight if as marker
     - `fig`: `matplotlib.figure` instance to which the parallel coordinate plot is plotted.
              If not provided, use current axes or create a new one.  Optional.
     - `ax`: `matplotlib.axes.Axes` instance to which the parallel coordinate plot is plotted.
@@ -76,7 +85,10 @@ def parallel_coordinate_plot(
     - `group2_name`: string, needed for violin plot legend if splited to two groups, for the 2nd group. Default is 'group2'.
     - `comparing_models`: tuple or list containing two strings for models to compare with colors filled between the two lines.
     - `fill_between_lines`: bool, default=False, fill color between lines for models in comparing_models
-    - `fill_between_lines_colors`: tuple or list containing two strings for colors filled between the two lines. Default=('green', 'red')
+    - `fill_between_lines_colors`: tuple or list containing two strings of colors for filled between the two lines. Default=('red', 'green')
+    - `arrow_between_lines`: bool, default=False, place arrows between two lines for models in comparing_models
+    - `arrow_between_lines_colors`: tuple or list containing two strings of colors for arrow between the two lines. Default=('red', 'green')
+    - `arrow_alpha`: float, default=1, transparency of arrow (faction between 0 to 1)
     - `vertical_center`: string ("median", "mean")/float/integer, default=None, adjust range of vertical axis to set center of vertical axis as median, mean, or given number
     - `vertical_center_line`: bool, default=False, show median as line
     - `vertical_center_line_label`: str, default=None, label in legend for the horizontal vertical center line. If not given, it will be automatically assigned. It can be turned off by "off"
@@ -230,8 +242,14 @@ def parallel_coordinate_plot(
                 label = models_to_highlight_labels[mh_index]
             else:
                 label = model
-
-            ax.plot(range(N), zs[j, :], "-", c=color, label=label, lw=3)
+            
+            if models_to_highlight_by_line:
+                ax.plot(range(N), zs[j, :], "-", c=color, label=label, lw=3)
+            else:
+                ax.plot(range(N), zs[j, :], models_to_highlight_markers[mh_index], 
+                        c=color, label=label, 
+                        markersize=models_to_highlight_markers_size)
+                
             mh_index += 1
         else:
             if identify_all_models:
@@ -251,8 +269,8 @@ def parallel_coordinate_plot(
             vertical_center_line_label = None
         ax.plot(range(N), zs_middle, "-", c="k", label=vertical_center_line_label, lw=1)
 
-    # Fill between lines
-    if fill_between_lines and (comparing_models is not None):
+    # Compare two models
+    if comparing_models is not None:
         if isinstance(comparing_models, tuple) or (
             isinstance(comparing_models, list) and len(comparing_models) == 2
         ):
@@ -261,24 +279,41 @@ def parallel_coordinate_plot(
             m2 = model_names.index(comparing_models[1])
             y1 = zs[m1, :]
             y2 = zs[m2, :]
-            ax.fill_between(
-                x,
-                y1,
-                y2,
-                where=y2 >= y1,
-                facecolor=fill_between_lines_colors[0],
-                interpolate=True,
-                alpha=0.5,
-            )
-            ax.fill_between(
-                x,
-                y1,
-                y2,
-                where=y2 <= y1,
-                facecolor=fill_between_lines_colors[1],
-                interpolate=True,
-                alpha=0.5,
-            )
+
+            # Fill between lines
+            if fill_between_lines:
+                ax.fill_between(
+                    x,
+                    y1,
+                    y2,
+                    where=(y2 > y1),
+                    facecolor=fill_between_lines_colors[0],
+                    interpolate=False,
+                    alpha=0.5,
+                )
+                ax.fill_between(
+                    x,
+                    y1,
+                    y2,
+                    where=(y2 < y1),
+                    facecolor=fill_between_lines_colors[1],
+                    interpolate=False,
+                    alpha=0.5,
+                )
+                
+            if arrow_between_lines:
+                # Add vertical arrows
+                for xi, yi1, yi2 in zip(x, y1, y2):
+                    if (yi2 > yi1):
+                        arrow_color = arrow_between_lines_colors[0]
+                    elif (yi2 < yi1):
+                        arrow_color = arrow_between_lines_colors[1]
+                    else:
+                        arrow_color = None
+                    arrow_length = yi2 - yi1
+                    ax.arrow(xi, yi1, 0, arrow_length, color=arrow_color, 
+                            length_includes_head=True,
+                            alpha=arrow_alpha, width=0.05, head_width=0.15)
 
     ax.set_xlim(-0.5, N - 0.5)
     ax.set_xticks(range(N))

From 668f3523795818f6428a067ab428eedefd690d74 Mon Sep 17 00:00:00 2001
From: Jiwoo Lee <lee1043@llnl.gov>
Date: Mon, 6 Nov 2023 21:28:13 -0800
Subject: [PATCH 108/133] pre-commit fix

---
 .../parallel_coordinate_plot_lib.py           | 39 ++++++++++++-------
 1 file changed, 26 insertions(+), 13 deletions(-)

diff --git a/pcmdi_metrics/graphics/parallel_coordinate_plot/parallel_coordinate_plot_lib.py b/pcmdi_metrics/graphics/parallel_coordinate_plot/parallel_coordinate_plot_lib.py
index c59450b62..8c37181a3 100644
--- a/pcmdi_metrics/graphics/parallel_coordinate_plot/parallel_coordinate_plot_lib.py
+++ b/pcmdi_metrics/graphics/parallel_coordinate_plot/parallel_coordinate_plot_lib.py
@@ -18,7 +18,7 @@ def parallel_coordinate_plot(
     models_to_highlight_by_line=True,
     models_to_highlight_colors=None,
     models_to_highlight_labels=None,
-    models_to_highlight_markers=['s', 'o', '^', '*'],
+    models_to_highlight_markers=["s", "o", "^", "*"],
     models_to_highlight_markers_size=10,
     fig=None,
     ax=None,
@@ -56,7 +56,7 @@ def parallel_coordinate_plot(
     - `data`: 2-d numpy array for metrics
     - `metric_names`: list, names of metrics for individual vertical axes (axis=1)
     - `model_names`: list, name of models for markers/lines (axis=0)
-    - `models_to_highlight`: list, default=None, List of models to highlight as lines or marker 
+    - `models_to_highlight`: list, default=None, List of models to highlight as lines or marker
     - `models_to_highlight_by_line`: bool, default=True, highlight as lines. If False, as marker
     - `models_to_highlight_colors`: list, default=None, List of colors for models to highlight as lines
     - `models_to_highlight_labels`: list, default=None, List of string labels for models to highlight as lines
@@ -242,14 +242,19 @@ def parallel_coordinate_plot(
                 label = models_to_highlight_labels[mh_index]
             else:
                 label = model
-            
+
             if models_to_highlight_by_line:
                 ax.plot(range(N), zs[j, :], "-", c=color, label=label, lw=3)
             else:
-                ax.plot(range(N), zs[j, :], models_to_highlight_markers[mh_index], 
-                        c=color, label=label, 
-                        markersize=models_to_highlight_markers_size)
-                
+                ax.plot(
+                    range(N),
+                    zs[j, :],
+                    models_to_highlight_markers[mh_index],
+                    c=color,
+                    label=label,
+                    markersize=models_to_highlight_markers_size,
+                )
+
             mh_index += 1
         else:
             if identify_all_models:
@@ -300,20 +305,28 @@ def parallel_coordinate_plot(
                     interpolate=False,
                     alpha=0.5,
                 )
-                
+
             if arrow_between_lines:
                 # Add vertical arrows
                 for xi, yi1, yi2 in zip(x, y1, y2):
-                    if (yi2 > yi1):
+                    if yi2 > yi1:
                         arrow_color = arrow_between_lines_colors[0]
-                    elif (yi2 < yi1):
+                    elif yi2 < yi1:
                         arrow_color = arrow_between_lines_colors[1]
                     else:
                         arrow_color = None
                     arrow_length = yi2 - yi1
-                    ax.arrow(xi, yi1, 0, arrow_length, color=arrow_color, 
-                            length_includes_head=True,
-                            alpha=arrow_alpha, width=0.05, head_width=0.15)
+                    ax.arrow(
+                        xi,
+                        yi1,
+                        0,
+                        arrow_length,
+                        color=arrow_color,
+                        length_includes_head=True,
+                        alpha=arrow_alpha,
+                        width=0.05,
+                        head_width=0.15,
+                    )
 
     ax.set_xlim(-0.5, N - 0.5)
     ax.set_xticks(range(N))

From e6345a1a927635deeb75eeddea3b7fbe963888b0 Mon Sep 17 00:00:00 2001
From: Jiwoo Lee <lee1043@llnl.gov>
Date: Mon, 6 Nov 2023 21:31:54 -0800
Subject: [PATCH 109/133] style fix for pre-commit test

---
 .../mean_climate/lib/compute_metrics.py       | 38 ++++++++-------
 .../mean_climate/lib/load_and_regrid.py       | 44 +++++++++---------
 .../lib/mean_climate_metrics_to_json.py       | 17 +++++--
 .../mean_climate/mean_climate_driver.py       | 46 +++++++++----------
 4 files changed, 76 insertions(+), 69 deletions(-)

diff --git a/pcmdi_metrics/mean_climate/lib/compute_metrics.py b/pcmdi_metrics/mean_climate/lib/compute_metrics.py
index 410649f91..67d80a5ee 100644
--- a/pcmdi_metrics/mean_climate/lib/compute_metrics.py
+++ b/pcmdi_metrics/mean_climate/lib/compute_metrics.py
@@ -35,23 +35,23 @@ def compute_metrics(Var, dm, do, debug=False):
 
     # unify time and time bounds between observation and model
     if debug:
-        print('before time and time bounds unifying')
-        print('dm.time: ', dm['time'])
-        print('do.time: ', do['time'])
+        print("before time and time bounds unifying")
+        print("dm.time: ", dm["time"])
+        print("do.time: ", do["time"])
 
     """
     # Below is temporary...
     dm['time'] = do['time']
     dm[dm.time.encoding['bounds']] = do[do.time.attrs['bounds']]
     """
-    
+
     if debug:
-        print('after time and time bounds unifying')
-        print('dm.time: ', dm['time'])
-        print('do.time: ', do['time'])
-        
-        dm.to_netcdf('dm.nc')
-        do.to_netcdf('do.nc')
+        print("after time and time bounds unifying")
+        print("dm.time: ", dm["time"])
+        print("do.time: ", do["time"])
+
+        dm.to_netcdf("dm.nc")
+        do.to_netcdf("do.nc")
 
     metrics_dictionary = OrderedDict()
 
@@ -65,15 +65,15 @@ def compute_metrics(Var, dm, do, debug=False):
 
     print("compute_metrics, rms_xyt")
     rms_xyt = pcmdi_metrics.mean_climate.lib.rms_xyt(dm, do, var)
-    print('compute_metrics, rms_xyt:', rms_xyt)
+    print("compute_metrics, rms_xyt:", rms_xyt)
 
     print("compute_metrics, stdObs_xyt")
     stdObs_xyt = pcmdi_metrics.mean_climate.lib.std_xyt(do, var)
-    print('compute_metrics, stdObs_xyt:', stdObs_xyt)
+    print("compute_metrics, stdObs_xyt:", stdObs_xyt)
 
     print("compute_metrics, std_xyt")
     std_xyt = pcmdi_metrics.mean_climate.lib.std_xyt(dm, var)
-    print('compute_metrics, std_xyt:', std_xyt)
+    print("compute_metrics, std_xyt:", std_xyt)
 
     # CALCULATE ANNUAL MEANS
     print("compute_metrics-CALCULATE ANNUAL MEANS")
@@ -82,23 +82,23 @@ def compute_metrics(Var, dm, do, debug=False):
     # CALCULATE ANNUAL MEAN BIAS
     print("compute_metrics-CALCULATE ANNUAL MEAN BIAS")
     bias_xy = pcmdi_metrics.mean_climate.lib.bias_xy(dm_am, do_am, var)
-    print('compute_metrics-CALCULATE ANNUAL MEAN BIAS, bias_xy:', bias_xy)
+    print("compute_metrics-CALCULATE ANNUAL MEAN BIAS, bias_xy:", bias_xy)
 
     # CALCULATE MEAN ABSOLUTE ERROR
     print("compute_metrics-CALCULATE MSE")
     mae_xy = pcmdi_metrics.mean_climate.lib.meanabs_xy(dm_am, do_am, var)
-    print('compute_metrics-CALCULATE MSE, mae_xy:', mae_xy)
+    print("compute_metrics-CALCULATE MSE, mae_xy:", mae_xy)
 
     # CALCULATE ANNUAL MEAN RMS (centered and uncentered)
     print("compute_metrics-CALCULATE MEAN RMS")
     rms_xy = pcmdi_metrics.mean_climate.lib.rms_xy(dm_am, do_am, var)
     rmsc_xy = pcmdi_metrics.mean_climate.lib.rmsc_xy(dm_am, do_am, var)
-    print('compute_metrics-CALCULATE MEAN RMS: rms_xy, rmsc_xy: ', rms_xy, rmsc_xy)
+    print("compute_metrics-CALCULATE MEAN RMS: rms_xy, rmsc_xy: ", rms_xy, rmsc_xy)
 
     # CALCULATE ANNUAL MEAN CORRELATION
     print("compute_metrics-CALCULATE MEAN CORR")
     cor_xy = pcmdi_metrics.mean_climate.lib.cor_xy(dm_am, do_am, var)
-    print('compute_metrics-CALCULATE MEAN CORR: cor_xy:', cor_xy)
+    print("compute_metrics-CALCULATE MEAN CORR: cor_xy:", cor_xy)
 
     # CALCULATE ANNUAL OBS and MOD STD
     print("compute_metrics-CALCULATE ANNUAL OBS AND MOD STD")
@@ -166,9 +166,7 @@ def compute_metrics(Var, dm, do, debug=False):
     metrics_dictionary["std_xy"]["ann"] = format(std_xy, sig_digits)
     metrics_dictionary["std-obs_xyt"]["ann"] = format(stdObs_xyt, sig_digits)
     metrics_dictionary["std_xyt"]["ann"] = format(std_xyt, sig_digits)
-    metrics_dictionary["std-obs_xy_devzm"]["ann"] = format(
-        stdObs_xy_devzm, sig_digits
-    )
+    metrics_dictionary["std-obs_xy_devzm"]["ann"] = format(stdObs_xy_devzm, sig_digits)
     metrics_dictionary["std_xy_devzm"]["ann"] = format(std_xy_devzm, sig_digits)
     metrics_dictionary["rms_xyt"]["ann"] = format(rms_xyt, sig_digits)
     metrics_dictionary["rms_xy"]["ann"] = format(rms_xy, sig_digits)
diff --git a/pcmdi_metrics/mean_climate/lib/load_and_regrid.py b/pcmdi_metrics/mean_climate/lib/load_and_regrid.py
index 53d8a1264..bcf6d0b88 100644
--- a/pcmdi_metrics/mean_climate/lib/load_and_regrid.py
+++ b/pcmdi_metrics/mean_climate/lib/load_and_regrid.py
@@ -34,16 +34,15 @@ def load_and_regrid(
 
     # load data
     ds = xcdat_open(
-        data_path, 
-        data_var=varname_in_file, 
-        decode_times=decode_times)  # NOTE: decode_times=False will be removed once obs4MIP written using xcdat
-    
+        data_path, data_var=varname_in_file, decode_times=decode_times
+    )  # NOTE: decode_times=False will be removed once obs4MIP written using xcdat
+
     # SET CONDITIONAL ON INPUT VARIABLE
     if varname == "pr":
-        print('Adjust units for pr')
+        print("Adjust units for pr")
         if ds[varname_in_file].units == "kg m-2 s-1":
             ds[varname_in_file] = ds[varname_in_file] * 86400
-            print('pr units adjusted to [mm d-1] from [kg m-2 s-1] by 86400 multiplied')
+            print("pr units adjusted to [mm d-1] from [kg m-2 s-1] by 86400 multiplied")
 
     """
     # calendar quality check
@@ -64,20 +63,22 @@ def load_and_regrid(
             ds.time.attrs["calendar"] = 'standard'
             print('[WARNING]: calendar info not found for time axis. ds.time.attrs["calendar"] is adjusted to standard')
     """
-    
+
     # time bound check #1 -- add proper time bound info if cdms-generated annual cycle is loaded
-    if isinstance(ds.time.values[0], np.float64):  # and "units" not in list(ds.time.attrs.keys()):
-        ds.time.attrs['units'] = "days since 0001-01-01"
+    if isinstance(
+        ds.time.values[0], np.float64
+    ):  # and "units" not in list(ds.time.attrs.keys()):
+        ds.time.attrs["units"] = "days since 0001-01-01"
         ds = xc.decode_time(ds)
         if debug:
-            print('decode_time done')
-            
+            print("decode_time done")
+
     # time bound check #2 -- add time bounds itself it it is missing
-    if 'bounds' in list(ds.time.attrs.keys()):
-        time_bnds_key = ds.time.attrs['bounds']
+    if "bounds" in list(ds.time.attrs.keys()):
+        time_bnds_key = ds.time.attrs["bounds"]
         if time_bnds_key not in list(ds.keys()):
-            ds = ds.bounds.add_missing_bounds(['T'])
-            print('[WARNING]: bounds.add_missing_bounds conducted for T axis')
+            ds = ds.bounds.add_missing_bounds(["T"])
+            print("[WARNING]: bounds.add_missing_bounds conducted for T axis")
 
     # level - extract a specific level if needed
     if level is not None:
@@ -143,15 +144,14 @@ def load_and_regrid(
 
     ds_regridded[varname] = ds_regridded[varname].assign_attrs({"units": units})
 
-    ds_regridded[varname] = ds_regridded[varname].assign_attrs({'units': units})
-    
+    ds_regridded[varname] = ds_regridded[varname].assign_attrs({"units": units})
+
     # time bound check #3 -- preserve time bnds in regridded dataset
-    if 'bounds' in list(ds_regridded.time.attrs.keys()):
-        time_bnds_key = ds_regridded.time.attrs['bounds']
+    if "bounds" in list(ds_regridded.time.attrs.keys()):
+        time_bnds_key = ds_regridded.time.attrs["bounds"]
         if time_bnds_key not in list(ds_regridded.keys()):
-            ds_regridded = ds_regridded.bounds.add_missing_bounds(['T'])
-            print('[WARNING]: bounds.add_missing_bounds conducted for T axis')
-        
+            ds_regridded = ds_regridded.bounds.add_missing_bounds(["T"])
+            print("[WARNING]: bounds.add_missing_bounds conducted for T axis")
 
     if debug:
         print("ds_regridded:", ds_regridded)
diff --git a/pcmdi_metrics/mean_climate/lib/mean_climate_metrics_to_json.py b/pcmdi_metrics/mean_climate/lib/mean_climate_metrics_to_json.py
index 337e52ff9..cf13f3375 100644
--- a/pcmdi_metrics/mean_climate/lib/mean_climate_metrics_to_json.py
+++ b/pcmdi_metrics/mean_climate/lib/mean_climate_metrics_to_json.py
@@ -24,12 +24,21 @@ def mean_climate_metrics_to_json(
             if m == model:
                 ref_list = list(json_dict["RESULTS"][m].keys())
                 if debug:
-                    print('debug:: mean_climate_metrics_to_json:: m, ref_list: ', m, ref_list)
-                if 'units' in ref_list:
-                    ref_list.remove('units')
+                    print(
+                        "debug:: mean_climate_metrics_to_json:: m, ref_list: ",
+                        m,
+                        ref_list,
+                    )
+                if "units" in ref_list:
+                    ref_list.remove("units")
                 for ref in ref_list:
                     if debug:
-                        print('debug:: mean_climate_metrics_to_json:: m, ref, json_dict["RESULTS"][m][ref]: ', m, ref, json_dict["RESULTS"][m][ref])
+                        print(
+                            'debug:: mean_climate_metrics_to_json:: m, ref, json_dict["RESULTS"][m][ref]: ',
+                            m,
+                            ref,
+                            json_dict["RESULTS"][m][ref],
+                        )
                     runs_in_model_dict = list(json_dict["RESULTS"][m][ref].keys())
                     for r in runs_in_model_dict:
                         if (r != run) and (run is not None):
diff --git a/pcmdi_metrics/mean_climate/mean_climate_driver.py b/pcmdi_metrics/mean_climate/mean_climate_driver.py
index 61e1dffbf..6b61fdcfd 100755
--- a/pcmdi_metrics/mean_climate/mean_climate_driver.py
+++ b/pcmdi_metrics/mean_climate/mean_climate_driver.py
@@ -233,32 +233,36 @@
     # ----------------
     if "all" in reference_data_set:
         reference_data_set = [
-            x for x in list(obs_dict[varname].keys()) if (x == "default" or "alternate" in x)]
+            x
+            for x in list(obs_dict[varname].keys())
+            if (x == "default" or "alternate" in x)
+        ]
         print("reference_data_set (all): ", reference_data_set)
 
     for ref in reference_data_set:
-        print('ref:', ref)
-        
+        print("ref:", ref)
+
         # identify data to load (annual cycle (AC) data is loading in)
         ref_dataset_name = obs_dict[varname][ref]
         ref_data_full_path = os.path.join(
-            reference_data_path,
-            obs_dict[varname][ref_dataset_name]["template"])
-        print('ref_data_full_path:', ref_data_full_path)
-        
+            reference_data_path, obs_dict[varname][ref_dataset_name]["template"]
+        )
+        print("ref_data_full_path:", ref_data_full_path)
+
         # load data and regrid
         ds_ref = load_and_regrid(
-            data_path=ref_data_full_path, 
-            varname=varname, 
-            level=level, 
-            t_grid=t_grid, 
-            # decode_times=False, 
-            decode_times=True, 
-            regrid_tool=regrid_tool, 
-            debug=debug)
+            data_path=ref_data_full_path,
+            varname=varname,
+            level=level,
+            t_grid=t_grid,
+            # decode_times=False,
+            decode_times=True,
+            regrid_tool=regrid_tool,
+            debug=debug,
+        )
 
         ds_ref_dict = OrderedDict()
-        
+
         # for record in output json
         result_dict["References"][ref] = obs_dict[varname][ref_dataset_name]
 
@@ -421,7 +425,7 @@
                                     )
 
                             # compute metrics
-                            
+
                             print("compute metrics start")
                             result_dict["RESULTS"][model][ref][run][
                                 region
@@ -469,11 +473,7 @@
         # write collective JSON --- all models / all obs / single variable
         json_filename = "_".join([var, target_grid, regrid_tool, "metrics", case_id])
         mean_climate_metrics_to_json(
-            metrics_output_path,
-            json_filename,
-            result_dict,
-            cmec_flag=cmec,
-            debug=debug
+            metrics_output_path, json_filename, result_dict, cmec_flag=cmec, debug=debug
         )
 
-print("pmp mean clim driver completed")
\ No newline at end of file
+print("pmp mean clim driver completed")

From 3b4959d64d1edc0332b99af591033dc3ae660a74 Mon Sep 17 00:00:00 2001
From: Jiwoo Lee <lee1043@llnl.gov>
Date: Wed, 8 Nov 2023 15:23:52 -0800
Subject: [PATCH 110/133] add annotate_text_colors parameter

---
 pcmdi_metrics/graphics/portrait_plot/portrait_plot_lib.py | 3 +++
 1 file changed, 3 insertions(+)

diff --git a/pcmdi_metrics/graphics/portrait_plot/portrait_plot_lib.py b/pcmdi_metrics/graphics/portrait_plot/portrait_plot_lib.py
index 5914e47c0..bc04665fa 100644
--- a/pcmdi_metrics/graphics/portrait_plot/portrait_plot_lib.py
+++ b/pcmdi_metrics/graphics/portrait_plot/portrait_plot_lib.py
@@ -16,6 +16,7 @@ def portrait_plot(
     ax=None,
     annotate=False,
     annotate_data=None,
+    annotate_textcolors=("black", "white"),
     annotate_fontsize=15,
     annotate_format="{x:.2f}",
     figsize=(12, 10),
@@ -61,6 +62,7 @@ def portrait_plot(
     - `annotate`: bool, default=False, add annotating text if true,
                   but work only for heatmap style map (i.e., no triangles)
     - `annotate_data`: 2d numpy array, default=None. If None, the image's data is used.  Optional.
+    - `annotate_textcolors`: Tuple. A pair of colors for annotation text. Default is ("black", "white")
     - `annotate_fontsize`: number (int/float), default=15. Font size for annotation
     - `annotate_format`: format for annotate value, default="{x:.2f}"
     - `figsize`: tuple of two numbers (width, height), default=(12, 10), figure size in inches
@@ -175,6 +177,7 @@ def portrait_plot(
                 data=data,
                 annotate_data=annotate_data,
                 valfmt=annotate_format,
+                textcolors=annotate_textcolors,
                 threshold=(2, -2),
                 fontsize=annotate_fontsize,
             )

From dca315b2f557a846d0c79750b4004ac86d7cae8a Mon Sep 17 00:00:00 2001
From: Jiwoo Lee <lee1043@llnl.gov>
Date: Wed, 8 Nov 2023 15:44:02 -0800
Subject: [PATCH 111/133] update example notebook, add
 annotate_textcolors_threshold parameter

---
 .../portrait_plot/portrait_plot_example.ipynb | 100 ++++++++----------
 .../portrait_plot/portrait_plot_lib.py        |   8 +-
 2 files changed, 50 insertions(+), 58 deletions(-)

diff --git a/pcmdi_metrics/graphics/portrait_plot/portrait_plot_example.ipynb b/pcmdi_metrics/graphics/portrait_plot/portrait_plot_example.ipynb
index 69d68b73a..922976736 100644
--- a/pcmdi_metrics/graphics/portrait_plot/portrait_plot_example.ipynb
+++ b/pcmdi_metrics/graphics/portrait_plot/portrait_plot_example.ipynb
@@ -76,16 +76,18 @@
     "----------\n",
     "- `data`: 2d numpy array, a list of 2d numpy arrays, or a 3d numpy array (i.e. stacked 2d numpy arrays)\n",
     "- `xaxis_labels`: list of strings, labels for xaixs. Number of list element must consistent to x-axis,\n",
-    "                  or 0 (empty list) to turn off xaxis tick labels\n",
+    "                or 0 (empty list) to turn off xaxis tick labels\n",
     "- `yaxis_labels`: list of strings, labels for yaxis. Number of list element must consistent to y-axis,\n",
-    "                  or 0 (empty list) to turn off yaxis tick labels\n",
+    "                or 0 (empty list) to turn off yaxis tick labels\n",
     "- `fig`: `matplotlib.figure` instance to which the portrait plot is plotted.\n",
-    "         If not provided, use current axes or create a new one.  Optional.\n",
+    "        If not provided, use current axes or create a new one.  Optional.\n",
     "- `ax`: `matplotlib.axes.Axes` instance to which the portrait plot is plotted.\n",
     "        If not provided, use current axes or create a new one.  Optional.\n",
     "- `annotate`: bool, default=False, add annotating text if true,\n",
-    "              but work only for heatmap style map (i.e., no triangles)\n",
+    "                but work only for heatmap style map (i.e., no triangles)\n",
     "- `annotate_data`: 2d numpy array, default=None. If None, the image's data is used.  Optional.\n",
+    "- `annotate_textcolors`: Tuple. A pair of colors for annotation text. Default is (\"black\", \"white\")\n",
+    "- `annotate_textcolors_threshold`: Tuple or float. Value in data units according to which the colors from textcolors are applied. Default=(-2, 2)\n",
     "- `annotate_fontsize`: number (int/float), default=15. Font size for annotation\n",
     "- `annotate_format`: format for annotate value, default=\"{x:.2f}\"\n",
     "- `figsize`: tuple of two numbers (width, height), default=(12, 10), figure size in inches\n",
@@ -109,12 +111,12 @@
     "- `legend_on`: bool, default=False, show legend (only for 2 or 4 triangles portrait plot).  Optional.\n",
     "- `legend_labels`: list of strings, legend labels for triangls.  Optional.\n",
     "- `legend_box_xy`: tuple of numbers, position of legend box's upper-left corner.  Optional.\n",
-    "                   (lower-left if `invert_yaxis=False`), in `axes` coordinate.  Optional.\n",
+    "                (lower-left if `invert_yaxis=False`), in `axes` coordinate.  Optional.\n",
     "- `legend_box_size`: number, size of legend box.  Optional.\n",
     "- `legend_lw`: number, line width of legend, default=1.  Optional.\n",
     "- `legend_fontsize`: number, font size for legend, default=14.  Optional.\n",
     "- `logo_rect`: sequence of float. The dimensions [left, bottom, width, height] of the the PMP logo.  Optional.\n",
-    "               All quantities are in fractions of figure width and height.  Optional\n",
+    "                All quantities are in fractions of figure width and height.  Optional\n",
     "- `logo_off`: bool, default=False, turn off PMP logo.  Optional.\n",
     "- `debug`: bool, default=False, if true print more message when running that help debugging.  Optional.\n",
     "\n",
@@ -143,14 +145,12 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
-       "<Figure size 864x720 with 3 Axes>"
+       "<Figure size 1200x1000 with 3 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -179,14 +179,12 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": 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",
       "text/plain": [
-       "<Figure size 864x720 with 3 Axes>"
+       "<Figure size 1200x1000 with 3 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -216,14 +214,12 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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On1EnrVab8ue5c+dKlpaWUpkyZaQnT578qtgZzvfU6dPGHUlJSVKDBg1SnjNlypSUx3z6HRgXFye9fPkyzd5HRvBfavV3n/9MzZw5UzI0NJSqVKkiPXv27Jdmz0i+p06f12PGjBmSubm5JAiCVKJECalfv37SunXrpC5dukjm5uZSjhw5vtjwiH7Mz/iMkiRJev78uVSnTh1JEARp2LBhvzAxERH9KDbUiH4hURQlJycnSRAEydXVVbK3t5cEQZDMzMykevXqScuXL5c0Gk3KicrnB8J/FxISwmbaL/Ijdfp024IFCyRBEKQGDRpIBQoUYDPtF/jZP085c+aUsmfPLt24cSOt3kKG8L11+rRr8eLFi6XixYtLkyZNSnmtT7vjccfcX+Nn/kwFBwdLmTNnlrJnzy7dvHkzrd5ChvC9dUpMTEx57s6dO6X27dtLFhYWKY0eIyMjqVKlSvzd95P9zJ+nw4cPS4IgSFWrVpXevXuXVm+BiIi+ExtqRL+IKIqSVquVRo0aJQmCIDVu3FhKTEyUxo4dm3LA9emga9KkSV8cAP/dvHnzJEEQpKxZs7KZ9pP9aJ0+HRhv2LAh5bHW1tbSlStX5Hg76daP1ik5OVmKi4uTpk+fLlWsWFESBEEqU6YMTyh/sh+p04cPH6Rbt26l/P1TM41+jZ/xuy8hIUFavny5VLt27ZRRUPyZ+rl+xrFEbGysdPv2bWn16tXS3LlzpTNnznzXiCn6Zz/zmE8URenly5eSp6cnf56IiBSODTWiXywyMlLKly+fZGhoKJ04cUKSJEl6/fq1tGLFCql8+fIpV43Lli0rzZw5U7p8+fJXr/HgwQPJ0dFRunbtWlrHzzB+tE6bN2+WBEGQLCws2PT8hX60TnPnzpWKFSsmDRgwQHr8+LEcbyFD+N46Xbx48Yvnfz7VnX6tH/2ZWrBggZQ/f36pS5cu0sOHD+V4CxnCzziWoF/vR+v0+ai1uLi4NM1ORETfjw01ol/o0wiLKVOmSIIgSP369fvi/sePH0t//fWXVKxYsZRmTPbs2aXp06dLf/755xeP/TQtin6+H6nT+fPnUx43depUTvP8hX6kTufOnUt53KNHj6T4+Pg0zZ6R/Mzfe/Rr/ayfqdu3b0tv375N0+wZCX+m1OFn1YlT3ImI1IMNNaI0cObMGcnS0lISBEE6evToF/cdP35csrGxkQRBkBwdHVOmBRQuXFhq166dFBERIUkSD7DSwn+tk7u7OxfgTkP/tU5t2rSRnj59KlPqjOdHfu9xk4i09V9r1bp1a/5MpSH+TKkD60RElHGwoUaURoKCgiRBEKTBgwen3LZ3716pUKFCkiAI0vTp0yVJ+jh1sFu3bikHWZxCk7b+a50ePHggU+KMiT9P6sA6qQdrpQ6skzqwTkREGQMbakS/2KeRZX/++aeUK1cuKV++fFJ8fLx08uTJlAOrqVOnfvEcrVYrXb58mdvZpyHWSR1YJ3VgndSDtVIH1kkdWCciooyFDTWiNNS2bVtJEASpXr16KUP+Pz+w+rROGne3kxfrpA6skzqwTurBWqkD66QOrBMRUfonSJIkgYh+KVEUoaenh2vXrqFhw4Z4+vQpAGD69Ono1avXF48h+bBO6sA6qQPrpB6slTqwTurAOhERZRz8TU6UBj4dNOXJkweVK1cGALRo0SLlwCo5OZkHVgrAOqkD66QOrJN6sFbqwDqpA+tERJRx8Lc5URrKkiULunfvDgC4cOECrly5AlEUoa+vL3My+hzrpA6skzqwTurBWqkD66QOrBMRUfrHhhrRfySK4n96npOTE9q2bYuIiAhcvHgRenp64MzrX4d1UgfWSR1YJ/VgrdSBdVIH1omIiL6FDTWi/+DTcP0PHz7g+PHjuHjxIj58+PCvnquvrw9HR0ckJydj4MCBePjwIQRB+MWJMybWSR1YJ3VgndSDtVIH1kkdWCciItKFDTWi76TVaqGvr4/4+Hh4enqiTp06GDp0KO7fv/+Pz/10VbJLly6oUKECNBoNDA0Nf3XkDIl1UgfWSR1YJ/VgrdSBdVIH1omIiFLDXT6JvsOnXZni4+Ph4OCAK1euoHHjxggKCkKZMmX+1VXH5ORk6OvrY/Xq1ahcuTKKFCmSBskzFtZJHVgndWCd1IO1UgfWSR1YJyIi+idsqBF9pw8fPqB9+/bYtGkTAgMDERAQABMTk+9+HUmSOOz/F2Kd1IF1UgfWST1YK3VgndSBdSIiotSwoUb0nU6ePIlGjRqhUqVK2L59O0xMTFKuYpJysE7qwDqpA+ukHqyVOrBO6sA6ERFRavhpQPQNUVFROu87f/483r59C3d3d5iYmKQsVktpj3VSB9ZJHVgn9WCt1IF1UgfWiYiI/it+IhD9zZAhQ2Bvb4+rV69+8/7k5OQv/v6tA6tPAz8fPnz4r3eCou/DOqkD66QOrJN6sFbqwDqpA+tEREQ/gg01os/ExMTgypUruH37Nm7fvv3FfZ8OmExNTQEAa9aswevXr79aE0MURQiCgFevXmHo0KE4f/582oTPQFgndWCd1IF1Ug/WSh1YJ3VgnYiI6IdJRPSF+/fvS/v27ZMkSZLev38v3bt374v7o6OjJTs7Oylr1qzSkiVLpPj4eEmSJEkURUmr1aY8rn///pIgCNL27dvTLnwGwjqpA+ukDqyTerBW6sA6qQPrREREP4INNaLPiKKY8ufExETJ1tZWsrW1la5du5Zye1JSkjR58mTJwsJCKlasmLR8+XLp9evXX7zOvHnzpGzZskk1a9aUoqKi0ix/RsE6qQPrpA6sk3qwVurAOqkD60RERD+KDTWiz3z48CHlzy9evJCaNWsm6enpSU5OTtLVq1dTDr6eP38u9e3bVzIzM5Ny5colNW3aVNq6dau0e/duqXv37pKlpaWUK1cu6datW3K9lXSNdVIH1kkdWCf1YK3UgXVSB9aJiIh+FBtqRP/v09D9uLg4afPmzdKHDx+kJ0+eSB07dpQEQZCcnJykK1euSMnJyZIkSdKzZ8+ksWPHSqVLl5YEQZAMDAwkQRAkPT09qWLFitKNGzfkfDvpFuukDqyTOrBO6sFaqQPrpA6sExER/QyCJP3/qptEhISEBFSoUAGPHz/Ghg0b4OLiggcPHiAkJARLly6Fo6Mjpk6dilKlSkFPTw+JiYl49eoVli5dipiYGCQmJsLBwQE1a9ZEzpw55X476RbrpA6skzqwTurBWqkD66QOrBMREf0wuTt6RHL7fA2NoUOHSlmyZJEGDhwoxcXFpdz+4MGDr65afv48+vVYJ3VgndSBdVIP1kodWCd1YJ2IiOhnYkONMrRPQ/41Go0UHR0tubm5Sc7Ozinrany+g9PfD7CuXr2act/nB1o86Pr5WCd1YJ3UgXVSD9ZKHVgndWCdiIjoZ2NDjTK89+/fS5UrV5a8vb2l3377TZo+fbokSR8PuP7u7wdYn+8ERb8W66QOrJM6sE7qwVqpA+ukDqwTERH9THpyTzklkoP02dKB169fx+vXr7F+/Xo8evQIb9++BQAYGhp+9TwbGxsEBQXB29sbx44dQ4cOHXDz5s00y53RsE7qwDqpA+ukHqyVOrBO6sA6ERHRLyNjM48oTXzaoemTpKQkSZI+Xo2Mi4uTRFGUDh8+LDk7O0uCIEgFCxaUrly5kuprPnz4UGrZsqVkZWUlPXz48Jdlz0hYJ3VgndSBdVIP1kodWCd1YJ2IiCgtsaFG6dqntS1u3LghHThwQEpISJAkSZLevn0rlSxZUho1apSk1WolrVYrHTx4UKpXr54kCILUrVu3fzxoevTokRQZGfnL30NGwDqpA+ukDqyTerBW6sA6qQPrREREac1A7hFyRL+SIAiIjIxEzZo1kTlzZqxZswZFihSBk5MTbt68CUEQIIoiDA0N4eDgAEEQ8P79eyxZsgQWFhbw9fVFgQIFvvnaum6n78c6qQPrpA6sk3qwVurAOqkD60RERGlO7o4e0a8WGRkp9e7dW7K2tpbs7OwkGxsbycjISJowYYIUHx8vSdL/pggkJydLhw8flmrUqCEZGxtLAwYMkB49eiRn/AyDdVIH1kkdWCf1YK3UgXVSB9aJiIjSEhtqlCEkJSVJAwcOlPT19SV9fX3J29tbio2NlSRJStku/ZNvHWA9fvxYjtgZDuukDqyTOrBO6sFaqQPrpA6sExERpRU21Cjd+7Smxu+//y4ZGBhIRkZGUvny5aVjx45JiYmJ33zO5wdY5ubmUvfu3aWIiIi0jJ3hsE7qwDqpA+ukHqyVOrBO6sA6ERFRWhIk6bO9pInSsYCAAEgfm8iYOXMmSpQogdDQUDg5OcHA4H/LCYqiCD09PYiiiBMnTsDHxwevXr3ClStXkCNHDhnfQcbAOqkD66QOrJN6sFbqwDqpA+tERERpIu17eES/3qcrlJIkSVqt9ov73rx5IwUEBEimpqZShQoVpH379qVMAfh8KoAoipIoitKJEyekBw8epEnujIZ1UgfWSR1YJ/VgrdSBdVIH1omIiOTCEWqU7iQnJ0NfXx+iKAIAXr58CWNjY1hbW6c85smTJ5g7dy6mTJmSctXS3t4exsbGAIAVK1YgOjoaXbt2hbm5uRxvI91jndSBdVIH1kk9WCt1YJ3UgXUiIiJZyd3RI/qZPl1tjI+Pl3x9faWKFStKJiYmUqFChaRBgwZJt2/fTrl6GRERIQ0dOjTlquXOnTulpKQkafny5VLWrFklCwsLKSoqSs63k26xTurAOqkD66QerJU6sE7qwDoREZHc2FCjdOPTNuhxcXFSxYoVJUEQpJIlS0pNmzaVChYsKAmCIFWpUkVatWqVpNFoJEmSpKdPn0rDhg2TrKyspLx580oVK1aUMmXKJOXIkUO6fPmynG8n3WKd1IF1UgfWST1YK3VgndSBdSIiIiVgQ43SlaSkJKlJkyaSnp6eFBAQIMXFxUmSJEmvXr2Shg0bJgmCILm6ukoXLlxIec6LFy+kefPmSYULF5ayZs0qOTg4SLdu3ZLrLWQIrJM6sE7qwDqpB2ulDqyTOrBOREQkNzbUKF05deqUZGJiIrm5uUkJCQlf3FexYkXJzMxMGjBggBQbG/vVc+Pi4qSHDx9+8z76uVgndWCd1IF1Ug/WSh1YJ3VgnYiISG56cq/hRvS9oqKiACBlAdrPXbp0CUlJSejbty8yZcoE4OOCtdWrV8eFCxfQr18/BAUFwcLCAvHx8SnP02q1MDMzQ8GCBWFhYZE2bySdY53UgXVSB9ZJPVgrdWCd1IF1IiIiJWNDjVTFz88Ptra2uHHjBvT09L46wHr//j0AIDIyEsDHA6tatWrhzJkzGDp0KPz8/GBpaQkA2LJlCwIDAwEABgYGafgu0j/WSR1YJ3VgndSDtVIH1kkdWCciIlI8uYfIEf1bQ4cOlQRBkARBkGxtbaUbN25IkvS/hWklSZK2bNkiCYIgTZ8+XZIkSapSpYokCIIUGBj41bD+WrVqSTly5JBevHiRdm8iA2Cd1IF1UgfWST1YK3VgndSBdSIiIjVgQ41UISEhQerevbskCIKUNWtWSRAEqXTp0tLNmzclSZJStkW/deuWVKhQIUlfX18qWbKkJAiCNGTIECkmJuaL1wsODpYMDQ2lgICAlN2f6MexTurAOqkD66QerJU6sE7qwDoREZFasKFGqrF582bJ2NhYatq0qeTk5JRygPX33ZnGjBkjCYIg6enpSV5eXl+9zqxZs6Rs2bJJtra20uPHj9MofcbBOqkD66QOrJN6sFbqwDqpA+tERERqwIYaqYq7u7uUN29e6fLly1KHDh10HmD5+PhIgiBI+vr60rJly6SwsDDp3LlzUteuXSUTExMpR44cKVc66edjndSBdVIH1kk9WCt1YJ3UgXUiIiKlY0ONVOHTmhmbN2+WBEGQunbtKr169Upyc3PTeYAVFBQkZc6cOWUNDkEQJENDQ6lmzZpfPZZ+DtZJHVgndWCd1IO1UgfWSR1YJyIiUgtBkiRJ7o0RiHSRJAmCIKT8PSEhAfXq1UN4eDhOnDiBXLlyoXXr1tizZw9KlSqFjRs3olixYimPP3v2LK5fv45bt27BxMQEDg4OKFOmDLJnzy7H20m3WCd1YJ3UgXVSD9ZKHVgndWCdiIhIdWRr5RHpsHbtWunPP/+U4uPjJUmSJFEUJUmSpA8fPkiSJEkHDhyQDAwMpICAAEmSJOndu3dS48aNdV61pF+DdVIH1kkdWCf1YK3UgXVSB9aJiIjUjA01UhQ/Pz9JEASpSJEikouLi3Tp0iXp7du3Xzzm0aNHUoUKFaQsWbJI58+flyRJkmJjY6UmTZp8dYD1aSco+rlYJ3VgndSBdVIP1kodWCd1YJ2IiEjt2FAjxVizZk3Kuhf58uWTChQoIJmYmEgtWrSQtmzZ8sVjFy5cKAmCIE2bNi3ltrdv337zAOvTWhz0c7BO6sA6qQPrpB6slTqwTurAOhERUXrAhhopSrt27SRBECQLCwtp1KhR0ogRI1IOuFq0aCEtXLhQSk5OlpKSkiR7e3spX7580tOnT1Oe//lVy7x580p37tyR8d2kX6yTOrBO6sA6qQdrpQ6skzqwTkREpHZsqJEifH5F0cPDQxIEQcqcObN079496caNG1KvXr0kS0tLSRAEqUqVKtKGDRukjh07SmZmZtLYsWOl5OTklNeIjY2V7O3tJUEQpPDwcLneUrrEOqkD66QOrJN6sFbqwDqpA+tERETpBRtqpBifH2B5eXlJgiBIWbJkka5fvy5JkiTdvXtX6tWrl2RjYyNlypRJKl68uCQIguTg4CBpNJovXuvdu3fS48eP0zR/RsE6qQPrpA6sk3qwVurAOqkD60REROkBG2qkKJ8fYHl7e0uCIEjW1tbS2bNnJUmSpPj4eOnZs2eSn5+fVKFCBUkQBKlSpUopu0JJkvTFn+nXYJ3UgXVSB9ZJPVgrdWCd1IF1IiIitRMkSZJApCCiKEJPTw8A0LFjRyxbtgxWVlYICwtDxYoVUx5369YthIeHo0GDBtDX1//iefTrsU7qwDqpA+ukHqyVOrBO6sA6ERGRmrGhRoqk6wDr4MGDKF++/FePT05Ohr6+flrHzPBYJ3VgndSBdVIP1kodWCd1YJ2IiEit2FAjxfqnAyxenVQG1kkdWCd1YJ3Ug7VSB9ZJHVgnIiJSI34ykWLp6elBFEUAwJIlS+Dl5YW3b9+iTp06uHjx4hf3k3xYJ3VgndSBdVIP1kodWCd1YJ2IiEiN2FAjRdN1gNWgQQOcO3eOVysVgnVSB9ZJHVgn9WCt1IF1UgfWiYiI1IZTPkkVPh/q37VrVyxatAgFCxbErVu3YGRkBEEQZE5IAOukFqyTOrBO6sFaqQPrpA6sExERqYWB3AGI/o1PVy319PSwYMECGBsbo0uXLjA2NpY7Gn2GdVIH1kkdWCf1YK3UgXVSB9aJiIjUgiPUSFW4KK06sE7qwDqpA+ukHqyVOrBO6sA6ERGR0vFTihQtKSkJwcHBSEpKAgAeWCnY57VinZSLdVIH1kk9WCt1YJ3UgXUiIiI14Qg1UrTY2FhYWVnh7du3sLS0lDsOpYK1UgfWSR1YJ/VgrdSBdVIH1omIiNSEl36IiIiIiIiIiIi+AxtqRERERERERERE34G7fKYjoigiMjISFhYW6WZL8djY2C/+S8rFWqkD66QOrJN6sFbqwDqpQ3qskyRJePfuHfLkyaPKdeESExOh0WjkjvEVIyMjmJiYyB0jXdq9ezfmzp0rdwzKgGJiYmBvb4+RI0eqpp/BNdTSkSdPniB//vxyxyAiIiIios9EREQgX758csf4LomJiciayRwJSJY7yldy5cqFBw8esKn2C9SuXRv3799H+fLl5Y5CGcjp06cRHR0N4GNjzcrKSuZE/w5HqKUjFhYWAIDwtVNgYZpJ5jT0d/HvE9Fw0Dh4162K5YfPY92hs3JHIh2G9O8NbdxbmJmaoHXpvKhlW0TuSPQNm09cwrR95+HT7Q8c2r0dS0cNlDsSfcPjyBeo12sEqnv1x5lV03Frx1K5I9E3iKIIp1Z/QHz4Ci+gwewzl+WORDpsnD8Vdw/tgJNtYehnzwO/zm3ljkTfcO7KTXTzH41eNWwxaPuxlON0NdFoNEhAMtojL4wUtFKQBiJWPX8KjUbDhtov4uTkhOXLl8sdgzKIHj16IDo6GsbGxkhKSpI7zndhQy0d+TQs0sI0EyzN2FBTkmfRb1C//xiM9GyEptVsseroBVhYcPcqpRFFER3dm+F3m3yYPncy/ugzEKYmRrBkg1pxpm49hE2XHuDIgTBcuHQJxw0MYGluJncs+puLN+7Apd8YOA+YiDy/l8G5NTNhaWEudyz6m8TERNjWaYVcL7UonWyF7QbRMDVX38l/RjBrWD98CL+EsOAuWHDgHBKMjPi7T4G2hh1DQOg0rPFogIQkLQCoZvrStxhBT1ENNSJKP5o1a4Zt27ahcOHCGD58ODp06CB3pO/C34xEv9iNh09Rt99ozO7VBk2r2codh3TQaDRoXt8BDlUqYOaEUFWuc5JR+C3airB70TiwdzcyZ7aWOw7psO/En3AdMB5Nh81Bnt/LyB2HdIh+HYMi1RujUJSI0slszChZaPe2sHp5BxsHecDYkNfElWr2qi0YPn4mNnq5okDm9HHxVB+AvqCgL7n/QYjoh4miiCpVqmDbtm2oWrUq7ty5g0yZ1DeIgZ/GRL/Qsb9uocekRVgX0BklC+SWOw7p8DYmBu6uddGvexd09GgjdxxKhefklYB1LuzYuhAGBvwIU6pFW/YiaPE2tBy1BOZZsssdh3QIfxSBGq4dUCXRDHklTptSKq1Wi2HtXVDntywY2bq5qkc6pXeBU+bh0IFj2OzVCGbGhnLHISJSpISEBNja2iI8PBzNmjXDli1b5I70n/FshOgX2XD4DEKXb8XukT2QL3tmueOQDk+fRKBD80aYGDIMLvXryh2HdBBFES4j5qNMlRqYOG4sTygVLGTeaiw68Cdaj1kOYzNOG1Sqs5euonG7nnDQWCMbjOSOQzokxMdhiHs9dLO3RXfnKnLHoVR0HDwaL8LDsdajIYwMOIaKiOhboqKiULp0abx8+RI9evTArFmz5I70Q9hQI/oFpm3Yi3UHTiIs1BdZLDiFRqluXLuKXh3bYensaahSkTsZKVWiRgOnobPRsp0HBvbrK3ccSkW3kBk4fO8lWo1aAgMjY7njkA7bw46ii28g6n/IAktwFI1SvX4ZhWHtGiCklSPcqpSSOw7pIIoiGnUZgGzJGixsVRd6eunvgo++IEBfQRey9CEAktwpiOh73b17F3Z2doiPj0doaCgCAgLkjvTD2FAj+sn8567Bpdv3sT/UF6bGvOqvVMcPH8RI//7YunopihUpLHcc0uF1bBzqDJuLwQEBaNemtdxxKBWuviPwBFZwGzYHevocnaFUs1dsRMio6XDRZoMpVyJSrIjwOxjTtSXmdm2KWiULyR2HdNBoNKjVxgf2ebOiX61KHD1NRKTD6dOn4eDggA8fPmDZsmWq23xAFzbUiH4ir9FzkZT0HtuHd4chh/sr1qZ1q7Fk1hTs37oOeXLlkjsO6fDgeTSahC7B1ClTUK+Ok9xxSIfk5GRU9RoE4yIV0bBDP55QKtiQsTOwYslGuGqzccc+Bbv+52nM8euG9f3boFR+fkYpVWxcHKq3/AMd7X5H+/LF5I5DRKRYW7ZsQcuWLaGnp4c9e/bA2dlZ7kg/DRtqRD+BKIpw8ZuA4nmzY1Lv1jyhVLDZ0ybi8K7tOLh9I6ytrOSOQzpcuPcY3tPXY/nSpahQ3k7uOKRDQkIi7Nr3RSHHFrBr7Cl3HEpFh96BOLXvJFy02T5OlyJFOrF3OzZNCsKuIV4owPVXFevJ85dwatcDgU4VUL9YQbnj/HJ6/7+7plLoAZzySaQSs2fPRq9evWBsbIzTp0+jXLlyckf6qdhQI/pBSZoPcPANgVv1shjYoo7ccSgVwf4DEHHvFsK2rlPltswZxd7z1+C/5gC2b92Cwr9xqpNSRb16g4odBqFim54oVrOh3HFIB1EUUb9Ndzy/fB/1tFkgsJmmWNuWzcWZ9QuxP6gzslly/VWlunIrHM27D8LUJrVQkSMIiYh0Gjp0KEJDQ2FlZYUrV66gQIECckf66dhQI/oBMXHxcPANQX83J3g4VZY7DqWiZ0cPmBsCO9Yuhz7Xd1KsxftOYv6RKwjbuxs5c+SQOw7pcPtBBBy7B8GxRzAK2HLnQaXSarWo4NwGJk/eoZbWis00BVsyfjienT2AsKAuMDPh+qtKdej0BXQPGI0l7nVRlCMIiYh08vLywvLly5E3b15cu3YN1tbWckf6JdhQI/qPHj1/CZdB4zGxS3M4VyghdxzSQRRFtGvaEFXtSmNscCCn4yrY6LX7cCg8Cgf27YGlpaXccUiHU5euoXnAZLgOnoochbhukFLFxSWgjFNL2MQAJZIt5I5DqZgysBtMXoZj5xBvrr+qYKu270fotAVY69EQeazM5Y6TphS5yycRKZIoinB2dsaBAwdQqlQpXLx4EUZG6fdCERtqRP/BpTsP4REyC0v6e6Bi0fS/doZaJSYmomUDR3i6N0dfn65yx6FU9Jq7EZEfDLF353YYGxvLHYd02HLgBHpMXo7mwQtglTOv3HFIh8gXL1GxfhuUS8gEG9FE7jikgyiKGNm5BcpYSJjWry0v+CjYhIWrsW7DdmzycoV1Jn5GERF9i0ajQcWKFXH16lU4OjriwIED0NNL35sgsaFG9J32nbuCQbNWYsuwriiSh1PSlOr1q2i0dq2HwIF90KZFM7njkA6iKKL1+GXIYlMMm2fPTPcfumo2Y/VWjF9/EO6hS2FqlUXuOKTDjTv34eDWETUSLZEbPPFXKo1GgyGt66NF2QLwd3OQOw6lou+oafjr3AVs8HJFJkOeOhERfUtsbCxKlSqFJ0+eoF27dli1apXckdIEPxWIvsPSPccwc+Ne7B3VC7kyc0qaUj28H45OrZth9sQxcKxdU+44pINWq0W9oHmwr98QI4YP4+gMBfObsggbz92Be+gyGGUylTsO6XD49Hm07jgAdT5YIzPS7/QKtYuLjcEQ93oY0LAKOjiUlzsOpaJ172H4EPUCK9s5w0A/417w0VfYLp+cGE2kLE+ePIGtrS3evHkDPz8/jBs3Tu5IaYYNNaJ/acyKbdhz5hLCQn1hZcYdIpXq0oXzGNi9M9Ytngfb0iXljkM6xCUkwilwNjp384FPN07HVbL2QybgUpQGLUcshL6hodxxSIe12/eiz6BQNNBmgTkP7xQr6tlTBHu4YpKnMxrYcQ1CpRJFEXU8fFHM1BDDmzvwgg8RkQ5XrlxB1apV8f79e0yfPh2+vr5yR0pTPOIi+hd8py7Fw8gX2BvSEyZGPKFUqv27d2LSqCDs2rAShQqmv22Z04vnb2LhPHweRoaEwK1pE7njkA6iKKJO90C8syyAJgFBEDgdV7EmzF2GKVMWwVWbFSYcu6FY4TevYXLPdljSozkqF+VnlFIlJmpQvVVXNC6SFz7Vy8gdh4hIsQ4ePIgGDRpAFEVs2LABLVu2lDtSmmNDjegftAqaBnMjA2we2hX6GXi4v9KtXLIQG5YvxIGt65Ejeza545AOtyKeodX4lZg3dw5q1qgudxzSQaPRoKLnQGS1c0S91j5yx6FU9B42DtvX74GrNhsMwc8opbpw/DCWBffBlkEeKJqHn1FKFf0mBrXcu6FX1dJoYVtE7jiKwV0+iejvVq1aBU9PTxgYGODo0aOoWTNjLrPDhhqRDlqtFvX6j0G14jYI8WzE4f4KNil0JC6eOoqD2zfCwjxjbWWvJqeu30P3+duwYf1alCxRQu44pENsXDzs2vZDiUYdUKZ+xrvSqCYtugzAteOX0ECbFXo8wVSsA5vXYu+8cdgb2Am5s3D9VaUKf/wUDbz6YHSDKqj9Wz654xARKdaECRPg5+cHMzMznD9/HiUy8HE9G2pE35CQmIjavULgVbcKejaqLXccSsXgPj0QF/0MezetgZERF+FWqs0nLmH01uPYvXM7CuTPL3cc0uHJi5eo6j0Y1b0HoXBlR7njkA6iKKJmM2+8v/0MTtrMENhMU6z1c6fg2u41CBveBdZcf1Wxzl+9hba9hmB2c0fYcgQhEZFOffv2xbRp05A1a1Zcu3YNuXLlkjuSrDg3gOhvot7Eokq3IPi3qsdmmoKJoojObVogEz5g4/JFbKYp2MwdRzF53584sG8vm2kKduXOfVTq4Aen3qPZTFOwxMRElLJvDuFWFKpprdhMU7B5Iwbh8ZEt2DesM5tpCrbr8Cl49h6Kle2c2UzT4dMun0r6ImV4+PAhBEH44svU1BR58uRBnTp1EBQUhPDw8B/+PsHBwRAEAUeOHPnx0ACOHTuGgQMHwtHREVZWVhAEAd7e3j/ltdOzli1bYtq0aShUqBAePnyY4ZtpAEeoEX3hTsQzuA2ZhJk92sC+DNfOUCqtVgt317po4FALQYP7czqugg1ZtgMXX8QjbO9umJmZyR2HdDhw+iI8R85C48BZyJrvN7njkA6vY96iXJ1WKBZrgKIip7cr2dhensilicaKwR2gzw09FGv+uu2YtWAlNnRwQXZzU7njEKlW4cKF4eHhAQBISkpCVFQUzp07h5CQEISGhsLPzw+jR49WzDH74sWLsWzZMpiamqJAgQKIjY2VO5KiiaKIWrVq4dSpU6hQoQLOnDkDAwO2kgA21IhSnLp2B3+MW4A1gzuhtE0eueOQDu9iY9HKpQ56dfZCV28PueNQKjpOW40k06zYtW0lDA25O65Srdx5AIPnbUKLkMWwyJpT7jikw6Onz1ClQXtUem+K/JKJ3HFIB1EUMczDFbXyWSC0S0vFnDzS14KnL8TevYew0csVFiYc5U70I4oUKYLg4OCvbj9+/Dg6dOiAMWPGQF9fHyEhIWkf7ht69eqFQYMGoXjx4jh//jyqVasmdyTFSkxMhK2tLe7evQtXV1fs3LlT7kiKwktmRAC2HjuPHhMXYUdwdzbTFOzFs2dwq1cLIQED2UxTMFEU0WjkfFgXKok1K5ezmaZgoQvXYeiSnXAPXcpmmoJduHoDleq1Qa0EMzbTFCwxIQEDmtZC6zJ5MaZ9AzbTFKzrkLE4feQk1nm6sJn2L3za5VNJX6QOtWrVwr59+2BsbIzx48cjIiICAPD27VuMGzcO9vb2yJMnD4yMjJAnTx506NDhqymiDg4OGDFiBADA0dExZWqpjY1NymMOHz6MTp06oVixYjA3N4e5uTkqVqyI+fPnfzNXxYoVUapUKejr6/+aN55OREdHw8bGBnfv3kXXrl3ZTPsGjlCjDG/OljAs23MU+0N9kc2SU2iU6vbNG/Dp0BqLZkxG9SqV5I5DOmg0WjgGzkbTVu7wHzRQ7jiUil5j5mDfjadoNXopDI3ZpFGqXYdOoGOPIaj3ITOswOa0UsW8eonAts4Y5lYb7tVt5Y5DOoiiiKbdB8PifRyWtK4HPT02Zoh+td9//x2tW7fG8uXLsXXrVvj6+uLmzZsICgqCo6Mj3NzcYGZmhlu3bmH16tXYtWsXLl68iIIFCwJAytpmR48ehZeXV0ojzdraOuV7jBs3Dvfu3UPVqlXh5uaGmJgY7N27F926dcPt27cxadKkNH7X6hceHg47Ozu8e/cOI0aMQFBQkNyRFIkNNcrQAheux5mrtxEW6gszE2O545AOZ04cR2D/Xti8YhFKFPtd7jikQ0xcApwC56Bf/wHw6sARhErm1n80whNN0Hz4XOjp81BAqRas3oJhI6bARZsVpuBVdKV6+vA+Rnd2w6xOjeBQurDccUgHrVaL2m18UDWHFQY1rsURhERpyN7eHsuXL8f58+cBACVKlMCzZ8+QJUuWLx53+PBh1K1bF6NGjcKCBQsAfGyoPXz4EEePHoW3tzccHBy+ev05c+agUKFCX9ym1Wrh4uKCadOmoU+fPihQoMCveXPp0Pnz51GrVi1oNBosXLgQnTt3ljuSYnHKJ2VYncfNx+0HEdg1ogebaQq2bdMGjPTvh31b1rKZpmCPXrxCrYBZGDtuHJtpCiaKIqp6DcRz03xwGTiezTQFGz5pDkaMmApXbTY20xTs5qU/MaZTM6zp485mmoLFxSfArrEXmvyWG36OFdhM+04CPp40KuWL1VOfPHk+LqkTHR0NALCysvqqmQZ8nNJZqlQpHDhw4Lte/+/NNAAwMDBA9+7dkZycjMOHD/+H1BnTzp07Ua1aNWi1WuzcuZPNtH/AI2nKcERRRJOASSiYzRrzBneEHnffUqwFs6Zh79YNOLh9A7Jkzix3HNLhr/AIeExdi6WLF6FypYpyxyEdEhM1KNeuL/LXaoKKzbzljkOp6NR/OI7uOgZXbTbo89RRsU6F7cb68QHYEdABNjm+PjEkZYiMioZjWx8Mti8PlxI2cschypAkSfrqtiNHjmDq1Kk4e/YsoqOjodVqU+4zMvq+tQ3fvXuHiRMnYuvWrQgPD0d8fPwX90dGRv634BnM/Pnz0b17dxgZGeHEiROoWJHH9f+EDTXKUDQaLRz7jkLDCiUwpLWz3HEoFaMCByP8+mUc2LoBpqaZ5I5DOoRdvIFBK/Zh2+ZNKFq0iNxxSIfoNzGo4DEQdq26o4R9I7njUCoatOuBxxduo542C/TYTFOsXasW4djK2dgf1BnZuf6qYt249wBNugzExEY1ULVgbrnjEGVYz549AwBkz54dALBhwwa0bt0a5ubmcHZ2ho2NDUxNTSEIApYuXYpHjx7969fWaDRwcHDAxYsXYWdnB09PT2TNmhUGBgZ4+PAhli1bhqSkpF/yvtKT4OBgjBgxApaWlvjrr7++OeqPvsaGGmUYsXEJsO8dgl6N7dGxXlW541Aqenf1hmFyEnauXwkDA/6aUqoVB89gRthF7NuzC7lz5ZI7DukQHvEUtboGwqHbMBQsV13uOKSDVqtFZZf20H/0BvZaawhspinWismj8PDELoQN7wKLTFwyQqmOnf8LnQeNxKJWdVCMIwh/iNJ21uTIXfU5cuQIAKBSpY8biwUHB8PExAQXLlxA0aJFv3js2rVrv+u1t23bhosXL6JLly4p6659/lrLli3778EziC5dumDRokXIlSsXrl+//s3puPRtPFOlDCHixSs0HDQO4zo2RcNKpeSOQzqIogjP5o1QtkQRTB49gmucKNi4DWHYc+MJDu7bAysrK7njkA7nrtxCk0Hj4TJoMnIWLiF3HNIhISERZZxaIP9rESWTLeWOQ6mY7t8Tek9vYvfQjjDiBR/FWr/7EIInzsba9g2R15ojCInkdOfOHaxfvx7GxsZwc3MD8HEHyVKlSn3VTIuMjER4ePhXr6Gv/3Et0eTk5K/u+/T4Jk2afHXf8ePHfzh/eiaKIho1aoQ9e/agePHiuHTpEkxMuPP79+DiUZTuXQl/DOeBY7CgT1s20xQsMTERTevWQkPHmpgSOpLNNAXrO38zTj19h/17drGZpmA7j55GU/9JaDZ8LptpCvb8ZTSKVm+E318BJZPN5I5DOoiiiJFdWiJnXATW9W/HZpqCTVm6DmOmzsMmL1c204hkduLECTg7OyMpKQkBAQHImzcvAKBgwYK4d+8eXrx4kfLYxMRE+Pj4fLGW2iefRkw9efLkq/sKFiyY8r0+d/To0a9GrNH/aLVaVKxYEXv27EGtWrVw/fp1NtP+Ax4NULp26OJ19Jm6DJuHdsXv+XLKHYd0iHn9Gu6udeHfpyfat24hdxxKRdvxy2GapyC2rpibcrWQlGfehl0IWbkbrUYtgVnmbHLHIR1u33+E2o29UC3RAnnAqYNKpdVqMaRNfTQukQdDWzTkBR8F8xs3E2dOnMEmL1eYGhnKHSfd0Bc+fikFjz6U5969ewgODgbwcU2zqKgonD17FteuXYO+vj4CAwMRFBSU8nhfX1/4+vrCzs4OLVu2hFarRVhYGCRJQtmyZXH58uUvXt/R0RGCIGDo0KG4desWrKysYGVlBR8fHzRu3Bg2NjYYP348rl27htKlS+P27dvYuXMnmjVrhk2bNn2V98SJE1i4cCEA4OXLlym3eXt7AwCKFy8Of3//X/AvpQxxcXEoXbo0Hj16hFatWmH9+vVyR1ItNtQo3VoVdhKT1+zE7pAeyJvVWu44pEPEowfwbtUUU8eMRH0nB7njkA6iKKL+8Hmo6lAHo0dyOq6SBc5chlXHr6F16HIYmXLEk1Kd/PMvuHn2hqPGGlnxfbuZUdpJiIuFf6t66F2vPDrXqSR3HEpF+/7BePfkCVa1bwhDfU7CIUpL4eHhGDFiBAAgU6ZMsLa2RvHixTFs2DB4eXmhcOHCXzy+Z8+eMDQ0xIwZM7BgwQJYW1vD1dUVoaGhcHd3/+r1S5YsiSVLlmDSpEmYMmUKkpKSULBgQfj4+MDc3ByHDh3CoEGDcOzYMRw5cgSlSpXCqlWrkDNnzm821O7du/fV2mrh4eEp00ft7e3TbUMtMjIStra2ePXqFfr164fJkyfLHUnV2FCjdGnimp3Ydvw8wkJ9YW1uKncc0uHKX5fQt6sXVs6fiQrlbOWOQzokJGrgOHQ2vDp3Rq8ePnLHoVR4B03GmSfxaBmyCAaGbNIo1cbdB9Cz30g4a7PAgodiihX9PBLDPVwxpm1dNK7IadNKJYoi6nv1hY0RMLGlEy/4EKUhGxsbSJL03c8TBAHdunVDt27dvrrv0wYGf+fl5QUvL69v3leoUCFs3Ljxm/d9K5+3t3fKaLSM5Pr166hcuTISEhIwadIk9O/fX+5IqsfLN5Tu9J+5EocvXMXekJ5spinYobB9GNi9I3asXcZmmoJFxcSiut90DAoIYDNNwURRRP0eQbjyzgjNhs5gM03Bpixchd79RsJFm5XNNAV7eOcmgto1wMJuTdhMUzCNRoPKbp1QJYspRjWoxmbaL/JxyqegoK/vy3/o0CF06tQJxYsXh5mZGfLmzYumTZviwoULv+YfjEiBjh07Bjs7OyQmJmL16tVspv0kPJKjdKXtyJkwhIgtw/6AAdd3Uqx1K5Zh5cJZCNu6Hrly5pA7Dulw7+kLuI1djlkzZ8DBvrbccUgHrVaLSp4DYVGqBuq368UTSgXrHzIJm1bugKs2Gwx5TVOxLp8+joWBPbFpYDsUz8vPKKWKiY1DjZZd8Uel4mhd7ne545CCzZkzB69evUKfPn1QsmRJvHz5EpMmTULVqlWxb98+ODk5yR2R6Jdat24d2rVrBz09PRw8eBAODg5yR0o32FCjdEEURdQfMBZ2v+XFWO8mPKFUsOkTxuDUof04tGMTLC0s5I5DOpy9eR9d52zB2jWrUKZ0abnjkA5xCQko17Yffndui7IN28gdh1LRuvtgXDp8Hg20WaEPfkYp1ZEdm7F9xkjsGdoRebNyF2OlevT0Oep69MKI+pXhVCS/3HFI4WbNmoUcOb5sjjdo0ABFihRBaGgoG2qUrk2dOhX9+vVDpkyZcO7cOZTmcf1PxYYaqV6iRgP7XiFoY18BfZo6yB2HUjGkvy9ePX2E/VvWwtiYO9op1bbTf2HExqPYsX0LCtnYyB2HdIiMikYVr8Go6tkPRarVlTsO6SCKIhxadMbbGxGoo80Mgc00xdq8aCYubl2GA8O7IrN5JrnjkA4Xb9yBew9/zGxmj3IcQZgm1L7L59+baQBgbm6OkiVLIiIi4ueEIlKgQYMGYeLEiciSJQuuXr2KPHnyyB0p3WFDjVQtOiYWTn1HI8DdGa1rl5c7DqXiDw935LAyw5ZVS6DP6biKNW/3cSw7eQNhe/cge/ZscschHW7ce4g6PUegru8o5CtVQe44pINGo0H5+m1gEZmAGsnWcsehVCwcPQSvLh/HvmGdYWrMNQiVat/xs+g9bDyWt66H37JZyx2HZBYbG/vF342Njf/1Bdu3b9/i4sWLHJ1G6Vbbtm2xdu1aFChQANevX4e5ubnckdKl717AQxCElK/Tp0/rfNz69etTHmeTBiMcftb3CQ4OhiAIWLp06Xd9779/GRkZIX/+/Gjfvj2uXr36zedFRERg9uzZ8PLyQokSJaCnpwdBEHDmzJkffh8ZQfjTF7D3DcHkrs3ZTFMwrVaLVi51UK7Yb1g4YzKbaQoWvGo3Nl5+jAP72ExTsqPnL8OpVwgaDZnBZpqCxcTG4vfqTZArMhF2yTyIVbKJfToC9y9gu38HNtMUbMmmXRgYPBHrO7iwmUYAgPz588PKyirla8yYMf/6uT179kR8fDyGDh36CxMSpT1RFGFvb4+1a9eibNmyCA8PZzPtF/qhEWqrVq1CtWrVvnnfypUrf+SlVenzbXzfvn2LCxcuYPXq1di4cSP27t0LR0fHLx6/adMm9OvXL61jpgvnbt5Dx9C5WDnIG2V/yyd3HNIhPj4erRo6oatnW/To4i13HEpFt5nrEGNggT07tsHIiCeUSrV2z2H0n7UWLUYuhGX23HLHIR0iIp+jcoN2qJCQCQUkTh1UKlEUMbxDE1TJaYLxvu5cf1XBRs9Zhu3b92KTdyNYmvAzKq192l1TKT6tQxkREQFLS8uU2//t6LRhw4Zh1apVmDFjBipU4IUpSj80Gg3Kli2LW7duwdnZGbt374aeHjdB+pX+07+usbExSpYsiXXr1kGr1X51/6tXr7B3716UL5+xRg0tXbo05WvLli0IDw+Hp6cnNBoN+vTp89Xjf/vtN/Tr1w+rV6/G3bt3YW9vL0Nq9dl56iK6jluA7cO7s5mmYFEvXqCpUw0MG9ibzTQFE0URzUYtgmGuQli3eiWbaQo2adkm+C3YCvfRy9hMU7C/btxBhbqtUT3ejM00BdMkJmKgmz2aFc+OCR1c2ExTsB7DJ+LwvkNY5+nCZhp9wdLS8ouvf9NQGzFiBEaNGoXRo0ejV69eaZCSKG3ExMTAxsYGt27dQseOHbF3714209LAf/4Xbt++PaKjo7Fv376v7lu3bh0+fPgADw+PHwqndoaGhggODgYAXL16FTExMV/c36RJE0yePBlt27ZFkSJF0j6gCi3YcQghSzZh36heKJSLU9KU6t6dW2jTqC7mTBqD5o1d5Y5DOnz4oIXDkFmo7OSM2TOn80NXwfqOn4c5+y/APXQpMllayx2HdNh/7AzqN++CuknWyAluvKJUsW9eo3/TmhhY3w79G9eSOw6lws3HH9F372JZ2/owMeTSz/RjRowYgeDgYAQHB2PIkCFyxyH6aR49eoSCBQvi2bNnCAwMxOLFi+WOlGH8UENNEIRvTu1cuXIlzM3N0bRp01RfY/fu3ahXrx4yZ84MExMTFCtWDP7+/l81nj6Jj4/H4MGDUaBAAZiYmKB48eKYPHkyJElK9fucOHECbm5uyJEjB4yNjWFjY4PevXvj5cuX//r9/lc5c+ZM+fO3RvPRvxe8eBNW7z+B/aN9kcPaQu44pMO5M6fQzcMdG5ctQO0a354STvKLTXiPan7T0bFbDwQO8Zc7DqWild9YHLj/Fs2D58HQmCOelGrphu3o0NUPDT9kgTUM5Y5DOjyLeAT/lo6Y4lEfbWuWkzsO6aDValHLvRvyiRpMa2YPfV7wkZWe8L+dPpXwpfcfBpSGhIQgODgYgYGBGD58+M//RyKSycWLF1G8eHG8e/cOc+fORUhIiNyRMpT/fKmnYMGCqFGjBrZv3464uLiUhe4ePHiA06dPo0OHDjA1NdX5/DFjxmDIkCEwMDCAvb09smXLhpMnT2LcuHHYsmULjh079kUzKikpCfXr18epU6eQLVs2NG7cGO/evYO/vz/Cw8N1fp/p06ejb9++0NPTQ+XKlZE3b15cu3YNM2bMwM6dO3Hy5Enkzv3rps5cuHABAJAtWzZky8YRVf9V94mLEPXqDfaM7AEjXqFUrN3bt2L6uBDs2bgaBfNzOq5SPY1+g4YjF2LMmDFo7OoidxzSQRRF2HcJgCbH72jkN4RT0hRs1IwFmDNzJVy12WD8369V0i925+olTOvjhRW+LVGeS0YoVkJCIqq16oJWJWzQpUopueNQOjBp0iQEBQWhQYMGcHV1/WoDuKpVq8qUjOjH7N27F40aNQIAbN26FU2aNJE5UcbzQ50JDw8PnDhxAps3b0aHDh0A/G8zgvbt2+t83vnz5xEYGAgLCwscOHAAlStXBvCxaebp6YkNGzbA19cX69evT3nO5MmTcerUKVSuXBn79++HlZUVgI8d2b8v9v/JmTNn0K9fPxQoUADbt2+Hra0tAECSJIwaNQpBQUHo3bs3NmzY8CP/DN/09u1bnDt3LmVu/q8YVpyUlISkpKSUv/996+j0QBRFNA+cipyWplgf0IlT0hRsybzZ2L5uJQ5u24BsWbPIHYd0uPbgKdpMXo3FC+ejapUqcschHZKSNCjfvh9yVWmAmi27yh2HUtHdfxT2bTkAV21WGLCZplhnD+/D6tF+2D7YE7/lyip3HNIh6tUb1G7dDQNqlUXjkr/JHYfSiR07dgD42HzYu3fvV/f/02wnIiVasmQJOnfuDENDQxw7dgxVeFwvix868nN3d4eRkRFWrVqVctuqVauQK1cu1KlTR+fzZs6cCVEU0bdv35RmGvBxs4OZM2ciU6ZM2LRpE54+fZpy35w5cwAAU6ZMSWmmAUD58uXRs2fPb36fsWPHQhRFzJ8/P6WZBgCCICAwMBB2dnbYvHkzoqOjv//Nf4MgCClf1tbWqF+/PmJiYrB69epfspvnmDFjvtgqOn/+/D/9e8hJq9XCofcolCuUG7N7tmYzTcHGBA/DoV1bcHD7RjbTFOzI5dtoP20dNm9Yz2aagsXExqF4i54o3LADKrOZpmiNvXrj6JbDcGYzTdH2rl+OTeMCsG9YJzbTFOz2gwjUaNEFoc5V2ExTmE+7fCrp63scOXIEkiTp/CJSm9GjR6NTp04wMzPDjRs32EyT0Q8d/WXOnBkuLi44ePAgnj9/jvPnz+P27dto27Yt9PX1dT7v+PHjAL49ii1HjhyoX78+RFHEqVOnAACPHz9GREQE8ubNi+rVq3/1nLZt2351myiKOHjwICwsLL7Z3BMEATVq1IAoiinTMn+Ul5dXylebNm1QrVo1REdHw8/PD0ePHv0p3+NzAQEBePv2bcpXRETET/8ecolLSETlbkFo71ABQW0bcqqTgvXr3hnPH9zCno2rYWame5o3yWvNkfMYuCoMe3btQPHixeSOQzo8evocpdx7o4q3H0o5pb4OKclHFEVUatgekadvwUFrDT3wM0qp1kwfi7OrZyNseBfk5PqrinXq0lU06tgX81o4orpNHrnjEBEplo+PDwIDA5EjRw48ePAAhQsXljtShvbDi1F5eHhg69atWLt2LR48eJByW2oiIyMhCAIKFiz4zfttbGxSHvf5fwsUKPDNx3/r9levXiEuLg4AYGCQ+tv8WSPUli5d+tVtly5dgr29PZydnXHz5k0UKlTop3wv4OOIvn+zPbTaPIt+g/r9xyCkQ2M0qVpG7jikgyiK8G7VFMUK5ceM2fPZ9FSwyVsOYutfD3Fw315kzmwtdxzS4eKNO3DpNwYN+k9A7t9Lyx2HdEhISES5uq2QK1qL0smWcsehVMwK7IMP9y9jT2AnGHP9VcXaEnYMQ0KnYXU7Z+TPzKYnEZEuTZo0wY4dO1CkSBFcuXIFmTJxsyq5/fDRRaNGjWBtbY3ly5cjMjISJUqUQPny5X9GtpQT9E9DcXWdsH/r9uTkZACAhYUFmjdvnur30dXY+xns7OzQrVs3TJw4ETNnzsSkSZN+2fdKD248fIpWw6Zinm9bVOdwf8XSaDRo5VIHbg3rIaB/b7njUCr8Fm3FjZgPCNu7mx+6Crbn+Dl0Cp2PZkFzkTnPr/tMoh8T/ToG5eq2Qql3Rigsmskdh3QQRRFjfNqhoPAO8wZ5cMkIBZu5chMWLluPjV6uyGrGzyil+rS7plLongdFlD5ptVpUr14d58+fR7Vq1XDixAl+tinEDzfUjI2N0bJlSyxcuBAA0Lv3P59c58mTBw8ePMCjR49QrNjXU48ePXoEACm7b+bJk+eL23U9/nPZsmWDsbExDA0NvzlyLC19GpV2+/ZtWXMo3dG/bqLX5MVYH9AZJQrkkjsO6fA2JgbuLnXQv+cf8G7XWu44lAqPSSugnyUPtm9Z8I8jdUk+i7bsRdCS7Wg5agnMs2SXOw7pcPfBY9Rq7IUq782QFyZyxyEdtFotAts1hHORbBju3pyjpxVsyKS5OHroODZ5ucLM2FDuOEREipSQkIAyZcrg/v37cHNzw+bNm+WORJ/5KW3NDh06IGvWrMiWLVuqu3t+UqtWLQD4YjODT16+fIn9+/dDT08vZb20ggULIl++fHj69ClOnz791XPWrl371W0GBgZwcHDA69evcezYse99Sz/V/fv3AQBmZryarcuGw2fQb9oy7BrRg800BXv6JAJu9WtjXPAQNtMUTBRFOAfNRd6S5bF86WI20xRsxNyVCFkdhtahy9hMU7Czl66ihosHar+3YDNNwRLi4zCgaU14VfwNwa3rsZmmYN5+o3D59Hms8WjIZhoRkQ5RUVGwsbHB/fv34evry2aaAv2UhlqtWrUQHR2Nly9f/qvpkz179oSenh6mTZuGP//8M+V2jUYDX19fJCQkoHnz5sibN2/Kfd26dQMADBgwALGxsSm3//XXX5g1a9Y3v8+QIUOgp6cHLy8vnDhx4qv7IyMjdT73Z7l06RLmz58PAHBxcfml30utpm7Yg6nrdmN/qC/yZc8sdxzS4cbVK/B0c8XSWdPQsJ7uXXxJXokaDWoMno6GzVthwrgxPKFUsK4jp2P1mXtoNWoxjM24bpBSbd1/GI3b9kB9TWZkg5HccUiH1y+j4NesNoKb1kB3Z+52plSiKKJBx36QXjzDglZ1YGTAyXtq8GnKp5K+iNK727dv47fffsPLly8xZswYTJ8+Xe5I9A2yDFuoXLkyQkJCMHToUFSrVg0ODg7Ili0bTp48iYiICBQtWhQzZ8784jmDBg3Czp07cfr0aRQuXBiOjo549+4dDh06hM6dO2POnDlffZ/atWtj2rRp6Nu3L2rVqgVbW1sULVoUiYmJePToEW7evAlzc3P07Nnzp7wvb2/vlD9rNBo8evQIZ86cgSiKaNy4MTw9Pb94/LNnz+Dm5pby9xs3bgAAunTpAnNzcwCAq6srhg0b9lPyKdHgOavx190H2De6F0yNeaKiVMcOH0SIf39sW7MUvxfm2nZKFf02DvWC5mJwQADateEIQiVz8Q3GM73MaBY4C3qp7IpN8pq9fD1CRs+EqzYbMnHVHsWKCL+DMV1bYl7XpqhZ8udt/kQ/l0ajQc3WPnDMlxX9aleWOw4RkWKdPHkSTk5O+PDhA1asWPGPmz6SfGSbBzRkyBCULVsWU6ZMwfnz5/H+/XsUKFAAfn5+8Pf3R+bMX45UMjY2xoEDBzBixAisWbMG27Ztg42NDUaNGoUBAwZ8s6EGAL169UK1atUwZcoUHDt2DNu3b4eFhQXy5cuH7t27o1WrVj/tPS1btizlz3p6erC2tkbt2rXh6ekJb2/vrxYOTEpKwtmzZ796nevXr6f8uXjx4j8tn9J4jp4DbVISdgzvDgOeUCrWxrWrsGTWVOzfug55cnE6rlKFP3uJZqFLMW3aFNR1cpI7Dumg1WpR1csPJkUrwblDX44gVLCAMdOxaukmuGqzwejnDOinX+DquVOY598d6/u3Qan8/IxSqti4OFRr0RWdyxdDu/Jfr59MREQfbd68Ga1atYKenh727duHevXqyR2JUvHdDbVPO27+G7ly5Ur18a6urnB1df3Xr2dubo4JEyZgwoQJ35WrQoUKWLly5b/6HsHBwQgODv7Xmf7pe6fGxsbmPz9XzURRhIvfeJTImwMTe7fhCaWCzZ46EUf27MChHRthbWUldxzS4c87D9Fx5kasWrkc5cqWlTsO6ZCQkIhy7fritzotYdeIVxqVzMN3KM7sP4WG2mzQBz+jlOrY7m3YOnU4dg/1Rv5s1nLHIR2ePI+CU7seGOZUCfWKFZA7Dv0H+oIAfQUdr/P3MqVXM2fOhK+vL0xMTHD27FnY2trKHYn+AVeqpjSVqNHAwXcUmtcoi4HNuQ6Xkg0f3B9P799B2NZ1MDHhItxKtfvcNQxdewDbt25B4d841Umpol69QUXPgajY1hfFajaQOw7pIIoi6rXphqi/HqBechYIPGlTrG3L5uLs+oXYH9QFWS1M5Y5DOly5FY7m3QdhWpPaqJA/p9xxiIgUKyAgAGPHjoW1tTUuX76MAgV4AUIN2FCjNPM6Ng6OvUdhYIs6aO9YSe44lIoe3m1haWyA7WuWQZ/TcRVr4d6TWHj0Cvbv3Y2cOXLIHYd0uP0gAo4+w+HYIxgFynDdIKXSarWoUL8NTJ68Q81kKzbTFGzp+OF4du4A9gd1gZkJ119VqoOnLsBnyGgsda+HItmt5Y5DRKRYHTp0wIoVK5A3b15cv34dVpwZpBpsqFGaePT8JVwGjcekrs1Rv3wJueOQDqIoom2TBqhW3hZjg4dyOq6CjVq7F4fvReHAvj2wtLSUOw7pcOrSNTQPmIxGg6ciu83vcschHWLfxaFsnVYoFCOgeDJ3XFWyyQP+gGn0A+wI8IYhd4hUrJXb92HMtAVY5+mC3JZmcsehH6QPZe2sqZ/xVsyhdEoURdSrVw+HDh1C6dKlceHCBRgZ8UKRmrChRr/cpTsP4REyC0v7e6JCUQ5dVaqEhAS0augE7zYt0Lt7V7njUCp6ztmAZ1oj7N21A8bGxnLHIR02hR1Drykr0Tx4Aaxy5pU7DukQ+eIlKtZvA7uETCgocnq7UomiiBEdm6NcZgFT+nH9VSUbv2AVNmzagU1ejWCdiZ9RRETfotFoULFiRVy9ehVOTk4ICwv7ahNDUj421OiX2nfuCgbNWoWtQX+gcO7scschHV5Fv0SbRvUxbFBftG7eVO44pIMoinAfvwxZbYph8+yZ/NBVsBmrt2L8+oNwH7MMppaZ//kJJItrt+/BqXln1Ey0RC7wxF+pNBoNhrSuh1blbODXzF7uOJSKPqOm4ur5i1jfwRWZDHmaQUT0LbGxsShVqhSePHkCT09PLF++XO5I9B/xk45+mcW7j2LO5n3YO6oncmXmlDSlehB+D53buGH2xDFwrF1T7jikg1arRb2guXBs4IrhwwI5OkPBBk1ZhE3n7sI9dBmMMnGxdKU6dOo82nQagDofrJEZnF6hVHGxMQhwr4eBDaugg0N5ueNQKtx7ByL5ZRSWt3WGgT4v+KQnegrb5VNPQVmIvldERARsbW0RExMDf39/jBkzRu5I9APYUKNfInTFVuw7exlhob1hacopNEp14fxZ+PXsinWL58G2dEm545AOcQmJcAycja7dfdD9D07HVbJ2ARNw+ZUWLUcugL6BodxxSIc12/agr98YNNBmgTkPhRTrxdMIjOzQGJM8neFsV0zuOKSDKIpw8uiFEmZGCHJz4AUfIiIdrly5gqpVq+L9+/eYPn06fH195Y5EP4hHkfTT9ZqyBI+fRWFvSE8Yc7i/Yu3bvROTQ4Zh1/qVKFSQa9sp1bNXMWgwYgFGhoTArWkTueOQDqIook63QMRZ26Dx4EAInI6rWONmL8X0aUvgqs0KE3BRe6W6d+MKJvfywNKeLVCpSH6545AOiYkaVGvVBU2L5kP3amXkjkNEpFgHDx5EgwYNIIoiNm7ciBYtWsgdiX4Cdjvop2o5bCosTQyxaWhX6HO4v2ItXzQfm1YsxsHtG5E9W1a545AONx49Q+uJKzFv7hzUrFFd7jikg0ajQQWPAchW3gl1W/vIHYdS0StwDHZu2AcXbVYYgp9RSnXh+CEsC+6HrYM8UDRPNrnjkA7Rb2JQq1U3+FYvg+ZlCssdh34hfUFhu3wqKAvRv7Fq1Sp4enrC0NAQR44cQY0aNeSORD8JG2r0U2i1WtTtNwY1ShTCSE9XDvdXsEmjR+Di6WM4uGMjLMzN5Y5DOpy4ehc9Fm7HhvVrUbJECbnjkA6xcfEo17YvSjX2Rul6vNKoZM0798f1E3+hgTYr9MDPKKU6sHkt9s4bh73DOiI3119VrPDHT9HAqw9GN6iK2r9xF2MiIl3Gjx+PwYMHw8zMDBcuXECxYlzCID1hQ41+WEJiImr3CoF3varo4VpL7jiUCr/e3ZHwOgp7N62BkREX4VaqTScuIHTrKezeuR0F8nOqk1JFPHuBqp0CUMPbD4UrO8gdh3QQRRE1m3rj/Z1ncNJmhsBmmmKtmzMZ1/esw4HgrrDi+quKde7KTbT3HYo5LRxROjdHEBIR6dK7d2/MmDED2bJlw/Xr15EjRw65I9FPxoYa/ZDnr2NQr18ohrdzQfMa5eSOQzqIoojObVrAJnc2LFu2EHpc30mxZmw/gnXn7+LAvj3ImjWL3HFIhyt37sPZNwT1+45BnuLl5I5DOiQmJqJcvdbI+iIJ1ZKt5I5DqZgbPBDxt89j37BOMDHihh5KtfPQSQwMmYyV7ZxRMAtHEGYU+grb5VNJWYh0adGiBTZv3oxChQrh2rVrMDXlzu/pERtq9J/diXgGtyGTMatna9QuXUTuOKSDVqtFK5c6cKljj2GD+nE6roIFLN2Bv6LiEbZ3N8zMzOSOQzocOH0RniNnoUngbGTJV0juOKTD65i3KFunFUrEGqCIyOntSja2pydyf4jGSj9P6POCj2LNX7cdsxasxIYOLshuzhNDIqJvEUURNWvWxOnTp1GxYkWcPn0aBgZsu6RXrCz9J6eu3cEf4xZgzeCOKG2TR+44pMO72Fi0cqkD367e6NKhvdxxKBUdp61GkmlW7Ny2EoaGHJ2hVCt2hMF//ma0CFkMi6w55Y5DOjyIeIpqLh6o9N4M+SVOHVQqURQR2N4F9vmtMLpdS17wUbDh0xdi/95D2OTtCnNjLhlBRPQtiYmJsLW1xd27d9GoUSPs2LFD7kj0i7GhRt9t67HzCF6yCTtH+KBADk5JU6pnkU/h0cwFY4OHoElDZ7njkA6iKKJxyEL8blcZUydN4Amlgo1esAbz952De+hSmJhz+qBSXbh6Aw3dfWCvsUR2GMsdh3RITEiAv3tddKpREr4u3MVYyboMGYsnt29jracLjA305Y5DMuAun0T/LDo6GqVKlUJUVBS6d++OOXPmyB2J0gAbavRdZm7aj5X7jmP/6F7IZskpNEp168YN9PBqjUUzJqN6lUpyxyEdNBotHANnw829NfwGDpA7DqWiR+gsHLj1HK1GLYGhMUc8KdWuQyfQsccQ1PuQGVbgSE+linn1EoFtGyCoeS20qmYrdxzSQRRFNO3mB8ukBCx2rwc9PXYxiIi+JTw8HHZ2dnj37h1CQkIQGBgodyRKI2yo0b8WuGA9zl67g7DQXjAz4VV/pTp1/CiCBvbG5pWLUeL3onLHIR1i4hLgFDgH/QcORAcPTsdVsqb9QvBAYwq3oDnQ0+fHplItWL0Fw0ZMgYs2K0zBUTRK9fThfYzu1AyzuzSBfanf5I5DOmi1WtRq7YPquawwsF5Njp4mItLh7NmzqF27Nj58+ICFCxeic+fOckeiNMQzA/pXOo2dh7i4eOwc4QNDDvdXrK0b1mH+tAnYt3kt8uXJLXcc0uHRi1doNGoxJk2cgAbO9eWOQzqIoojq3n7QK2gLl46DeEKpYMMnzMbihevQSJsNRuCi9kp189KfmDWgE9b2bY0yBfkZpVRx8Qmo3rIrPGyLoEPF4nLHIQXgLp9E37Zjxw64ubkBAHbt2oWGDRvKnIjSGhtqlCpRFNHYfxIK5bDGfD9v6HH3LcWaN2Mq9m/biIPbNyBL5sxyxyEdLt17jA7T1mHJ4kWoXKmi3HFIh8REDcq164MCtZqiQjNvueNQKjr2H45ju47BRZsN+uBJllKd2r8L6ycMwY6ADrDh+quKFRkVDce2PvC3L4+GJWzkjkNEpFjz589H9+7dYWxsjJMnT6J8+fJyRyIZsKFGOmk0Wjj2GQXXSiXh785RNEoWMnQw7t+4jAPbNsDUNJPccUiHsIs3MGjFPmzdvAlFixaROw7pEP0mBhU8BqK8ew8Ur+0idxxKhXNbHzy9eBf1tFmgx2aaYu1auQjHV8/G/qDOyM71VxXrxr0HaNJlICY3ronKBXLJHYeISLGCgoIQEhICS0tLXLlyBQULFpQ7EsmEDTX6ppi4eDj2HgXfJg7wrltF7jiUCt/OnjBCMnauXwkDA/5IK9WyA6cx68Al7NuzC7lz8URFqcIjnqJW10A4dA9CwbLV5I5DOmi1WlR2aQ+DRzGopbWCwGaaYq2YPAqPT+xG2PAuMOf6q4p19Nxf6Oo3Eotb1cXvOTjKnb6kJwjQU9A0SyVloYync+fOWLx4MXLnzo1r164hSxaOus7IePZNX4l48QoNB47FuE7N0LBSKbnjkA6iKMLDzQXlSxXHxFHDub6Tgo3bEIa9N5/i4L49sLKykjsO6XD2yk00HTQBLoMmI2fhEnLHIR3i4hJQtm4r5H8tomSyhdxxKBXTBveAfuQt7BzqDSNe8FGsdbsOYcSk2Vjr0RB5rDiCkIjoW0RRhKurK/bu3YvixYvj8uXLMDIykjsWyYxHN/SFy/ceoe2IGVjc1wOVi9nIHYd0SExMRKuGTmjj1hgDfX3kjkOp6DN/Ex4mCNi3eydMTEzkjkM6bD9yGt3GL4Zb8DxY58ovdxzS4fnLaFSo1wa28cYoJJrJHYd0EEURo7q2QvFMWszo347rryrY5KXrsHL1FmzyckVmU35GERF9i1arRaVKlfDXX3+hdu3aOHz4MD/bCAAbavSZg39eQ9/py7F5aFf8ni+n3HFIhzevX8HdtS6G9PNFu1bN5Y5DqWgzbhnM8tpg64q50Nfn7rhKNWfdToxevRetRi+FmXVWueOQDjfvPYBD046olmiBPODUQaXSaDQY2rYBmpbKg6EtnOSOQ6kYNHYmzp48i01ershkxFMC0k3QFyDoKWcmBGdlUFqKi4tDqVKl8PjxY7Rp0wZr1qyROxIpCD89CQCwct8JTF2/C3tCeiJPVk5JU6rHDx/Au1VTTB8XgnqO9nLHIR20Wi2cg+ejmmNdjB45ggd+CjZk+lKsOXkdrUOXwciUI56U6vi5i2jh1RdOGmtkAadXKFVCXCz8W9VFn/oV0cmJuxgrWbt+wxEf+RSr2jeAoT5HWRARfUtkZCTKlCmD169fo3///pg0aZLckUhh2FAjTFi9A9tP/In9o31hbW4qdxzS4cqli+j7hxdWzp+FCuVs5Y5DOiQkauA4dBa8OndBrx6cjqtkXsMm41xkPFqFLIa+oaHccUiHDbvC0Kt/CJy1WWDBwxbFin4eieEeLhjbrh4aVeAahEoliiLqefXBb0YCJrVw4gUfIiIdrl+/jsqVKyMhIQGTJ09Gv3795I5ECsQj0wyu34yVuPPoCfaN6gUTI55QKtXBsL0YF+SPnetWoHAhG7njkA5RMbGoP3w+goYPR8vmbnLHIR1EUYRzz+F4nSkXmg4ZC4FrYCjWlAUrMWHifLhosyITOG1aqe7fuoGJPdpgkY8bqv5eUO44pENSkgbV3f+AS6Fc6FmjrNxxSEX09AXoKWjKJ3f5pF/tyJEjqF+/PpKTk7F27Vq0bt1a7kikUGyoZWBtR8yAoSBh67A/oM/h/oq1ZtkSrF48Fwe2bkDOHNnljkM63HnyAi3GLcfsWTNhX7uW3HFIB61Wi4oeA2BVpibqte3F0RkK1i94Ijav2QlXbTYYgp9RSnXp1DEsHtYLmwa2Q/G8OeSOQzq8efsONVp1hU/lkmhVtqjccYiIFGvdunVo164d9PX1cejQIdjbc5kd0o0NtQxIFEXU7z8WFQrnRah3E55QKtjUcaNx5ugBHNqxEZYWFnLHIR1O37iPbvO2YO2aVShTurTccUiHd/HxKNeuP4o1aIeyDXilUcladRuEy0cuoIE2K/TBzyilOrx9A3bOCsWeoR2Rl+uvKtajp89R16MXRtavDMci3MWYiEiXKVOmoH///jA1NcW5c+dQqlQpuSORwrGhlsEkajSo3SsE7R0qwrcJu+1KNqS/L15FPsL+zWthbMwd7ZRq2+m/MGLjUezcvhU2BTnVSakio6JRxcsPVTsMQJGqdeSOQzqIogj75p0Re/MJ6mgzQ2AzTbE2LZiOS9tXICyoCzKbZ5I7Dulw8cYdtO7hj5nN7FGWIwjpv9LXU9byCIIkdwJKhwYMGIDJkycjS5YsuHr1KvLkySN3JFIBNtQykOiYWDj1HY0hrZ3hXqu83HFIB1EU0c2zNXJltsDWVUuhp6QDGPrCnF1HseLULYTt3YPs2bPJHYd0uHHvIer0HIG6vUcjX0n+7lMqjUYDu3qtYfXsPWokc7STki0Y5Y83V05if1AXZOL6q4q199gZ9Bk+Acva1MdvHEFIRKRTmzZtsG7dOhQsWBDXrl2Dubm53JFIJdhQyyDuPnmOZgGTMMOnFRxsf5c7Dumg1WrRpnF9ONWogpFD/DgdV8GCVu7CmYgYHNi3hx+6Cnbk7F9oM3wGGg+ZiWwFCssdh3SIiY1FWadWKPpWH7+L/HlSsgm9vZEl4Rm2+XeAgT43ilCqxZt2Ycqcpdjg6YIcFtzBnYjoW0RRhIODA44fP45y5crh/PnzMDBgi4T+Pf7fkgGcu3kPHUPnYpVfR9gWyit3HNIhLi4OrRo64Y8O7dCji7fccSgVf8xYi7eGlti9YxuMjIzkjkM6rN1zGP1nrUOLkQthmT233HFIh4jI56js3A4V3puigGQidxzSQRRFBHVojGq5MmFcJ3de8FGwUbOWYsfOfdjk1QiWJvyMoh8n6AkQ9JXzM8/lAOhnSExMhJ2dHW7duoUGDRpg165dnBlE340NtXRux8kLCFywHjuCfWCTM6vccUiHqOfP0a6pM0YFDoZbIxe545AOoiiiWehiFCxRFuumTeGHroJNXLoR07cfh3voMmSy4FQnpfrrxh3Ub9kVtZIskRNcK1KpNImJ8HevC4/Kv6Nf45pyx6FU+ARNwL2r17HO0wUmhjzMJyL6ljdv3qBUqVJ49uwZOnfujIULF8odiVSKn7Tp2IIdh7Bw+0HsG9ULOay5Q6RS3b19E9083DF/6kTUql5F7jikg0ajRZ2gOXBp2hxDAwbLHYdS0XvcXOy+8hjuoUthaMzF0pVq37FT8PzDH3U/ZIY1uA6XUr19/QpD29THkKY10LZmObnjUCqadfeH8bsYLG1TD/q84ENE9E2PHj2Cra0tYmNjERQUhBEjRsgdiVSMDbV0KnjxJhz76zr2j/aFhSmn0CjV2dMnMaSPDzYuW4BSJYrLHYd0iE14D8ehs9Grd1907ugldxxKRcuBY3A7Xh8tRsyHnj4/4pRqyfrtCBg2EQ21WWDGQxHFevboAUI6NcP0jq6oU6aI3HFIB61WC8d2PWGXxQwBTWtzOi79dHr6AvQUNOVTj1M+6T+6cOECatSoAY1Gg3nz5uGPP/6QOxKpHI9i06E+05fhbWwcdo/oASMO91esXdu2YMa4EOzdtAYF8nFtO6WKinmHmv4zMW7sWLi6NJQ7DukgSRJqdhoMba5icO0RwBNKBQuZvgDzZq2EqzYbjMFRNEp15+olTOvjhZW+rWD3Gz+jlEqT9AEVmnqjdclC6FS5pNxxiIgUa8+ePWjcuDEAYOvWrWjSpInMiSg9YLclHXr15i1m9WyNV+/i5Y5COsTGJWDamBFYs2gODA0M8Oz5C7kj0TdER0cj8OgJTJwwDuXL2+HZ8+dyR6JviIqKwoE/r+P3mg1QqXknxL+JljsS6fAu+jXmzFgBJzEztBChhSh3JPqGxOQPmNSzPRb3bIlcmS3w7E2s3JHoG16+jcPyg4fQq6YdXEvY4MW7BLkj0TdEx7MuRHJbvHgxunTpAiMjIxw7dgyVK1eWOxKlE2yopUNPI1+ixbDZcscgHV5rtIjTaGFjBXTp6iN3HNLhbUICoqJfIW++fJg4frzccUiHD1otXr58CX1BwLNbl7A91FfuSKRDfMwrCAZ6sMiRGecBAJLMiehbXr2NRUxiMrJYWMNn+UG545AOGk0iXrx5hywmxtgY/gIbw3lhTokkUcTLl1Fyx/hhgp4eBAWtyydI/Pygfy8kJARBQUGwsLDApUuXULhwYbkjUTrChlo6NLVIUZgZsLRKFPIoAoXKl8bdu3eww72W3HFIh81X72HO6WuoXCgv/Pp0RZ2q5eWORN/w6OkLNPDxR1vn2jj2NAHuw2fKHYm+QRRFLOzngbJVmuDayQO4cfqA3JFIh6GjJ2Dq0s3Q0zNG5eFr5Y5DOry8cxlnFo5EjnL2cK9VFv38/OWORN8QGxsLN8dqaFG1NGbtOyN3HKIMqXv37pg3bx5y5MiB69evI1u2bHJHonRGOZcaiNIxURTRP/wh8tepizVrVkNfX1/uSKTD7FNXsPTCLWzzaY7sFqZyxyEdLly/gwbd/bFi1CDUY8NTsTSJCZjZrRmq1G2INn0DAS4krVgePfpj6oqdkGzqQ9DjrqtKFXH+EM4uHo0KvlNgkec3ueOQDs+ePkXjmhUQ1KgK2tcoK3ccogypcePGmDdvHooWLYpHjx6xmUa/BIcxEf1iGlFEr/sP0apTJ/j5+ckdh1IxfP8ZhL9+hy3dmyMTN/RQrN3Hz8Jv0nzsmB6MIvnz4MCZS3JHom+IexON+X3bo3m3/qjq3FTuOKSDKIqo06IDzt+NhlTACYIgcDKuQt0OW4fwYztRuf8sGJlbyx2HdLh14zq6tmqMOd4uqFY0P64/Uf+UT+7ySWqi1WpRrVo1/Pnnn6hevTqOHz8OPQVNWab0hWeMRL/Quw9a9HzwCIOGBaKDp6fccSgVPpsPw9DQAKs7NYaBPj90lWrRlj2YvWY7Dswdg1zZMssdh3R4GfEAy4f8Ae8hY1Cqck2545AOWq0W5Rwb42FcJoh5qnF3XAX7a/1MRN29isr9Z8HAOJPccUiHk8eOYIhPR6zr2QLF8mSXOw5RhpOQkIDSpUvjwYMHaN68OTZt2iR3JErn2FAj+kWevU/EwIinmDxjOho4O8sdh3QQRRFtVu2Fbf5cGNGoBk8oFWzUvJXYf+pPHJ4/FpbmnI6rVA+vX8KmsX7wHT8PBX4vKXcc0iEuLg4lajbEG8MCkHIU43gPBTszbzjeJ8SjYu+p0NPnobtSbd24HjNHBmDHgHbIm8VS7jhEGc6LFy9QunRpREdHo3fv3pg2bZrckSgD4Kcy0S9w6907hERFY9mqlahUqZLccUiHRK0WzZbuQqsKJeBTu5zccSgVPUZNR8SzFwibEwpjI67vpFTXjofhwOLJ8Ju1CtnzFpA7DukQ+fwFbB0a433mMoBlQbnjkA6iKOLYpD4wypIb5boOUdQui/SludMnY/fy+djj54ks5ulvBKGgL0BQ0JRPgZcA6G9u376NChUqID4+HuPGjeMyO5Rm2FAj+snOvHqNOXEJ2LZ9O4oWLSp3HNLhdUIiWizfjf71KqNFud/ljkOpcOsbDGtzU2ydPIwbeijY6a0rcXHPRgxZsBGWmbPKHYd0uHHrDqo1bosPOapCMM8pdxzSQavV4OCoP5C9TA381tBb7jiUiuDB/XHn5AHsGuQBU2Ne8CFKaydPnoSjoyO0Wi1WrFgBDw8PuSNRBsKGGtFPtPP5C2yDHvbt34/cuXPJHYd0ePj6LTqsDcO45o6wL5pf7jikg1arhVPnQahpVwqje3bgdFwF27doMp5cu4ChCzfBxNRM7jikw7FTZ+Hq6YPkvPYQMnENQqXSxMfiwKiuKFinNfJWbyx3HEpFjw5toR/1AFv6teX6q0Qy2LRpE9zd3aGnp4ewsDDUqVNH7kiUwbChRvSTLHsaiYuWVji4fTusrKzkjkM6/PU0Cr23HcMCjwYokzeH3HFIh4T3iajRoS+6uDmjp3sjueNQKjaO94c2PhaD56yBgaGR3HFIh3VbdqDzwBEQC9SFYGQudxzSIf7Vcxwa1xPFW/gie5kacschHURRRBvXuihjqYdx3Zql+ws+H6d8KqdhKECUOwIpwIwZM9C7d29kypQJZ86cga2trdyRKANiQ43oJ5j8+AlibGywf/16mJiYyB2HdDhw5xFCD13A2i5NYZOVTU+leh79GnU6D8LIHp5oUYcnlEoliiKWD/kDOXLlgfeIhdySXsEmzlqA4VMWQrKpD8GAn1FK9ebRHZyY6Y8yXoGw/q2M3HFIh8TERLg5VUfLMgXQt2E1ueMQZUj+/v4YN24crK2tcfXqVeTLl0/uSJRBsaFG9IOGPniMnFUqY+uCBVzfScFWXriJlZfuYHP35shhwR0iler2wwg09Q3CvEBf2FfgCaVSabVazO/dFnY1HdDsj/7pfnSGmvUeMhKLNu6HVMgZgh4P+5Tq2bWz+HP5BNj5jId5Lm4UoVRvXr9Gc6fqGOBcEW2r8TOKSA6enp5YuXIl8uXLh+vXr8PSkrvqknx4OZnoP0oWRfQKf4ByzZpi8aJFbKYp2MQjF7DlxgNs92nBZpqCnbh4Fc16D8fGCUPYTFOwxLh3mPlHUzi6tYFbtwFspimYm5cPFm05AsmmLptpCnb/xC5cXD0FlfpOZzNNwR49eoAmtSthbMvaGa6ZpqcvKO6LMh5RFOHk5ISVK1eiTJkyCA8PT5fNtIcPH0IQhC++TE1NkSdPHtSpUwdBQUEIDw//4e8THBwMQRBw5MiRH36t+Ph4rFy5Eu7u7vj999+RKVMmWFtbw97eHmvWrPnh11cyHl0R/QeJWi16PHiEzr690atXT7njUCoG7TyB14kabPrDDUYGbHoq1aawYxgxZwX2zgpBwdxc206pYl4+w6IBXmjbZyjKOzjLHYd0EEUR1Vxa4XpkEqT89mx6KtiNnUvx+M8jqNx/NgxNLeSOQzpcvnQRvh4tsKRrE9jZ5JE7DlGGo9FoUL58eVy/fh116tTB/v370/1SE4ULF07ZsTQpKQlRUVE4d+4cQkJCEBoaCj8/P4wePVoRn/HHjx+Hp6cnsmbNijp16qBFixaIiorC5s2b0a5dO5w6dQozZsyQO+YvwYYa0Xd6rdGgz8MIDB89Gq1atZQ7DukgiiI6bTiEHFZmWOblCj09+T9s6NtmrN6CFdsP4ND8MchmzbXtlOrZ/TtYPbwn/hgxBUXLVpQ7Dumg0WhQslZDPNdmgZS7MvibT7kurJyIN08foFLfmdA3MpY7DulwcN9ejBrQAxt7u6NwzixyxyHKcGJiYlC6dGk8ffoUHTp0wLJly+SOlCaKFCmC4ODgr24/fvw4OnTogDFjxkBfXx8hISFpH+5vcufOjVWrVqFVq1YwNDRMuT00NBRVqlTBzJkz0aFDB1SqVEnGlL9G+m7rEv1kjxMS4PvoCWYumM9mmoJpRRFuy3fDNn9OTGnpxGaaggVMXYRtB0/i8IKxbKYp2L2Lp7EmuBf6TlnMZpqCxcTEomB5BzxDPkjZMtaUNLU5OdMf8TGvUb7HRDbTFGz1skWYMNgXuwa1z9DNNEEQIOgp6EsBI3IobTx+/BiFChXC06dPERAQkGGaaampVasW9u3bB2NjY4wfPx4REREAgLdv32LcuHGwt7dHnjx5YGRkhDx58qBDhw5fTRF1cHDAiBEjAACOjo4pU0ttbGxSHnP48GF06tQJxYoVg7m5OczNzVGxYkXMnz//q0xly5ZFu3btvmimAUDOnDnRrVs3AMDRo0d/5j+DYnCEGtG/dOXtW4x/HYN1GzeiTJnScschHeI0Grgt3YWONcrCuyrrpGTeQ8cjPuE99swKgaEBP46U6uKBHTixZi78565Dlpy55Y5DOjyKeAK7Om5IymYHWHC3M6USRRGHx/rAIn9xlGzek40BBZs8ZhRObF2NPYM9YZmJTU+itHb58mVUrVoVSUlJmDFjBnr16iV3JMX4/fff0bp1ayxfvhxbt26Fr68vbt68iaCgIDg6OsLNzQ1mZma4desWVq9ejV27duHixYsoWPDjOp3e3t4APja5vLy8Uhpp1tbWKd9j3LhxuHfvHqpWrQo3NzfExMRg79696NatG27fvo1Jkyb9q6yfmmwG6fRYP32+K6Kf7MjLV1iWlITdu3en/CIi5XkRGw/3VXsR5FoDDUv9Jncc0kEURbj2DMRveXNiSXBfnlAq2NG1C3Hz+F4MXbgJZpbWcschHS5duQZ7Ny98yFMDeqbZ5I5DOmg1iTgQ0hm5KzujYJ22csehVAz27Y6oa+ewc2B7GBvydIkorYWFhcHFxQWiKGLTpk1wc3OTO5Li2NvbY/ny5Th//jwAoESJEnj27BmyZPlyNO3hw4dRt25djBo1CgsWLADwsaH28OFDHD16FN7e3nBwcPjq9efMmYNChQp9cZtWq4WLiwumTZuGPn36oECBAqlmTE5OxvLlyyEIAurWrfsD71a5OOWT6B9sjHyOdYIewsLC2ExTsDsv36DVyj2Y0qoOm2kKptFoUN2zD2qVK4lZ/j3YTFOwHTNH4+HFEwiYt4HNNAXbc/Awarl1gDafA5tpCpYYG4N9wzxRsG57NtMUTBRFdGrVFNKja1jn24rNtP+np6+nuC9Kv1asWAFnZ2fo6enh2LFjbKbpkCfPxw1SoqOjAQBWVlZfNdOAj1M6S5UqhQMHDnzX6/+9mQZ8HGXWvXt3JCcn4/Dhw//4GsOGDcPVq1fRsWNHlC6dPmcO8VOCKBXznjzFvWw5cGDrFpibm8sdh3Q4/fAZ/PecwjJvVxTLmVXuOKRDTGwcanv3Q38PN3g3qSd3HErFmpF9YKSvj4EzVkI/nQ7RTw8WrVyL3sMnQSpYH4KhqdxxSIfY5xE4OqkPSrYdiKzF09+CzOmFVqtFy3q1UTu/FYLaNeIFHyIZjB07FgEBATAzM8OFCxdQrFgxuSMpliRJX9125MgRTJ06FWfPnkV0dDS0Wm3KfUZGRt/1+u/evcPEiROxdetWhIeHIz4+/ov7IyMjU33+/PnzMWbMGNjZ2WHatGnf9b3VhEfJRDqEPooASpTA7pUrv/sXEKWd7dfCMf3UFWz8oxnyWlvIHYd0iHgWhfrd/DGpfxe41OQJpVKJoohFA73wW7FSaDdgOE8oFSx43FRMWLgOUiFnCPr8jFKq6HvXcHpeEMp2CYFl/t/ljkM6xMXFwc2xGjpVK44/nLjxCpEcfH19MXPmTGTPnh3Xrl1Djhw55I6kaM+ePQMAZM+eHQCwYcMGtG7dGubm5nB2doaNjQ1MTU0hCAKWLl2KR48e/evX1mg0cHBwwMWLF2FnZwdPT09kzZoVBgYGePjwIZYtW4akpCSdz1+yZAm6d++OMmXKICwsLF0PTGFDjehvRFHE4IePUczRCTNmTIeeHoeVK9X8s1ex/cZDbPNpgcymJnLHIR3+unUPbQeNxtKRA1ClDK80KpUmKRHzfdugeoOmcPHykTsOpaJT78FYt+80JJt6EPR4KKdUEReO4PL6WajQazJMs+eVOw7pEPXiBVrVq4XgpjXQpEJxueMokqAvQNBXzgUWQVJOFvo5mjdvji1btuC3337D1atXYWrKUdf/5MiRIwCASpU+XqgODg6GiYkJLly4gKJFi37x2LVr137Xa2/btg0XL15Ely5dUtZd+/y1UtttdfHixejatStKliyJgwcPImvW9D17iEdhRJ/RiCJ633+EJh08MXTIELnjUCpGhp3Dzeg32Nq9OUyNDP/5CSSLsNMX0XfsLGybOhy/F+QJpVLFv32D+X3aomnn3qju0lzuOJSK+q28cOrGM0gFnSAIvOCjVHcPbcLdQ5tRqd8sGFtmljsO6XD3zi10cnPBLK8GqPE718klSmuiKKJGjRo4c+YMKleujNOnT3Mww79w584drF+/HsbGxilrzIWHh6NUqVJfNdMiIyMRHh7+1Wvo6+sD+LhxwN99enyTJk2+uu/48eM6cy1evBhdunRBiRIlcOjQoZTRc+kZ/28l+n9xWi26hj9ANz8/NtMUznfrUTxPeI91nZuymaZgK7aHwW/SPITNHc1mmoK9ehaBub3c4TlwBJtpCqbValHWoRFO3X0LMW8NNtMU7MqmOXhwci8q92czTcnOnT6Jzm4NsdrHjc00Ihm8f/8exYoVw5kzZ9CkSROcPXuWzbR/4cSJE3B2dkZSUhICAgKQN+/HY+yCBQvi3r17ePHiRcpjExMT4ePj88Vaap982sDgyZMnX933aSO+EydOfHH70aNHvxqx9smiRYvQpUsXFC9eHIcOHcowU3Y5Qo0IQFRiIvpHPMX4yZPh6uoqdxzSQRRFtFuzD8VyZ0dok1pc30nBxi1ai+1HTuHwgrGwtki/6yao3eObl7E+dCB6jpmFQiVs5Y5DOiQkJKBEzYZ4pZ8XUs7i4G8+5Tq3aBTiY16jYp9p0DPgBR+l2rltC6YMG4jt/dsif1YrueMoHqd80s8WHR2NUqVKISoqCj4+Ppg9e7bckRTn3r17CA4OBvBxTbOoqCicPXsW165dg76+PgIDAxEUFJTyeF9fX/j6+sLOzg4tW7aEVqtFWFgYJElC2bJlcfny5S9e39HREYIgYOjQobh16xasrKxgZWUFHx8fNG7cGDY2Nhg/fjyuXbuG0qVL4/bt29i5cyeaNWuGTZs2ffFahw4dQteuXSFJEmrXro05c+Z89X7KlSuHZs2a/fR/J7mxoUYZ3r13cRj+4iUWL1uGqlWryh2HdEjUatF82W40KVsUvR0ryB2HUtFn7CzcefgEB+eOgYkxF0tXqhunD2HfvPEYNGMFcua3kTsO6RD1MhqlarsiwaoUYGUjdxzSQRRFHJ/SHwYWWVCu2xgIHGWhWAtnTceWxbOwZ5AHslpwnSaitHbv3j3Y2dkhLi4Oo0aNwtChQ+WOpEjh4eEYMWIEACBTpkywtrZG8eLFMWzYMHh5eaFw4cJfPL5nz54wNDTEjBkzsGDBAlhbW8PV1RWhoaFwd3f/6vVLliyJJUuWYNKkSZgyZQqSkpJQsGBB+Pj4wNzcHIcOHcKgQYNw7NgxHDlyBKVKlcKqVauQM2fOrxpqjx8/Ttl1dN68ed98P15eXmyoEaU3f75+g+mxcdiybSu3ZVawmPeJaL5sN3ydKqI1FwxWtNYDR8HIQB87pw1PWZuBlOfMzrX4c9tqDJm/AVZZ0//6Fmp1+144qjRsDU2OyhDMc8kdh3QQtVocDP0DWYpXwm+unTl6WsFGD/PH1UO7sMfPA2a84EOU5s6ePYvatWvjw4cPWLx4MTp27Ch3JMWxsbFJaU59D0EQ0K1bN3Tr1u2r+z5tYPB3Xl5e8PLy+uZ9hQoVwsaNG79539/zeXt7w9vb+7vyphdsqFGGtS/qJTYki9i7fx/y5skjdxzSISLmHdqv3ofQpvZwKs41TpRKFEXU7eKHiiWLYlyfjjyhVLADS6fjwaXTGLpwEzKZW8gdh3Q4de5POLf9A8l5a0PIlEXuOKSDJiEOB0Z1QQH75shXy03uOJSK3p088CHiDrb2awtDA17w+R56+nrQ01fOqEs9STlZ6N/bvn07mjf/uFbrnj174OzsLHMioh/HhhplSCufPsNZM3Mc2LEdmTNzwWCluvrsJXpuOYo57Zxhlz+n3HFIh4T3iajt1R+ejZzQp11TueNQKrZMCkTC6yj4z10LQyNjueOQDpu274ZX32EQC9SBYMymp1IlvHmJg2O6o1izHshRrrbccUgHURTh0aQBipp8wKQezXnBh0gG8+bNg4+PD4yNjXHy5EmUL19e7khEPwXb+5ThTI94gpu58yAsbD+baQp2+G4Eem87htWdmrCZpmDRb96ictteGOTVgs00BRNFEcuHdocBRPSbupTNNAWbPm8JOvQLhljImc00BXv79D4OhnZDac8hbKYpmEajQePalVE7twkmezRgM41IBsOGDUP37t1haWmJW7dusZlG6QpHqFGGMvzhY1jZlcf2JYthYMD//ZVq7V+3seTPm9jSrTlyWJrJHYd0uPvoKRr3CsTsgJ5wqlxW7jikg1arxcK+7VG6cg206DGIJ5QKNnB4KOas2QWpkDMEfe4QqVTPb17A+SWhKNd9LCxyF5I7DunwNiYGbk7V0NvJDp41+Rn1QxS2yye4y6dqdOrUCUuWLEGePHlw/fp1WFtbyx2J6KdiR4EyBFEU0e/BI1Rp3Bjjx43jCaWCTTt+CUceRGJb9xawzMRRNEp19upNeA8dj3Xj/FH299/kjkM6JCbEYZ5vG9Rp4YG6rb3ljkOpaN3FFztPXoNUsC4EPa7vpFQPz+zDtW2LUbH3NGTKwtHTSvUk4jHaNXTEWHcnONsWkTsOUYYjiiIaNmyI/fv3o0SJEvjrr79gZMSNQCj9YUON0r3EZBG97j+Ah08P9OvbR+44lIqA3acQGZeATX+4wcSQv56UatvhUwicthi7Z4xEobzceVCpYqOjsKC/B1r38kfFOi5yxyEdRFFE7SZt8dejd5DyO/CCj4Ld3LMKD0/vQ+X+s2FkZil3HNLh2pXL8GnTDAu7NEbF3/LKHYcow9FqtahYsSIuX74Me3t7HDp0CHp6XGmK0ieesVK6FqPRoPfDCAwZOQJt27SROw6lovOGg7AyNcHKjo2gzw9dxZq3ficWbtqNg/PHIEcWa7njkA4vHt3DikAfdB0+EcXsqsgdh3TQaDQo49AITxMtIeWpCrbSlOvSmqmIfngblfvPgr6RidxxSIejB8MwvM8f2Ni7FYrkyip3nHRDTxCgp6ec31B6vPCgWLGxsShTpgweP36Mtm3bYvXq1XJHIvql2FCjdOtpQgL8nj7HjDmzUadOHbnjkA5aUYT7ij2o8ls+BDasytEZChY0cxmOXbiMQ/PHwMLMVO44pMP9y+ewZVIg+k5aiHyFi8kdh3SIjX2H4jUb4K1JYSA7p6Qp2anZQ/FBm4wKvSZDT5+Hzkq1btUKLBo/AjsHtEfuzNzQgyitRUZGokyZMnj9+jUGDhyICRMmyB2J6JfjUQGlSzfexmJ09GusXrcO5cpxIVqlStBo0WzpTnhUK40u1W3ljkOp6Dp8MqJjYrB/9igYGXKxdKW6fHg3jqyYhcGz1yBbbk51UqonkZEo69gU77OUg2CZX+44pIMoijgyvhdMc9nAtlVfXvBRsBkTxuLghmXYM9gT1qYcQUiU1q5du4YqVaogISEBU6ZMQd++feWORJQm2FCjdOdk9CsseJ+EXbt3oVAh7r6lVC/jEtBqxR74N6iGJlwwWLFEUUTT3sORJ1tmbJowlGtgKNiJjUtx9eB2DFmwERbWmeWOQzpcuXETtZp44EOu6hDMsssdh3TQahJxYFRX5CzvhEL1PeSOQ6kY2s8XTy6ewK5BHlx/9RcR9PUg6Cvn818QlZOFgMOHD8PZ2RnJyclYt24d3N3d5Y5ElGb4qUPpyrbnL7BbzwD79+9DzpzcfUupwqNj4L3+ACa3dEKNwvnkjkM6aLVaOHQcgHpVyyPoj7YcnaFge+aOx/N71zBkwUYYZ+J0XKU6cPQEmnXsDTG/AwQTa7njkA5JcW9xYFRXFHL2QJ4qDeWOQ6n4o10LmL19hg193Ln+KpEM1q5di/bt20NfXx+HDx9G7dq15Y5ElKbYUKN0Y9HTSNzInBUHt22FhQXXzlCq8//H3p3Hxbj+/wN/zbRHCskSlX099n1tU2StZFdkKVLKkoSEyL5GdqGiok3a7fu+70uSiJIkyRh3vz/O7/T9nA/3fJxjuS56Px+PeRxm5m5ewzFzX+/7ut7XkyxMjTuB7fZWaFRVl3UcIiK/4B26OnjAZVAfjLXpwToOUSBswRRIP33EtIAQKNNyXG7tDNuH8d7+KDbsDolqGdZxiIiC7EwcXuqGhgM9oNuINvTglSAIsLM0Rjs9Dcwb05cu+BDCwPLlyzF16lRoamriwoULaNiwIetIhPx0VFAjv4XFT57iQ526SNgdCjU1NdZxiIj4W2lYfvwKwsf1R43y5VjHISKevXwF87HTsMhtFPp2a886DhEhCAK2ezqiRq3aGOHpRwNKji1YGYCF64JRbGQJiTJ9R/Hq1aNbOBU4C00dfaFtSANDXhUWFsLapAOGta4Dl+5tWccpFaRKEkiV+PmOkQr8ZCmtPDw8sGrVKlSsWBHXr19H1apVWUcihAkqqJFfmiAI8E7PgGGnztiwIZD6O3Fs+7mbiLjxENHjbVCxjAbrOETEjftpGDB5HrbN8UDH5jSg5JVcJsMGt8FoZ9YTfRxdWcchCjhNnYXg/UdRXNMCEimddvEq88pJXNq9Ci1dlqOMHrUi4FVO9ksMMO8M714dYNu2Ees4hJRKgwYNQnh4OAwNDXHjxg2ULVuWdSRCmPnH1QeJRFJyO336tOjzwsPDS55nZGT0LRm/Otf3eB1fX19IJBIEBQX9o9f+75uqqipq1KiBYcOG4fr1658dIwgCjh8/Dk9PT7Rr1w56enpQU1ND7dq14ezsjLS0tG9+L787uSDA5VE6OtgNxMaNG6iYxjH/QxeQcD8DMeNtqZjGscPnrmDQ1PmIWjGbimkcK3ybj7Xj+sFyoAMV0zjXa+gYhCScRbGhGRXTOPbwaAyuRqxDW/e1VEzjWNrDB7A2bo+Vg82omEYIA4IgoEuXLggPD0fLli3x4MEDKqaRUu+bzu5CQkLQoUOHLz4WHBz8LT/6l+Tg4FDy6zdv3uDixYsIDQ3F3r17kZiYCBMTk5LHHz16VNK0UV9fHx07doRUKsW5c+ewceNGhIaGIj4+Hp07d/7p7+NXUCiXwyXtCcZPmQynceNYxyEKeMQexzv5J0SM7QcVJSXWcYiI3fGHsHjrHiStX4Dqlam3Ha9yn2diu+dIjJg6F007m7KOQ0QIgoDWFta4l10MQb8zLcfl2I3oLXh67RTaTF4PFQ3qbceri+fPwmPkYOxy6o8/DKqwjlPqSJQkkHC05FNCSz5/uqKiIjRv3hx3796FlZUV9u/fT5MZCMG/LKj9NZMqLCwMq1atgrLy33/Mq1evkJiYiJYtW+LSpUvfJeiv4L9ntX38+BGjR4/Grl27MGnSJFy7dq3kMYlEAktLS3h7e/9tN5QPHz7A2dkZQUFBGDZsGB48eAAVajL9N9lFH+CR8RQLlixB/379WMchIgRBwIg9KTCqVB5r+3eDVEonP7xaviMCe5OO4dCmRaigTRt68OrpvZsIm+cO5wVrUbtJc9ZxiIiioiI07NwT2aiM4iqNQJ98/Dq/fSHe5rxAm0lroaSiyjoOEZGwPwZLvT0Q5T4IRpXKs45DSKmTm5uLxo0bIysrC2PGjMHmzZtZRyKEG/+6rDxs2DDk5OQgKSnps8fCwsLw8eNHDB8+/JvC/epUVFTg6+sLALh+/Try8vJKHqtduzYSExM/21pYTU0NgYGB0NbWxpMnT3Dq1KmfmJh/jwrewf3pM2zcto2KaRyTyeXoG3QAHepUx1IbYyqmcWzykg1IOnEBBzctpGIax+6eO46IBZMxec0OKqZxLOdVLgxbGiNb2QjFFWlJGs+Or5qKD0Uf0GL8YiqmcSxoUyDWzpmGhGnDqZhGCANpaWmoWbMmsrKyMGfOHCqmEfJfvqmgJpFIvri0Mzg4GGXLlkW//1HwiI+PR/fu3VG+fHmoq6ujfv368PLy+lvh6T+9e/cO06dPh4GBAdTV1dGgQQOsWLECxcXFCl/nxIkTsLa2LulTZmRkBDc3N2RnZ3/1+/23KleuXPJruVz+Vceoq6ujXr16AIBnz579kFy/osuv8zAn+xX2Ru5D506dWMchIvKLPqDn1v1w6NgUk83asI5DFBjq5Y/n2TmIXzsXmurqrOMQEecT9iJ50xLM2BiOqoa1WMchIh6mpaNuBwsUlG+OYu2arOMQEYJcjhS/MVCvbIjG9jMhkVIrAl4tnjsbB7avQ4LnCOiWo+W4LEmUpNzdyI934cIFNGzYEG/fvsXmzZtLJooQQv7Pv+6hZmhoiE6dOiE2NhYFBQUlDQnT0tJw+vRp2NvbQ1NTU/R4f39/eHt7Q1lZGd26dYOuri5OnjyJxYsXIyoqCseOHftbMerDhw+wsLDAqVOnoKuriz59+uDt27fw8vLCw4cPRV9nzZo1cHd3h1QqRdu2baGvr48bN25g7dq1iIuLw8mTJ3/oNr8XL14EAOjq6kJX9+v6En369Anp6ekAgCpVqE8EAKS8zMbuj5+QkJSIGtWpYTCvMt8UYEhIIub17QqLhkas4xARgiDA0skLTWobYsWUsdTfiWOHgtfjwdmjmLllHzS1yrGOQ0ScvXgZ3QeOgbxaZ0g0K7KOQ0TIi4qQ4ueI6p36okY3W9ZxiAKTnR1R8OAaYqcMhaoyFT0J+dlu3LiB9u3bAwBiY2PRu3dvxokI4dM3bUowfPhwnDhxApGRkbC3twfwf5sRDBs2TPS48+fPY9asWdDS0kJqairatm0L4M+i2YgRIxAREQFXV1eEh4eXHLNixQqcOnUKbdu2RXJyMrS1tQEAly5d+luz//905swZeHh4wMDAALGxsWjatCkAoLi4GH5+fvDx8YGbmxsiIiK+5Y/hi968eYNz585h4sSJAABvb++vPnbPnj14+fIlKlWqhI4dO4o+78OHD/jw4UPJ7/Pz8/99YI7teZaF4xoaOJi4HxUqVGAdh4i4mfUK4yMPI2Bwd7Q2/HFFavJtiopk6DLSAwMtumCaPQ0oeRaz2hdvs57Ca2MYVNVoBiGvYhNTMHSCFwQDU0jUaNk0r97n5eDgQmfU7TcOlVt8+byRsCcIAhxsesNA8g4bJwyglhGEMPD27VtcuXIFEokEvr6+kMlkiIyMZB2LlAJnzpxhHeEf+6aC2sCBA+Hm5oaQkJCSglpISAiqVKkCMzMz0SWVAQEBEAQB7u7uJcU04M/+YQEBAYiLi8O+ffuQmZkJfX19AEBgYCAAYOXKlSXFNABo2bIlXFxc4O/v/9nrLFq0CIIgYNOmTSXFNODPDQFmzZqFqKgoREZGIicn56tnjynypVkeenp6CA0NxZAhQ77qZ2RkZMDd3R0AMG/ePKipqYk+19/fH3Pnzv1XWX8V655m4mmVakiN3KdwxiNh69ijp/BJOovgUX1QR496nPAqNy8fXUdNhteogRhuRQNKngX7uKBsGU1MXr0DUtodl1sbtu/CFL81KDaygERFg3UcIuJNZhqOrZqCxsO9UKFuC9ZxiIiPHz/CxrwzLGtXglefnjR7miNSJUDK0S6fUoF1gt9bx44dceXKFRQXF2POnDms45BSpkGDBlD/hVrRfFNBrXz58iXb5mZlZSEjIwN3796Fh4cHlBQMAI4fPw7gy7PY9PT0YGFhgZiYGJw6dQp2dnZ48uQJMjIyoK+v/8UZW0OGDPmsoCYIAg4ePAgtLS2YmZl9doxEIkGnTp1w+fJlXLx4EZaWlv/07X/GwcGh5NcfPnxAeno6zp49C09PT1SrVg3dunVTePy7d+9gbW2NnJwc9O/fH87OzgqfP2PGDEyePLnk9/n5+ahRo8a3vQmOzE9/ArU/mmH/jiDa6ZRje6/ex8ZzN7HPyRpVtcuyjkNEpGU+R09nb6zxdIJFh1as4xARgiBgs/swNGjeGgPdvGlAybEZ85dgzc5oFNfsAYkSfUfx6uXdyzi71Q/Nx/lDS596EPIqPz8fNiYdML5bU4zs2px1HEJKtbVr12L+/PmsY5BSSlNTU+GkIt58U0EN+HPZZ3R0NPbs2YO0tLSS+xR59uwZJBIJDA0Nv/i4kZFRyfP+878GBgZffP6X7n/16hUKCgoAAMrKit9mTk6Owse/VlBQ0Gf3Xb58Gd26dYOlpSVu376NmjW/3Kj448ePsLW1xcWLF9G5c2eEhob+z9dTU1P7pf5n+1qCIGDK4ydobmmJlStW0ICSY+tOXkXS/QzEjreFtsbv9//i7+LizXsY7uWPkIXT0KphXdZxiAhZUSE2TBwE4/6DYTFkNOs4RIFhzh6IPnIZxUbdqak9x56cO4hrkZvQ2nUVNCpST1pePc/MxKAe3eBn2w29mtdjHYeQUk8qlVKbHUK+0jcX1Hr37g0dHR3s3LkTz549Q8OGDdGyZcvvka2kkPLXLp5ihZUv3f/p0ycAgJaWFmxsbBS+jlhh73to0aIFnJycsGzZMgQEBGD58uWfPUcQBAwfPhxJSUlo1qwZ9u/fDw2N0rl0RCYIcHn0GANHj4bntGms4xAFfJLO4NHrt4hytoGGyjd/lJAfJP74WXgu34T9a3xRp0Y11nGIiILXOdjkPgy2zlPRzqIP6zhEhCAIMLUZgQsPXqHYwIQu+HDsbtJuPDwRj7aTA6BaVod1HCLi9s0bGDewDzaO6oV2dX6fVRa/G4lUAglH/ex4ykIIKd2+eRSspqaGAQMGYMuWLQAANze3/3lMtWrVkJaWhvT0dNSvX/+zx//a4fKv3TerVav2t/vFnv+fdHV1oaamBhUVlS/OHPuZ/pqVdvfu3S8+PmHCBISHh6NevXpITk6Gjo7OT0zHj7cf5Zj4OB2es30wfLj4phaEPad9h6GmqoxQxz5Qpq3LubU1KgHrQmOQusEfVXSptx2vsjPSsGPGODjOWoRGbTqxjkNEyOVyNDXugyfvNCBU60DFNI5dCV+Ll/dvoO3kdVBWK50XKH8FJ48dgff4UQhzsUX9apVYxyGEEEL+se8yEra3t0fFihWhq6urcHfPv3Tp0gXAnxsY/Lfs7GwkJydDKpWW9EszNDRE9erVkZmZidOnT392zJ49ez67T1lZGcbGxsjNzcWxY8f+6Vv6rh49egQAKFOmzGePeXt7Y+PGjTAwMEBKSgr09PR+djwuPH9fBOfHT7B07VoqpnFMEATY7YqHga42AodYUDGNY/M3BmNHdBKObF5MxTSOPb55GTtnOsFt2SYqpnGsoKAANVub4ImsIor1mlMxjWOnN/rgdWY6WrutomIax6LC98DXdQz2TxlKxTRCCCG/rO8yGu7SpQtycnKQnZ39VcsnXVxcIJVKsXr1aly4cKHkfplMBldXVxQWFsLGxqZkh08AcHJyAgBMmTIF+fn5JfdfuXIF69at++LreHt7QyqVwsHBASdOnPjs8WfPnoke+71cvnwZmzZtAgBYWVn97bEVK1bA398fVapUQWpqqmiPuN/dnbdvMfVZFnaGBMPSwoJ1HCKiSC6H1bb96NGkDub27kwDSo45z1+Nc9duIyVwIcqVpd1xeXXjWDKil3nDMyAYBnUbso5DRDzLegGj1qbI1aiP4vLU34lXgiDgyFJXQEUDzccugFSJWhHwKnD1cmxf4osEz+HQr1COdRzyFaRSKaRKHN2kdEGXEMIHJmcbbdu2xfz58zFz5kx06NABxsbG0NXVxcmTJ5GRkYG6desiICDgb8dMmzYNcXFxOH36NGrXrg0TExO8ffsWhw4dwujRoxEYGPjZ63Tt2hWrV6+Gu7s7unTpgqZNm6Ju3booKipCeno6bt++jbJly8LFxeW7vK+RI0eW/FomkyE9PR1nzpyBIAjo06cPRowYUfL4lStXMHXqVAB/LgldsGDBF3/mmDFj0Llz5++Sj0enX+ViQ0EhYvfHok6dOqzjEBG5hUWw3XEAUyzawYYaBnPNepIvymuVQfSK2Qp3WyZsnYrehSuJkfDevBflyldkHYeIuHXnHjr0HoKPVdpDUqYy6zhEhFwuw8H5Y1GpWWfU6jGSdRyigO/0ybh/6iDiPYdDQ5V2xyWEEPJrY3b5ztvbG82aNcPKlStx/vx5vH//HgYGBvD09ISXlxfKl//7EiU1NTWkpqZi7ty52L17N2JiYmBkZAQ/Pz9MmTLliwU1AJg4cSI6dOiAlStX4tixY4iNjYWWlhaqV68OZ2dn2NnZfbf3tGPHjpJfS6VS6OjooGvXrhgxYgRGjhz5t6speXl5JZstnD59+otLWQHA2Nj4ty2oxb14iVhIkZScjKpVafctXqXlvoHDnhQssTFB17rUMJhXcrkcpqOnoWvLJpg/YQTNIORY0tYVyLx5Cd6b90Jd8/NWAIQPR06cRh8HF3yqbgyJug7rOESE7F0+UheMhaHZYOh36M06DlFgvP1gqGQ/RqT7YGoZQQgh5LfwjwtqfxWBvkaVKlUUPr9Xr17o1avXV/+8smXLYunSpVi6dOk/ytWqVSsEBwd/1Wv4+vrC19f3qzP9r9cWY2xs/K+O+10EZT7HFW1tpMbEQFtbm3UcIuLy0xeYFHscW4b3RBN96nHCq4LCQnRxmIwx1pZwGUgDSp7tXTwd8sK38FwfCmUVVdZxiIg9kfsxZtpcCAbmkKiWZR2HiHj3KguHFrugga0rKv1BPQh5JQgCBluZoamOEhaN608XfH5BEiUJJEr8/L3xlIUQUrpRgwny0y1/koH8mrWRFLYH6urqrOMQEcl307Ho8EXsGdMPRhWp6MmrrJxcmI2ehvku9rAx7cg6DhEhCAJ2eo+FXtXqGDlvJfV/4djStRvhu3obio0sIVFWYx2HiHidfg8nArzwx8jZ0KnZhHUcIqKoqAj9TTpgYDMjTOrRnnUcQggh5Luighr5qWampaNK+/aI2rSJ+jtxbNeF2wi9cg9R421QiZrac+vOoyfoP2kONs12Q9eWNKDklVwmwyb3oWjZ1Qz9x3qwjkMUcPXyxbbIVBTXtIRESqdIvHp+4ywu7FyKlhOWokzl0rmh068gN/cVbM06YZplGwxqT99RhBBCfj90tkh+CrkgwD0tHcY2tvDzm0/T/Tm29MgFnM3IRsx4W5RVpyVpvDpx6TrG+a7E3qXeaFLHiHUcIqKo4C02uA1Cj6FjYGw9lHUcokA/e2ekXriPYiNzSCQ0g5BXj04cwO34YLRxXwN1HWpFwKv0x48wond3LB9iDtPGtVjHId9IoiSFhKO+dzxlIYSUblRQIz9ckVyOCWnpGO02CRNdJrCOQxSYFncCuR9k2DuuP1SVaQYhr/alHMO8wF1IXD8fBlX0WMchIvKyn2PrFAcMcZ+Flt0sWMchIgRBQPuedrj1/AOKq3elCz4cu7V/O55cPIq2k9dBRVOLdRwi4vLFi3CzH4DtY/uihVE11nEIIYSQH4YKauSHypXJ4PY4A/MW+cPWxoZ1HCJCEASMCj+IKuW1sGNQd0ilNKDk1eqQSITuP4iDm/yhq0O97Xj1/MEdhPhOhNO8VajbrDXrOEREUVERmnTthaxPFVFctS3ok49fF3cuxevn6WjjEQAlFeptx6vUxHgsmDoRkZMGopZeBdZxCCGEkB+KCmrkh0l/Vwjv51kI3LIZ3bp2ZR2HiJALAmx2xsOkvhE8u7eh2Rkc81q5GRdu3MWhzYtQRoM29ODV/QunsH/tXExeFYRqNeuwjkNE5L7OQ6MuVnhbpi6gW5t1HKLAibXTIUiV0cplGSRSmj3Nq5Cgrdi1ehHipw2Dnjbtjvs7kUilkHC0mQ5PWQghpRsV1MgPcfXNGyzLfYPwvXvRpAk1ouVVfpEMNjsOYHTnZnCghsFcs/dejPdFRUhYNx8qyvTRzatLKTE4EbYZXoF7UKFyVdZxiIj0jKdoYdYfH3RbAVr6rOMQEYIg4JD/eJQzbIC61i50wYdjKxbOxcmYMCR4jkA5DZpBSAghpHSgURn57o5k52DHh484EH8AhoaGrOMQES/y32FgcCJ8endCT2oYzC1BEGA1YSbqVK+KHXM9aEDJsSOhG3HnZApmbt6LMuV0WMchIi5fu4Fu1g6QV+sEiaYu6zhEhLyoCCkLRkO/XU8YmA5iHYcoMH2iE7Jvnsf+qcOgpkJDC0IIIaUHfeuR72rvsywcVFVDamo8dHVpoMKruy9zMWbvIawZZI521DCYWzKZDF1HTkY/4w6Y4TiQdRyiwP61C5Cb8QDemyKgqq7BOg4RkXDwMOzGTYVQwxgSNepByKui/DwcXDAWtXuNQpXW3VnHISIEQcDoQf2hJ3uNMNeB1H/1NyZVkkLK0c6aPGUhhJRuVFAj383Gp8/wUE8PqZGRKFuWemfw6tTjZ5iRcBo7R/ZC/coVWcchIvLyC9B1pAcmD7fGyL40oORZ6NxJUFdRwtS1u6BEy3G5tXnnbrjPXYFiw+6QqGiyjkNE5Gc9wdHl7mg0xBMVG7RiHYeIkMvlGNC9K7oZ6GD20F40e5oQQkipRGf+5LtY+DgDkkaNEB8SDBUVFdZxiIiYGw+x9tQ17B3XH/o6WqzjEBEZz1/Cwmk6lk8eC6vObVjHISIEQcDWKfao3bAJhkyeQwNKjs1ZvBLLtoSjuKYlJEqqrOMQEdkPruPMxjloNtYP5arXZR2HiCgoKIC1SQeM7tgQY02o6EkIIaT0ooIa+SaCIGD64ydoYGqGNWtWQ0q77nBrw+lriLuTjpjxtiivSTtE8urKnQcYMm0BdsyfgrZN6rOOQ0TIPhRho+sgdOppDSt7Z9ZxiAIjXT0RkXIWxUbdIZHSaQ+vMi4ewdXwdWjluhKautSKgFcvsrIw0KILfPt1Rt9WDVjHIT+LkhQSnpZZ8pSFEFKq0Zkl+ddkggDXh4/Rz8EeM729WcchCsxNOYs72XmIdraBpirNIORV8qkL8FgciJhVc1DPkHYe5FVBXi42uQ9F/9Fu6GhlwzoOUcB8gD1O38lCsYEJJBIagPHq/sEI3D8cgzYe66BWrjzrOETEvbu3McamFwIceqJTPQPWcQghhBDmqKBG/pUCuRwuaU/g4eWFUaNGso5DFJgYdQSCVIqwMf2gTFf0uLUjJgmrdkUiZcMCVKtEve14lZOZjh1eY+DgtQBN2ndlHYeIkMvlaGneD4/yVFBcrRMtx+XYtX2ByLp9CW0nB0BZvQzrOETEmZPH4TnOHqETbNBQX491HEIIIYQLVFAj/9jLoiJMzsjE0lWrYNWzJ+s4RIQgCBgSmoyG1XSxoG8XGlBybNGW3Yg7egaHNy+CjhZt6MGrJ7evImLhVLgsCoRRgyas4xARhYWFaNC5J3KV9FFcuQHok49f57bOx7s3r9HabTWkyjR7mlf7o/Zh9RxPxE4eghoVaXfc0kgi5WvJp4RazBBCOEEFNfKPPHhbgDkvsrF95060a9eOdRwiokguh3VQPPq3qAdX45as4xAFXP0D8OhJJlI3LIS6GjVL59Wt04eQtHEJpq7dhco1jFjHISKyXmbjj259UKjdCNA2Yh2HiBAEAcdXeUBZSxfNx/nT4JhjW9atQfS2dUjwHI4KZWl3XEIIIeQ/UUGNfLULua+xJr8A0bExqFevHus4RMTrwiLY7IzHJNPWGEgNg7lmN2U+NFSVEbtqDpSUlFjHISLOxO7Ghbg98N4UAe2KlVjHISLuPniIdj0HQqbXDpKyVVjHISIEuRypC8eiYsN2qGXlSLOnOeY3czpuHIlHvOdwlKELPoQQQshnqKBGvkriy5fY+wlITE6CfjXafYtXT/LyMTw0GQv7d4NpfUPWcYgIQRBgOnoa2v1RD4tcR9GAkmMp21cj/epZzNyyDxplaDkur06duwDLIU74pN8FEo0KrOMQEbLCAqT6jYGB8QBU79yPdRyigKvjcHx6eg/RHkOgokwXfEo7iVTK1UxSnrIQQko3KqiR/ynkWRbOlimLg/tjoaOjwzoOEXH1WTYmRh/FhqGWaFGjMus4RETh+yJ0cfCAfW8zTBpKA0qeRS6biaK8V5geuBsqqmqs4xAR+2Lj4eAxG4KBGSSqVPTkVWHuCxxa7IJ6/SdAr1kX1nGICEEQMLyvJeppfMKy8TZ0wYcQQghRgApqRKHVGU+RXcMAyRER0NDQYB2HiDh0/wnmHTyP3Y59UauSDus4RMTL3DyYOE6Bz9ihGGRJO0TyShAE7Jo1HhV1deG8chukdCWcWys3bMWsZRtRbGQJibI66zhExOuMBzixxhNN7GeifO2mrOMQETKZDNYmHdCvcXVMserIOg4hhBDCPSqoEVE+jzNQoVUrxGzdAmVl+l+FV7sv3UXQpTuIdrKBXrkyrOMQEffTM9Fn4iwEek+ESRsaUPJKLpdj86QhaNq+K2zGT6XZGRybMnsBNoTF/1lMU6IdInn1/NYFXAjyRwvnxShb1Yh1HCLiTV4erE07YJJZSwzvRN9R5O8kSlJIOOr1KlH6xDoCIYQAoIIa+QJBEOCelo4Offti8aJFNKDk2Mpjl3Hs8TPEjreBljotSePVmau3MGr2UoQt9kKzerVYxyEiigoLsNF1MLrb2cPUzp51HKKAneNEJJy+hWKj7pBIaAYhrx6fScKNmG1oPWkNNMrrsY5DRGRkpGNYT1MsGWSK7n/UYR2HEEII+WVQQY38TdEnARMfPcaICePhPmkS6zhEAa8DJ/H83XtEOllDjWYQciv60EnMXrsd8WvnoaY+7TzIqzc5L7Bl8ggMmuiF1mZWrOMQEYIgoHOfQbj25B2EGt3ogg/HbscHI/1sCtpOXg/VMuVYxyEirl+9gglDrLFlTB+0rqXPOg4hhBDyS6FROCmRJ5PBLT0DM+fNw+BBg1jHIQo4hqeifFlNBI/qDSXq78StwD2x2BaViEObFqFSeW3WcYiIF2n3EewzAWPmLEP9Fu1YxyEiZDIZmnTrhWcftFFcrT2olMavSyEr8OrJA7TxCICSKvW249XRgymYM8kJe93sUKdKRdZxCMf+XPLJz/kmT1kIIaUbFdQIACCzsBCemVkICAyEqakp6zhEhFwQYLcrAR1qV8fMHu1pdgbHZq/djhOXruPw5kUoq0kbevDq0dVziF4+C5OWb0H12vVZxyEi8vLy0ahrT+Sr10ZxJVqSxrNT673x8VMxWk1cDqkSnWbyKix4J7YunYcDU4eiio4W6ziEEELIL4nOdAhuvnkD/1d5CA0LQ/PmzVjHISLeyT7CescBDG/fBGM6UsNgno32WY7cN2+QtN4PqirULJ1XVw7G4WhIIDzX74ZuVVrqxKuMzEw0N+2PoorNAa0arOMQEYIg4MgSF2hWrYWmAybRBR+OrVnqj0MRO5E4fQS0NWkGISGEEPJvUUGtlDuek4Ot72WIOxCHmjVrso5DRLwsKMTAXQnw6tEBfZvS7AxeCYKAfm4+qKZbAfuWzoSUluNy63jENtw4fADem/dCS6c86zhExJUbt9Ct/wh8rNIRkjKVWMchIuSyIqT6jUWVVmYw6j6MdRyiwEyPiXh66SQOTBsOdRUaBpCvI5VKuTqn4SkLIaR0o2/SUiwm6wUSlFSQkpIMPT3afYtXD3LyMCo8FSvtTNGxVnXWcYgIuVyObiOnwLJjK8weO5hmZ3AsPnARXj66De9Ne6GmQctxeZVy5BisHd0h1DCBRJ16EPLqQ8EbpPqNQa0eDqja1pJ1HKLA2CE20Hr7AhGTBlL/VUIIIeQ7oIJaKbXlaSZuV9BFakw0tLSodwavzj3JwtS4E9hub4VGVXVZxyEi8gveoauDB1wG9cFYmx6s4xAFwvwmQ6n4E6auDYYyLcfl1o7dezFh1iIUG1lAoqLJOg4RUZCdicNLXNFw0BToNmrLOg4R8enTJ9hZGqN9FU3MG92HLvgQQggh3wkV1EqhxU+eQla3HhJ3h0JVVZV1HCLiwK1HWHH8KiLG9UeN8uVYxyEinr7IRvex07F40ij07daedRwiQhAEbJ82Cga162K453waUHLMb3kA/AODUWxkCYmyGus4RMSrR7dwKnAWmjrOhbZhA9ZxiIjCwkL0N2kP+zb1MN68Des45BdFu3wSQsiXUUGtFBEEATMeZ6BW165Yv34d9R/g2NZzNxB54xGix9ugYhlaksarG/fTMGDyPGz39UCHZg1ZxyEi5DIZNrgOQjvzXujjOJF1HKKA05SZCI47huKaFpBI6RSFV5lXTuDy7tVoOXE5ylSiVgS8ysl+iQHmnTGzdwfYtGnEOg4hhBDy26Gz1VJCLghwfZSOnkOGwMdnNs3O4NiCg+dwLSsX0eNtUUaVlqTx6vC5K3BZsAZRK2ajYU3aeZBXhW/zsdF1EPqMnIDOfexYxyEKWA0ZjWPXnqDY0AwSCV3w4dWDw1G4lxqBNh4BUCtXgXUcIuLRg/tw6GeJtSMs0aWBEes4hBBCyG+JCmqlQKFcDpe0dEyYOhXjxo5lHYco4BZzFEWfihE+th9UlJRYxyEiQg8cxOJtYUhevwDVK1NvO17lPs/Eds+RGDF1Lpp2NmUdh4gQBAGtzPvj/isJBP3OdMGHY9ejNuPZ9TNoM3k9VDTKsI5DRFw4dwaTRw1BsLM1mtSozDoO+Q3Qkk9CCPkyKqj95rKLPsAjIxP+S5ehb98+rOMQEYIgYPieFNTSK4/1/bpBKqUBJa+WBoUjMuU4jmxejPLlyrKOQ0Q8vXcTYfPdMX7BWtRq3Jx1HCKiqKgIDTr1QI6kCoqrNAJ98vHr/PaFeJvzAq0nrYGSCvVf5VXC/hgs9fZAtMdgGOrqsI5DCCGE/NaooPYbe1TwDrOzXmDz9m3o1LEj6zhEhEwuh83OePRoUhuTzahhMM8mL9mAmw/ScGijPzTUqVk6r+6cPYqE9f6YsmYnqhjUZB2HiMh5lYvGXaxQoNUQ0KG/J14JgoCTazwhUS+LFuOXQEL9V7m1fVMgIjasRMK04dAtRzMICSGEkB+NCmq/qUuvX2PVmwLsi4pCw4bULJ1Xb4qKYLMjAeO7tcTQNvT3xLMh0xZAAuDAmrlQVqbluLw6Hx+BM1G7MGNjGHQq0VInXt1/mIa2PezwoVJrSLSqsY5DRAhyOQ76O0GnbgvU6TOOluNybLHvLFxMikaC5wiUVacZhOT7kkikXBXTqc8mIYQXVFD7DR3KzkF0sQTxiQmoUZ123+JV5psCDAlJxLy+XWHR0Ih1HCJCKC6G+VhPNK1jhOWTx9CAkmMHd63Dw/PHMHPzXmhqlWMdh4g4e/EyzAeOxqdqXSDRrMg6DhEhKypE6vzRqNG5H2p0s2Udhyjg4TQKhQ9vIGbKUKjSBR9CCCHkp6GC2m8oXkUNqfEHUKEC7b7FK5lMhsEhiVg/uDtaGVZlHYeIEIqLMWnROozqZ4Fp9jSg5FnGvduQFRXBa0MYVNXUWcchIt4VFsLMbgwEAzNI1LRYxyEiiosFJM+xR92+Tqjcwph1HCKiuLgYkSHb0a12FWyaYEv9VwkhhJCfTFJcXFzMOgT5PvLz86GtrY26tWpBnfo7cet90Qfkvc6FhpIEZdTo74lXgiAg5/0HKKEYVSqWZx2HKJCXn4+89zJUqKIPKS0D4VZRYSHe5WajSKIKqZSu5/Hqk/AJytJPUFIvA7UyNNOTV8WCALzPh7wwHwZ6NNOTZ/nvP+DJixy8efMG5cr9Wv+m/hpb3PYaCS2OlhK/LZKh4aKgX/LPlBDye6Ez2t9QSPt6KKuqwjoG+YKoh08R+vITtMuXR/fFUazjEBH5LzJxfJkr9HUrw62SBtpX0WUdiXyBXBAw9sRl1KxUHhW0NLHL1Y51JCJiXvhBHL50F7maqkgZYcU6DhFx7ulLTD37AHJBQPqd66zjEBH5+fnoZmaBCjVqw9TUDN7e3qwjERH2Dg54kpGBJy9yWEf5JhIlKSRK/Fyw4ikLIaR0o08jQn6SzTfTEPNOwOG4vVBX12Adh4h48eAGTi13xe6QYDRt2pR1HCKiUC7H0CMXMMisDab26wJa6MSvCZuicOlWGoIHmEJKf1Pcir/3BF6X0jFv+z5UoA09uJWZmYmOXU3gN3cO+vftwzoOESEIAnr07IkKFSti1eo1rOMQQgj5QaigRshPsODSPVzXLI/U6HDoaGuzjkNEpF88huvb5iEmNhbNmjdnHYeIeFVUhKFHLmDGIHOMt2zHOg4RIQgCbBbvwoe8d9jYtytUlahZOq+2Xb2P1Y/fYMGuGOhVo82MeHXtxg10t+qNLRvXo5dVT9ZxiIiioiJ06twZJqZmWLZ8BW1mRAghvzFa8knID+Zx5hbKN2qC/etWQokGlNy6k7oP2SejkZSUBL3KNDuDV2lvCjDp3HWsc7JG10Y1WcchIuTyTzCfuwUdq1aCe/smNKDk2MIzN3H+Uxn47wyDumYZ1nGIiEOHj2DS5KmICg9D/fr1WMchInJzc2FqZobJU6Zi6LBhrON8N7TkkxBCvowKaoT8IIIgwPHEdXS0tMRi35k0oOTY5b0bUPz4CpJTUqi5LccuZ7/G3Gv3sHvKUDQxqMI6DhGRX1gEszmbMaxJHQxrWod1HKKA+6HLeKVXE3OXb4KyCvVe5dXuPeFYtnIlEg/EQr9aNdZxiIj09HT06t0bS5cvR/fuFqzjEEII+QmooEbID1Aol8P+2DWMGuOISc5jWMchCpzZ4gc9FCA0IQFqtOsqt1KePMfGR08R6+0Aw0q06yqvnr/OR4+5W+HZuRksatdgHYeIEAQB9okXUKFlZ3jNXkQXfDi2bMVKRMfuR2piAsqX12Edh4i4ePEi7B1GYltQEFq1asU6DiGEkJ+ECmqEfGev3hdh1MmbmDNrBgbZ9GUdh4gQBAHHV01By9r6WB8YBKmUlg/wKvReOuJzXiPJZzR0y9GSNF7dyniBQUtDsMSiPVrrV2Idh4iQyeWw2X8WrfsNxUBnd9ZxiAKTp03HnTt3kJJwABoatJkRrxITE+Hp5YV9UVGoU+f3nJUrVZJCytEyS56yEEJKNyqoEfIdpb8pgMuFe9iwejmMu3RkHYeIEORypC4YCxur7vDxmUOzMzi2+vp93JHLkewzGmXUVVnHISKO33oEl43R2Ni3C+pW1GEdh4jIL5LBOu4s+jlPgbnNENZxiAJDR4yEkrISYqP2QVmZTtd5tX37dqwPDERCQiIqV6FWBIQQUtrQNzQh38nll7nwufkEYTu2oFmTRqzjEBGy94U4ON8RruPHwcnJmXUcosCsCzfxqVwZ7J82DCrKtKEHryJOXsOivYewy8YU1WgGIbcy3xRgaNIljPZZjNZdzVnHISIEQYCFVW+0bN4ci/0X0AUfji1YuBDJySlISkml/quEEFJKUUGNkO8gNf051j15hfi9oahpaMA6DhHxLjcbRxY5Y6HfPPTvb806DhEhCAJcTl9DvTrVsWpUL1qOy7HVcScQfvQy9tiZQ0eDehDy6ubLXDgfvYUpKzajXtOWrOMQEUVFRehq1h1DBg2Eh5sr6zhEgYmursjIyEB8YmKp6L8qkUog4ei7WCKlQjMhhA9UUCPkG4Xee4L9b2Q4tH8fKulWZB2HiHj99BFOr56KzZs2oFOnzqzjEBEyuQDHE5fQu+MfmGFjzDoOUcArOAGXb6djt505NFTodIJXxx4/w+zLT+CzeQ/0jWqzjkNE5OTkwMyyJ7w8p2HIoIGs4xAFBtjZoZy2NsL37oOSEs2eJoSQ0oyfSw2E/IJWX3+Iw3IVHKZiGtee3byIs2unYW9EOBXTOFYg+4ghR89jbK+OVEzjnMOacKQ9zkKQjQkV0zi291Ya5t7Mgt+OKCqmcezhozR0M7PA8qWLqZjGMblcDhNTU9Rv0AAbN22mYtov5u3bt/D09ISFhQUqVaoEiUQCX19f1rEIIb84KqgR8i/NunAHmbrVkBS5G2XLUt8gXj08lYS7e5YiPj4eDRtRbztePX9XiGHHLmCBfU+MNGnFOg4RIQgCes7fDi25gLVWnaDM0RIg8ncBF+5gW9YH+O+KRUU9apbOq3Pnz6OvtS1Cdm6Huakp6zhERGFhIdp36ADbAQMwd978UtfbTqIk5e72T7169QqbNm3Chw8f0L9//+//h0QIKZXosjIh/9Cf/Z1uolbbdghcvoj6O3HsRnwICq6kIik5BRUr0gxCXt17nY+pF29hi8sAtKtLPQh5VST7CNM5m9GzVnU4t27IOg5RYPbxa7ijrouFO3ZAVU2ddRwiIj4hETNm+WB/dCRq1TRiHYeIyMrKgoWFJWbPmQNrGxvWcci/ZGhoiNevX0MikSAnJwdbtmxhHYkQ8hugghoh/4BcEOBw7BqsbG3g4+lR6q5Q/kouhqyC2qtHSEpOgaamJus4RMSZrBwsvvUQ+zyHo161SqzjEBG5BYUwn7MZTq0awqZhTdZxiAJOKRchr9kEPv5raUkax7Zs24ZNW7YhOSEOlfX0WMchIu7duwdrW1sErFuHLl26so5DvgGdsxNCfgQqqBHylQpkMow4fgNukyZirP1Q1nGIAifXz0LNckoI2r8fKioqrOMQEfsfZyL4SRbiZzmiWoVyrOMQEWkvX6PfgiDMMW6FrkZVWcchIgRBwKADZ2HUrSdGTvGhwSPH5s5fgCPHjuNgUgK0tLRYxyEiTp46BScnJ4SG7kbjJk1Yx2Hq3y6z/FF4ykIIKd2ooEbIV8h69x5jTt/CkgVz0benBes4RIQgCDiyZCK6tfoDy1esoAElx7beScOJ/AIkzxkNnTIarOMQEZcfZcJ+VRhW9eyIplVo2TSvCmVy2MSdRbcho9HPwYl1HKKAs4srXrx8icS4GKipqbGOQ0RERUVh3vz5iI07AAMDakXAq/z8/L/9Xk1Njf5dEUJ+KirvE/I/3HudjzFn7mD7xgAqpnFM/lGGFF8HDO5jgRUrV1IxjWOLr97BtY8fkTBrFBXTOJZ0+S5GrQ7Dtv7GVEzjWE5hEXrFnIb1JG8qpnFMEARY2w3CJ0HAvrDdNOjn2Lr167F46VIkJCVTMY1zNWrUgLa2dsnN39+fdSRCSClDM9QIUeDMs2wsvPccUbuD0LBeXdZxiIiignwc8hsNr2lTMGKEPes4RIFpZ69Dq3IFRI63hjL1d+LWjkMXsC7uJELtzFGJip7cSnudD4eUq5jgtwpN23dmHYeIkMvlMLXoAVNjY8yZPZMu+HBs5syZOHf+ApKSU1CmDO3g/heJRAoJR5twSSR/ZsnIyEC5cv/XMoIK1YSQn40KaoSIiEvLRNDzfCRHh6F6NeobxKv8l5k4vtQVK1csg6VlD9ZxiAhBEDD25BW0a1obC4da0oCSY/6Rh5F49hb2DDSHlpoq6zhExOVn2Zh0+h6mB+xAzQaNWcchIgoKCtDNrDvGjRkDp7GjWcchCjg6OuLtu3eIjYuj/qu/iHLlyv2toEYIIT8bFdQI+YLttx/jUBFwOC4S5XW0WcchIrIf3cb5QG/s3LEDrVu3Zh2HiCiSyzHy+CUMNW2DSb06so5DFHDdHIPHT18iZIAZ1JRpBiGvEh88xaKbz+C7NQJVqhuyjkNEPH/+HN2tesNvri/69+3DOg4RIQgC+vTrh5pGNbFh8xZIOZqJRQghhG9UUCPkvyy+ch8ZZSvgUPh2aGios45DRDy5cgp3w1YiOjoadevSclxevS6SwfHEJXgPNMPAjk1ZxyEKDFweAo2PAjb37QolGlBya+e1hwh+9g7+u2KgXUGXdRwi4tbtOxgweAg2B65Hp44dWMchImQyGUxMTWFl1QvTZ8xgHYdbEiUlSDlq0yD5l1kSEhLw7t07vH37FgBw69Yt7N27FwBgZWUFTU3N75aREFI6UEGNkP8w7ewtaNRtgLgNa6CsTP88eHXvcCyyjoYjMTERVarSclxeZbx9h4lnrmHV2H4wbVKbdRwiQi7/BMv529CyUnlMM2lKy3E5tvTMLRyXqcF/Vyw0qL8Tt44dP4EJrpOwd08oGjVsyDoOEZGXlwcTMzO4urrC3mEk6zjkJxg/fjzS09NLfh8REYGIiAgAQFpaGoyMjBglI4T8qqhiQAj+nO4/5uQNtDY1xXK/OTSg5NjVyC2QPziH5JQUaGvTclxeXct5jdlX7iLYYwiaGlHRk1fvimQw8dmEQY1qwr5ZPdZxiAJTj1zBs/I1MH/DFqioUm87XkXs3YeFi5ciIS4GNapXZx2HiMjIyEBPKyv4L16Cnj17so5DfpLHjx+zjkAI+c1QQY2UekVyOeyPX8dQ++GY6jqedRyiwLnti1BeloM9iYlQV6fluLw68jQLax9kIGaGA2pWrsA6DhHxIu8tLOduxeQOf6BnXQPWcYgIQRAwKukiyvzRDt6+S6m/E8dWr12HsIgIHExKQIUK5VnHISKuXr2KocOGYcvWbWjTti3rOL8EiZIUEiV+Pnt4ykIIKd2ooEZKtbwiGRxO3ID3jKkYZmfDOg5R4NjKqWhSQxebNkdBiaM+HuTvwh88QcyLV0ic7Qg97bKs4xARdzKzYbd4F/zN26FdDT3WcYgImVyOAXHn0KzXAAyeMJVmT3NsuvcsXLl6FamJ8dSHiWMHDx6Ex+TJiNgXiXr1aFYuIYSQb0MFNVJqZbx9h/Fn72Ltcn90N+nGOg4RIXz6hIMLxqG3WVfM9/OjASXH1t18gCtFH5DkMxpaGmqs4xARp+48hnNgJAL7dEF9XR3WcYiItx9ksIk7B6sxbrC0G8E6DlHAwXEMZLKPiIuOhIqKCus4RERwSAhWrlqFAwkJqFq1Gus4hBBCfgNUUCOl0vXs15hx/TFCtm1Eq+Z/sI5DRHwseo/UeY4YP9YRLi4urOMQBXwv3UKhpjriZ46EKm3owa3oszcwb3cKdtqYQL8czSDk1fO3hRiceAEjZyxAO9MerOMQEYIgwKqvNRrUr4eVy5bQBR+OLV26FLH79yMpOQU6Ojqs4/xyaMknIYR8GY16SKlzJCMLq9JycCAiFLVrGrKOQ0QU5uXiyMKxmD/XFza2tqzjEBGCIGDSmeswMKqC7WP6Un8njgUknELIwQvYbWeOCprUg5BXd7JfY+yRG3BfsgENW7RhHYeIKCoqgrG5JWz694Xn1Cms4xAFPCZPxr179xCfmAQNDQ3WcQghhPxGqKBGSpWI+xnYm1uEg7ERqKxXiXUcIiLv2WOcWumBwMD16NqVluPySi4IcDx+CRZtG2PWABOancGxWbuTceb6Q+wZ2B2aKvTVz6tTT7Iw40IaZm0IRY3a1N+JV3l5eTA2t8QUj0kYMWwo6zhEgSFDh0JFRRWR0THUf5UQQsh3R2fVpNRYd+MRLkENh+P2opyWFus4RMTzO1dwdfs8hO3ZgyZ/0HJcXhXK5HA4fhHOvTthjBnNouHZ6HV7kffqDXbamEKFlslwK/ruY6y9/wrzgyKhW4X6O/EqPT0dVn2tsWLpElhamLOOQ0QIggALyx5o0bIlFvr70wWfbySRSiHhaAY6T1kIIaUbFdRIqTD34l3k6VVDctAmqKlRs3RepZ09hEf7NyIu7gAMDWk5Lq9eFhZh3Kkr8BveA31aN2Qdh4gQBAH9Fu2Evpoq1vfuAikNKLm18dJdROd+gv+uWJTV1mEdh4i4fOUqhjuMQtDWzWjTuhXrOETE+/fv0c3YGEOGDoOrmxvrOIQQQn5jVFAjv7U/+zvdQrUWLRGzehn1d+LYraQ9eH0+AUlJyahUiZbj8upB3ltMPn8DG8fbomMDKnrySiaXw9RnC8wMqmBiu8as4xAF5p68jmtKOli4cxfU1Km/E6+SklMwzcsbMfsiUKdObdZxiIicnByYmpvDa8YMDBw4iHUcQgghvzkqqJHfllwQMOr4dZj37YN53tNouj/HLoUFQPn5bSQnp6BsWdp5kFfnXrzCwhsPED5tOBpW12Mdh4jIe/ceZj6bMbpFfdg1rsU6DlHAJfUiCms0gO/i9VCi3XG5FbRzF9YFbkByQhyqVK7MOg4R8eDBA/Tr3x+r16yFsYkJ6zi/FdrlkxBCvozO3shv6Z1MDvvj1+A8wQnjRzuwjkMUOLVxDqqrfcKuuANQVVVlHYeISEh/hu3pz3Bg1ihUr6jNOg4RkZGTh17zt2FWt5YwqanPOg4RIQgChsSfh35HM0yYPo8u+HDMf/ESJCanIDUxHtra9NnHq3Pnz2OUoyOCQ0LxR9OmrOMQQggpJaigRn472YVFGH36Jvzm+sCmd0/WcYgIQRBwdNkkdGhSF6vXrKHluBzbefcxDr5+gySf0ahQVpN1HCLi2uPnGLZiN1ZYdkDzarqs4xARRXI5bPafRUc7B9g4urCOQxSYOMkDTzIykHRgP9TV1VnHISL279+PWT4+iIndDyMjI9ZxCCGElCJUUCO/lYd5bzHp4n1sDliNLh3aso5DRMg/ynDQbyyGWPfGDO+ZrOMQBZZdu4fHxcVInO0ITTWaQcirg9ceYPLWWGzp1w21KpRjHYeIyC0swoAD5zHAzQvGfQawjkMUsBs8FFpaWoiKCIOSkhLrOETE5s2bsWXbNiQmJqGSHrUi+FEkUglXyywlUprVSwjhAxXUyG/jQtYrzL39FPtCgtC4QT3WcYgIWWEBUuc5Yqq7G0Y5OrKOQxTwOn8DqhXKIWbiACjTgJJbIUcvYWX0cYQMMENlmkHIrfS8txiRcgXOc5ejecdurOMQEXK5HN179kLH9u3hN8+XluNyzHfuXBw7fhyJScnQ0tJiHYcQQkgpRAU18ltIfPwcmzNfIylqDwyqU98gXhW8eoGji8dj2ZIlsLKyYh2HiBAEAc6nrqJZQyMste9JA0qOLY0+ithT1xE20Bzl1GkGIa+uZr3CxBO3MW3VFtRp3Jx1HCKisLAQXU27Y5TDCLiMd2Ydhyjg5OyM7JwcxB2Ip/6rhBBCmKGCGvnl7brzGEmFxTi0fx8qVijPOg4R8erxPZxdNx1B27ahbbt2rOMQEUVyOUYdvww745aY0qcz6zhEgcnb43Dn0TPstjOHmjLNIOTVwUeZmH/tKXy3RKCqgRHrOETEy5fZMOvRE76zZ8LW2pp1HCJCEATY2NqicpUq2BMWTv1XfxKJVAoJR3/WPGUhhJRu//jTSCKRlNxOnz4t+rzw8PCS5/2MBqHf63V8ff+c3h8UFPSPXvu/b6qqqqhRowaGDRuG69evf/G42NhYODg44I8//oCuri5UVFSgp6cHKysrHDhw4JvfS2mw/OoDnJRo4mBMBBXTOPb02hlc3OCNyMhIKqZxLK9IhqFHLsCtf1cqpnFu6MrdePH8Fbb170bFNI7tvvEIi+5kY8HOGCqmcezevfsw6W6JgFUrqZjGMblcDmMTEzRv0RIB69ZTMY0QQghz3zRDLSQkBB06dPjiY8HBwd/yo39JDg4OJb9+8+YNLl68iNDQUOzduxeJiYkwMTH52/N37tyJyMhING7cGO3atYOWlhYeP36MhIQEJCQkYPbs2Zg3b97Pfhu/DK/zdyAxrIWEzeugoqLCOg4Rcf/4AWSmhCAhMQHVqtFyXF5lFhRiwumrWO7YB92b1WUdh4gQBAE95m1Dk/Ll4NWjAy3H5djK87dx6J0y/INjoVmW+jvx6tSZMxjn7IKw0F1o0rgx6zhERH5+PoxNTOA0fjxGjx7DOg4hhBAC4F8W1NTU1FC7dm2EhYVh1apVUFb++4959eoVEhMT0bJlS1y6dOm7BP0V/Pesto8fP2L06NHYtWsXJk2ahGvXrv3t8ZkzZ2Ljxo2oWLHi3+4/e/YszM3N4efnh8GDB6NRo0Y/Ovov5c/+TjfRpEsXrF40nwaUHLseE4TC28eRnJKC8uVpBiGvbr16gxmXb2P7pEFoVYuKnrwqLJLBdM5mWNczhGPL+qzjEAVmHLuGtLJV4Be0DSqqaqzjEBHRMbHwne+HuJhIGBkaso5DRDx//hwWlpaY77cAvfv0YR2nVJJIlSCR8jMbmqcshJDS7V/PlR42bBhycnKQlJT02WNhYWH4+PEjhg8f/k3hfnUqKirw9fUFAFy/fh15eXl/e7xFixafFdMAoF27dhg8eDCKi4tx5MiRHx/0FyKTCxh69Cq62w3AmsV+VEzj2PkdS6GceRVJSclUTOPY8WcvMevaXUROt6diGsdy8t+hk/d6jGtRn4ppHBMEAaOTLiDXoDFmrd9FxTSOBW7chEVLlyE1MYGKaRy7ceMGzLt3x/rADVRMI4QQwp1vKqhJJJIvLu0MDg5G2bJl0a9fP4U/Iz4+Ht27d0f58uWhrq6O+vXrw8vL67PC01/evXuH6dOnw8DAAOrq6mjQoAFWrFiB4uJiha9z4sQJWFtbQ09PD2pqajAyMoKbmxuys7O/+v3+W5UrVy75tVwu/+rjlJT+vPJCOxf9n/wPMgw8chkTPSZhhocr6zhEgeNrp8NI/QOio2OgoaHBOg4REfnwKdY/eorE2Y6oU/Xz4j7hw4Pnr2AyeyN8u7VCn/o08OeVXBBgu/8M9LpYwW3BaurvxLFZPr7YFxWN1MR46OrSZx+vjh49iiFDhyIsPAIdOnZkHYcQQgj5zL/uoWZoaIhOnTohNjYWBQUFKFu2LAAgLS0Np0+fhr29PTQ1NUWP9/f3h7e3N5SVldGtWzfo6uri5MmTWLx4MaKionDs2LG/FaM+fPgACwsLnDp1Crq6uujTpw/evn0LLy8vPHz4UPR11qxZA3d3d0ilUrRt2xb6+vq4ceMG1q5di7i4OJw8eRJVq1b9t38M/9PFixcBALq6utDV1f2qY65du4awsDCoqKjAzMzsh2X7lWS+LYTT2dtYuWQhepqb/O8DCBOCIOCwvzMsOreD/6JFNIOQYxtvPcS5d4VI9hmNcprqrOMQEWfvP8GYtXuxrldnNNSjmZ68KpDJYLP/LLo7TECvoY6s4xAFRo91wtuCAiTsj6H+qxwLCwvD4iVLEBcfD3396qzjEKnSnzde8JSFEFKqfdOmBMOHD8eJEycQGRkJe3t7AP+3GcGwYcNEjzt//jxmzZoFLS0tpKamom3btgD+LJqNGDECERERcHV1RXh4eMkxK1aswKlTp9C2bVskJydDW1sbAHDp0qXPmv3/5cyZM/Dw8ICBgQFiY2PRtGlTAEBxcTH8/Pzg4+MDNzc3REREfMsfwxe9efMG586dw8SJEwEA3t7eos/dv38/9u3bh48fP+LJkyc4deoUVFRUsGnTJtSsWfO7Z/vV3HqVB88radi5ORBtWzVnHYeIkMuKkDrPEWPsh2OSuzvrOEQBv8u38VpNBQmzHKGm8k1fA+QHirtwG7ODE7HDxgQ1tMuyjkNEvCgoxKCEixju6YuO3XuzjkNECIKAvtYDYGRkiM0bA2kGIcdWrlqFvfv2ITE5BRUqVGAdhxBCCBH1TWcTAwcOhKqqKkJCQkruCwkJQZUqVRTOrAoICIAgCHB3dy8ppgF/bnYQEBAADQ0N7Nu3D5mZmSWPBQYGAgBWrlxZUkwDgJYtW8LFxeWLr7No0SIIgoBNmzaVFNMAQCKRYNasWWjRogUiIyORk5Pzz9/8F0gkkpKbjo4OLCwskJeXh9DQUHh4eIged/XqVezYsQOhoaE4ceIEVFVVsXr16pIipZgPHz4gPz//b7ffzYmnLzHjRgZiw3dRMY1j7/NfI3n2cMzynErFNM65n74Gqa4OIqYOo2IaxzannIXf7hTstjOnYhrH7r16A7uES3DxD6BiGsdkMhk6m5ihQ/t2CFi9koppHJvm6YmkpGQkJCZRMY0QQgj3vumMonz58rCyssLBgweRlZWF8+fP4+7duxgyZEhJD7AvOX78OIAvz2LT09ODhYUFBEHAqVOnAABPnjxBRkYG9PX10fELPRSGDBny2X2CIODgwYPQ0tL6YnFPIpGgU6dOEAShZFnmt3JwcCi5DR48GB06dEBOTg48PT1x9OhR0eNmzZqF4uJivH//HtevX4ejoyPGjx+Pfv36QSaTiR7n7+8PbW3tkluNGjW+y/vgRdTDp1jz9DVSY8NRr3Yt1nGIiDfPM3Bo/misXbUSgwYPZh2HiJALAkYeu4hmf9TCJmdrKNGAkltzw1Ox+9BF7B5ojoq0HJdbZ5++xJgjNzFj/S40bt2edRwi4s2bN2jXqQvGOI7CzBnTWcchCowYMQKZmc8QHRursG0MYUAq5e9GCCEc+ObpCcOHD0d0dDT27NmDtLS0kvsUefbsGSQSCQxFdlUyMjIqed5//tfAwOCLz//S/a9evUJBQQEAQFlZ8dv8XjPUgoKCPrvv8uXL6NatGywtLXH79m2FSzjV1dXRpEkTrFu3DsrKylizZg3Wrl2LKVOmfPH5M2bMwOTJk0t+n5+f/9sU1TbeTMPpT8o4HLcP2uXKsY5DRLy4fx2XNs/B7pBgNGvenHUcIqJQLofD0YsY3bMDxlu0Yx2HKOC8MQovsnKxy9YUqgouTBG2Dtx7guV3XmLe9n3Qq0b9nXiVkZGBnn36Y4n/Alj17ME6DhEhCAJ69uqFRo0aYcnSZdR/lRBCyC/jmwtqvXv3ho6ODnbu3Ilnz56hYcOGaNmy5ffIVvKF+tcunmJfsF+6/9OnTwAALS0t2NjYKHwdscLe99CiRQs4OTlh2bJlCAgIwPLly7/quOHDh2PNmjWIiYkRLaipqalBTU3te8blgt+lu8iuUAWpOzdDXZ1mZ/Dq8YWjeBC5Dvv376defxzLeV+EMSevYM4QC9i0a8w6DhEhCAJsl4SgopIUG/p0gZQGlNzaeuUeIl7KsDA4BuV0aEkar67duIHBw+yxbfMGtP+P9iKEL0VFRTA2MYHtADt4/MdFYkIIIeRX8M0FNTU1NQwYMABbtmwBALi5uf3PY6pVq4a0tDSkp6ejfv36nz2enp4OACW7b1arVu1v94s9/z/p6upCTU0NKioqX5w59jP9VWy4e/fuVx/z146g2dnZPyQTr9zP3EKFxk0QG7BS4bJhwtbt1H14dSoaSUlJ0PuP3XgJX9LeFGDSuetY72SNLo2o6MkrufwTzHy3oLN+JUxq14RmZ3BswekbuCCUxcKd4VDXLMM6DhFx8OBhuE+dhuiIMNSrV5d1HCIiNzcXJmZmmDp1GoYMHco6DlFAoqQECUfn5TxlIYSUbt9lAbq9vT0qVqwIXV1dhbt7/qVLly4A8LfNDP6SnZ2N5ORkSKXSkn5phoaGqF69OjIzM3H69OnPjtmzZ89n9ykrK8PY2Bi5ubk4duzYP31L39WjR48AAGXKfP3J918912rXrv1DMvFGEAQ4HL2KP8zMsWP9aiqmcexyRCCKrqYiOSWVimkcu5z9Gh4XbmL3lKFUTONYfmER2nuth209Q7i3/4OKaRxzP3QZd8vVwNwtVEzjWcjuPfD0nonEA7FUTONYWloaunTrhkWLF1MxjRBCyC/ruxTUunTpgpycHGRnZ3/V8kkXFxdIpVKsXr0aFy5cKLlfJpPB1dUVhYWFsLGxgb6+fsljTk5OAIApU6b8bTfLK1euYN26dV98HW9vb0ilUjg4OODEiROfPf7s2TPRY7+Xy5cvY9OmTQAAKyurkvtfvnyJ5cuXIy8v77NjUlJS4OnpCQAYNWrUD83Hg0K5HAOPXMEgx5Hwn+NNA0qOnd48Hzr5T3AgPh5aWlqs4xARKU+eY8GtB9jvPRJNDKqwjkNEZL56gy7egZjcvgmG/FE6Lp78igRBwLD4sxAat8f0VVuhrKLCOhIRsXT5CmzYtBkHk+Kh//9XNxD+XLx4Eb379sX2oB3o3t2CdRxCCCHkX/vmJZ//Rtu2bTF//nzMnDkTHTp0gLGxMXR1dXHy5ElkZGSgbt26CAgI+Nsx06ZNQ1xcHE6fPo3atWvDxMQEb9++xaFDhzB69GgEBgZ+9jpdu3bF6tWr4e7uji5duqBp06aoW7cuioqKkJ6ejtu3b6Ns2bJwcXH5Lu9r5MiRJb+WyWRIT0/HmTNnIAgC+vTpgxEjRpQ8XlhYiKlTp2L27Nlo3bo1qlevjnfv3uHevXu4c+cOAMDDwwO2trbfJRuvXr0vwqiTN+HrMwMD+/dlHYeIEAQBx1ZMRqu6NbA+cAektLsSt0LvpSMh5zWSZo+GbjmaRcOrWxkvMGhpCJZYtEdr/Uqs4xARMrkcNvvPok3/YbBzmsQ6DlFg8jRP3L17H8nxcdDQ0GAdh4hITEzEdC8vREZFl5pVGL8FqdKfN17wlIUQUqoxKagBf84ea9asGVauXInz58/j/fv3MDAwgKenJ7y8vFC+fPm/PV9NTQ2pqamYO3cudu/ejZiYGBgZGcHPzw9Tpkz5YkENACZOnIgOHTpg5cqVOHbsGGJjY6GlpYXq1avD2dkZdnZ23+097dixo+TXUqkUOjo66Nq1K0aMGIGRI0f+rQihp6eHJUuW4MiRI7h58yYuXLgAQRBQtWpVDB48GE5OTjA2Nv5u2XiU/qYALufvYcPq5TDu0pF1HCJCkMtxcMFYWFt1h4/PHJpByLHV1+/jjlyOJJ/RKKOuyjoOEXH0xkO4bo7Bpr5dUaeiNus4RER+kQzWcWfRf/xUmFkPZh2HKDBkuD1UVFQQExnxP3d2J+xs374d6wM3ID4hEZWr0OxpQgghv75/fNbx146bX6NKlSoKn9+rVy/06tXrq39e2bJlsXTpUixduvQf5WrVqhWCg4O/6jV8fX3h6+v71Zn+12uL0dTUxLRp0zBt2rR/fOzv4PLLXPjcfILwXVvRtHFD1nGICNn7QhycNwqTXMZj7LhxrOMQBWZduAmhXBnsnzYMKsp05ZZX4SevYvHewwi2NUVVLZpByKvMNwUYmnwZo2cvRuuuZqzjEBGCIMCiZ2+0bNkcixcuoAs+HFuwcCFSUlKRlJKCcuXKsY5DCCGEfBd0GY/8dKlPnmNdei7i94aipqEB6zhExLvclzji7wz/hX7o168/6zhEhCAImHDqKhrUq4GVI3vRclyOrYo7joijV7HHzhw6Gmqs4xARN168woTjtzF5+SbUa9qSdRwioqioCF1NzTF08GC4u01kHYcoMHHiRGQ8fYoDCQlQU6PPvl+SVMrXMks61yGEcIIKauSnCr2Xjrh8OQ7t34tKuhVZxyEicjMe4vSaqdiyeRM6duzEOg4RIZMLGHXiIvp2agYv626s4xAFpu+Mx5W7T7DbzgwaKvTVy6tjj59h9uUnmL1pD/SNqL8Tr3JycmBq0RMzvTwxaOD3a91Bvr8BdnbQ1tFB+N59tIM7IYSQ3w6d1ZOfZvX1h7ipXAaHYvegbFla6sSrZzcv4PpOf+yLiEDDRo1YxyEi8mUyjDp+CZP6dYODMc2i4Zn9mjDI8gsRZGMCZbqqzq3wW2nYlJaHBTujUaFSZdZxiIiHj9LQ19oWa1Yth5mJCes4RIRcLkd3Cwt06twZc3zn0nJcQgghvyUqqJGfYtaFO5DpGyJxayBUValZOq8enkzEk8QgxCckoHr16qzjEBHP3xXC+dRVLB7ZCz1b1Gcdh4gQBAFWC4JQr2wZzLLqRANKjgVcuIPEtxL4B8eijBb1d+LVufPnMWqME0J3BaFZ06as4xAR7969g7GJCUaOGgUn5/Gs45DvQCKVQsLRBSGeshBCSjcqqJEfShAEuJy+hdpt22H9cn/q78SxGweCUXD1IJJTUlGhQgXWcYiIO6/fYPrF29jiaoe2dWqwjkNEFMk+wnTOZljVqg6n1rTxCs9mHb+Ge+qV4BcUBFU1ddZxiIi4A/GY6eOL/dGRqFXTiHUcIiIrKwsWFpbwmeuL/v2tWcchhBBCfigqqJEfRiYXMPLENfS1G4BZ09xZxyEKXAheCY3XaUhKToGmpibrOETEqWfZWHrnEfZ6Dke9apVYxyEicgsKYe6zGeNbN0L/hkas4xAFxiVfgFDrD8z2X0v9nTi2actWbNkWhJTEA9CrRJ99vLpz5w5sBwzAusBAdO7chXUcQggh5Iejghr5IQpkMow4fgPukyZitP1Q1nGIAifWzURtbRVsj90PFRUV1nGIiJi0p9j99AUSZjuianlaksartJev0XfBdsw1aY0uhlVZxyEi5IKAwXFnUcvYCg5TZtNyXI75zp+Po8dO4mBSPLS0tFjHISJOnjoFJycn7N4ThkaNG7OOQ743iRJfu3xKOMpCCCnVqKBGvrusd+8x9vQtLFkwD316dmcdh4gQBAGHF0+AcetmWL5iBQ0oObblziOcfFOAJJ/R0CmjwToOEXHx4VOMXB2O1T074o8qtIsxrwplcljvPwPT4WPRZ8Q41nGIAs4urnjx8iWSDsRS/1WORUZGYr6fH2LjDsDAwIB1HEIIIeSnoYIa+a7uvHqDqVceYfvGAHRo04p1HCJCLvuA1PmjYT/YDtOmTWMdhyiw6ModPFeSImG2IzRUaQYhrxIu3cGMHQnYbm0MQx2aRcOrnMIi2B04hyFTfNC5Rz/WcYgIQRBgYzcIVSpXxr6w3dR/lWNrAwIQGrobCUnJ0NXVZR2HEEII+amooEa+mzPPsrHw3nNE79mBBnXrsI5DRBQVvMGh+WPgPX0ahg0fzjoOUWDq2esoV7kCoibYQIkGlNzafug8AuNOIdTODJVoBiG3HuXmY+TBq3DxW4M/2nViHYeIkMvlMO3eA6amJpgzy5tmT3PM29sbFy5eQmJyMsqUKcM6DvmRpJwt+eQpCyGkVKOCGvku9j96ih1ZBUiODkP1atQ3iFf5LzNxfOlErFq5AhYWlqzjEBGCIGDsySto17Q2Fg61pAElxxbuO4Tkc7exZ6A5tNRoSRqvLj7Lhsfp+5gesBM16zdiHYeIyM/Ph7G5JZzHjcW4MY6s4xAFRo0ahXfv3yNmP/VfJYQQUnpRQY18s6230nBUJsXhuH0or6PNOg4Rkf3oNi5smImdO3agdevWrOMQEUVyORyOXcKI7m3h2rMD6zhEAdfNMXj89CWCB5hBTZmulvMq8cFTLL75DHO3RaCyPvV34tXz58/R3ao3Fsybi359erOOQ0QIgoA+ffuiVu3a2LR1G13wIYQQUqpRQY18k0VX7uOpVgUcjNgODQ111nGIiCeXTuLu3lWIjo5GnTq0HJdXr4tkcDxxCd4DzTCwY1PWcYgIQRAwaMVulPkkYEu/bpDSgJJbO649QMizQizcFQPtCtTfiVe3bt+B3eAh2BS4Hp060oUEXslkMpiYmqJXr97w9PJiHYf8RBKpFBKOWk/wlIUQUrpRQY38a1PP3kKZeg0RF7gaysr0vxKv7h6KRtbRCCQmJKJKVVqOy6v0/AK4nr2OtWP7w7hJLdZxiAi5/BMs5m1Fa70KmNqxKc3O4NiSs7dwUqYO/12x0KD+Ttw6evwEXFwnYW/YbjRs0IB1HCIiLy8PJqamcJ00Cfb2DqzjEEIIIVygKgj5xwRBwJiTN9DGzAzL5vvQgJJjVyI3Q/7gPFJSU6GtTctxeXUt5zVmX7mLEI8haGpERU9eFRR9gOnszRjUuCbsm9VjHYcoMOXIFWRVMMC8wM1QUaXedryK2LsPCxcvRUJcDGpUr846DhGRkZGBnlZWWLRkCXr06Mk6DiGEEMINKqiRf6RILof98esYPtIek12cWMchCpzdthAV5a+xOzER6uq0HJdXhzKysP5hBmK9R8JIrzzrOETEi7y3sJy7FVM6NEWPujVYxyEiBEHAyKQL0GraATPmLIGUlgVxa9WatYjYF4mDSQmoUIE++3h19epVDB0+HFu2bkObNm1YxyGs0C6fhBDyRVRQI18tt+gDRp24gZnenhg6wJp1HKLAsZVT8IeBHjZuioSSEp108Cr8wRPEvHiFhNmO0NMuyzoOEXEnMxt2i3dhUfd2aFtdj3UcIkIml2NA3Dk0722HwROmso5DFPCc4Y2r164jJeEANDU1WcchIlJTUzF5yhTs3ReJunXrso5DCCGEcIcKauSrPMkvwIRz9xCwYjHMjbuyjkNECHI5Di4chz7mxpg3fz4tx+VYwI2HuPahCEk+o6GlocY6DhFx8nYaxgdGIbBPF9TX1WEdh4h4+0EG6/1n0XusOyzshrOOQxQYMWo0Pn36hAMxUdR/lWO7goOxevVqHEhIQNWq1VjHIYQQQrhEZzLkf7qe/Rre19Oxe8cmtPijCes4RMTHovdIneeICeMcMWGCC+s4RAHfS7dQqKmOA1NHQpUGlNyKOnMd8/ccxE5bU+iXo6b2vHr+thCDEy9ilPdCtDWxYB2HiBAEAT379EOjhg2xYuliuuDDsSVLlyIuLg6JySnQ0dFhHYfwQCrla5klLecnhHCCRnJEoSMZWViVloO4iBDUrmnIOg4RUZj3CocXjoPfXF/Y2NqyjkNECIIAtzPXYWhUBdvH9KX+ThwLSDiFkIMXsWegOcrTDEJu3cl+jbFHbsJj6QY0aN6adRwioqioCCbmlrC16Y+pkz1YxyEKeHh44P7Dh4hPTKL+q4QQQsj/QAU1Iir8/hNEvv6Ag7ERqKxXiXUcIuJ15mOcXuWBDYHr0bVrN9ZxiAi5IGDU8Uvo0a4xZtqa0OwMjs0MScK5m2nYM9Acmir0NcmrE0+yMPNCGmZtDEWNWtTfiVe5ubkwseiBaZM9MHzoENZxiAKDhwyBmro69kVGUf9VQggh5CvQSIF8UcCNR7gsUcfhuFBolaVm6bx6fucyrm73Q3hYGBo3oeW4vCqQfcTI45cwoXdnjDajWTQ8cwzYi/zcfOywMYGKEs0g5FXUnccIePAKfjuiULFyVdZxiIi0tHT0trbGyqVLYdHdjHUcIkIQBHS3sEDrNm3ht2ABXfAhn5EoKUHCUZGVpyyEkNKNCmrkM74X7+JtZX0kb98INTVa6sSrR2dSkbZ/M+Li4mBoSMtxefWysAjjTl3GghFW6N2qAes4RIQgCOjrvxM11NWwvndnGlByLPDyfcS++gj/XbEoq63DOg4RcfHyZdiPHI0d27agdauWrOMQEYWFhTA2McHQYcMx0dWVdRxCCCHkl0IFNVJCEARMOnsb+s1bYufqpdTfiWO3EkPx+kISklNSoKuryzoOEXHvdT6mXryJTRNs0aEeFT15JZPLYeqzGeaGVeHStjHrOEQB35M3cF1JBwt3RkBNXYN1HCIiMSkZnjNmImZfBOrUqc06DhGRnZ0Nc/Pu8PL2ht3AgazjEEIIIb8cKqgRAH/2dxp57Bos+vfD3BlTaXYGxy7tWQPlrLtITk5BWVqOy61zL15h4Y0HCJ86HA2r67GOQ0TkvXsPU59NGNOiAewa12IdhygwIfUSigwawHfROijR7rjc2rZjBwI3bEJyQhyqVK7MOg4R8eDBA/SztsaatQHo1o36r5L/QSrla2dNnrIQQko1OiMleCeTw/74NYx3cYazoz3rOESBU4E+qKFZjF0H4qGiosI6DhER/zgTQU+eI37WKOhX1GYdh4hIz36NPn7bMbtbKxjXrMY6DhEhCAIGx59DjY7d4TJ9Ll3w4dgC/8VITk3FwaQElCtXjnUcIuLsuXMYPXo0goND8EfTpqzjEEIIIb8sKqiVci8LizD61E0smOcDm949WcchIgRBwNGlbujwRz2sXrOGluNyLOjOYxzOe4Mkn9GoUFaTdRwi4srjZxixYg9W9uiAZlVp2TSviuRyWO8/g852o2DtOIF1HKKAi5s7nmZmIjk+jvqvciw2NhY+c+YgOnY/jIyMWMchhBBCfmlUUCvFHrzOh/ulB9i8bjW6dGjLOg4RIf8ow0G/MRhi3RczvL1ZxyEKLL16D+koRuJsR2iqqbKOQ0SkXr2HKdvisKVfN9SqQLNoeJVbWIQB8edh5zYD3Xrbso5DFBgweCi0y5VDZPgeKNHue9zauHEjtgcFISEpGZUqVWIdh/xKpEp/3njBUxZCSKlGBbVS6nzWK8y/8xT7QoLQuEE91nGIiA/v3uLgfEdM83DHyFGjWMchCkw/fwPqFbUR42ILZRpQciv46CWsjjmO0AFm0KMZhNxKz3uLESlX4DxvOZp3oP5OvJLL5TDvYYXOnTpivu8cWo7LMV9fXxw7cQKJ1H+VEEII+W6ooFYKJTx+hi2Zb5AUFYYa+tQ3iFdvs5/j2JIJWLZ0KaysrFjHISIEQYDzqSto3rAmltj3pAElxxZHHcWB09exZ6A5ytEMQm5dfp6DSSfvYtrq7ajd6A/WcYiIwsJCdDU1h+NIB0xwdmIdhygwzskJr3JzEXcgHqqq9NlHCCGEfC9UUCtldtxOQ8p74ND+vahYoTzrOERETtpdnFvvhaBt29C2XTvWcYiIIrkco45fgp1xK0zp05l1HKKA+7b9uJ/2HKF25lBTphmEvEp99BR+1zLhuzUCVWoYso5DRGS9eIHuPXrB12cmbK2tWcchIgRBgLWNDapWrYbde8Ko/yr51yRSJUg4WmbJUxZCSOlGBbVSZNnVB3ioqY1DsUHQ1NRgHYeIeHr1DG7tXoaoqCjUq0fLcXmVVySD44lL8BxgiiGdm7GOQxQYsmI3lIpk2Nq/G5RoQMmt3TceYVvGWyzYGYPyutTfiVd3796DzcDBCAxYg65d6EICr+RyOUxNTWFuYYkZ3t40e5oQQgj5AaigVkp4nb8DJaM6iN+0FioqKqzjEBH3ju3H84O7kZCYgGrV9FnHISIyCgox8fRVrBjdF+ZN67COQ0QIggDLedvQtII2pvdoRQNKjq04dxuH3yvDPzgWmmWovxOvTpw6BacJExEWugtNGjdmHYeIyM/Ph7GJCZzHT4Dj6NGs4xBCCCG/LSqo/eYEQYDTqZto2rUrVvnPowElx65Fb0PR3VNISk5B+fK0HJdXN1+9gffl29g+aRBa1aKiJ68Ki2Qw8dkE2wZGGNWiPus4RAHPo1eRUa4a/NZvhYqqGus4RERkdDTmLfBHfGw0DA0MWMchIjIzM2HZsyf8/Bagd58+rOOQ34VECvA0w1vCURZCSKlGBbXfmEwuYMTxq7AbOgReHhNZxyEKnNuxBOXeZSE6MQkaGrQcl1fHMl9i1b3HiPKyR+0qFVnHISJevimAhe8WTGrXBL3rUx8uXgmCgNHJF6HWsDVmzl9B/Z04FrB+A0J270ZqQjx0demzj1c3btzAoMGDsWnzFrRr3551HEIIIeS3RwW131T+Bxnsj1+Hp+dk2A+2Yx2HKHB8zXQ0qFwOW0KioaxM/yR5FfnwKSKev0TibEdU1tFiHYeIePD8FawX7YCfaVt0NKjMOg4RIRcEDNh/Fo0t+2Oo63SaPc0x79lzcP7CBaQmxqNMmTKs4xARR44cgaubG8Ii9qJBgwas4xBCCCGlAo3ef0PPCt5j8qUHWLlkIXqam7COQ0QIgoBDC51g2aU9/BctogElxzbcfIjzhYVI9hmNcprqrOMQEWfvP8GYtXuxrldnNNSjZdO8KpDJYLP/LCxGToTVkJGs4xAFRo0Zh3fv3iE+Npr6r3Jsz549WLJ0GeLi46GvX511HPIbol0+CSHky6ig9hvyuHQfwVs2oE1L2nmQV8XFApJmD8e4kSPgNmkS6zhEgR33n0CzYjkkzHKEmgp9ZPLq8ctcOK/bhx02JqihTU3teSUXBPSOOYMRnvPQoXsv1nGIiOJiAb36WaNWTSNs3bSBluNy7MiRIziQkICkFOq/SgghhPxskuLi4mLWIcj3kZ+fD21tbbRo2gRGBjVYxyEi5J8+4dipMyhbVgutW7dhHYcocPjIERQV5MOiRQNIaQYht+49e4m0F7loWU0P2urU1J5XH+SfcOFlHnT0qsCgTj3WcYiI4uJiXD15BMoqKjA27sY6DlHg/IWLePniBczNzaGmRp99vHr+/DnOnTuHN2/eoFy5cqzj/CN/jS1eHQlHubKarOOUyC8oREXjgb/knykh5PdC0y1+Q7MWraQ+J5x6mp6O+TOnolyZMtgcEs46DhEhl8sxaeJ4CBIphqlVQp0HhawjEREpn96iQO0TWlQoj3EVq7COQ0TcepuPfXI59CpVwNadO1jHISLevn0LN0d7KBcL8N5E31E8C14xH+9lHzGyZ1eM7duZdRwiIurYeVw6fpd1jG8nlQI8LbOkWbOEEE5QQe031KhRY2jR1RruXL5wHv4+Xti3aSUcp/ig8R9NWUciX1BQUIBeFqZoaTUQGXeuo8rJCzBSpp1XebT1Uw7KNqyK1d3bIzDoAOpp0VJPHqVmZyMOxUjduws9h43FH02pHQGPnmZkwHGgNeb3ao+VBy/AqEET1pHIFwiCAL9xg1CrQSO0Ne6Oai+voVkdI9axyBcs3BGF1JMXsMG+J3qt2sM6DiGEkB+AyvuE/ARJB/Zj6oTRSNgViOaNafctXr188QLm3Tqh24iJ6GxrzzoOESEIAlYK2TBsUwd7po+EqjJHV83J34RlZSFGWYoj0aGooV+VdRwi4vq1q+hv2hkBdsawaFSTdRwiQlZUhJlDe6J1F1OM8ZpPmxlxzHX5Vpw4dxnRE+2gRa0ICCHkt0Uz1Aj5wXZt24I92zbgcPhW6OlWZB2HiLh/7w6GDrSD3XR/1G7elnUcIkIuCFiMbFhbtMXMQRas4xAF1j19imeVK+Hwro3Q1KBZnrw6fDAV051HI2SUFeroVWAdh4jIf50L31HWGOQ0Cd36DGAdhygwwHsZND59xG6n/lD6XZYmSqV8LbPkKQshpFSjghohP9DSBb64cOwQjuzdDq2y1NeOV2dPn4LLBGeMXLAeVWvVZx2HiCgU5FiEHHgM7g7H7u1ZxyEKzH/yBJqN6iN+/XKoqKiwjkNE7A4Jxjq/2Yhy7o8qtDsut7KePsai8cPhPGshmncyZh2HiJDL5TB3m4821XXh08eMZhASQkgpQAU1Qn6Qaa7OePviKVJ2b4aqKg0oeRUXG4158+bBecVOlK9SjXUcIuLVJxlWSHOxzMkGVm0as45DRAiCgKmP09HSvBtW+82iASXHVi1djAO7tiB2gg20NdRZxyEiHty4jIAZEzF12QbUaUz9B3lVWFSELs4+GNamIcZ1a8E6DiGEkJ+ECmqEfGeCIGDUYBvUqKiFHVtWQUrT0rm1OTAAQbtC4LI2FGV1aKkTr9LlRdik8gbbJo9A+wZGrOMQEUWCALe0Rxg8fDC83JxYxyEKTPdww/2TBxHlbAMNVToV5NXFo6kIXTkfczbuRlUDI9ZxiIiXr9/A1MUXXj3bo3+L33OWu0RJCRIlfvqV8pSFEFK60VkUId+RXC6HbU9T9OjcFr6Tx9PsDI7N85mFI6fPwCVgN9Q0NFnHISJuyN4hXLMQUTPHon71yqzjEBH5Hz/CLT0dXtPcYD/QmnUcooDDIFuovkzHnjF9f5/+Tr+h1L3BSA3fgQVBkdDRrcQ6DhFxL+M5+k1bhJWDzNG5bg3WcQghhPxkVFAj5DvJz8+HbQ8TTLQfCKfhdqzjEAVcxo1Ges4bOK/YASVlWo7Lq5Mf3uCwjoDEOROgX1GHdRwi4nlREaY9fYo1S+aih0lX1nGICEEQ0NesG5qXk8J3qAVd8OFYROAy3Dx7Agt3RkOzrBbrOETEqev3MGbBOuxw7I1G1ajoSQghpREV1Aj5DrKeP8fgPhZYNMMN/S3NWMchIgRBwCDbflCpqI+RfutoQMmxA7I83K2sgtQ541C+LM0g5NXdt2/hl52NkM1r0Kb5H6zjEBGFhYXo2aU97BrXwPiuzVnHIQpsnueJvOwszN8WARVVNdZxiIjoo+fgszEUe8fbwKCiNus4P55U6c8bL3jKQggp1aigRsg3unv7JsYOs8P25fPRqQ01ouWVTCZDLwtT1OlgBnN7F9ZxiAKh8lwUGmkj2dsRGmqqrOMQEadzc7HpXQHidm9F3VpGrOMQEa9ycmDVrQOmGjeDzW/a3+l3IAgClrs7olz58pi5bif1X+XY+sgkbI9OQazrQOhq0QUfQggpzaigRsg3OHXiGLwnjUf0ltVoVK826zhExJu8PFhZmKHzwNFo22sA6zhEgcBPOajSpDp2uw+FMjUd5taBFy+xXyLgYGQwqujRUidePXzwAIOszLHMuiu6UH8nbsnlcsxztEHTdp0wzM2LZk9zbNbGPTh54Sr2TxqIsnTBhxBCSj0qqBHyL0Xvi8D6pQuQsnszalSrwjoOEZH5NAPWfXqhr+ssNOxgzDoOESEIApYhB126NMHikX1pQMmxoGfPca1cGRwO2QLtctTfiVcXz5+H09AB2DrcEk30qejJq6LCAvjY90OPQfawGjKKdRyiwOgFgcjNfonIiQOgUtou+NCST0II+SIqqBHyL2wMWIX4vbtxZO92VNApBb0zflE3r1+Dw4hhGDp7BQwbNWMdh4iQCQL88RIj+3SBe39j1nGIAssznuKdoT5St66Dujr1d+JVfFws5k12RfiYPjAsDf2dflG5L7Mwf4wd7D280aF7L9ZxiAhBENDHczH0NVSww7EvpFK64EMIIeRPVFAj5B+a5+2J+9cv4nDENmhqaLCOQ0QcPXQQU6dOxujFW6BnUJN1HCIiX5BjqSQHs+x7YXDXlqzjEAVmpaejWqvmCF3lD6XSNjvjF7Jt0wbsXL0U0eNtUIn6O3Er89F9LJ00Eq5+K9G4VXvWcYgImUwO44lz0L1eDUzrQX9PhBBC/o4KaoT8AxNHj4CS7B0SdgVCWZn++fAqfHcIVqxcifGrg6GtW5l1HCLihVyGNcq5WDtxIMyaUbN0XskFAR6PH8O0b08s9J5Cy3E5ttB3Nk7EhCNmvA3KqlN/J17dunAGm+dNhffaIBjUoc8+XuUXFKLL+Nlw7tocIzqU7l2MJVIpJBxtlMFTFkJI6UYVAUK+giAIGNrfCs3qGGDVXF8aUHJs9fKl2BcTC5eAPdDUKsc6DhHx4ON7BKm9Rej0UWheqzrrOEREkVwOl8ePMc5pFFzH2LOOQxRwHeeIVzcuYp+TNVSVaQYhr04lxSJq40rM2xKOStXos49XT1/mwHKSH+b264oeTWqxjkMIIYRTVFAj5H8oKiqCjaUxBvcyh+cER9ZxiAIzpk7GxVt3MWFNKFTUqL8Try59eIv9WjLEzXZGzSoVWcchInJlMrg/eQI/n+kY0KcH6zhEhCAIGNK/F/SKXmPHyF7U34lj8cGbceJAJBbsjEI5nQqs4xAR1x4+wSDvZVg/vAfa1KzGOg4hhBCOUUGNEAVe5+bCtqcJZox3xIgBfVjHIQo4jhiK13Ipxi7ZAin1d+LWoQ95OFdBgiRfF1TWoR0ieZVeWIhZz59j4+rFMO7YlnUcIkIul8OqW0cYVysHzz6mNHuaYyEr/ZB26yoW7oiCuib1tuPVoYvX4bp0C0LH9kNduuDzfySc7fIp4SgLIaRUo4IaISLS09Ngb9Mba+Z6wdK4E+s4RIQgCOjfqwcq1GmCYeOn04CSY1EfX+OpvgZSfcZCS0OddRwi4tqbN1j2Ohd7dwTij4bU34lX+fn56NG5Hca0rQeH9k1YxyEKBHhPhPzDB/hu2gNlFRXWcYiIkKTjWLYrEtETB6AqXfAhhBDyFaigRsgXXL18CW6jRyAkYBFaN23MOg4RUVhYCKvupmhq0R/dBtJyXJ4FyV9BqY4uEqaPhJoKffXw6kjOK+z68B6J4UEwqkH9nXj17Nkz9DftAp8ebdGT+jtxSxAE+DsPRfVatTFu5kK64MOxpSGxiEo9gf1ug6CjSRd8CCGEfB0a1RDyXw4mJ2LBzGk4sHMd6hgZsI5DRLzKyUYvy+4wH+WGFma9WcchIgRBQABeoV7LWgh0GQgp7czFrX1ZL3BYRQmHo0OhW6E86zhExK1bN2DfrxfWDjRFW+rvxC25TAYfh37o2N0KA8ZNYh2HKOCxegdu3X2AGFc7aKjSDMIvkkgACUff31ScJoRwggpqhPyH3buCsHPDGhwK24oqerqs4xARaQ8fYKCtNWyn+aFuyw6s4xARckHAEmSjl1krzBnSg2ZncGxj5jOk6ZbH4eBNKEP9nbh1/MgRTB7rgF0jrVCvMjW151VB/hvMcegHG8cJMLMezDoOUWCIzyrgfSHCnKyhrMRRwYgQQsgvgQpqhPx/K5csxMmUAzi6NwjltMqyjkNEXDx/Fk5jx8B+XgD06zZkHYeIKBIE+OMlJg4ww7ieHVnHIQoszHgCpXp1kLRxFVSovxO39oWHYbnPdEQ69UM16u/ErezMDCxwHoKxM+ajVVcz1nGICEEQ0H3SfDTV08E8u550wYcQQsi/QgU1QgB4uU9ETsZDHNyzFWpqqqzjEBEJB+Iwx2cWxi0PQsVqNVjHISJef/qI5dJXWDCmP/q3/4N1HCJCEARMf/IEjbt2RID/HFqOy7GAVSsQvXU9YsfbonwZ6u/Eq0e3rmPNdGdMXrwO9Zq2ZB2HiCiSydDVeTZsW9SDi0kr1nF+DRIpZ0s+OcpCCCnVqKBGSr0xQ+2gW0YFMdvWQEmJtuHm1fYtm7B56za4rN2NsuVpK3tePZUXIVDlDTa5D0PnRtQsnVcyQYBbWhqsB9ti9mQX1nGIAjOneuDm0SREjbeBJvV34taVk0ewc4kPZgcGQ9+oNus4REROXj5MXOZgSvd2GNC6Aes4hBBCfnFUUCOlllwuh12v7jBp0xQLprvRdH+O+c/3RfLhY5gYsBtqmmVYxyEibsveIVTjHfZ6j0Ejgyqs4xARb+VyuD1+jMkeEzB6qB3rOESB0UMHQci8j7Ax/ai/E8cOR+1BYugW+AXtQ4VKlVnHISIeZr5AnykLscTOFMb1DVnHIYQQ8hugghoplQoLC2Ft0Q3jBveHy8ghrOMQBSZNcMb9Zy/gvGonlFVoOS6vznx4g5Ryn5DoOwHVdWmHSF69KCrC1KdPsWLhHPTqbsw6DhEhCAKsLc3QQE2OBcNpQw+eRW5egyvHU7BwZzTKaJVjHYeIOHfrAUbOXYMtI3uhaQ0qev5TxRIpijlaZslTFkJI6UYFNVLqvHzxAgN7m2PB1Imw7dWddRwiQhAEDBtoC6FsRYxaEEj9nTiWIHuNm5VUkDJnPCqWoxmEvHpQUIC5L19gx4YVaN+qBes4RERRURF6dGkP63pVMdGE+nDxbOtCb+Q8TYff9n1QVaPedryKO3ERM9btQvh4Gxjp6rCOQwgh5DdCBTVSqty/dwejB9tg8xJfdGvfmnUcIkIul6OXpRkMW3WBxUhXmp3BsTB5LvIMtJAyaww0aUMPbp17/Rrr3+YjNmQL6teh3na8ys3NhVWX9pjU9Q8MbEX9nXi2fPIYaGpoYFZgMPVf5djm2FRsjEhAjKsd9OiCDyGEkO+MCmqk1Dh3+iQ8J45D5OaVaFK/Lus4RER+fj6supuivY09OvQdzDoOUWDzpxzoNKyG/ZOHQ0WZBpS8Snz5EpHCJ6Ts3QX9qrTUiVfpj9Nga2EC/76dYdqA+jvxShAEzHW0RaMWrWE/eRZd8OGY79YIHD59EfsnDYKWOl3w+Sa0yychhHwRFdRIqRAXE4XVC32RFLIRhtWrsY5DRDzPzES/Plbo5eyJJl1oOS6vBEHASuSgTYeGWDHGmgaUHAt+9hwXymjgyO6t0NGm/k68unz5EsYOtMbGod3RnPo7cavofSHm2PeDuc1g9BkxlnUcooDT4k14nvkMURPtoEoXfAghhPwgVFAjv72tG9YhevcOHI7YBt0K1CydV3du3cLwoQMx2Hspav7RinUcIkIuCPBHNgZbdYCnrRnrOESBNU+fIke/Cg5uD4SGBvV34lVyUgJmuzpjt2Nv1KqkwzoOEfHmVQ7mOtpgyMRp6NKzH+s4RIQgCLCZsQwVlIDgMf0gldIFH0IIIT8OFdTIb22h7yzcOHcSRyK2oYymJus4RMSJY0fhPskVo/w3oYpRHdZxiIh3ghyLkAOv4T0w3KQN6zhEgTnp6ajQrAni1i6BsjJ91fNq5/at2LJkAaKcrVGZ+jtx69njh1jiao8JvkvRtF1n1nGICLlcDpOJc9G1VlV49exAs6e/J4nkzxsveMryD3zrBluCIHynJISQ74XOsslva5LTKHx6m4ukkA1QUVFhHYeIiIwIw+IlSzB+1S7o6FVlHYeIyJbLsEopFysn2MGyJTVL55UgCPB4/Bidrbpjic90GlBybInfPBzaG4xYFxtoqauxjkNE3L1yARt83OG1aiuM6jdiHYeIKCgsQhfn2XDs9AdGdWrKOg4hX6ShoYHi4uJ/fNyHDx/+1XGEkB+PCmrktyMIAkYM6IsG1fUQsGwFDSg5tn7NKoRGRGBiwB5oltNmHYeISPv4HltV87FzmgNa1zVgHYeIKBIETHz0CKNGj4CHsyPrOEQBjwlOyLx0CpHO1lCjGYTcOncwAWEBi+G7OQyV9WuwjkNEPM95je5uczG7d2f0akqz3Am/3r1799XPzcjIQEhICEJCQnDr1q0fmIoQ8i3oLI78VmQyGWwsTWDdvQtmuo1jHYcoMHvGdJy+dAUua3dDVY36O/Hqyoe3iCr7ATGznFCnWiXWcYiIPJkMbk+ewNd7CgZb92Ydh4gQBAEjbPtB++1LBDv2htI3Lv8hP07C7m04FhOGhTsioV1Bl3UcIuJW2lPYei3B2qEWaF+7Ous4vy+p9M8bL3jK8h3l5eUhPDwcwcHBOHnyJIqLi1GpUiW4uLhg2LBhrOMRQr6ACmrkt/EmLw+2PUwwZewIjBrUn3UcosDYUSPw8p0c45ZthxLNzuDWUdkbnCxfjKQ5LqhagXaI5FVm4Xt4Pc/E+hULYNalI+s4RIRcLkdvk87oWEkT3oPMaPY0x3avWYT7V89j4Y5oaJSh3na8Onr5FsYv3ojgMX1RvyoVPcmvSSaTIS4uDsHBwYiPj8fHjx+hqamJwYMHY/jw4bC0tPzm3muEkB+HRrLkt5D5NAND+/XASp9psDLryjoOESEIAmz6WkGrRl2MmDaLBpQci/34GmlV1JDqMw7aZTRYxyEibr7Jx6LcHIRtW4/mTRqyjkNEFBQUoGeXdhjRvBZGU38nrgX6uON9fj7mbg6Diqoq6zhERMSh0/DbGo4oF1vol6cLPuTXc+LECezcuRN79+7FmzdvoKSkBDMzMwwbNgw2NjbQpM3UCPklUEGN/PJuXL8GF4ch2LnKD+1aNmMdh4goKiqCVXdTNDLuBZOhY1nHIQrs/JgLoVZ5JHqNgroqbejBq2M5OQgqeo+EsCDUMqT+Trx68eIF+hh3hHf31uhD/Z24JQgCFk8cAb2q+pi0ZhvNCOHYpTkhZAABAABJREFU6vB47I4/jDi3gShPF3x+imKJFMUSfv5N8JTl37KxsUFOTg5atGgBBwcHDB48GHp6eqxjEUL+oX/8aSSRSEpup0+fFn1eeHh4yfOMjIy+JeNX5/oer+Pr6wuJRIKgoKB/9Nr/fVNVVUWNGjUwbNgwXL9+/at/lrm5ecnPyMrK+hfvoHQ5evggXEcNxf6gtVRM41hu7iuYdO6A9gNGUjGNc+s+5aBC8xqInDmGimkci3rxAruLBRyKCqFiGsfu3b2DXl3aYXn/zlRM45hcJoOPfR80atEaLnOXUTGNY57rgxF36CRiqZhGfnFSqRQSiQTPnj3Do0ePkJ6ezjoSIeRf+KYzhpCQENHHgoODv+VH/5IcHBxKbr169YJEIkFoaChat26Nw4cP/8/jg4KCcPDgQVoG95UidodgofdUHNyzBfVqGbGOQ0SkP06DpakxervOQiuL/qzjEBFyQcAi4SXaGf+BLZOGQEmJBpS82pKZiVNaZXAkOgR6uhVZxyEizpw8iWG9LRE0whIdqVk6twoL8uE12BIWtsMwaPwU1nGIAiPmrkXag8eIGG8LTbrgQ35x58+fx8KFC1GxYkWsXbsW7dq1Q8OGDTF//nw8fvyYdTxCyFf6VyMmNTU1NGrUCGFhYZDL5Z89/urVKyQmJqJly5bfHPBXEhQUVHKLiorCw4cPMWLECMhkMkyaNEnhsdnZ2Zg6dSosLCxgYGDwkxL/utatXIbdWwJwdO92VKtC06N5deXiRdj064Ohc1ahQdsurOMQEUWCAD+8hL11Vyyw701FfY4tycjAC6MaSInYAa2yZVnHISJioyIx2XEYIsb2RUNqls6tVy+eY+bQXhjh7g0Lu+Gs4xARgiDA0t0P2pBj26heUKYLPj+fRMrf7RdXo0YNTJ8+HTdu3MDFixcxZcoU5Ofnw9fXF7Vq1ULHjh2xfv165Obmso5KCFHgX38aDRs2DDk5OUhKSvrssbCwMHz8+BHDh5fukxMVFRX4+voCAK5fv468vDzR57q7u+Pdu3dYv379zwn3C5vtORnnjybjUNg26GhTI1pepSQmYOzY0Ri3fDsMGvzBOg4R8UaQw0/yEj6OfeDSm4qevBIEAV6PH6N8u1aICgqEKjVL59aGgDVYOXsaosfboAbtjsut9Hu3MH/MAExasArtTC1ZxyEiimQydBg7E12MKmORrQld8CG/pebNm2Pp0qV4+vQpkpOTMXLkSNy8eROurq6oWrUq+vXrh/DwcNYxCSFf8E0FNYlE8sWlncHBwShbtiz69eun8GfEx8eje/fuKF++PNTV1VG/fn14eXmJFp7evXuH6dOnw8DAAOrq6mjQoAFWrFiB4uJiha9z4sQJWFtbQ09PD2pqajAyMoKbmxuys7O/+v3+W5UrVy759Zdm8wFAUlISQkNDMXPmTNSuXfuHZ/qVOdkPxvvspziwYx3U1dVYxyEigndsh8+cOXBZGwpdfUPWcYiI5/IPWCLNwfpJQ2DbqTnrOESEXBAwMS0NXax7Y9MyP+rvxDGfGZ6I3x6ImPG2qFiW+jvx6vqZ41g1zQmz1u1EgxZtWMchInLzC9DWcQbGdvoD7ub090R+fS9evFB4e/nyJZo0aQJ/f39cvXoVK1asQNOmTREXF4fBgwezjk8I+YJ/vcunoaEhOnXqhNjYWBQUFKDs/196kpaWhtOnT8Pe3l7hdr/+/v7w9vaGsrIyunXrBl1dXZw8eRKLFy9GVFQUjh079rdi1IcPH2BhYYFTp05BV1cXffr0wdu3b+Hl5YWHDx+Kvs6aNWvg7u4OqVSKtm3bQl9fHzdu3MDatWsRFxeHkydPomrVqv/2j+F/unjxIgBAV1cXurqfL/soLCyEs7MzGjRoAE9Pzx+W41cnCAIG9rZAh6b1sWTmZLpCybGlixYgLjEFE9fthnoZLdZxiIi7skIEqxcg3Hs0mhhWYx2HiCiQy+H6+DHcXMfByX4I6zhEASeHYXj/6BbCx/WDipIS6zhExPEDkdi/fR3mb42AbhX67ONVelY2err7wd/GGGaNarKOQ3hbZslTln+gWrVq/3MiyJfQuIcQfv3rghoADB8+HCdOnEBkZCTs7e0B/N9mBMOGDRM97vz585g1axa0tLSQmpqKtm3bAvizaDZixAhERETA1dX1b1NbV6xYgVOnTqFt27ZITk6GtrY2AODSpUswMTH54uucOXMGHh4eMDAwQGxsLJo2bQoAKC4uhp+fH3x8fODm5oaIiIhv+WP4ojdv3uDcuXOYOHEiAMDb2/uLz5s9ezYeP36Mw4cP0xIeEYWFhbCxNMZI215wHzOCdRyiwNRJrrj5KB0TVu+CsirNIOTVeVk+ErQ+4oDPeBjqVWAdh4jI/vABUzIysHj+TPTrYc46DhEhCAIGWFmgpvQ91tn3pIEPx2K2BeD8oQQs3BGFsto6rOMQEZfupmGYz0pssrdCC8MqrOMQ8t0YGRn9q4IaIYRf31RQGzhwINzc3BASElJSUAsJCUGVKlVgZmYmuqQyICAAgiDA3d29pJgG/LnZQUBAAOLi4rBv3z5kZmZCX18fABAYGAgAWLlyZUkxDQBatmwJFxcX+Pv7f/Y6ixYtgiAI2LRpU0kxDfizyj9r1ixERUUhMjISOTk5X5w99k996SRaT08PoaGhGDLk85kFly5dwurVq+Hg4ABjY+N//HofPnzAhw8fSn6fn5//j38G73Jf5cC2hynmuDthcL+erOMQEYIgwGHoILxXKQPHRZtoSRrHkj/k4YquFMlzJqKSNjW151Xau3fwycrC9vXL0bFN6drg51cik8nQo0t7WNWqBA8zY9ZxiAJBi33w7NE9LAiKhJo6LcflVdLpy5iyJgh7nKxRW6886ziEfFeKVlURQn5N3zTqLV++PKysrHDw4EFkZWXh/PnzuHv3LoYMGQIlBcsdjh8/DuDLs9j09PRgYWEBQRBw6tQpAMCTJ0+QkZEBfX19dOzY8bNjvlSsEgQBBw8ehJaWFszMzD57XCKRoFOnThAEoWRZ5rdycHAouQ0ePBgdOnRATk4OPD09cfTo0b8999OnTxg7dix0dHSwbNmyf/V6/v7+0NbWLrnVqFHje7wNbqQ9fID+3bshwG8GFdM4JpfL0dvSDKpVa2HwjMVUTOPY3o+v8bCGJlIWUDGNZ5de58E3JxtRwZuomMaxvLw8dGv1B0Y1M4SHWWvWcYgCqzydUPA6B3M2hlIxjWPbDxyG97pdiHEdSMU03rDe0fM33OWTEPJ7+KYZasCfyz6jo6OxZ88epKWlldynyLNnzyCRSGBo+OVm5UZGRiXP+8//GhgYfPH5X7r/1atXKCgoAAAoKyt+mzk5OQof/1pBQUGf3Xf58mV069YNlpaWuH37NmrW/LMPxKpVq3Dp0iVs3br1X8+OmzFjBiZPnlzy+/z8/N+mqHbp/DlMdh6FiA3L0KxRfdZxiIiCggJYdTdF696D0MmGluPybIs8B2XqV8aBaQ5QUab+TrxKyc5G2EcZkiN2oob+j+vvSb5NRno6bCyM4de7I8wbGrGOQ0QIggC/MQNRu3ETOHrOpeW4HFu4IxIJx85i/6RBKKdBLSMIIYT8Gr65oNa7d2/o6Ohg586dePbsGRo2bIiWLb/PFfW/Tnz+WmsudiL0pfs/ffoEANDS0oKNjY3C1xEr7H0PLVq0gJOTE5YtW4aAgAAsX74cALB//35IJBLs2LEDO3fu/NsxWVlZAAAbGxuoqqrCz88PnTt3/uxnq6mpQU3t9zvpSDqwH0vmzkTCrkDUNKjOOg4R8SIrC32tLGE5dgqaGfdgHYeIEAQBq/AKzdrWxRonW5pByLE9WVk4paaKIxGhqFBeh3UcIuL6tasYZdsX6weboZUhFT15JSsqgo9DXxj3tkX/UeNZxyEKuCzbgvTHGYieaAc1lW8emhDCvQcPHmD58uVITU3F06dPoaSkBCMjI5ibm8Pd3b1kcgkhhH/f/K2lpqaGAQMGYMuWLQAANze3/3lMtWrVkJaWhvT0dNSv//nso/T0dAAo2X2zWrVqf7tf7Pn/SVdXF2pqalBRUfnizLGf6a9ZaXfv3v3b/cXFxTh27JjocadPnwbw/WbQ/Qp2bN2MiKCNOBKxDZUqUrN0Xt2/extDBg3EQK9FqN2MtrLnlVwQsAjZsLFoi5mDLFjHIQoEZGbiuZ4uDu3aCE0NWpLGq0MpKfByGYPQUb1oSRrH8l/nYu4oawx0dke33ras4xAFBngvh6bwESHj+kGJLvhwq1giQTFHyyyLf+HZplFRURg+fDiKiooA/DkxRCaT4fbt27h9+za2bNmCHTt2wNaWPrsI+RV8l09Ge3t7VKxYEbq6ugp39/xLly5dAPy5gcF/y87ORnJyMqRSaUm/NENDQ1SvXh2ZmZklRab/tGfPns/uU1ZWhrGxMXJzcxUWrX6GR48eAQDKlClTct+RI0dQXFz8xdtfM+aeP3+O4uJi9O/fn0Xsn26pny/iI3bh/7F353E15v0fx9/t+0IhJPs+9hGh7AmJInubfU1kSUgpQpElUpb2RaXNln0Z2zB2YxtJmmwtKslxHNf5/TG/cd/ucTX27xef5+NxHrepc/TK3KNzfa7v9b2OJIXTMI1jp0+ewIhhQ+G8NISGaRwrF2RYgieYNrw3DdM455NzH9ImDbE3YRsN0zgWFx0NL9cJSJ04iIZpHHuUew9ejjYY7+lHwzSOyWQydJuyGPW01bBhVB8appEfQlZWFhwcHKCpqYnIyEiUlJTA0dERcrkcjx49QlhYGFRVVTFq1Chcu3aNdS4h5D18lp9e5ubmKCgoQH5+/ntdPjl16lQoKipi7dq1+O233958XCqVYvr06SgvL4ednd2bO3wCwMSJEwEA7u7ub93N8tKlS9iwYcM7v46npycUFRXh5OSEEydO/OPzDx48EH3t53Lx4kWEhYUBAPr16/dFv9a3bPa0ici6+hsOxG2GjrbWv7+AMLEzLQUzZrhiUlA0qtdrxDqHiCh8LcVShQKsmGQHl94dWOcQEYIgYGZ2Nhr06IL40DX/ut8nYSdopT8iApYgY4odjOiGHty6c+0ilk8ZhbmrQ9G6U1fWOUREuUSCDuM8MfCnuvCy6UJ725EfxqpVq1BeXo7w8HCMHj0a2tr/+XlSpUoVjB07Fjt37sSrV6/g7+/PsJQQ8r6YvHs3NTWFr68vFixYADMzM3Tr1g2GhoY4efIkcnNz0bBhQwQHB7/1mjlz5mDXrl04ffo06tevj+7du+PZs2c4fPgwxo4di5CQkH98HQsLC6xduxZubm4wNzdHy5Yt0bBhQ0gkEuTk5ODGjRvQ1tbG1KlTP8v35ezs/ObXUqkUOTk5OHPmDARBwIABA+DgQJu2/y9BEOAyzA61q+giavMa2t+JY2Eb1yMyNg7TguOhpUerM3iVI5MgTKUE4e4O6NC4DuscIkIiCHDNvouRjiMxd9p41jmkAnNnTMPdM0eROtkO6rS/E7fOHzuIuCBfLA6NR3WTOqxziIjHRcXoNc0HHn3NMLANnZj7ZvB2Z02eWj7AgQMH0LhxY1hbW4s+p3PnzmjXrh2OHTv2FcsIIR+L2TtDT09PtGrVCkFBQTh37hxevHgBExMTzJ07Fx4eHqhU6e0DdjU1NRw8eBA+Pj6Ij49Heno66tSpAz8/P7i7u79zoAYA06ZNg5mZGYKCgnD8+HFkZGRAR0cHxsbGmDRpEuzt7T/b9xQZGfnm14qKitDX14eFhQUcHBzg7OxMw6L/IZPJMNiqB/pamGLxzMl0hpJjPosW4PiZs5i6Ph5qGpqsc4iIa9LnSNQsR9rCCWhUsyrrHCKiVCrF9Pv34Tl3BhzsB7HOIRVwsreFWuGfiBs7gC5J49iB5GgcSorG0ogU6BtWYZ1DRNzOfYiBs5cjaHgvdGn4fdyVnpAPkZeXByurf7+RV926dXHlypWvUEQI+VQfPFD7+46b78PIyKjC5/fv3x/9+/d/799PW1sbAQEBCAgI+KCudu3aISYm5r2+hre3N7y9vd+76d++9se4d+/eZ/39eFRaWorBfbpjuvMwTBg1hHUOqcCU8WOQW/QME1dHQElZhXUOEXHiZQmO6QvIXDwFNQ30WecQEXkvXsDjQR7WB/jCsts/795M+CAIAgb0sEA7fWV4jehNJ3w4lrQxAL+fPYVlUWnQ1KLLcXl16uptjFu6AZFjrdGsBg09yY9JXV39vY4bs7KyYGBg8BWKCCGfiq5dIF/dowcPMNzGEis83TDQsgfrHCJCEAQMtbWBatVacPL1pwNKju2SPsUf1VRxYPEEVNKmFYS8uvnsGZYW5CN283r83Oon1jlERHl5OazMO2D4TyaYaN6adQ6pQJjPbJQU5sN3WyJUVNVY5xARacfOYnFoHHZMsUOtynqsc8jHUFD468ELnlo+QI0aNZCTk1Phc6Kjo3Hx4kU4OTl9pSpCyKeggRr5qm5e/x0TRg9FxKol6NS+DescIkIqlaKfZQ807NQLvRymsM4hFYiVFUJSVx/75o+Bhpoq6xwi4lRhETaXl2FX3FY0rFeHdQ4RkZ//BNZdO2FO9zawpf2duCUIAgLdXKBfyQALgiNpSw2ObUjZh8j0A8hwHQoDOuFDfnBmZmaIjo7G06dP/7G90apVq3Dy5EmkpaXBwMAAixcvZlRJCPkQ9A6EfDUnjx/DZMdhSN+6loZpHCspLka3Lh3RbsBIGqZxLkQogNZPNZG2aDwN0zi28/FjRL1+hUMpMTRM41jWnTvo29kUK2w60TCNYzKZDIudBqJ+k58w3S+IhmkcWxAajx2Zx7CThmmEAAAGDx6MV69eISEh4a2PKygoYO7cuUhPT4epqSmOHz+O2rVrM6okhHwIWqFGvoq0HUkICVyGA/FhMK5uxDqHiMjNzYGdjTUGui5C047dWOcQEYIgIBAFsOjyE5Y729DluBwLf/AA13S1cTRuK3R1aH8nXp379VdMGT0UW0f3wU81aX8nXknKy7DIwQb9Rjij73Bn1jmkAmOWbkRxfgF2TB0MFSUl1jnkU9FdPj8LKysrPH36FGpq/7lEfdCgQWjYsCEMDQ3Rvn17tG7dml0gIeSD0UCNfHGb1gchM2U7jiaFo5K+LuscIuLq5UtwcRqNkYuCULtZK9Y5RIRUEOCPfLjYdMGMgd1Y55AKBObmorx2LRzctgFqtIKQW7sz0uE72xXbxw1AbQPa34lXRU8ewXecPRxnLYBZr36sc4gIQRAwYM4KGGupIGLMACgq0gkfQv6bru7bx0I2NjawsbFhVEMI+VTf5niffDO8Pefg5P5dOJy4lYZpHDt66CDGuDhh7IqtNEzjWKkgwxI8wXzn/jRM49yCnBxotWuF9KhNNEzj2JbQjQiYPwvpU+xomMax3Du3sGTMYEzzXUXDNI5JpTJ0mbQI7WtUxqqhvWiYRshHmjt3LurUqcM6gxDyHmiFGvlipo5xgKqsHHujQ6CsTP9X49X22GgErV2LyWtjoWdYlXUOEfFYJsU65SIETxuGHq1ofydeyQQBbvfuodfAflg6fxZdjssxv8ULcTojGelT7KBNQ09uXf/tDDYvmQ3P4AiYNGjMOoeIKC57jq6TF2Ny19YY3ZHuYvy9kSsoQs7RZZY8tXyohw8fYv/+/Xjw4AGkUuk7n3Pw4EHcv38f3t7eUFBQgFwuh7e399cNJYS8F5pykM9OEASMHNQPbRrVxurF3nRAybE1gSuQkrEL0zZsh4a2DuscIuLOq3JEqJUhbp4LWtczZp1DREhkMky9dw8TJ4/BtDEOrHNIBaaNc8HTGxeRPHEQVJVpfydencpMR+rmNViyNQlVqtdknUNE5D4ugJWbH5YMskCf5vVY5xDCrYMHD8LGxgYSieRfj48UFBTg6+sLADRQI4RjNFAjn5VEIoFdn24YYd0bcya7sM4hFfCY7YYL129jyro4qPzX5qiEL+dfPsNO7ZfY5TUJdY0MWOcQEYVSKWbevw+/xfMwxNqKdQ4RIQgChtv0g9GrEkQ49aNL0ji2O3ozTu1NxdLIVOjqV2adQ0RcycrBMM9V2DjaCu3r1mCdQwjXlixZAplMhjlz5qBx48aiV/Bs2bIFJ06cQERExNcNJIR8MBqokc/maVERBlt1x4LpYzHK1pp1DqmAy+jhKH6tjPErt0KR7r7FrcMvi3G2sgL2e09DNX1aQcire8+fY+Gjh9i8dgW6djJlnUNEyGQy9LMwQw9jfczu3Z1WT3MsJnAJcm5dw7LIFKhpaLLOISIOnbsC11VbETdhEBpWo6Hnd01BEVDk6DLLb/SSz8uXL6NXr15YsWJFhc87evQoTpw4AUdHx69URgj5WDRQI59FTk42HGytEew7H5ZdO7HOISJev36NQf37wKBRK4yaNJcOKDmW8qoIeTU1cdBrPHQ01FnnEBGXS0qw6mkRdkRuQoumtL8Tr0pLS9G3SweMM20Mx47NWeeQCqyfPxWC7BUWh8VDWUWFdQ4REbPvOFZFpyJtuj2q62mzziHkm1BWVoYaNWglJyHfExqokU92+eIFuI5zQHzwCrRr2Yx1DhFRXl6Ovr27o7XlYFgMdWadQyoQLiuEcgNDZM5zhqoK/TXNq8P5BYh99RL7kiJR25j2d+JVXl4eBvU0x2KrDuj7E+3vxCtBELBs0giY1G+I8Z5L6YQPx1bGpCPt0Ensch0GPU064UPIh3ifv9vo7z9Cvh10pEY+yaH9mVi2YA72RG5E/Tq1WOcQEQX5T2BtZYleLq5o05Mux+WVIAhYj0I0blcfIVPsocjT5RXkLUmPHuGYqjKOJMbCsHIl1jlExPXr1+A4sD+Ch/VA+zq0KoBXMqkUXk426GRpjSHjXVnnkAq4rY3EzVt3kD7dHhqqtILwh6GgyNdlljy1fIAjR46gevXq//q8efPmwcnJ6SsUEUI+FQ3UyEeLiwpHdGgwDiduQ7UqtFk6r+7e+QNDh9hhyBw/NGxrxjqHiJAJAlYiH9Y928FrhBWdneTYpj/zcK9KZRyJCYOWJu3vxKtfjh6F+3gnRDv3QyPa34lbZaUl8HYaCLtx09Bj4FDWOaQCw73WQPFFObZPsoUSnfAh5INZWFi81/MaNWqERo0afeEaQsjnQAM18lGCVi7FqQN7cSw5HLo6tHcGr347ewaTJoyHk+8G1GjQhHUOESERBPjjCabb98R4K9qDkGd+9+9DpXED7AtdAxXa34lbiQnxWOs9HzsmDkQNuqEHt/LzcrF00giMn++LdhY9WecQEYIgoLerL1oa6WOJfV864UPIR4qMjPyg59MqNUL4RwM18sE83Kah8M+7OJiwBWpqqqxziIg9u3fC28sLE1ZFwKAGXY7Lq6evXyFQsRD+4wZhYMcWrHOICEEQMO9+Dn6y6Iz1/ovpclyOrQsKRMa2TciYMhj6tL8Tt+5ev4p18ybBfeVGNGzRhnUOESGRSmE+cRHs2zXClG7tWOcQVuiSz89izJgxkMvl//o8BQUFyOVyGqgR8g2ggRp5b3K5HONG2aOajhrSt62jA0qOhW8OxeZt4Zi6Pg7alehyXF79KZMgRKUEm91GoXMz2iydV1JBgGt2NoaMtIen22TWOaQCC2bPxPVj+5E2eTA0VOktDq8unTiMqEBveG2KRY3a9HcfrwqKS9F96mLMtuyAwe1olTshn8rb2/udAzWJRIL79+/j4MGDyM/Px7Bhw9CkCf03R8i3gN5tkvcik8lg378XenZoA98502i5P8eWLfHGgaPHMS04HmqaWqxziIjr0ueI1yjHjgXj0LSWEescIuKZTAbXe/cwe9ZUuIwYwjqHVGDMiKFQeJiFhHE2UFaiEz68OpIaj8y4rfAL34HKVaqxziEi7vz5CDaz/RFg3xNdG5uwziHku7Bo0aIKPy+RSODh4YGIiAj4+Ph8pSpCyKegd5zkX5WVlaF/144YPaA3/OZOp2Eax6ZPnogTFy5j0pooGqZx7NTLEuzQeYlMv8k0TOPYY4kEU+7dw8pli2mYxjFBEGDTsyuqlj1C6Kg+NEzj2I7QNTiaGo9lUWk0TOPY2et3YOO+DFud+9Mwjfzl70s+eXp8h9TV1REUFARDQ8N/Hb4RQvhAK9RIhZ48foyh1r2xbM402PXrxTqHiBAEASPtbSHXrQKXpSF0OS7HMqUluFZFCQe9J6OyDg09eXX7WRl8858gatMadGjXinUOESGRSGDVpQNsm9TAtG5tWeeQCmxdOh8FD3LhG54MVTXa245Xu06ch+eGaCRNtkNtQ33WOYT8cBQUFGBqaorDhw+zTiGEvAcaqBFRf9y+ibHD7bAlwAcWHWgjWl5JpVIMsOqFOj9bwNLFlXUOqUCCrAiltXVxYMFYaNINPbh19ulTbHxWiozYzWjcgPZ34lVhQQH6dzWDW9eWsG9Le83wLNBtLLS1tbFwYzSUlJRY5xARYekHsHlHJtJd7VGFTvgQwszTp0+Rn5/POoMQ8h5ooEbe6ezpk5g7bQJSNgfhp8YNWecQEaWlpejXuwfMBjui44DhrHNIBUJfF6Jy0+rY6T4aynRAya3MJ0+QIrzGwR3RqGFEl6TxKvtuFoZa9YL/wM7o3rg26xwiQiaTwXfsEDRvZwqHmQtoywiOLd6SiGO/XkSG6zDoqNMJH/I2uYIC5BxdZin/zv8ucXV1xeTJdBMkQr4FNFAj/7ArPRVr/b2xPy4MJjWrs84hIh7m5WHggL7oP3k+furSk3UOESEIAlajAB3MmmHVuEF0QMmxmAcP8ZuWBo7Gb4W+ni7rHCLi/PnzmDjcFptH9UFL46qsc4gIyYtyeDnawHLwSFiPHsc6h1RgwopQPM57iJSpQ6CqTCd8CGGtb9++rBMIIe+JBmrkLVtCgpGeEIWjSeEwqKTPOoeIuH7tGhxHD8dwzwDUbUGX4/JKKghYjnyM7GeGOYNp6MmzNX/+iafG1XE4PATq6mqsc4iIfXv3YPGMyUgYOwB1aX8nbpUUFsBnjB1GTZ+DzlYDWecQEYIgwNYjAIYqCogeNxCKinTCh5AvycXF5b2fK5fLERERgcePH8PDw+PNPxNC+EIDNfLGUi9PXD9/GkeTtkFLU5N1DhFx/OgRzJo5A2OWb0a12vVZ5xARZYIMK1CA+aP7YlT3n1nnkAp43c+BYasW2LV+Je3vxLGIrZuxLdAfqZNtUZX2d+JWXnYWAlwdMdU7EC06dGadQ0TIZDJ0n+qDrvWrY15fM1o9TSrG2501eWr5AFFRUe/93L8HaCUlJYiKiqKBGiGcooEaAQC4TnCB/PlTZMZsgoqKCuscImJHYgJWBgRg8ppo6Fely3F5lS+TYo1yEYIm26MPbZbOLUEQ4JZzDxb9LLFi0Vw6oOTYcl9vHNsRj4ypdtChFYTcunXxHDYtngmPtdtQp1FT1jlExLPnL9B1ihfGdGoB584tWecQ8sM4cuTIB7/GxMTko15HCPk6aKD2gxMEAQ6DbdDEpBqCV62iA0qObVgbhPjkZEwLToCmrh7rHCIi+9ULbFUtRfRcJ7RrYMI6h4iQyGSYnpMD53GOmDnBmXUOqYDb5Al4eOkMdkwaBDVletvCq18P7kbSxkB4b96OajVrsc4hIh4WPEXv6T5YNKAL+rdswDqHkB+KhYXFB79GXV39o15HCPk6vs31suSzkEqlsOlpjp6mLbBhKd19i2cL5s1G2p59mLo+noZpHLv08hkiNZ8j3XsiDdM4ViyVYuK9e5g/fxYN0zgmCAJG2lpD+sdlxLhY0zCNY3vjtyFty3osjdhBwzSO/Z6dix5TF2PNiN40TCMfRkGBv8cHKisrg5ubG2rUqAF1dXW0bt0aCQkJX+APixDyI6F3pz+okuJi2Fl1x+zxDnAZNoh1DqnAOOfRyC9/jfGB26BEB5TcOiYtwalKcuzzngqjSnSHSF7llpfD8+EDhAQtQ48uZqxziAiZTIb+3Tqji5EW5g/rSSd8OBa3dhnuXDmPZZGp0NCive14dezidUxZEYqYcTZoXN2QdQ4hX52dnR3OnTuH5cuXo1GjRoiLi8OIESP+OnkzcuRX7zl37hwOHjyI3NxcAECtWrXQq1cvtG/f/qu3EEI+Hh2d/4Dy/szFqIF9EeQ9B327m7POISIEQYDtgL7Qrd0YDnNpBSHP0l8V4Z6ROg54TYCelgbrHCLi95JSrCgqRGJ4CFo1p73teFVWVgarLh3g2KYextL+TlzbuHAGXpaXYcmWRCjT/qvcSjx0Csu2JSF16hDUqKTDOoeQr27Pnj04cODAmyEaAHTv3h05OTmYM2cOhg0b9tVuSvTgwQM4ODi82Rft7/f3crkcCxYsQI8ePRATEwMjI6Ov0kMI+TQ0UPvBXLt6BVOdRiB63TKYtm7BOoeIkEgk6Nu7O5p3s0b3keNZ55AKRMkKIdQzQKaHM9RV6YCSV8cLChAheYG9iRGoa2LMOoeIePjwIQb26IIFlu1h3YLuYswrQRCwYupoGBmbwG3ZWigq0g4ivApK2IXEzGPY6ToUleiED/lY3/hdPlNTU6GtrQ17e/u3Pu7i4oKRI0fi119/RadOnT5n4TuVlZWhZ8+euH37NqpXr45Ro0ahQYMGkMvlyMrKQmxsLI4cOYKePXvi3Llz0NTU/OJNhJBPQwO1H8ixQwfhM88NOyPWo1G9OqxziIiiwgL079ML3R2moJ3lINY5pALrX+ejbqs6CJs+HEpKHL3RJG9JefwYB5UUcTg1FlUNDVjnEBE3b1zH6IH9sGZIN5jVq8k6h4iQSaVY7DIIpt16Y9hkd9Y5pAKz10fj8u+3kO46FJp0wof8wK5du4amTZtC+X+2TmnZsuWbz3+NgVpQUBBu376NUaNGISwsDOrq6m993sfHBxMmTEBsbCzWrl2L+fPnf/EmQsinoSPAH8T2uBj4L5qLQ9u30jCNY/ey76JPz+4Y4OpFwzSOyQQB/sITdOrWEltmjKBhGse25OXhjK42jqTG0DCNY6d++QUONn0R6WhFwzSOlZeVwmN4H1jZO9AwjXOjfdbj/r0cJE22o2Ea+W6Vlpa+9Xj58uU7n1dYWIjKlSv/4+N/f6ywsPCLdv4tOTkZlStXRmho6D+GaQCgoaGBsLAwGBgYIDk5+as0EUI+DR0F/gDWrw7A9q0bcSw5HNWrVWGdQ0RcOv8bhtgOxGifdWjcvgvrHCJCIgjwwxM423WFn6M17W3HsRW5uXhS1wT7EyOgo63NOoeISNuRjNnjHZA8wQZNjGjoyauCh3lYMLIfnGYtQO8ho1jnEBGCIMByhh8qQYatzv2hTCd8yGcgV1Dk7gH8tZG/np7em4e/v7/o91DR+7Wv9V4uKysLXbp0gYaG+OXXGhoa6NKlC/7444+v0kQI+TR0yed3buGcmXhw5zoOb98KdXU11jlExP69e7BgoSfGB26DYc3arHOIiGJBhkCFAviOsYFdp1asc4gIQRAw//59NOhkitAAX9rfiWMb169Fcug6pE+2Q2Xa34lbObevI8h9PGb6r0fj1j+zziEiJFIpuk5eDJsW9TCjF90pkHz/cnNzoav7nzurq6m9+1jHwMDgnavQioqKAOCdq9e+BGVlZai8xw1cVFRUIJfLv0IRIeRT0UDtOzbRcTj0VICdEcFf7c415MNFR2zDxk2bMHVdHHQq063sefVQ9hLByk8R4joCXVs0YJ1DRMgEAa73sjHA3hZe7tNoBSHHvDzm4NKBXUibPBhaanRJGq+unvkF4f6eWLgxGsZ16e8+XhWVlqHbFC/M6Nkew9o3ZZ1DyFehq6v71kBNTIsWLRAfHw+ZTPbWPmpXr14FAPz0009frPG/1apVC3fu3PnX5925cwcmJiZfoYgQ8qnotP13SJALGNKvF5rUNETkmqU0TOPYymV+2BIRhWnB8TRM49gtaTk2qpYgceFYGqZxrEwmw/i7dzFh2gQsnj2dhmkcm+g0CjknDyJxwkAapnHs+M5kRAd6w3dbMg3TOHbv0RN0mbAAfgMtaJhGvoy/7/LJ0+MD2NraoqysDDt27Hjr45GRkahRowY6dOjwOf+0RFlZWeHy5cvIysoSfU5WVhauXLkCS0vLr9JECPk0tELtOzSkb0+MHWoDt3EOrFNIBWZOm4KbOX9iytpoKKvS5bi8uv6qDDcrKWP34smoXfXrXBJAPtwL2WtMuXcPK/0WwqZPT9Y5RIRcLodtn56or/wSGxz70tCTY2lbg3H+SCaWRaZCW0+fdQ4RkfMoH/1m+CHUsR/a1DZinUMIl/r27YvevXtj8uTJKC0tRYMGDRAfH4/MzEzExMR8tcUHM2bMQIsWLSCTyUSf8/r1a2zbtg3du3f/Kk2EkE9DA7XvUH1jIxhVMUBC+h7WKeQd5HI5HuQX4cHxY7Aa64bLx/axTiIicq5fwi3Fcizq1xe/3rqHX2/dY51E3uHEtSzcePkSw22tUf7iBRLSdrFOIu8gCAKK859AV1aODl1aIfXiLdZJRMT94jLcidkMx5meuHDiCOscIuK344eQffFXTO7xM+7mP8Xd/Kesk8g7ZOcXs04gAFJSUrBgwQJ4eXmhqKgITZo0QXx8PIYPH/7VGoyNjeHo6Fjhcxo1aoRGjRp9pSJCyKdSkNOOh9+N0tJS6OnpYazDCKiqqrLOIe8gCHJkZB7AC8lL9LaxY51DKnD+9Enk3ruL3t3MUbtWTdY5RMTdezk4f+kK9NWU0aMp3dCDVy+kr7D79O9QAGDdsj7rHFKBvfeeIL+4BD1VdFinkArkCC/xSEmA3KA2dOs2Z51DRLx6XgLZ/St4WliAkpKS99rviyd/H1s8evyYq/bS0lIYVav2Tf6ZEkK+L7RC7Tu03GcRdHXojTBvysrK0KWfHabNmoPYyHDM8w1gnURELJoxCUYmdVC3STNMcxyMnl3NWSeRd0jYkYbjJ8/A32s+9iTEINCxH+sk8g45+cXo67UF03RrIPJFAZZYdmSdRN5BEAQMTPoFRh37QuHWJTgWv2adRERkvCpCSRVt2LdsgLgXdVGnjzPrJPIOJfd+R35aAOb5LcfsyeNY5xBCCPkC6KYEhHwFDx49Rvte1pjpsRCOY8azziEiBEHA1NF2UFRTh+farVBSonMOvFq9YRPWh27B4Ywk1KxenXUOEXHp3kNYLQzDDI1qaKeqzTqHiJDIZOgeexiaXQbhZwd32tuOY1GyQpTU1sXuuQ7QUaf9V3mVf+U4nu4KQkRSGho2acY6hxBCyBdCR4uEfGHXb96GreM4rFq/CaZmnVjnEBFSqRRj7azQvmtvDJ8yi3UOqYD7Am9cv3kTh9ISoaGhzjqHiDhw5Q7cglOwQKcmairTgT+visolsNp+HM3sp6Ju576sc0gFNrzOR93mxtg4xhpKinROnFcPT6ZC4eZhxGVkorKhIW5d/5110ieTy/968IKnFkLIj40GaoR8QcdOnMJEd09sidmOxk3oVva8Ki0pxhhbKwx0mog+Q0ayziEVGDVuCgA5diVEQVmZfoTxKuroRQTGHcQS3VqorKTCOoeIyHlaCtvU02g/zgs1WnRgnUNEyAQBq+T5sOzUHIsHd6MVhBz7M3MLdIvvYHPGXmhp0apcQgj53tHRCCFfSFJqBpasWo+EtF2oUdOYdQ4R8SgvF5NGDMK4ed4w7WbJOoeIEAQBVnYj0KJZE6zy86IDSo6tSDmGHft+wzI9E2gpKrHOISIu5uVjzP5LMHdbBYO6TVjnEBESQcBKPMHE/maY0PNn1jmkAveSVqCujhxrkzPo5mCEEPKDoIEaIV/A2pDNiEnZieTd+1CpUmXWOUTEretXMWeiE2YvD0aT1u1Y5xAREokEXfvZYqitNWZPm8w6h1RgxtaduHzuDnz1akFVgS5J49X+P+5j3uks9JgfAt1qdMKHV8WCDKuRjyUje2NgO1rlzitBEJAdtQAdfmqIJYFroPgdXo4ryOUQOLrOkqcWQsiPjQZqhHxmcxcvwbkrt5C8MxMampqsc4iIM78cwfIFc+AdEg3jug1Y5xARRU+folt/O3i4TcMoe1vWOaQCw1bG4cW9QizUNYYSrSDkVtTF21h7Mx99Fm2Bhr4B6xwiIk/2EpuUnmLj+IHo0rg26xwiQpBJcWfzLNjZ9MfU2R60epoQQn4wNFAj5DMaPXE6nr+SIyY5DSoqtG8Qr3btSEBkyDr4R+6AQVUj1jlERHZODqyHjsZa/yXo3b0r6xwi4vVrAb29tsCk9DUmahvRASXHVpy4gtT81+i7eBtUNLRY5xARt6TPEadWhgTXYWhmXJV1DhEhk5QhK3QGJk+bjqEOzqxzyDege/fuH/1auVyOo0ePfr4YQshnQQM1Qj4DQRBgNWQU6jVujlXLA+iAkmPhwatxKHM3VkSnQVtXj3UOEXH+0mU4TpiG6NBgtGvdgnUOEVEukcLCcxMsXmtggGYV1jmkAm77zuGCQmVYLgyEkjKd8OHVWWkp9mtLkTFzNEwM9VnnEBGS4nzkhM/BQt+l6GnVn3XOFyf//wcveGr5EL/88gvkH3i5qlwuh4KCwge/jhDyddBAjZBPJJFIYNHfDn0HDsZUN3fWOaQCAV7z8Mcft+EfuQNq6hqsc4iIvQcOY57XEuxMiEL9unSpE6/yS8vQwzMMQ5X10UVdl3UOESEIAkaln0JR9WboMd4LCt/h/k7fi/3SYlytBGS6O8FAh7aM4NWzvDt4mOiL1RvD0KY93R2XvL9Tp06993PlcjmSk5MREhKCFy9efMEqQsinoIEaIZ/g6dNimPe3w+QZ7rAfMYp1DqnA3ElOEBSU4BMaCyUluvMgr7ZExSJ0WxQOpm1Htaq04olXfzwswECfcEzUqIaWqnTgzyuZIKB/wlFo/mwJsyGTafU0x5JeFaGougb2zhwOLTW6QySvnt4+j6LMDdgcm4j6jRqzziHfGFNT0/d6XkpKCnx8fHD16lUoKCjAwsICPj4+X7iOEPIxaKBGyEfKyc1Fn8Gj4bMiAN17WrLOISIEQcDEoQNQr3krjJnjRQeUHPNduRqHj/2CwxmJ0NXRYZ1DRJy5fR8uqxIwV6sG6qios84hIsqkUvSJP4ba/RzRuJc96xxSga2vC6DTwBDpk+ygokwnfHj1+PwBSM/tQHTKThjVqMk656sS5H89eMFTy+eUlpYGHx8fXL58GQoKCjA3N4ePjw+6devGOo0QIoIGaoR8hItXrmL4uGlYH7YVrdv+zDqHiJBIJHAZ2BvdbIbA1nkS6xxSgSnuHnjw4AH27YiDmpoa6xwiIuO3G/AM24nFOsaopkyraHj1+Fk5rJNPoOVod5j8/PGbYJMvSxAErJMXoHWbelg92opO+HAs70g81O6fRVzGXujpV2KdQ74zGRkZ8Pb2xqVLl6CgoIDOnTvDx8cHPXr0YJ1GCPkXNFAj5ANlHjqCWQt9EbV9B+rWb8A6h4goLirEGDsrDJ8yC93627HOISIEQcAQx3EwqKSP1JhtUKT9nbgVsu8MwlJOwE/PBHqK9PaBV7fzn2LYznMwm+yLak3asM4hImSCgJXyJxjcvQ3mDujCOodU4P7ODaj66jE2pe2Bugbtv0o+n127dsHb2xsXLlyAgoICzMzM4OPjg169erFOI4S8J3pHTMgHCI/djrWbw5GYsRdVq1VjnUNE3L93F9MdhmDq4pVobWbOOoeIkMlk6GkzBF07m2HJ/Nm0OoNjXvEHcPDYVSzVrQUNRbokjVen7z/E5MO/o9ucdahkXI91DhHxXJAhAPmYZdcVDl1asc4hFciO80WzGroI2JgCZeUf97BJLpdzdZdJnlo+xp49e+Dt7Y3ffvsNCgoK6NChA3x8fGBpSVvIEPKt+XF/MhDygZYGrsWuw8eRvGsfdHX1WOcQEVcv/oaFrhMwf80W1GvSnHUOEVFWVoau/Wwx3nEUJo91ZJ1DKjB+QwruXbsPb11jqCjQCkJepV/Phs+F++i9MAzaBkasc4iIwtdSrFEoRIBjX/Rp1ZB1DhEhCALubpuDHp3aY76vP53wIZ9FZmYmvL29cfbsWSgoKMDU1BTe3t6wsrJinUYI+Ug0UCPkPUyd7Yk7fz7E9rTdUFenTbh5dXTfbqxb7gPfLQkwMq7NOoeIePT4CXoNtIfvgrmws+7LOoeIEAQBA5dFQf3hc3jo1IQiHVBya9O5G9iaXQqrxdugpk0nfHiVI5Ngq3Ixtk6yQ/v6xqxziIjX0pfICpuBkSNHYNy0GaxzyHekf//+kMvl0NDQwJQpU2BtbQ0AOHbs2Hu9vmvXrl8yjxDyEWigRsi/GOw0Hmra+oiIT4aSEl3qxKvk6G1IignH8sgU6BtUYZ1DRNy8/QfsRo9B2JoAmJu93+3jydcnlcnQY8Fm/PRCEcO06fJ2ni0+egkHninDymsLlNVofydeXZWWIUWjHDvcRqKBkQHrHCJCWlaM7C2zMHPuPNgMGcY6hxt0l8/PSyKRYNWqVVi1atUHvU4QhC9URAj5WDRQI0SETCZDz4HD0LZjZ3h4+dByf45tDPDD2VMnsCI6DZpa2qxziIgTZ85i0ozZSI7cjOZNGrHOISJKyyXoOn8T+irooLcGrXbi2YTdZ/CHZk30nu8PRSV6S8erEy9L8IuuDLvdHVG9kg7rHCKiPD8PuTGe8AsIQududHdF8vlZWVl98/u/EULeRu++CHmH8vJydOlri6GjnTFm4mTWOaQCS2ZPx5P8J1i6LQkqqqqsc4iIlIzdWLJyFfYmx8LEuCbrHCIir6gUlgvD4KhmCFNVGk7zShAEDN7xC6QNTNHVaS6d8OHYLulTZFVRRqa7M/Q1acsIXpXcv4EnKSuwfnMEfmpNd8clX8bu3btZJxBCPjMaqBHyP57kF6C7jT3c53vBepAt6xwiQhAEuLkMg07lqlgYHAFFRdosnVfrN21FXPIOHE5PgkHlSqxziIhr9x/DfmkkXDWN0ERVk3UOESGRydA34SgMzW3R2saFdQ6pQKysCFITbexxHQZ1FXrLzauC30/h2ZFtCE9MgUkdujuuGFpXRQgh/0Q/3Qn5L7fvZMFm5BgsX7MenbpYsM4hImQyGcbaWaFVp64YNW0Orc7g2HxvP5y/dAWH0hKhpUVDGl4dvpaFaet2wFOnJoyV1VjnEBHF5RJYJR5HI7tJqG9uzTqHVCDkdT6Mm9ZA6DgbKNEJH249PL0T8muZiE3bA8OqVVnnEEII+cbQT3hC/t+pX8/BZtRYhEbF0TCNY2WlpRhhZY4eg4Zh9HS61IlnTpNckZ1zH3sSo2mYxrGEE1fgti4FS3Rr0TCNY7nFz9Az4RhaOnvSMI1jgiAg4PUTtOvQCJvHD6RhGsf+PBABjbvHEZexl4Zp5KuJi4tDixYtoKamBiMjI0yaNAmlpaVvPl9UVIRbt27h5cuXDCsJIe+LfsoTAiB15x5MmDUfsTvS0bRZc9Y5RET+44cYbd0Do1090HeYI+scIkIQBPQbMhL6ujpI2BoCFRUV1klExKqMXxAYvR9L9UxgoET/nnh15WEBbFLPoJNrAGq26sQ6h4iQCAKWyp9ghFV7LB3Wi074cCwnZRVqvMxDVMouaOvoss7h3t93+eTp8S3KzMyEg4MDrl+/Dm1tbRQVFSEsLAy2tv/ZYubChQto2rQpYmJiGJYSQt4XDdTID2/DlnAsXbMRybsyUcukNuscIiLr1g2MHdIfbkuDYNbTinUOESGVStHJ0hoWnTpi3QpfOqDk2OyI3Ujf8xv89EygrajEOoeIOJSVC6f9V9B93gYY1m/GOoeIKBVkWIbH8BjWE1MtTVnnEBGCICAraiFa19DGppjtUFWjVbnk6/H19YVcLkd8fDwKCwtRVFSEbt264ciRIzh58iQAoFevXjAyMsLOnTsZ1xJC3gcN1MgPzXPJciTu3IfkXftQ2cCQdQ4R8dvpE5g1fjS8giPQrC0dqPCquLgE7btbYbKLI+bPnMY6h1Rg1OoE3DibBS9dY6gp0FsBXsVeuYO5v+bA0msL9KqbsM4hIh7KpAhQyMe6sQMwpAMNPXklyGTICpuB/l07YNnajXQzI/LV/f777zA3N8fQoUMBANra2liyZAkUFBRw9OjRN89r0qQJrl69yqiSEPIh6KYE5Ic1ZtpMFDyTIC5lJ12SxrHM9GRsWRcI//BkGBrVYJ1DROTk5qLfkJFY5eeNvr26s84hIgRBQB/vbaj2VAp37eq0gpBjgaeuIumhFFaLt0JVU4d1DhHxx6tyxKg+Q+y0oWhhYsQ6h4iQScqRFTYD4yZMwKgx41nnfHPkcjnkcn6us+Sp5UMoKSnB2Nj4rY81b/7XVjN5eXlvPmZkZIQzZ8581TZCyMehgRr54QiCAOvhjqhuUh9hwVvpDCXHokPXY29GClZEpUFHvxLrHCLi8tXfMWLsJERuXAvTdq1Z5xAREqkMFvNDYCZTxyBN2oCbZ7MP/oZfZXros2gDlFRUWecQEedfPsMeLQnSZo1CnSr0M4pXkpIC5GybDY/FS9DH2oZ1DvmBtW/fHpcvX37rY/r6+gAAiUTy5mOPHj2CGl2OTMg3gSYJ5IcilUrRqc9AtO7QGf6r19IwjWOrlyzAsUP7sTwyhYZpHNt/5BhGj5+C9LhwGqZxrKC0HKaz1qGfoI1B6vTfE88c0k7hsoYJesxZQ8M0jh2WFuNwpdfInO9EwzSOlT3Mxv1t7ghYt4GGaYS5hQsX4tatWwgKCnrzsb9Xiv+96i4nJwenTp1Cy5YtmTQSQj4MrVAjP4ySklKY97fD2MnTMcLBiXUOqcD8qWPwUvYavpsToKRMf03xKio+Ees2bcaBtARUr1aNdQ4RcfdxEay9t2K8elW0VtVinUNEyAQBAxKPQbVld3QePp0ux+VYyqsiPDZSx1634dDRoFUkvHp65zIKd69BaHQCGjZpyjrnmyb8/4MXPLV8iNevX2PGjBlwd3fHzp07MWzYMNSrVw8A8PDhQ2zduhVLly6FVCrFlClTGNcSQt4HHamSH0JuXh4s7UZikd9y9OrTl3UOESEIAiaPGAjjBk3gNp/uEMmz5WvWY3fmARzOSIKeri7rHCLi/N08jF4Ri9la1VFPRYN1DhFRLpXBMuEIalmORJM+I1jnkAqEywqhXq8yMqYMhqoy3R2XV/kXj+DF6XhE7chADeNarHMIAQD06NEDcrkcCgoKOHbs2JsbESgoKODAgQM4cOAAFBUV4e3tjWHDhrGNJYS8Fxqoke/eld+vY6jLZASFbEa79nSHSF5JJBKMse2DzlY2sB9Hd4jk2Yx5C5GVfQ8HUxOgrq7OOoeI2HvxFmaHpGORjjGqK9Olg7x6UlYO66QTaD58Bup07M06h4gQBAHB8gI0bVUH6xz7QVGRTvjw6sGxJCjfPYHYjExUqlyZdQ4hbzg7O7/zhgoKCgrQ0dFBw4YNMWDAAJiY0F2dCflW0ECNfNcOHT2OaR6LER6fhPoNG7HOISJKioswxtYK9hNc0cPGnnUOqcAw5wnQ1FBHRlw4lJRodQavth76Deu3H4Gfrgn0lehHPa+yCkswJP0MOkzwQfXmP7POISJkgoAA5MOma0vMtzGn1dMcy929CZXLcxGatgeaWnSJ++cil//14AVPLR9i69atrBMIIZ8Zvcsm363YxB1YGRyGhLTdqF6jBuscIiLvfg6mjLLDpIVL0a5Ld9Y5RIQgCOg10B4d27XBMq/5dEDJMd/Ew9h96CKW6plAU5GGnrw6l/sY4w9eQVf3Nahs0pB1DhFRLsgQgAJMt+kMl65tWeeQCtxLWIaGhqpYHZEGFRUV1jmEEEJ+ADRQI9+lFWs3IHXvQSTv3gc9PX3WOUTE9SsXMX/qWMxdFYKGzVuzziEiysvL0bWfLRxH2MN1whjWOaQCUzal4dble1iiVwsqCnQXY17tvnkPi87dQ+8FodCuQid8eFX0WooghUIsG90H1m0as84hIgRBwN2IebBo3wqLlgXQCR9CCCFfDQ3UyHdnpqcXrv5xD9vT90BDgzbh5tXJw/sR6LMAPqFxqFG7LuscIuJJfgF62gyG19yZGDrIhnUOESEIAoasiAX+LIGnTk0o0gElt7b8dhOhWcXo47UV6rqVWOcQEbkyCcKUi7F5oi06NKBN7XklyKS4EzoDQ+0HY5LbbNY53y1B/teDFzy1fAgXF5f3fq5cLkdERMSXiyGEfBY0UCPflWFjJ0GurIGo7SlQVqb/e/MqLT4KcdtC4R+ZgspVqrLOISL+yMrGwBEOCAlcjm7mnVjnEBEymYAei0LRqEwBI7Wq0eoMjvkev4w9TxXQx2srVNTphA+vfpc+R5J6GZJmDEfjGlVY5xARr8qf4e5mN0yfORN2w0ezziHkX0VFRVX4+b9vWKCgoEADNUK+ETRxIN8FQRDQa9AwNGv9Mxb5LqMDSo6FrVmBE0cOYWVMOjS1dVjnEBFnzp3H2Glu2L4tFC2bN2WdQ0SUSaSw8AhBL7kW+mrqs84hFZi69yx+V60GS8/lUFSm/Z14dfplCY7oyLDL3RE1K+uyziEiXhQ9xP0ID3ivCEDXnpascwh5L0eOHHnnx+VyOfLy8nDw4EHExMRgypQpsLOz+8p1hJCPQQM18s2TSCTo0tcWg4aOwISprqxzSAX8PNyQ9+ef8I9IhoqqGuscIiJ99154LVuJ3dujUbc23bqdVw+flqL3ws0YqVoZZmo0nOaVIAgYmnICZXXaoJvLfCgo0t52vMqUFuOGoSIy3Z1QSYtWEPLqWe5tPEpeinVh29CibTvWOT8EuVz+ZvUUD3hq+RAWFhYVfn7kyJGwsbHBkCFDYG1t/ZWqCCGfggZq5JtWUFiErtaDMWPufAwaPJR1DqnArLEjoaqlA6+NUVBSojsP8ipkayQiYuNxKC0RVQwNWOcQETfynsBuSSSmalVDcxVN1jlEhFQmQ7/tx6BnZg0z2/Gsc0gFEl4VocxYC3tch0FTjVYQ8qrwxq8oORiGLQk7ULd+A9Y5hHx2gwYNQtu2beHr64tevXqxziGE/IsPPk2qoKDw5nH69GnR5yUmJr55Xp06dT6l8b27PsfX8fb2hoKCwgdds/7ffyZ/P1RVVVGrVi2MGjUKV69erfBriT08PDw++fv5nmVl30OXfrZYsmI1DdM4JpPJ4GLbB9XrNsTMZWtpmMYxr6UrkZyWgcPpSTRM49gvN7Mx2CcCHlrVaZjGsRLJS3SNPYLqfV3QkoZpXAt7XQC1xlWQOmskDdM49ujXPZD8EomYtN00TCPftdq1a+PixYusMwgh7+GTVqjFxsbCzMzsnZ+LiYn5lN/6m+Tk5PTm1yUlJTh//jzi4uKQnJyMzMxMdO/e/Z2v69y5Mxo0+Ocbg3btaBm7mF/PX4TTZDeEhEejeYuWrHOIiBfPn8NpkCX62I/CgFFjWeeQCox3dcfTp8XITI6Fqqoq6xwiIvn0VfiE74W3bi1UUaIDf17llZTBJuUU2jjOQ6225qxziAhBELBano9O7Rth+fDetP8qx/48FAOth5ewJWMvdHT1WOf8cIT/f/CCp5bP7fnz5zh79iw0NOiyc0K+BR81UFNTU0P9+vWxfft2rFmz5h93UywsLERmZibatm2LCxcufJbQb8H/rmp79eoVxo4di+joaMyYMQNXrlx55+vGjRsHZ2fnLx/4ndiVuR/zlqxAdHIaatepyzqHiCh48hjj7fvDwW0+uljSPhC8EgQBA0c4oVaN6kiKCIUi7e/ErfW7TyEi4xT89Eygq0g7NvDq98eFGL3nPDpNXYaqDemED6+kgoCVeIJRvX+GW993nxwmfMhJW4MaCqXYmLILaurqrHMI+WjHjh0T/dzz589x9+5dhIWFITc3F+PGjfuKZYSQj/XR78hHjRqFBQsWYN++fejfv/9bn9u+fTtevXqF0aNH/1ADtf+loqICb29vREdH4+rVqyguLoa+vj7rrG9aaEQ0QqMSkLQzE4ZV6Fb2vMr+4xZmuIyAq98qtGzfiXUOESGTydCtvy2senbHojlurHNIBTyiMnHi1HUs1TOBmgINPXl1PPsBZhy/ge5zg6FXow7rHCKiTJAhAPmYb98dwzq2YJ1DKpAdsxgt61TF8vVJtGUE+eb16NHjX2+ooKCggG7duiEwMPArVRFCPsVHvysfNWoUFBQU3nlpZ0xMDLS1tTFw4MAKf489e/agd+/eqFSpEtTV1dG4cWN4eHiguLj4nc9//vw55s2bBxMTE6irq6NJkyZYvXr1v/7FdOLECdja2qJq1apQU1NDnTp14Orqivz8/Pf+fj9WtWrV3vxaJpN98a/3PVu8LADRyRlIpmEa1y6ePQ23MSOwcP02GqZxrLS0FO2798GYUcNpmMY5p3WJuHzmNhbr1qJhGseSrmbB7VQWLBduoWEax57IpFihkI9VY6xpmMYxQSbDH6Fu6NWxJVZuCKNhGmNyAHI5Rw/WfyAfydnZ+Z0PJycn9OrVC/r6+mjatCmSkpKgq6vLOpcQ8h4+eoVa7dq10blzZ2RkZKCsrAza2toAgOzsbJw+fRqOjo7Q1BTfLNnf3x+enp5QVlZG165dYWhoiJMnT2LFihVITU3F8ePH3xpGvXz5EpaWljh16hQMDQ0xYMAAPHv2DB4eHsjKyhL9OuvWrYObmxsUFRVhamqKmjVr4tq1a1i/fj127dqFkydPonr16h/7x/Cvzp8/DwAwNDSEoaHhO59z+PBhXLp0CRKJBMbGxujbty/tn/Y/xrvNwYP8YiSk7ab9nTh2cHcaQlb5w29rIqrVrMU6h4jIe/AQlrbDsMJ7AQZY9WadQ0QIgoD+vhHQz5dgjnZ12t+JY+vP/I7oP8thtXgb1LR0WOcQEXdfvUCESgkipw5Bmzo1WOcQETKpBFmhrnB2cYHThMmscwj5bLZu3Vrh50tLSzF58mSYmprizJkzqEILCAjh3idtwjJ69GicOHECKSkpcHR0BPCfmxGMGjVK9HXnzp3DwoULoaOjg4MHD8LU1BTAX0MzBwcHJCUlYfr06UhMTHzzmtWrV+PUqVMwNTXF/v37oaf314akFy5cEN3s/8yZM5g5cyZMTEyQkZGBli3/2stELpfDz88PXl5ecHV1RVJS0qf8MbxTSUkJzp49i2nTpgEAPD09RZ8bHR391j8vWrQIgwcPRkRExJtB5bu8fPkSL1++fPPPpaWln1jNH0EQMGjUGFSqVgNbY7fT/k4ci9sWgp1JCVgelQq9SnSHSF5d/f0GhrmMx7bgIJi1p8E9r16+kqGr5ya0e6mKIVpVWeeQCsw/fAG/vNBAn0WboaxK+zvx6uLLZ9ipJUHqzFGoV7Uy6xwiQvrsKbK3zMLchYvQd6Ad6xxCvipdXV2Eh4ejfv36WLhwIUJDQ1knEUL+xSdNJ4YOHQpVVVXExsa++VhsbCyMjIzQs2dP0dcFBwdDEAS4ubm9GaYBf93sIDg4GBoaGtixYwfy8vLefC4kJAQAEBQU9GaYBgBt27bF1KlT3/l1li9fDkEQEBYW9maYBvx1bfrChQvRpk0bpKSkoKCg4MO/+XdQUFB489DX14elpSWKi4sRFxeHmTNn/uP5DRo0QGBgIH7//XeUlZUhNzcXsbGxqFmzJnbs2AEHB4cKv56/vz/09PTePGrV+r5WBMlkMpj3s0WT1j9j5doNNEzj2NqlXji4eydW0DCNa0d+OYkRYyciJXorDdM4VvxcAlP39bB8pYkhGnTgzzOXjNM4q1IDPT2CaZjGsaPSEuzXf4W9Ho40TOPY88f3cW+LG5avWUfDNM4Icjl3j++VqqoqTE1NsWvXLtYphJD38EkTikqVKqFfv344dOgQHj16hHPnzuHWrVsYMWJEhXsd/PLLLwDevYqtatWqsLS0hCAIOHXqFADg/v37yM3NRc2aNdGp0z/3ZBoxYsQ/PiYIAg4dOgQdHZ13DvcUFBTQuXNnCILw5rLMT+Xk5PTmMXz4cJiZmaGgoABz5859511dRo8eDXd3dzRr1gxaWlowNjbGyJEjce7cORgYGCAtLe3Nn8G7zJ8/HyUlJW8eubm5n+X74MGzZ8/Qrntf2I5wwOz5C+lSJ44tmjEBd+9mwW9rItQ1tVjnEBHxySlw91yMfTvi0bRRQ9Y5REROfjE6zQ6Gg1JldFej/VN4JQgCBiQeQ0Hd9jCfuhSKirS/E6/SXz3FtapKyJzvjKp64qv+CVsld6/iYcJihETEokNnc9Y5hDD18OFDFBUVsc4ghLyHT7rkE/hrKJSWloaEhARkZ2e/+VhFHjx4AAUFBdSuXfudn69Tp86b5/33/5qYmLzz+e/6eGFhIcrKygAAysoVf5ufa4VaRETEPz528eJFdO3aFX369MGNGzdQt27df/19qlevDhcXFwQGBmLfvn3vHCICf63oU1NT+9Rs7jx49Bg9Bw3D/MW+sOo/gHUOESEIAqaNHoIqxibwXLqOhp4cW7U+BCk7d+NwRhIq6ev9+wsIE5fuPcQI/2jM1KqOhioarHOIiHKpDFbbj6J6j6Fo1q/i9zuErajXhVCorYdd0+yhpvLJb3nJF5J/5Rie/xKDiKQ0GJu8+9iAkB9BaWkpAgMDcebMGbRv3551DiHkPXzyuwtra2vo6+sjKioKDx48QNOmTdG2bdvP0fbmAP3vu3iKHbC/6+OvX78GAOjo6MDOruJl42KDvc+hTZs2mDhxIgIDAxEcHIxVq1a91+saNvxrBcnDhw+/WBuPrt+8DVvHcVgdHIr2Hc1Y5xARUqkUY2z7wLRHHwyf9M/LmQk/3Bd44/rNWziUlggNDbokjVf7L9/GzA1pWKhTEzWUv78TJd+LonIJrBKPo5n9NNTtZMU6h4gQBAEb5YWo39wYG10GQFGRTvjw6sGJFCjeOoK49L2oLHLzLsKeHHzdWZOnlg9R0cKK58+fo7CwEHK5HOrq6li5cuVXLCOEfKxPHqipqalhyJAh2LJlCwDA1dX1X19To0YNZGdnIycnB40bN/7H53NycgDgzd03a9So8dbHxZ7/3wwNDaGmpgYVFZV3rhz7mv7+y/PWrVvv/ZqnT58CQIU3JfjeHDtxCpPcPbE1JhGNmjRhnUNElJYUY4xtH9i6TEZvu39ebk34MXLsZCgqKmBXQuS/rtQl7EQdvYhVcQexRLcWKiupsM4hIrKLSjA47Ve0H++FGj+Z/vsLCBMyQcAqeT76dPkJXrZdafU0x3Izt0C/OAthGXuhpfXjvN8lP67c3Nw3C0X+l6qqKmrXro2uXbti9uzZaN68+VeuI4R8jM+yy7ujoyMMDAxgaGhY4d09/2Zu/tfeCP99M4O/5efnY//+/VBUVHxzqWPt2rVhbGyMvLw8nD59+h+vSUhI+MfHlJWV0a1bNxQVFeH48eMf+i19Vnfv3gUAaGm93/5ScrkcqampAIB27X6MjcOTUjMwzWMx4tN20TCNY4/ycuFg3RMusxfRMI1jgiCg98ChqFm9GmJC19MwjWPLdxzFhvjDWKpnQsM0jl3My4dd+ll0mbmKhmkckwgCluEJnPt3xGK7bjRM49i9pBWorVCI8OR0GqaRH4ZMJsPr16/f+Xjx4gXu3r2L8PBwGqYR8g35LAM1c3NzFBQUID8//70un5w6dSoUFRWxdu1a/Pbbb28+LpVKMX36dJSXl8POzg41a9Z887mJEycCANzd3VFaWvrm45cuXcKGDRve+XU8PT2hqKgIJycnnDhx4h+ff/DggehrP5eLFy8iLCwMANCvX783Hy8oKEBUVBRevnz51vPLysowefJk/PrrrzAyMoKtre0X7ePBmg1hCAjZiuTd+1CjpjHrHCLi1u9XMHH4QLivWI/2XXuzziEiJBIJzHr1Q99e3RHo60UHlBybvjkD+w5ewhK9WtCiTe25te92DsYcuoaenptgUOefq+oJH4oFGZbhMRaP7I0JPX9mnUNECIKArHAPtK9XFcHhsVBVVWWdRN6DIOfvQQghPGCybMHU1BS+vr5YsGABzMzM0K1bNxgaGuLkyZPIzc1Fw4YNERwc/NZr5syZg127duH06dOoX78+unfvjmfPnuHw4cMYO3YsQkJC/vF1LCwssHbtWri5ucHc3BwtW7ZEw4YNIZFIkJOTgxs3bkBbWxtTp079LN+Xs7Pzm19LpVLk5OTgzJkzf90NbMAAODg4vPl8WVkZnJycMH36dDRt2hQmJiYoLi7GhQsXUFhYCH19fSQnJ0NTU/OztPFqjtcS/HbtFpIy9kLjO/9ev2Vnjh/G8kVzsTgkBsZ167POISIKCovQY8BgeLhNwyj7738Y/y0bujIWL3OKsECnJpRo6MmtqIu3se5WAfp4bYWGXmXWOUREnuwlNik9RciEgejciDa155Ugk+LO5pkYPHAAps72YJ1DCHOvX79GQUEBlJSUYGBgQCdBCfkGMbsOyNPTE61atUJQUBDOnTuHFy9ewMTEBHPnzoWHhwcqVar01vPV1NRw8OBB+Pj4ID4+Hunp6ahTpw78/Pzg7u7+zoEaAEybNg1mZmYICgrC8ePHkZGRAR0dHRgbG2PSpEmwt7f/bN9TZGTkm18rKipCX18fFhYWcHBwgLOzMxQV/7Mg0MDAAPPmzcOZM2dw584dXLp0CUpKSqhbty6cnZ0xc+bMt1bofY9GT5iOckEBscnpdEkax3YmxSEqNBj+ETtgUNWIdQ4RkZV9DwOGO2D9cl/06mbBOoeIkMkE9F68GXVKBUzSMqI3zxxbfuIK0goEWHlthYrG+23ZQL6+m9LniFcvQ8L0YWhmXJV1DhEhk5QhK3QGJk+bjqEOzqxzCGHql19+wbJly3D06NE3Vytpa2ujR48e8PDwQMeOHRkXEkLe1wdPMcQ2UnwXIyOjCp/fv39/9O/f/71/P21tbQQEBCAgIOCDutq1a4eYmJj3+hre3t7w9vZ+76Z/+9pidHR0sHz58g9+3fdAEAT0GTIK9Zs0xyr/ADqg5Ni24FU4vG8PVkSnQVtXj3UOEXHu4iU4T5yOuC0b0KbFT6xziIhyiRQW8zehq6AJa00D1jmkAjP2ncMlxcqwXBAIJWXa245Xv0pLcVBbip2zHFDLgH5G8UpSnI+c8Dnw8vNH9z59WeeQjyEHPuJw58vhqeUDbdy4Ea6urpDL5ahcuTLU1NTw7NkzKCoqYufOndi1axdWr179Xjf6I4Sw91n2UCPkfUkkEnTsPQCdu/fGkuWBNEzj2IpFc/HrqRNYHplCwzSO7d5/EGOnumFnQhQN0ziWX1qGDrPXY5BcB9bq+qxziAhBEDA89QRu6jdA91lBNEzj2H5pMU5UEpA534mGaRx7lncH98NnY3VIGA3TyA/v4sWLcHNzg4mJCY4dO4b8/HzY2dlBLpejqKgI+/fvR+3atTFz5sx37v9NCOEPXWdHvpqip09hYT0EU2a4Y8jwkaxzSAXmTHAElFXgvSkWSkq0WTqvNkfEICwiGgdTt6Na1Sqsc4iIPx4WYKBPOCZpVEMLVdorklcyQUC/hKPQat8HZoMn0QkfjiXJivC0hib2ug2Dlhptas+rolu/oXjfRmyJS0K9ho1Y5xDCXFBQEGQyGaKjo9G5c+e3PqeoqIiePXsiIyMDbdq0wapVq9ClSxdGpYSQ90UDNfJV5OTmos+Q0ViyPBDdetIdInklCAIm2Fuj/k+tMWYO3SGSZ0tWrMLh47/gcEYidHV0WOcQEWdu34fLqgTM1aqBOirqrHOIiGcSKfpsP4a6/Z3QqOcQ1jmkAlteF0C3viHSJtlBRZlO+PDq8fkDeHVuB6JTd6Fa9Rqsc8gnEiCHwNF1ljy1fIjjx4+jZcuW/xim/bfmzZvD1NQUp06d+oplhJCPRQM18sVduHwFI8dPx/rN29CqTTvWOUSEpLwczraW6DFwKAY5TWSdQyoweeY8PHz0CPt3xENNTY11DhGRfvZ3LNiyG4t1jFFNmVbR8Orxs3L0Tz6BVqPdYfJzd9Y5RIQgCFgrL0C7tvUROKoPnfDhWN6ReKjlnkNExl7o6Vf69xcQ8oN49OgR2rdv/6/Pq1mzJn777bevUEQI+VQ0UCNfVObBw5i1yA+RiSmoW68+6xwi4mlhAcYO7osRU93RtZ8t6xwiQhAEDHEcC4NKlZAas/WtOwcTvoTsO4OwlBNYqmcCXUX6Ucurm/lPMWLnOXSa4oeqjVuzziEiZIKAFfInsO/RFnOsxVd2EPZydwajqiwfIam7oa6hwTqHEK7o6OhAIpH86/OuXbuG6tWrf4UiQsinonf55IvZFh2P9VujkJixF1WrVWOdQ0TkZGfB1dEeUxevRGszc9Y5RIRMJkOPAYPRw7wTvD1m0+oMji2K349Dx65hqW4taCjSJWm8OnnvAaYevYFuc9ahknE91jlExHNBhpXIh7tdVzh0acU6h1QgO24JmtfUx8oNO6CsTIcY3xM5Z3f55KnlQxgbGyMnJ6fC5/j5+eHGjRuYPn36V6oihHwK+mlHvgjfgCDsPXoCybv2QUdXl3UOEXHl/FkscpuE+Wu2oF6T5qxziIiysjJ07TcI451GY/IYR9Y5pALjgncg9/c/4aNbC8o09ORW2u/ZWHIxF5YLw6BlQCd8eJUvk2KdUiECnfrBskUD1jlEhCAIuLt1Nnqad4CHzzI64UOICAsLC2zcuBEPHjxAjRpv7y3o6uqKkydP4uLFi6hbty4WLVrEqJIQ8iHoeiHy2U2ZPR8nL1zF9rQ9NEzj2JHMXVjsPg2+WxJomMaxB48eo2Ovflg4ZyYN0zgmCAKsfSNQevMR5urUoGEax0LOXsey6/mwWryVhmkcuyd7gfXKRdg2ZTAN0zj2WvoSf2ycjBH2gzB/iT8N0wipwJAhQ6Cjo4O0tLS3Pq6goIANGzbgypUrsLe3x6lTp2BoaMgmkhDyQWiFGvms7BzHQkPXAOFxSVBSokudeJUYuQU74iKxMjoVepXpBzavrt+8jSGOYxG2JgDmZqasc4gIqUyG7p5haCFRwjAtGtDwbNGRizj8XBV9Fm2Gshrt78SrK9IypGqUY4fbSDQwMmCdQ0RIy4qRvWUmZs2bjwGDh7LOIV+QIP/rwQueWj6Eubk5ioqK3vrYuHHj0Lt3bxgaGqJdu3aoXLkyozpCyMeggRr5LGQyGXrYDMXPncwxb5E3naHk2IYAX5w7dRIrotOgqaXNOoeI+OXUGUyaORfJkZvRvEkj1jlERGm5BBYem9BPUQe9NfRY55AKjN91BlnaxujlsQyKSvT2h1e/vCzBCb3X2OPuCCN9HdY5RER5fh5yYxZgaWAQOnWlu+MS8rE6deqETp06sc4ghHwkekdJPll5eTk69x2EYQ5jMGbCJNY5pAI+7lORX1iIpduSoKKqyjqHiEhO24mlgUHITI6FiXFN1jlERG5hCawWbYaTmiHaq9JwmleCIMBuxy941bADLBzn0Akfju2UPkV2VWXsmzUaeprqrHOIiJKc63iSuhIbtkWiWQu6UQQhhJAfFw3UyCd59PgJeg4aBvf5XrAeZMs6h4gQBAEznIdB17AaFq4Ph6IibZ/Iq7Uhm5GwIxWH0pNgULkS6xwi4tr9x7BfGokZmkZorKrJOoeIkMhk6JtwFIbmtmhj48I6h1QgRlYIWW1d7J4+FOoq9PaUVwW/n0TZkXBEJKaiVp26rHPIV0J3+SSEkHejdyzko92+kwWbkWOwYm0wzDqbs84hImQyGcbYWaFN524YOXU2rc7gmIe3Ly5cuopDaYnQ0qIhDa8OX8vCtHU74KlTE8bKaqxziIjicgmsEo+jkd1k1DfvzzqHVCDkdQGMm1ZH6DgbKNEJH249PL0TuJaJmLQ9MKxalXUOIYQQwhwN1MhHOfnrOYx1nY3QqDg0bUZ3iORVWWkpXGz7wHr0WPQd6sA6h1TAaZIrXr6UYE9iNFRUVFjnEBFxv1zG8uj9WKJbCwZK9O+JV7nFzzAo9TTajfFEzZa0Nw2vBEFAoLwAPTo2wRL7HnTCh2N/7g+HTsFNbN6ZCW1t2tuOEEIIAWigRj5C6s7dWLQiCHEpO2Fcy4R1DhHx+GEeJg6zgctsL5j1tGKdQ0QIgoD+9qPQqH49rF0eRAeUHAtMP47te85iqZ4JtBXpLsa8uvKwAE6ZF9Fl+koY1m/GOoeIkAgCVuIJxlh1wFRLuosxz3J2BMJEXYr1OzKgqkarcn9EAuQQwM91ljy1EEJ+bDRQIx9kfdg2RCamInlnJiobGLLOISLu3LyOWeNGY5b/WjRrSwcqvJJKpbDoNwi2/fvCw20q6xxSAffw3fjt19vw1TOBmgJdksarQ1m5mH3iD/Tw2ABdIzrhw6tSQYZA5MNrWC8MNqWhJ68EQcC96EX4uWkd+K1eT/uvEkIIIf+DBmrkvc1f4o/TF64geWcmNLW0WOcQEedOHsfS+TPhtTESJvUbsc4hIoqLS9C1vy1mT50IxxH2rHNIBUauSkBp1hMs0jWGEq0g5FbslTtYfe0RLL22QFOfTvjw6qFMio1KhQgeawOLpnVY5xARgkyGrC0zMaBfH8yYt4BWTxNCCCHvQAM18l6cp8zE0/KXiN2RQfs7cWxvahK2BK/CsvBkGBrVYJ1DROTk5qLfkJFY5eeNvr26s84hIgRBgOXirahRLMMs7ep0QMmxwFPXkPRICqvF26Cqqc06h4j441U5YlSfIW76MPxUqxrrHCJCJilHVtgMTJg0CSOcx7LOIRygu3wSQsi70UCNVEgQBPQf5oCadRsidONqWu7PschN67AvIxUro9Kgo1+JdQ4RcfHKVYwaNwWRG9fCtF1r1jlEhEQqg8X8EHSSqWOgZhXWOaQC7gd+w9nXeuizMBhKKqqsc4iI36TPsFdLgrSZo1CnCv2M4pWkpAA522ZjvrcvLPsPYJ1DCCGEcI0GakSUVCqFRf/B6NnXGjNmz2OdQyoQ6D0fN69fw/KoFKhraLLOISL2HzkGd08vpMeFo1H9eqxziIiC0nL08NyEwcr6sFDXZZ1DKjA6/SSeVG2MHhMWQ5FuFMGtQ9JiXNAHMt2dYKhLW0bwquxhNh4keCNwfQjadaS74xJCCCH/hgZq5J2Ki0tgYT0Y46a4YvhoR9Y5pAIeU1wgfS3Ad3MClJTpP2lehccmYEPYVhxITUD1anSpE6/uPi5C/8VbMVGjKlqp0oE/r2SCgAGJx6Dauic6DZ1Kl+NyLOVVER4bqSNz5ghoq9MKQl49vXMJRbvXIixmOxo0bsI6h3BGkMshcHSdJU8thJAfGx19k3/IzcuDpd0oLPLzR68+fVnnEBGCIGDScBuYNGyG8fOX0AElx/xXr8XeA4dxOCMJerq04olX5+78CceAOMzWqo56Khqsc4iIMqkUVgnHUavPKDSxHMY6h1Qg/HUB1OsZIGPKYKgq0wpCXj25eAiSM4mIStmJ6jWNWecQQggh3wwaqJG3XL76O4aNnYI1m7ag7c/tWecQERKJBGNs+6CLlQ2GjJvGOodUYPrcBbh3LwcHUuKhrq7OOoeI2H3hJuZuyoCXjjGMlGkVDa+elJWjf9IJ/DTCDXU69GKdQ0QIgoD1KMRPrWpjjUM/KCrSCR9ePTiWBOW7JxCXsRf6lSqzziGEEEK+KTRQI28cPHIc0+cvRnh8Euo3bMQ6h4goflqIMbZ9MWziDHS3GcI6h1RgqNM4aGlqIj0uHEpKtDqDV1sOnkNw4lH46ZpAX4l+LPLqTkExhmb8ig4Tl8CoWTvWOUSETBAQIM/HwK4tMX+gBescUoH7u0Ng+CIPoel7oaFJ+68Sca+Fvx684KmFEPJjoyMHAgCI3p6EVRu3Ynv6HhhVr846h4j4M+cepo62w+RF/mjbuRvrHCJCJpOh96ChMPu5LZZ5zafLcTnms/0Q9h6+hKV6JtCkTe259ev9R5h46Bq6zV6LSrUasM4hIsoFGVYiHzMGmcPZog3rHFKBewlL0aiKBlZFpEJFRYV1DiGEEPJNooEawYq1G5C29yCSdmVCT0+fdQ4Rcf3yBXhMG4d5q0LQsHlr1jlERHl5OSz6DYLTiKFwnTCGdQ6pwOSQVNy+koMlerWgoqDIOoeI2H3zHhady0HvBZugXaUG6xwioui1FEEKhfB3sEL/1rTKnVeCIOBu+Dx0NW2NhctW0gkfQggh5BPQQO0H5zbfC79n5WB7xl7a34ljJw7twyrfhfANi0d1kzqsc4iIJ/kF6GkzBIvnzoL9IGvWOUSEIAgYvCIGCn+WwlOnJhTpgJJbm3+7ibCsYvTx2gJ13Uqsc4iI+zIJNisXY/NEW3RoUIt1DhEhvJLij7AZGDHUHhNmzGKdQ74hdJdPQgh5Nxqo/cCGjZkEuaomoran0P5OHEuJDUdC5BYsj0xFJcMqrHOIiNt3sjBopDM2rVqOrl3MWOcQETKZgB4LQ9H4uQJGaFWj1RkcW3LsEvYWK6KP11aoqNNdV3n1u/Q5ktSfI2nGcDSuQT+jeCUtL0V2mBtcZ82G7fCRrHMIIYSQ7wIN1H5AgiCg56Bh+Klteyz0WUoHlBzbtNofp44dwcrodGhq67DOISJO/XoO413dsX3bJrRs3pR1DhFRJpHCwiMEveVasNLUZ51DKjBl76+4rmYES8/lUFSm/Z14deplKY7pvsKuWQ6oWVmXdQ4R8aLoIe5HesBnRSAsevRmnUMIIYR8N2ig9oORSCTo0tcWtsNHYfzkaaxzSAWWzpuBP/Py4B+RDBVVNdY5RET67r1YvCwAexKjUceELnXi1cOnpei9cDNGq1ZGBzUaTvNKEAQMTTmJ8rpt0M2FbujBs73Sp7hVRQl7ZzmhkhatIOTVs9zbeJS8DOvDwvFTm7asc8g3SpDL8Zqjyyzpkk9CCC9ooPYDKSgsQlfrwXCb54mBdvasc4gIQRDgPnYU1HX1sDgkGoqKtFk6rzZuCUdk3HYcTNuOKoYGrHOIiBt5T2C3JBLTtKqhmYom6xwiQiqToe/249A3s0YH23Gsc0gF4l8VodxYC3tmDIOGKq0g5FXhjV9RejAMW7fvQJ169VnnEEIIId8dGqj9IP7IysaAEc5Ytnotulh0Y51DRMhkMowb0g/NfzaDoxutzuDZQr/lOH32HA6nJ0FbW4t1DhFx/Ho2Jq1JgodODZgo041XeFUieQmrhGNoMHA8GnQbyDqHVCD0dQGqNamGmPGDoKxEJ3x49ejMHry+sgsx6XtQpWo11jmEEELId4kGaj+AX89fhNMUN2yKiEGzn1qwziEiysvK4GxriT72ozFg1FjWOaQC46bPQnFxCTKTYqGqqso6h4hIPn0VPuF74a1bC1WUaBUNr/JKymCTchJtnTxg3MacdQ4RIQgCVssL0Ll9I/gP70UnfDj258EoaD26gi0Ze6Gjq8c6h3wHBDlfl1kK/KQQQn5wNFD7zu3cuw8evisRk5wOk9p1WOcQEQVPHmGcvTWc3Oajs6U16xwiQhAE2Ax3hIlxDSRFhNLluBxbu/skojJOY6meCXQU6Ucdr35/XIjRey6g89TlqNKQTvjwSioIWIknGN37Z8zoS3cx5llOahBqKpVhQ8ouqKnTqlxCCCHkS6KjjO9YaEQ0QqMSkLQzE4ZV6Fb2vLr7xy24uQzHDL8gtGhPByq8kkql6G5th369e2Lh7Bmsc0gF5kXtxclTN7BUzwRqCjT05NXRu39i5i+30X1eMPSq12adQ0SUCjKsQj7m23fHsI409OTZ3WgvtK5nBP9126CkpMQ6hxBCCPnu0UDtO7V4WQAOnz6H5J2Z0NahO9rx6sKvp+AzezoWrg9HnUZNWecQEaWlpejabxCmTxiLsQ4jWOeQCjiuSUTB7Ufw1q0FJbokjVuJV7Ow4soDWC7cDM3KdMKHV49lUgQrFWKNizV6NK/HOoeIEGQyZG2bjb49u8J9oTddjks+u9fCXw9e8NRCCPmx0UDtOzR93iIUlpYjPnUX7e/EsYO70rBxlT+WbktE1RrGrHOIiPyCQnTqbY0VPgth3acX6xwiQpDLYeW9FZULXmK2dnU6oOTYujPXEPOnBH0Wb4OaFp3w4dXdVy8QoVKCqKn2aF2nOuscIkKQyXAnZApcxoyBw/hJrHMIIYSQHwoN1L5Dj5+WYkVQMJ4WFbFOISJKS0sRHOCH+Wu2QFlZBUVPHrNOIu9QUlQAT5+lWL3UG+1at8DDx/TviUeP8/Nx4sItdFbVga1GZTwVZKyTiIhiuRzRuS/Q1XU5XkslKJdKWCeRd5A8f4atysXYPH4QjPS18aj4Gesk8g5PSp+j7PI+THZzR58Bg/Dk8SPWSeQdigoLWCcQQgj5QhTkco5u2UI+SWlpKfT09NCixU9QUqJZKa8KC/JRWvoMRpUqg9bQ8OvZSwmKy8pgUKUaNDQ1WecQEa9lMjwreQqh/AWqaWuxziEVePL8BV4qKkK7SnX6u49jpSVPUf60AC1qVWWdQirw4qUU90sk0NPSQFVDA9Y5RIQgF/D4wQM8eVqCkpIS6Orqsk76IH8fW+y+eBdaHG0h8/zZM/RvU++b/DMlhHxfaOryHTqyIxq6HP3QI//hNHUWXtSoitt/ZCG8ngnrHCJi76MnSJCoo/FPLVC12zDUaU03i+BR8eM87PadhMH2w/D47k1s37CCdRJ5B0EQYDF8Apq3NMWpwwfgG7eHdRIRkbJpFS6d/gVaWlo4ucaVdQ4Rcfr6XQxdk4Q6pp0xpK4KFoyxZ51E3qG0rBzmY+ZiUJPaCDt9hXUOIYSQL4Buf0bIVyAIAqyGOkJPVweJm9dDSZH+0+NV1P0HSFdUQ8a+g6hSlVZo8OrhH9ewx28StoRHoFuPnqxziIjy8hdo3X8U2vUeiMkePqCt7fi11XcObl29BKcVEVBV02CdQ0SknrqIYetT0W9RKAxM6rPOISLynhSio8NMzOnSCkNbNWKdQwgh5AuhFWqEfGFSqRQWA4bCtp8lPFxpw2CeBWXn4mGNWkhLSIKGBh1Q8irr3DGcjw1C4o401KtfH0cPH2KdRN7hSUEROg8dB4fp89BjgB3rHCJCEASsnTUGSlr6GO4TAkU64cOtDTuPIfDgVdgsCYeGbiXWOUTEtawcDJ7phyAbc7Q3McLNR9/+nsav5XK85miXIJ5aCCE/NhqoEfIFFZeUwmKAPWZPGQenYYNZ55AKLLqTA61WbRC/eRuUlemvRl5d3Z+C7MOJSN+TiWrVjFjnEBF/ZOegj8sMuC1ZhbadLFjnEBEymQzLxg2GSasO6OY4g+6OyzGPiF1IvZmPgUvCoaJO+3ry6si5K5jitx7hw3qhYRUaehJCyPeOjhoJ+UJycvNgNcwRQb4L0bdHV9Y5RIQgCJh+Oxvt+ttgybLldEDJsTPbQ1B26xx27t0PHdqEmFunz1/GSHdvLF4fjgZNf2KdQ0RIysuwxHkQ2vYbjvY2I1nnkAo4BMXi8nN19F8UCiVlFdY5RMT2fcfhtykWCaP7ooaeNuscQgghXwEN1Aj5Ai5euYaRE10RtWEVTNu0Yp1DREhkrzHp9l0MmzgFU6bPYJ1DKnA4ZAm0pU+Rumsv1NTUWOcQEWn7DmP2yhCsDE9C9Vq1WecQEU/zH2PZeHv0HDMLTbtYss4hIgRBgKX3Zjyr0hiW7gugQJfjcmt1TBoSMg5gh1N/VNJUZ53z2QkABI6ushRYBxBCyP+jgRohn9n+I79g1qIlyIjejEb167LOISKKpVJM+SMHsxcvweChw1jnkArs8p+BprWqYt3GZCgpKbHOISI2Rm1HcMIurInLgL6BIescIiIv6zaCZo2FzaylqN3iZ9Y5RIRUKkNHj2Dot7OExZCJrHNIBdxXb8XFi1eR7NgfGqp0aEUIIT8S+lufkM8oIj4J6zaH42ByNKpXoztE8ir3eTlm38tDQHAIunbvwTqHiBBkMqR6jYFVz65YuHgJXY7LMc+Vwdh39hrWxGdAU4sudeLVzQtnsGXJXAxbHIyqdRqyziEiisvK0WFuMBoNGIMmPWxZ55AKjPQMgLSgELEjraCsRCsICSHkR0MDNUI+E/81G7DnwGEcTY2Hnq4O6xwi4lpxCfweFWJr3Ha0aNWadQ4RIZWUI8XTERPHj8PYCXR3XJ45z/ZGdrEEgVE7oKKqyjqHiDizfydSwoLgsHwb9KvWYJ1DROTmF8F8YRhMneahdju6oQevBEGA5RQvNNRShc/g7t/9CZ/XghyvObrmk6cWQsiPjQZqhHwG0z0WIzvnPg4mx0BdnfZ34tWJJwUIKX2B7em7UacuXY7Lq7KifKQvHgtvHx8MGEirM3glCAL6jXGDelUT+G4MhSLt78StfbGbcXx3KlwCo6GpS3ce5NWV7D/Rf1kMurkug1Ej2n+VVxKJFOZj58K6oTGmdGrJOocQQghDNFAj5BPZj5kMLQ0NZESH0f5OHEvLe4QMuRLSMvejatVqrHOIiMI/s7FvuSvWbdiIzl3MWecQETKZDGZDxqKVeW84uc797ldnfMsSgvxw69plOAdGQ1Vdg3UOEXHw4g24bNqJPvM3oFLNOqxziIii0mewcJmLaWYtMLhlA9Y5hBBCGKOBGiEf6fXr1+hpNxJmP7eB/4I5dEDJsbCcP3FVtzJ2pqRDW4cux+XV/d/P41SoD6LjE9C0WXPWOURE6bMydBg8BjaOEzBguBPrHFKBkAXTUFYugYP/Figpq7DOISKiD/2KhSmnYb14C7QN6IQPr7LzHqPvlEXwteqIbvWNWed8VXK5HIKcn8ss5Ry1EEJ+bDRQI+QjlJeXw3zAUDgPHwzXcXRAyTP/rPsor98QKTHxUKX9nbh168Q+XEsNw470XahlYsI6h4j48+FjdB0xCePneaNL736sc4gIQRAQMGUk9GrWhf2sFXTCh2PLkw5i8+k7GOgbDjUtXdY5RMT5m3cwau4KbLDrjpY16C7GhBBC/kIDNUI+0JOCAvQYNAJes10xbGB/1jlEhCAImHfnHmp2Msem4BDa34ljFzJi8PDXvcjYuw8GBnSgwqtrN//AgIlzMC9gA35qa8o6h4iQSaVY4jIIjTv3RpfhE1nnkAq4hqXgwP1y2HhvhbKaOuscImLvyfNwXxmKqBGWqGugxzqHEEIIR2igRsgHuJ2VjYGjxyFk5RJ072LGOoeIkAkCpty6i55DR2DeQi9ancGxE5GrITy8jYy9+6GlpcU6h4g4fOosxi1YDr/QWNRu0Ih1DhHxvLQEvi62MLMfg9aWdqxzSAXslkciW8EA/TwDoKhEb8d5tTX9AIKjdiDJsR+q6miyzmHmtfyvBy94aiGE/NjoJzgh7+n0uQsYO2MuEresR8tmTVjnEBHlMhkm3srGuFmz4Tx2POscUoEDaz1RTU2OzWk7oaJC+zvxKi59DxYHRyIwKhVVq9dgnUNE5D/MxYrJI2E1yRMNTbuyziEiBEGAhWcIUL89ejrMohM+HPPdvB2Zh05gh5M1dNRpywhCCCH/RAM1Qt5D2t798PJfhb0J21Cn1o+1Ee23JF8iwfSsXCxeEYB+1jasc4gIQRCw03cyTFs0wcrVa+iAkmOBoVGI3H0E6xJ2QUdPn3UOEXHv5lWsnzcZdh6rYNy4BescIkIilaL97PWobmGHVgMcWeeQCkxZthF3b2dhu2M/qCnTHdwJIYS8Gw3UCPkXG7ZFITI+GYdTYlHFoDLrHCLi7rMyzM99hHWbw9GxUyfWOUSETCpFykJnDLW1way5HqxzSAXclgTi9I0cBMWmQV3jx73UiXeXTx5BTKA3RvqGwtC4LuscIqKgpAwd521AC/upaNClL+scIkIQBAye7Q+tly8QMbw3lGj/VQCAwNldPnlqIYT82GigRkgFFi4LxKmz53EkNRbatL8Tty4UPkVAQQmik1PRuElT1jlEhKSsFKkLnTDTbSZGOtDqDJ7ZT/NAsVwNK8IToaxMbxV4dTwjAXtitsJpZSR0DKqyziEish48QQ/vreg03gvGLTuyziEiZDIZekzwRPuq+pjXx5xWTxNCCPlX9C6ZEBFjZ8xFSUkp9m0Ph6oq7Z3BqwOPniD6hQw7dmeipjFdjsurkvyH2OUzActXBqB3HyvWOUSEIAjoPnIyqjdpBa8FfnRAybH0LWtx/vghuKyKgbqWDuscIuLcrXuwW5WAnrNWo0pd2n+VV2XlL2DuMhcjW9aHc/tmrHMIIYR8I2igRsj/EAQBA0aNRe2aNZC0NRiKtNyfWwl/PsRhZXVk7MtEpcp0OS6vHt+9iUNBcxC6ZSt+bm/KOoeIkEgk6Dh4DDr3H4IRE6azziEViPT3xJ/3c+C4MhIqqmqsc4iIjDNXMC1yP/ouDIVeNTrhw6tHhU/RY9x8zOvWFv2a1mGdw6XXghyvBX4us+SphRDyY6OBGiH/RSqVovugEejbsysWzZrGOodUYF12LnKq1UB64g5oatL+TrzKvnAKZyNXYHtyCuo3aMg6h4gofFoMsyFjMWKKO3oPtGedQyqw1n0cBGV1jFyyCYpKtFk6r0L3nIB/5gUM8NkGTT0D1jlExM17f2LgDB8EWndBx9rVWecQQgj5xtBAjZD/V1r6DBY2QzF9nBPGjRrKOodUYPGde1Bp3hKJ4VG0vxPHrh1Kwx+ZsUjfnQmj6nSgwqvs3Dz0HD0V031Won2X7qxziAiZTIblE4aietPW6DnGnS7H5dii6D3YfvUhBi6JgKoG7b/KqxOXrmOcVxC22PdEk2q0yp0QQsiHoyNRQgDkPXyI3oMdsHKxBwZY9mCdQ0QIggC329n4ybIvlgWsogNKjp1N3oLiayewK/MA9PT1WecQEecuX4O960IsWrsZjX9qzTqHiJBIyuHrNAit+wyG6SAH1jmkAmPWJeDsU0VYe22GkooK6xwiIuXQSSwOjkLC6L6oqa/NOod7dJdPQgh5NxqokR/eles3MWzsFISvXQmz9m1Z5xAREtlrTL59F7Yu4zDDfQ7rHFKBo2HLoPbsEdJ2Z0JdXZ11DhGx69BxzFi6Diu2JaJm7bqsc4iI0qIC+I0bjG4OrmjetS/rHCJCEAT0992KAr16sJrrBQXaf5Vb6+N3IjJlL5Id+6OyFv2MIoQQ8vFooEZ+aId+OQlXj8VIiwxFk4b1WecQEaVSKSbdvge3RYsxbMQo1jmkAnsCZqF+FV1s3JEKJdrfiVub41OwKnIHguLSUdmwKuscIuJhThZWuTrD2m0J6rbqwDqHiJDJZDDz2ADtlj3QdehkWj3NMY91kTjz6wXscOoPTVVaQUgIIeTT0ECN/LDiktMREByKA0lRqFndiHUOEfGw/AXcsv+E/9pg9OxtyTqHiBAEAemLx6JH547w9ltGB5Qc814TirTjv2Ft/E5o6eiwziEibl8+hzAvd9gvWgejeo1Z5xARpeUvYDonGPWtRqOZJe2/yjPHRatR+vAR4kb3hYoSrSD8EK/lfz14wVMLIeTHRgM18kMKCA5F6u59OJIai0r6eqxziIibJc+w+METhEbHoU3bdqxziAippBypC5zg4uSISVOns84hFRjn4Yubj0qwOjoVqmpqrHOIiHOH9yIpeCVG+29BJSNj1jlExMPCYnTy3IR2o91R15T2X+WVIAjoN90btVQUEWbfk074EEII+WxooEZ+OLMW+eLGrT9weEcMNDRo7wxenc4vxPri50hI24l69RuwziEiykueIm2RMxYsWADbIbQ6g1eCIMBmvDugVxXLwmKhSPs7cetgQjgOp22Hc2A0tPTpzoO8up7zEFZLI2Ex1Q/Vm7RhnUNESKWvYD52HizrVodrl1ascwghhHxnaKBGfigjJ0yHgqIidsVugbIy/d+fVzvzHiNFANIy96NaNbocl1dFeTnY6z8Na9ath3nXbqxziAiZTIYuQ8ejSceuGDvTk1ZncCwpeAV+v3AWzquioaahxTqHiDh29TZGr0+D5dx1qFyL9l/lVfGzMpiPmYsJ7ZtheOtGrHO+aXSXT0IIeTeaKJAfgiAI6D1kNNr81AyB3vPpgJJj4Tl5+E1bDxkp6dDVo8txeZV38zKOb1iIyJhY/NSiJescIqKsrBwdBrvAargzbB3Gsc4hFQjzcsPT4hI4+G+Fsooq6xwiYvux3zBn+3H0X7wZOoZ0wodX9x89QZ9JC+HVyxS9GpmwziGEEPKdooEa+e5JJBKYDxiKkXYDMGvSWNY5pAIr797HU5O6SInbDnV1uhyXV7dPH8blxPVITtuJ2nXqsM4hIh4+yYf5sIkY474IFlbWrHOICEEQsMrVEZoGRhjqtZ4ux+XYqpTD2PDLTQxcEgF1HTrhw6tLt7IxbM4yrB/UFW2M6S7GhBBCvhwaqJHvWkFhEboPGg5PtykYaWfDOodUYN7tbFQx7YjYkDAoKSmxziEiLu3djtzjacjYuw9VqtCBCq9u3LmLfmNnYc7ydWjZ3ox1DhEhk0rhN9YO9dp3RdfRU1nnkArM2pKGPXdLYeOzDSpqGqxziIj9Zy7CzX8joob3Rj1DfdY53w1BkEMQ+LnMkqcWQsiPjQZq5LuVlZ2DAaPGYP0yb/Tq2pl1DhEhEwRMu52NLoMGY6GPL12Oy7FTseshuXcFO/fuh7aODuscIuLYr+fhPM8PS0KiUbdRE9Y5RER5WSmWOA9CR1sntOlrzzqHVGBYQDRuyXTRf2EIFJXorTOvonYdxqptidju0A9GurQHISGEkC+P3hWQ79K5i5fhOGUW4kPXok2LZqxziAiJTIYJt7LhNH0Gxk2awjqHVOBAsBcMFSSIy9gNVVXa34lXibv2YcHabQiMSkG1Gsasc4iIokcP4D9pOHpPmIfGHbuzziEiBEFAj0WhkBq3RC+XeXTCh2P+4cnIyDyKHU79oaehxjqHEELID4IGauS7s3v/YXj4Lcee+K2oV5s2ouVVoUSKaVk5mO/nj4G2dqxziAhBELB72XS0blgbq9ZF0P5OHFuzLRabU/ZjTVwG9CpVZp1DRNy/fR1r50yE7dzlqNW0DescIkIilaLDnGBU7TQA7QeNYZ1DKjB9RShuXb+FRMd+UFehQ5svQZADrzm6ypKu+CSE8IJ+6pDvyuaoeIRGxeJQcgyqVTFknUNE5JQ9x9ych1i9aTO6mFuwziEiBJkMKQudMbC/FebOX0CrMzg2Z9kaHL38B9bEpUNDky514tW1sycQucwTI3xCUMWkHuscIqKotAwd5m1EU9sJaNx1AOscUoGhc/2h+OwZokZYQolO+BBCCPnKaKBGvhtLAtbg8C+ncTQ1Djra2qxziIjLT0vg/6QIEYnJaNb8J9Y5RISkvAypno6YPm0qHF3o7rg8G+m2EI9eyBEQkQRlFRXWOUTEyd0pyIjYCMeVkdA1rMY6h4jIflSAbou3oKOLJ0za0P6rvBIEAT0mLkCrSlpYMKgrnfAhhBDCBA3UyHdhorsnHj/Jx/7ESKip0f5OvDr8OB/hz18ieede1DKhy3F59azwCTK8x8HPbyn6WtPqDF4JgoBeDlNhULcpfAKW0wElx3ZHbMTpg3vgEhgNDR091jlExPk79zFoZRy6z1iJag2as84hIsolEpi7zMPgprUxviOdmPsaBLkcgpyf6yx5aiGE/NhooEa+aYIgwM5pIgwrV0JqRAjt78SxxD8fYL+iGtL3HYSBAV2Oy6v8e7exP3AWQkI3w7SjGescIkIqlaLj4DEw7W2D0VNmss4hFYgJ8MK9O7fhHBAFFTV11jlExN5z1zBh615YeYZAvzqd8OHVk6JidB83H7PMW8GmOV02TQghhK0Pnj4oKCi8eZw+fVr0eYmJiW+eV6dOnU9pfO+uz/F1vL29oaCggIiIiA/62v/7UFVVRa1atTBq1ChcvXq1wte/fPkSgYGB+Pnnn6GrqwttbW00btwYY8eORV5e3id+R98vmUyGrjZD0ap5E2wJ8qdhGsc23vsTp/QMkUHDNK7du3wGh4PmID4xmYZpHCsuKUWLfiPRd9R4GqZxLnjeJDx5ko+RfmE0TOPYtv2nMTnyEAZ4b6NhGsf+yH0IC5c5WNqnAw3TCCGEcOGTVqjFxsbCzOzdB10xMTGf8lt/k5ycnN78uqSkBOfPn0dcXBySk5ORmZmJ7t27/+M1T548Qa9evXD16lUYGRmhV69eAIA7d+5g27ZtcHFxQc2aNb/a9/CtKCsrg8WAoZjoOBKTnEeyziEVWJKVA3njZkiOjIEK7e/ErevHduPWrgik7tyDGvR3DrdycvPQ3WEapi5chg7derHOISIEQcDyicNgWK8prCfMo8txObYkPhNR5+9joG8EVDVp/1Venbl6C84LAhE6pAeaGRmwzvnhvJbL8Zqjyyx5aiGE/Ng+aqCmpqaG+vXrY/v27VizZg2Uld/+bQoLC5GZmYm2bdviwoULnyX0W/C/q9pevXqFsWPHIjo6GjNmzMCVK1fe+rwgCBg4cCCuXr2KBQsWwNvb+60/y7t370JXV/drpH9THjx6jN6DR2Oppzts+1myziEiBEGA+x/30KBbT6wMWksrCDn2W2oE8i8cRsaefahUuTLrHCLiwrUbGDx1PjxXb0LTVu1Y5xARUokEvi6D0Ly7NcyGjGGdQyowYUMiTj4RYOO9FUoqtP8qr9KPnsGCNdsQO6oPTCrR+2JCCCH8+Ogj3FGjRqGgoAD79u37x+e2b9+OV69eYfTo0Z8U961TUVGBt7c3AODq1asoLi5+6/MRERE4c+YMBg8eDD8/v38MJuvVqwdDQ7o87r9dv/UHetqORGjgUhqmcUwqCBh/8y7MRzogcO16GqZx7NjWFZD8cRbpezJpmMaxfcdOYcj0hVi2JYGGaRx7VlyEhSOt0GGwCw3TODfAbxsuSPRgNW8dDdM4tjFpD5YERyLZqT8N0wghhHDnkwZqCgoK77y0MyYmBtra2hg4cGCFv8eePXvQu3dvVKpUCerq6mjcuDE8PDz+MXj62/PnzzFv3jyYmJhAXV0dTZo0werVqyH/l2W/J06cgK2tLapWrQo1NTXUqVMHrq6uyM/Pf+/v92NVq1btza9lMtlbnwsNDQUAuLu7f/GO78HxM2cxxGUydoRvhHnHn1nnEBHPXsngciMLY+d6wH3efNY5pAKZq+eiCsqQmJoBTU1N1jlERERiBlyXrUdQbBpq1a3POoeIeJybAx+ngeg7ZSFa9KC74/JKJpOh49z1KKnRGt2m+kJRSYl1EhGxKCQW8amZ2OFkDQMtDdY5PzRBkHP3IIQQHnz0Hmq1a9dG586dkZGRgbKyMmhr/7XvRHZ2Nk6fPg1HR8cKD9D8/f3h6ekJZWVldO3aFYaGhjh58iRWrFiB1NRUHD9+/K1h1MuXL2FpaYlTp07B0NAQAwYMwLNnz+Dh4YGsrCzRr7Nu3Tq4ublBUVERpqamqFmzJq5du4b169dj165dOHnyJKpXr/6xfwz/6vz58wAAQ0PDt1abPXv2DL/99ht0dHTQoUMHnD59GhkZGSgqKoKJiQkGDhyIn36iW4H/LTFtF5YFBWN/YiRq1fxy/77Ip3n0QoIZd3Pht2oNLK36ss4hIgRBQLr3BJi3b42lKwJofyeOLV2/GYmHzmBtwi5o69DqDF7duXoRmxa6YsiCIFRv0Ix1DhFRLpHi5znrULvXCPxkNZx1DqnAuCXr8CQnFwmj+0JVmYaehBBC+PRJNyUYPXo0Tpw4gZSUFDg6OgL4z80IRo0aJfq6c+fOYeHChdDR0cHBgwdhamoK4K+hmYODA5KSkjB9+nQkJia+ec3q1atx6tQpmJqaYv/+/dDT0wMAXLhw4Z2b/QPAmTNnMHPmTJiYmCAjIwMtW7YEAMjlcvj5+cHLywuurq5ISkr6lD+GdyopKcHZs2cxbdo0AICnp+dbn79+/ToEQUCDBg3g6uqKDRs2vPX5RYsWYfbs2Vi5cqXo13j58iVevnz55p9LS0s/43fAj7Wh2xCfkoEjqXGoXEmfdQ4R8UfpMyz88wk2hkfhZ9MOrHOICJlUgh2eThg9Yhimu81inUMqMGWRPy7lPMHq2DSo0R0iuXXh+AHEr1mKUcu2oHL1WqxziIhHT0vRyWMj2ox0Q72OvVnnEBGCIMDGzRdV8Bqb7XtBUZFO+BBCCOHXJ21sNHToUKiqqiI2NvbNx2JjY2FkZISePXuKvi44OBiCIMDNze3NMA3462YHwcHB0NDQwI4dO5CXl/fmcyEhIQCAoKCgN8M0AGjbti2mTp36zq+zfPlyCIKAsLCwN8M0AFBQUMDChQvRpk0bpKSkoKCg4MO/+XdQUFB489DX14elpSWKi4sRFxeHmTNnvvXcp0+fAvhrb7UNGzZg9uzZyM7ORn5+PjZv3gwNDQ0EBARg06ZNol/P398fenp6bx61an1/b+Tn+fhj94HDOJISS8M0jp0tKILXo0LEpabTMI1j5aXFSJw9DO4z3WiYxrlBE2cju0QG/83xNEzj2JEd0UjaEADngCgapnHsZu5DmM7bgI4TfWiYxrFXr2To4jIXLXTVsaJ/ZxqmceQ1gNdyjh6s/0AIIeT/fdJArVKlSujXrx8OHTqER48e4dy5c7h16xZGjBgBpQr2pPjll18AvHsVW9WqVWFpaQlBEHDq1CkAwP3795Gbm4uaNWuiU6dO/3jNiBEj/vExQRBw6NAh6OjovHO4p6CggM6dO0MQhDeXZX4qJyenN4/hw4fDzMwMBQUFmDt3Lo4dO/bWc1+//utHgUwmw4gRIxAQEIA6derA0NAQ48aNe7MybenSpaJfb/78+SgpKXnzyM3N/SzfBy8cp8xETm4e9sRthaYm7Z3Bqz0PH2PD85dI3bMfDRo2Yp1DRBQ/ykWq52isDAjE0OH//DuT8EEQBHQeMhY6Jk3guSqkwp+lhK0dIYE4unMHXFbFQLsS3UCIV6d+v4PevlHoNWctajan/Vd5VVpWjp9HuWFo09qYZdGGtiIghBDyTfikSz6Bvy77TEtLQ0JCArKzs998rCIPHjyAgoICateu/c7P16lT583z/vt/TUxM3vn8d328sLAQZWVlAPCPu2f+r8+1Qi0iIuIfH7t48SK6du2KPn364MaNG6hbty4AQEdH581zxoz5553AXFxcMH36dPz555+4c+cOGjRo8I/nqKmpQU1N7bO080QQBPQd5oQmDethrZ8XvaniWNT9BziloYWdO3dCT1+fdQ4R8fCPazi6bj62RkShdZu2rHOIiPLyF+hg54JeQ0ZjsPNE1jmkAluXzMGTJ4/huCICynSHSG7tOHERM+OOoJ9XGHSr1GCdQ0TkPSlErwmeWNjzZ1g2fvexASGEEMKjTx6oWVtbQ19fH1FRUXjw4AGaNm2Ktm0/zwHb34OUv+/iKTZYedfH/14BpqOjAzs7uwq/jthg73No06YNJk6ciMDAQAQHB2PVqlUA/jM0FPv6mpqaqFKlCp48eYInT568c6D2PZJKpTAfMBR2/Szh4TqJdQ6pQFB2Lh7WqIW0hCRoaNAKQl5lnTuG83FrsH1HGurVpztE8upJQRE6Dx0Hhxke6NHflnUOESEIAtbMHAMVHX0M994IRcVPWuhPvqB16UcRdPh32Phsg4ZuJdY5RMTVrBwMmemHNQPN8XMtI9Y5RIQgl0OQ83NnTZ5aCCE/tk8eqKmpqWHIkCHYsmULAMDV1fVfX1OjRg1kZ2cjJycHjRs3/sfnc3JyAODN3Tdr1Kjx1sfFnv/fDA0NoaamBhUVlXeuHPua/l6VduvWrTcfMzExgYGBAQoLC1FUVPSP1wiCgOLiYgB4cwfV711xSSksBgzFnKnj4Di04iEoYWvBH/eg07ot4jdv+9cVoISdq/tTcPfQdqTv3otq1ehAhVd/ZOfA0nkGZvmtRhszc9Y5RIRMJsPScYNRu1VHdHN0pdXTHJsXsRNpNwswcMk2qKiL33GesHXk3BVM8VuP8GG90LAKDT0JIYR8ez7LqVVHR0cYGBjA0NCwwrt7/s3c/K8Dhv++mcHf8vPzsX//figqKr7ZL6127dowNjZGXl4eTp8+/Y/XJCQk/ONjysrK6NatG4qKinD8+PEP/ZY+q7t37wIAtLS03vr4gAEDAABHjhz5x2tOnToFqVQKDQ0NNGnS5MtHMpaTmwezvrYIWOxBwzSOCYKAKTey0KBvf2zaFknDNI6d2R6Cx2d2YVfmARqmcez0+cuwdHGD94YIGqZxTFJeBq+RVviphw26O82gYRrHRq2Oxd4/X6H/olAapnEsft9xzPDfgITRfWmYRggh5Jv1WQZq5ubmKCgoQH5+/ntdPjl16lQoKipi7dq1+O233958XCqVYvr06SgvL4ednR1q1qz55nMTJ/61n4y7uztKS0vffPzSpUvYsGHDO7+Op6cnFBUV4eTkhBMnTvzj8w8ePBB97edy8eJFhIWFAQD69ev31ufmzJkDJSUlBAQE4OLFi28+/uTJE8yYMQPAX/urqap+3/uzXLxyDVZDHRG9YRWseliwziEiJLLXcLmRhQETJmHJshV0QMmxwyFLoPToNlJ37YWOri7rHCIidd9hOMz1w8rwJNRv0px1DhHxNP8xFo3sBwuH6fjZmm7owStBENDLKxRZqsawdF8FJWUV1klExKqYNKzZuh07nKxRQ+/HuArjW/daLufuQQghPGCyvMTU1BS+vr5YsGABzMzM0K1bNxgaGuLkyZPIzc1Fw4YNERwc/NZr5syZg127duH06dOoX78+unfvjmfPnuHw4cMYO3YsQkJC/vF1LCwssHbtWri5ucHc3BwtW7ZEw4YNIZFIkJOTgxs3bkBbWxtTp079LN+Xs7Pzm19LpVLk5OTgzJkzEAQBAwYMgIODw1vPb9asGYKCguDq6gozMzOYmZlBW1sbJ0+exNOnT9G2bVv4+/t/ljZeZR4+htleftgZsxkN69VhnUNEFEulmPxHDuZ6+8LOfijrHFKBXf6uaF7bCGuCk+kOkRzbGJmA4MTdWBOXAX0DukMkr/KybmP1zDEYONsftX9qxzqHiJBKZegwbz0qt7eC+eAJrHNIBdxXb8Wli9eQ5NQfGiq0yp0QQsi3jdlPMk9PT7Rq1QpBQUE4d+4cXrx4ARMTE8ydOxceHh6oVOnt5d9qamo4ePAgfHx8EB8fj/T0dNSpUwd+fn5wd3d/50ANAKZNmwYzMzMEBQXh+PHjyMjIgI6ODoyNjTFp0iTY29t/tu8pMjLyza8VFRWhr68PCwsLODg4wNnZ+Z2bF0+fPh2NGzdGYGAgzp49C4lEgvr168PNzQ2zZ8+Gpub3e7lCeFwigrdE4mByNKpXq8o6h4jIfV6O2ffyELhhEyy6dWedQ0QIMhlSvcagb6/uWODlTSsIOea5Mhj7zv2ONXEZ0NSi1Rm8uv7baWzzm4fhPhtRtfaPcWOgb1FxWTk6zNuARtYuaNKDbujBs5HzA/CqqBAxI/tAWYlu6EEIIeTb98EDNfkHLLE1MjKq8Pn9+/dH//793/v309bWRkBAAAICAj6oq127doiJiXmvr+Ht7Q1vb+/3bvq3r/0+LC0tYWlp+Um/x7dmWdAGZB46giOpcdDT1WGdQ0Rce1oCv8eF2BqfiBYtW7HOISKk5c+RssAJEyeMw9gJdHdcnjm5L0ZOyUsERiZD5Tu/nP9bdmZfBlI3r4Hj8nDoVa3OOoeIyM0vgvmCUJg6e6B2O9oygleCIMBy8iI00laDt113OuHzDRIEOV4L/FxmKXDUQgj5sdFaa/LVTZvnhZzcP3EgKRrq6mqsc4iIY0/yEVYqwfb03ajz/3eqJfwpK8pH+uKx8FmyBNY2g1jnEBGCIKCvywxoGtXBko2B71yxTPiwNzoMJ/amwTkwBpq6+qxziIjLd3NhvTwW3aYvh1GjlqxziAiJRArzMXMxoFEtTO7UgnUOIYQQ8lnRQI18VUNcJkNHWxPpUaG0vxPHUvIeYrdcGen7DqBKFbocl1cFuVnYv8IN6zeGoFPnLqxziAipVIpO9uPQppsVnKbPYZ1DKhAf5Is716/COTAaquoarHOIiIMXb8Bl0y5Yzd8A/Rp1WOcQEYUlpeg6Zh6mmbXA4JZ02TQhhJDvDw3UyFchk8nQy24UOpm2xTLP2bTcn2Oh9/7E7/oGyNiRBm0duhyXV/d/P49ToT6Ijk9A02Z0h0helT4rQwc7Fwx0ngzrYQ7//gLCzEbPaSh/8RKjlm2BkhK9PeJV9KFfsTDlNAZ4b4FWZTrhw6u7eY/Qb4oXlvbtCIv/Y++uw6Qq2ziOf09Mb7N0d4kNqEjZikiYiITdikGInSgoooKBoiigIkiJoigGKmCBolLSS8PC5uzEOc95/zizs7vArIWeeeX5XNe4wcS9O8jM+Z37ee5GdZwuR/qbzCRb8plMtUiSdHiTa06kf1wwGKTdGT24oPvZjLh7sAzTkthj6zazpUFj3p07T4ZpSWz1Vx/x3auPMWPO+zJMS2Jbtu/kmO79GHjnfTJMS2JCCB6/7mJUfxoX3DNGhmlJbMS0j3ng/WX0ePg1GaYlse9WrOWc6+9lXK8uMkyT/q8VFhYyZMgQzjjjDKpWrYqiKH96r21Jkv7bZKAm/aN27dlDuzN6cPegG7j5qv5OlyMlIITgzjUbCJzQgTfenoZbbpaetJbOmcTaeW8wZ95H1Klb1+lypASWr1hDx0uuZfDIsXQ49Syny5ESiIRC3N+vG3WPPJGzb7hHnvBJYje9+C5vLN/FeQ++iieQ5nQ5UgLvf/U9A4ePZNKlZ3JkrWyny5GkvyU3N5fx48cTDofp2bOn0+VIkpSE5GlY6R+zeu16eva7mhdHPUyXDic4XY6UgCEE169az+l9+jJkuDygTGZfvj4aa/sa5sybTyAQcLocKYFPvlrCNfeO5JHxb1K/cVOny5ESKMrP45ErenHSRVdz1Ok9nS5HqkSvERPZpFXlnLueQpX7ryatV2bN5/lJM5jW/xyqpvidLkc6hA7XJZ/169dn3759KIrCnj17eOWVV/6Vx5Uk6f+HDNSkf8Sib3/gqkFDmTZhLG1aNne6HCmBIsPgutUbuPr2Oxl45dVOlyNVYv7Td1HDDy/Peg+Xy+V0OVICk2e+z0MvTOLJN2ZSrWYtp8uREti9PYcnrr+Us6+/myZtOzldjpSAEIJOw1+Axm05pd/t8oRPEnto/Nt89OnXTB9wLqle2eUu/TfIf3MkSfo9MlCTDrkZ73/Eg088zby3X6VBXbl3RrLaHQpx89oc7h85inPOPc/pcqQEhBC89/B1tD+yFU889bR8c5fEnnhhIpPnfcEzb71HanqG0+VICWxc9TNjh95A77ueonazI5wuR0ogGIrQbshz1O5yAUd2u8zpcqRKXPfoODavXc/U/ufg0WUHofTvKSgoqPC1x+PB4/E4VI0kSYcjGahJh9S4V9/g9bem8+mMyWRXyXK6HCmBdYVFDM/ZwXMTXqP9iSc5XY6UgBGJMOOeAVzcuxe3DR7idDlSJW55YCTfrtnCmDdn4/H6nC5HSuCnrz9j8pMPcOkjL1GldgOny5ES2JNfxAlDx9Hmopto0kHuQZishBD0vuMxUqMhXrv4DFRVnvD5rzJFck3WNIX9se5+e8nef//9cmiAJEn/KhmoSYfMPY89yeLvfuCzmVNIkfs7Ja0fcvfx5J58Jk2fSfMWLZ0uR0ogVFTAjLsHcPttt3FpPznQI5ldeONQCvDx+KtT0XX5spqsvpj1FvPefJUBo94gNauq0+VICazdupNTH3yNk665jzpt2jtdjpSAYRh0vXo47WtkMuSsjrJ7WnJETk4OaWllQ0oq6077/PPP6dq16x+632XLlnH00Uf/3fIkSToMyHf+0iFxxS2DKSgs4qOpE+X+Tkls/vadTA6ZvPv+h9SuI5fjJqv83duZ++A1PDFyFKedKbszkpUQgi59rqN2y2O45+6H5QFlEpv9yjP8sHABlz81GW8g1elypAS+WbmeC55+h9PuGE12A7n/arIqCpbQ8fIhXHpkEwa2lSfmJOekpaVVCNQq07x5c15++eU/dN169er9nbIkSTqMyEBN+luEEHTveyUN6tZmwtMjUFXV6ZKkBN7csp3PdS9zPnqPzCy5HDdZ7Vy/igVPD2b8hFc57vi2TpcjJRAKhWjf+wo6db+Qi6++yelypEpMfOwutuXk0H/k67jccm+dZDVnyXJuen0+59zzEmnV5QmfZLV9z15OvXo4Q7scyzktGzhdjvQv+S9M+axZsyZXXXXVP1CNJEmHMxmoSX9ZJBKhS49LOOe0Ltx7uzygTGbPbMhhc7WazJ42A79fjrJPVuuXfs13r49i6vQZNG7S1OlypAT27N3HSRdeRZ8b7uD0Hhc6XY5UiTG3XwluP30eegFVk5ulJ6sX3/+SJz76ke4Pvoo/vYrT5UgJrNyQQ69BDzHq3JM5oX5Np8uRJEmSJMfJQE36SwoKCul03kXcctUArux7kdPlSJW477eNeNoczTuvTpT7OyWxXz6Zydr5bzH7/XnUqCkPVJLVuk05nN7/Zm55cBTHn9zF6XKkBAzD4PFrLqRmy2M59Yrb5XLcJHbv5A+Yunw75z30Gm6f3H81WX259BeufvAZXrnwVJpXk13u0uFj3rx5FBcXU1hYCMCKFSuYPn06AOecc448US1Jhzl5dC39aVu2beOMC/vz5P130e30P7a5p/TvE0Jw65oNtDnzbB4b+ZQ8oExi304bT96vi3hv3nzSMzKcLkdK4LuffuGiW+/lvmcn0LT1kU6XIyUQCgV5eEBPjj7rfNr16Od0OVIlrnjmLb7Ld3HufS+jyf1Xk9b0T77mwXFv8Hbfs6mdkeJ0OZID/gtLPv+q66+/nk2bNsW/njZtGtOmTQNgw4YNNGjQ4F+rRZKk5CMDNelPWb5iFRdfeSMTnx3JCccf43Q5UgIhw+T61evpfdU13HLbHU6XI1Xis/GP4ivaxaz3P8Tr9TpdjpTA3AULufXRZ3l8wlRq12/odDlSAnl7dvHYNRfStf+ttOokB3okKyEE5zw0gb0ZjThz8H0ocv/VpPXsW+8xacY83h3QjUy/fI2SDj8bN250ugRJkpKYDNSkP2zBl19z67AHmP3GSzRv0sjpcqQE8iMRrl+zkUH33s/Fffo6XY5UiQ+euI2mNTIZO3EGmtzfKWm9NOVdxkyewdNvziYru5rT5UgJbN+0jidvGUD3QY/Q8Kh2TpcjJWAYBicMHUfq0afQ6cLrZfd0Ehv6zES+/W4Z0wd0w++WHYSSJEmStD8ZqEl/yJTps3hy3HjmT3ud2jVrOF2OlMC2YAm3bdjC48+O45TTTne6HCkBIQSz7r2C0zqfxP0PPSoPKJPYfaNf4L2vl/HMW3Pxp8ilTslqzU/fMf7+O7j4vueo3rC50+VICRQES2g3eCyNz+5Pq9MvcLocqRL97x1N4Y6dTOl7Ni5NdhAe7kSSLfkUSVSLJEmHNxmoSb9r1NiXmPXBR3w+800y0tOcLkdKYGV+AQ9s281Lk97kmGOPc7ocKYFIKMiMuwdw5YD+XHvjzU6XI1XiyqEPs2ZXAU+9MQO3x+N0OVIC3306j2njRtFvxAQyqtd2uhwpga179nHy3S9xXL87adhW7r+arIQQnHPzA9Rzqzx5wSnyhI8kSZIkVUIGalKlbrv7IVatXceC6ZPx+eTeGcnq6117GJdfzNuz3qNR4yZOlyMlUJyXy+x7r+Due+6m1wVyOm6yEkLQ/arbUTNr8uhLk1Hl/k5Ja/5br/L5nGkMfHISgfRMp8uREvhl41bOeWwSnW58hJot5P6rySoSidLxiiGc2agWN598lNPlSJIkSVLSk4GalFCfq29Gd2nMnfwyui7/qiSrOVt3MFOozPrwY6pXl8txk9XerZuYN+Imxjw3lo6dOjtdjpSAYRh0uPAqWp3YlStuu0t2ZySxd54bwcpl33P5k5Nw+/xOlyMl8PlPq+n3/GzOGPocWXXk/qvJKq+wiI6XD+Hadq24+OhmTpcjJRnTSq4ln6aVPLVIknR4kymJdAAhBKdfcBnHtmnNqPuHyQPKJDZh0xaWpmTw3sw5pKbJ5bjJasvKH/ny+Xt5ffIUjmhzpNPlSAkUFQVp13sg3fpeRY++lztdjlSJl+4dRH5BAf0efxVNl5ulJ6upX3zP4KkLOff+V0ipUt3pcqQENm3fxVnX38MDZ7TnlCZ1nS5HkiRJkv5vyEBNqiAUCtGx+0X07X0et113hdPlSJV4Yt0m8uo3YuZb7+CR+zslrTWLPuGn6eOYPus96jdo4HQ5UgLbdu6i0yXXcsUd99HprHOdLkdKQAjBUzf3w1+1Nhfe+6xcjpvEnpqxgOe/XEOPhybiTU13uhwpgWWr1nPJkBE817Mzx9SRU4wlSZIk6c+QgZoUtyd3L117XsLdg26kT+/uTpcjVWLImvVUb38SU55/CU3TnC5HSmDZ+2+y5au5vDdvPtnZVZ0uR0pgxZp1dLv6DgY//ixHtj3R6XKkBIxIhIev7E2Tdl3o1PcGp8uRKnHbyzOZt7GQ7g9OwOXxOV2OlMD8JcsYNOJ53rjkdBplZzhdjpTEzCSb8plMtUiSdHiTgZoEwLoNm+je9wrGjniQUzud5HQ5UgKGENy4ej2del/I3Q88JJfjJrGvJz9LZNPPvPfhfFJSUpwuR0rgi29+YODQR3nohUk0bNbC6XKkBIoL8nn4it6c0HsAx5x1gdPlSJW4aOQbrBEZdLv7BVRNvs1MVq/PXcDTr07jnf7dqJ4q9yCUJEmSpL9CvtOR+OaHZVx+8528+eIzHNOmldPlSAmUGAbXrt7AgJsHcdV11ztdjlSJT567lypqmLfmvI/b7Xa6HCmBt+d8yL3PvcaTb7xL9Vp1nC5HSiB3x1Yev64PZ1w7jGbtuzhdjpSAEIKu97xEtO5RnHb5EHnCJ4k99to03vvwC94deC5pXvkaJUmSJEl/lQzUDnPvffQJdz86ivffnECj+vWcLkdKIDcU5sZ1m7j7kcc5r1dvp8uREhBCMPexmzimWQOeeuZ1ub9TEnv6lclMmP0xY96cQ3pmltPlSAlsWv0rzw25jp5DHqduy2OcLkdKIBSJ0G7wWKp3OI92PeRAj2R28xMvsmbFGt7pfw5elzwMkP4YueRTkiTp4OQr6WHs5TfeYvykt1jw7mSqZVdxuhwpgY1FxQzdtJ2nX5pAh5M7Ol2OlIAwDN69ewC9u5/DncOGy+6MJHbno0+z8Od1jHlzDl6fXOqUrH5e8iVvPHEPfR56gey6jZwuR0pgb0ER7YeOo3Xv62jaSQ70SGYXDh6BXlTI633OQJMnfCRJkiTpb5OB2mHqgZFP8/lXS/hsxmRS5f5OSevHfft4fOc+Jr4znVatj3C6HCmBULCImcP7c/NNN9L/8iudLkeqRJ9b72Z3WGXka++gu1xOlyMl8NXc6cx9/UX6j5xIWpXqTpcjJbBhxx663PcyJ151D3WPkvuvJivTNDn12rs5OiuV4T07yxM+kiRJknSIyEDtMHTN7Xexe88ePp72utzfKYkt2LmbicVhps/9kLr15HLcZFWwZwfvPXA1jzz6GGefK6fjJishBKdddgPZjVpz/5Mj5AFlEpv72li+WfARA5+ajC8lzelypAS+/20TvUa9xSmDnqRaY7n/arIKhkKcPHAIF7ZuyFXtWztdjvR/yhAWWhItszSSqBZJkg5vMlA7jAgh6NX/GqpXzWbGay/I/Z2S2NtbtrFA8zL7o0+oUiXb6XKkBHZvXMP8J2/nxfGv0Lb9CU6XIyUQiURo3/tyTjizJ32vH+R0OVIlJo28j03r1jBg1Ou4PF6ny5ES+ODbX7j21XmcffeLpNeo63Q5UgK79ubR9aph3NHpaLq3ksumJUmSJOlQk4HaYcIwDLr2vIRTO57EA4Nvld0ZSWzsxhzWVanO7OkzCQQCTpcjJbDxpyV88+oI3npnOs2at3C6HCmBvPwC2p9/BRddcytn9r7E6XKkSowdch0RS6HvIy+japrT5UgJvPLh1zzy/vec9+Br+DPk/qvJas3mbZx38/080a0DJzWo5XQ5kiRJkvSfJAO1w0BRUREdu1/E9QMu5doBlzpdjlSJB9duhBatmfb6ZFxyf6ek9etn77H6/deZ+d4H1Kpd2+lypAQ25Wyla78bufGeEbTvcprT5UgJCCF4/NqLqNq4NedePUSe8EliD775IZOX5dDjoYm4/XL/1WS1ePkqLr/3KV46/xRa1ZChp/T3ySmfkiRJBycDtf+4bTt2cvr5lzHinjvpcdbpTpcjJSCE4PbfNtKs62k8MXqMXI6bxL6b8Sq5yz5n7ocfk5GZ6XQ5UgJLf1nJ+Tfexd1Pv0SLI491uhwpgUgoxEOX96TNqedxQu+BTpcjVeKace/w9S5B9/tfQXPJ/VeT1ezPl3D3mFd589KzqJuZ6nQ5kiRJkvSfJgO1/7AVq3/j/IHX8srTj3Ny++OdLkdKICIE169aR7d+A7lj6F1OlyNV4osJT6DvzWH2vI/w+XxOlyMlMO/zr7npodGMmPA2dRo0drocKYHCvL08fEVvOvW9gTZdz3W6HCkBIQTnPfoaOwJ1OXvYgyjyhE/Sev6dD5jwzntMH9CNKgH5GiVJkiRJ/zQZqP1HfbFoCTcMvocZE1+kVbMmTpcjJVAQiXL9bxu5cehw+vYf4HQ5UiXmPXkn9TP9vDhjFrou/+lMVq9Onc3I16YyevIsqlSr4XQ5UgI7Nm3gyVv60+2W+2l0zElOlyMlYBgGHe56Hm+rjnTpc7NcjpvE7n1+Ml989S3vDjiXgEduGSEdWiLJlnyKJKpFkqTDmzwq/A+aMfcjxrw0gY+mvk7d2jWdLkdKYEdJiFvX5/DIU2M446yznS5HSkAIi5n3XUmndsfyyOMj5QFlEnv42Zd597NvGPPWe6SkpjldjpTA2p+X8eI9t3DBPWOo2bil0+VICRQFQ7Qd8hwNTr+UI86SAz2S2ZUPPMvunC28ddnZuHU50EOSJEmS/i0yUPsPev7V1/lsxhSyMjOcLkVKIGIY3LphC89PnMTxbds5XY6UgBCCRa+O4IrLL+fmQbc7XY5UiR9+WUVOYYSnJs/E4/E6XY6UQElxMS/dfxt9H3uFrJp1nS5HSkAIkyNvH8MxfW6j0QlyoEeysiyLyXMXcGKdarx84amoqjzhI0mSJEn/JsWyLNkz+x9RUFBAeno6zRo3wuuRGwYnq5JQiK178qiWlUlKSsDpcqQETFOwbccO3LpG9erVnS5HqkR+Xh55+YVkVa8p93dKYuFQCcVFBXh9Kbg8HqfLkRIwjCjBPTtRUzLwpshN7ZOVJSyiudswolHqVslwuhypEoWhCDm5+8jPzyct7f+re7r02OLqN75Oqsm+kWARL/fv8H/5O5Uk6b9Fdqj9B30zbTxpqTKoSUavTZ/L7c++heZL5bdFHzldjpTAhk05nHrJVWRXr8nzd17OqSce53RJ0kEYhsEpVw6mSsMG/LrDwGh7ldMlSQmUrJhDtn8bOoKld5zvdDlSAkvWbWHQ1AV4Ah7GL1zmdDlSAsVFBQzu2ZmsND/tL72K628b6nRJUgLDrh/I3i3ryMnd53Qpf4tpWZhJ1IORTLVIknR4k6fyJelf8tC4idz54kxaXjMG3SWXpCWr75Yt55RLrmLo0y/TuOURTpcjJRAMhmh7yY1ccOHFDL59kNPlSJUI/TiZ+qlB7n5pqtyDMInNWbaGwe9+xpyh/aieITvTktXuHdu4/ZwTGXZcU85qKpdNJyshBFdfeA7103TGPTDY6XIkSZKkf4gM1CTpX3DVvU8ydt4PtLxqNLoveVrmpYrmfvwpl9w8jIdefpsmrdo4XY6UwK7cfRx/yY0MGzqUG667xulypASEEIS+eYE2TWtw+9MT0F1yK4Jk9eIXS3nu86XMu2sgdaukO12OlMD6VSu4+/xTGNXlGE5tWMvpcqQEQqEQfc/qyLknHMmz990uTyRIkiT9h8kln5L0Dzv72rtYvk+n+YARKKqcvpWsxk+ayujX3uaJN2aSmV3N6XKkBFZvyOG8m+/j+eeeoXPHk50uR0pACIPwojF0PvNMzr/uDnlAmcQenPMlP23bzbzhAwjI/VeT1rJFX/DS4Gt5pdtJNM6Se0Ylq7x9e7m8x6kMv6Yv/Xud43Q5h4wpLEyRPMssk6kWSZIObzJQk6R/iBCC4y+6gb0ZzWl00bXygDKJ3f/ks8z+/BtGTp5NQG7CnbQWLfuZK+57mqlTJnFE61ZOlyMlICJBwoufpseAqzntgn5OlyNV4rrJHxKxYNadl+HS5QmfZPXp7HeY9eT9TOnViRopfqfLkRLYmrOJ6y8+l+fuvY2zOp3gdDmSJP0f+7NzI+VxpnNkoCZJ/4BgMMQRva5Gb30G9Tpc4HQ5UiWuHnwvK7fkMmLiu7jdcvJgsprx8ULue2EKc2fNoEH9ek6XIyVgBPMwvnmWAXfey/Fdz3K6HCkBIQQXjZ9Nk1rZjO53tnwjnsSmvTiG76a+wtvndyXdKzsIk9UvPy1j+HX9ePPpB2nbpqXT5UiS9H+ifHD2Rz4vr/xrd+nn+7+ey9f3f54M1CTpENu1Zy9Hnn89VTr3p8pRpzhdjpSAEILzBt6AlVaN+1+YhKrKLSWT1bgpM5n4/ud8Mm8u2VWqOF2OlEAkbyvWjxO46dExNDu6rdPlSAlEDINznp3GeW1bMbi7XDadzF56cAg7F3/MW+d3wavLt+zJauEnHzHmgcF88MpTNG3w3xwUIZd8StKhsX9QVv4ihEAIgWmaCFMgLAHYoZiqKCiqiqqqKIpSFqARC8wU4t+v8Of7fS4devLVWZIOod825HBCv9uoc97tpDc+xulypAQMw+Dknn1p3q4z/W4dJl9gktjwMRNYvGI9n8ybSyAQcLocKYHQztVoq6dx57OvUadRM6fLkRLIC4bo9uw73NqtA/06Hu10OVIlHr+xHylbf+O18zqiyxM+SWvGlNeZNuE5Pps8jhpV5QkfSZIOVBqilQZnhmFQVFTErp072bx5M5s3bWLLli3s3rWbffv2EQwGCYfDCNMEQNN1PB4PgUCAjMwMqlWrRu3atalbrx61a9emSnY2fr8ftVzgpqoqCgqKqpR9LcO1f4QM1CTpEFm87GfOvukhGl/yAIFajZ0uR0qgqKiY9t0v4fSLB9L90iucLkeqxMC7R1IQVfhg9gxcLpfT5UgJBDd+g2/75wwbP5UqNeTkwWSVk5vPBS/NZGTfszjjqKZOlyMlIIRgeJ+zOdYVYfiZ7eVBTxJ7cfTjfL9gLgvffIG0VHnCR5Kkisp3nhUUFLBm9Rp++P47li1dxrq1a8nNzSUUClUI3P6I0tcFVVXx+nxUrZpN48ZNOOqYoznqqKOoX78BgZQAmqqhqAqaplUM2sp9LH9/0l8jAzVJOgRmzv+Cyx96nmYDn8CbVdPpcqQEtu/cRcfe/el/2910OONcp8uREhBC0O2Gu6nfpDnjRz8pl+MmseDqj8kIrmTYq++Skp7pdDlSAstzdnLVG/OYcF0vjm9cx+lypAQioRB39uxC7/pZXH3MUU6XI1Xi4SG3kJ/zG59OGovnMJiOK5d8StIfUxqKCSHIz89n6Q8/8Mn8j/n222/Zvn07RjR6QHCmaRper5dAIEBKagqBQAperxeXSwcUDMMgFAoRLC6mqKiIouIiQiUhDMOguKiI4qIiNm7YyKcLFuDxeKhduzbHt2tLp86dad6iBakpKWi6jqaqqJqGpmnxUK1855oM1v4aGahJ0t/03KTp3DdhDi2vHoMrJcPpcqQEVv22jrP7X8+tj46hzfEnOl2OlEAkEqHTwDs4u9u53D10sNPlSJUo+Xk6tbyF3PHKdDw+n9PlSAl8tnIjd89ayPQ7LqVpDbkkLVnl781l6PldueXoxvRoXt/pcqRKDBp4MTUDCnNeGommyem4kiSVBWnRaJR1a9fy3uw5fPzxfHI252CaZvzPVVUlMyuThg0b0bJVK1q0aEG9+vXJrppNIBDA5XLbJ5KVsv3RLPsBsCyLaDRKMFjM3txccnJyWLN6NSt+XcG6devYG+t4W7duHevXr2fGuzNo0KABp5x6Kl1P6Uqt2rVxu1zoLhdaLFjTVA1Vk8Ha3yEDNUn6G4aMepFXP15Kq2vGoHnkAWWy+mLJtwy8/V7uGfs6DZq2cLocKYGCoiI69LuNm2+6iSsG9HO6HKkSoe9fpWm9TG58bDKa3Cw9ab31za+8uPBH5g7rT83MVKfLkRLYtmkDD1zWjYc7HsnJ9Wo4XY6UgGEYXNnrDE49vjWP3XGdPOiUJAmww7RIJMKypcuYMnkSXy38kqKioniI5na7adCwISd16MBJHU6icZMmBAIpCCGIRqMYscAtEjWIRI2DPEL5aZ6gu9zUqFWb2nXr0rFTZzRNIxQqYePGjXy7ZAlfffkVa1avpqSkhN/WrGHtb7/xztSpnHzyyXTvcR5NmjbF4/Gg6zou3YXu0mWw9jfId8GS9Bf1ueMRPlu3jxZXPomqy/2dktU7cz7g7idf4LFXp1OtllzqlKxytu/k9GvuYuSIRznnrDOdLkdKQAhBZMlzHNf+OPoPeUi+2Upio+d/w8erNvHh8IFkBLxOlyMlsOqnHxh9Q1/GntmO1tWynC5HSiAYDDLg3C5ce2E3bu5/odPl/OtMS2AK4XQZcaaVPLVIh6/SAQM/L1/OK+Nf5suFC+N7oimKQu3atTnt9NM546wzadioEaAQjkQwDJPCoqJy91RxIuf+gZYdzFlYFgdMBI1Gjfj16jdoSNNmzeg3cCDbtmzl0wUL+GjePNavX8++vXuZ+957fPbZZ5zc8WQuuPBCGjRsiMfjwe1y4XK50XQNXdPjwVrplivyvV7lZKAmSX+SEIKuA+9gvahC074Pocj9nZLW0+Mn8sr09xk5aRZpmfJAJVktX7WOC+98hAnjX+SEdm2dLkdKQBgRwotGc0avC+l++Q1OlyNVYsj0T9mcV8QHwwbgdcu3esnqm88+4vV7b+X1806mXnqK0+VICezZtZOrep/BI4Ou5sKzT3G6HEmSHFYaauXk5PDyiy/x3nvvESwuxrIsdF2n9RGtueiSS+hwckfcHg+hUIjiYIjY4k1AQVU1u0PMZe9tpqgKxAIzEbv/8hRFQS0XtFlYCFNgmAaGYcYDtlAoDEBWdjZ9+/fn0sv6suyHpUx9+22++WYJRYWFfDTvQ5YsWszZ55zDeT17ULVqVTxeL263u6xjLbYkVIZqv0++y5KkP8EwDI7sfS3hOm1pcEp/+Y9LEhv80BN8tmwVIyfNwuvzO12OlMCCxT9w04gXmDntbZo1lZMHk5UIFRFeMoaLrruVjude4HQ5UiUGvvY+Ab+H6bf3QZMnfJLWvLcnMv+FkbzVqzPZAbllRLLasPY3bu3Xm5cfHUqX9sc6XY4kSQ6zLItQKMTsWbN4Yew4tm3bhmVZaJrGkUcdyeVXXMnx7dphmCbhcISoEQRiSzV1Fx6PG03TEMLubguHw5imiHehHejAY01FsQMuTbNDOZ/Pi6qqWMIiEo1iGEa8ToCjjj2Wtu3bs2rlSl5/7TW+XLiQvLw8pr79NosXL6LvZf1of8IJ+AN+vF4vbtONy+XC5XKhqqoM1n6HDNQk6Q8qKCyidc9rCBzfi9rt5ITIZHbpjXeyvdjk0VemorvkctxkNXnOfEZNmsWHc2dRu1Ytp8uREjCKdmN89wJX3/0oR57UxelypASEEPQY9y7tmtXj4YtOlW96k9ik0Y+y6oOpvH1+V1Lc8jUqWS39djEPDrqGGeMepU3zJk6X4yiRZFM+RRLVIh0+LMti08aNjHpiJJ8uWEA0GgWgYcOGXHPddXTu2pWoYVIcLAHs4Mue4OlB0zSMqEEoFMYwTEqXcAJEDUEwbFJYYlAUEhSWRDCiUYQwsbvZVHSXi1SfmxSfSprPhd+r4bIsTNMkHLa70jRNw+N24w0EELF93QzDiF8aNWnMiFEj+WnZj7wwbiw/LvuRTRs38eTIkXQ5pSuX9OlD9Ro18Pl8eD1ehBC4XK54YChDtYOTgZok/QFbtu/i2ItupPoZ15LZqoPT5UgJCCE4/ZIryajfnLsfe0z+g5/ERk54m5kLv2fBvPfJyEh3uhwpgUjuRvhlEoNGPU+jVkc5XY6UQDASoduz0+jb6RhuPKO90+VIlXh22E2U/LyEST0749HlhMhkNX/ubF4eeT8fTxxD/do1nS5HkiQHWbHg6vPPPuOxRx4lZ/NmLMvC7/dz0SWXMGDgQFweN8GSsiDN5XLh83oRsU6xaNSgNEQrLgmxbU8RG3aEWLctn5ztO8kvKMbQUomEigjnb8UMF6L7MlBUjWhxLponFU96LVy+VFxmEekpfurUqk7j2uk0ruGndtUUUvxeSkIhSkIhdF3H43Hj83qJRCNEIlFM06SkJETrI45g3Isv8t7sOYx/6SX27N7N/A8/Yu2a37j8qis54ogjiPr9eA0fXq833q1WugzU/hnlMVYpGahJ0u/4Zc06Ol4+lAbnDyW1/hFOlyMlEIlEOKF7H44/7VwuvnaQ0+VIlRj0+FhWbsll/tzZ+HxyqVOyKtm6HPeGuQweN4ka9Ro6XY6UwO7CIOeNnc7w3l24oH1rp8uREhBC8PBVF1KrYAfPnNsBVR6MJK3Jr7zAR1Mn8vmU58nOynC6HEmSHGRZFuFwmNcmvMqLL7wQ3yutRcsWDBl2F61at6Y4GIzvXeZ2ufD6fBiGQWFRMULYyzkLi4OsXJvDD6t3sXZ7Cbl79xEq3IWIFOFKq4HuTSOc9yMiEoyHVYpKbG81AzO0j2BoH6rLhzu9DgX5eaxb9QNfugJ4UrKpkpVFk5p+jmtelSOa1iUtNYBpmqiqisftJiUlQDQSJRK1u9ZQoGfvXpxw0ok8OXIkXy38kvXr1zPq8Se49LK+dOnalVTDwDRNvB4vlrBwuSt2q8lQzSYDNUmqxKeLvqf34Cdoctkj+KvVd7ocKYG8/AJO6N6HnlfcxOm9L3G6HKkSF9/xMHpKJrOnT0XX5UtQsipZ/yWBPd8w7JVpZGRXc7ocKYF1u/fRZ/xsnrv8XDq1kqFnsjIMg2EXnEanDJ3bTz1eHoQksTGP3Mua77/iizefJ+CXJ3xKmcJCTaJllsm0/FT677Isi4KCAkY9/gTvTp9ONBpF13XO69GDmwfdiu5yURy090jTNQ2/348pBEWFRZixIQE523by2aKf+GHNHvIiHqLBfRjFu7GsKKrmwle1CcIIEdqzFkWxUDXNfo1QFFRNR1E1NE0HRcHtduN2u/G5S9AyqxHNropVvAOXy4DoPn7ZUMwPv+3Fry7luKZVOOWko2hQpwaWFSYcieD1ekgJBOxlp6ZBKBwmOzubUU8+xVtvTmH8S+MpKCjg1VcmsGvnLs7r2YOMjEzMgIGwBMISuN3u+O9Hhmo2eTQjSQlMmfMRNz45keZXPoUnvarT5UgJbMrZyikXX8HVdz1K286nOl2OlIAQglOvHMyxbU/g8Ucfki/ASaxk5ftkGZsY+uoM/CmpTpcjJfDdhm3c9NbHTL75QtrUq+F0OVICweIiBvfszIBmtejbprHT5UiVuPvmq6BgF/MnPoPLJQ+RJOlwZlkWuXv2cN899/LJxx8jhCAlJYVbBt3Keb16UVISIhKJoigKXq8HXdcJFgcxTBNTCNZu2MKc+V/x45pdCF8NzFAR0aJ1KFgoqoqqu/FXb45Rsg+jOBdVU1FQQFHRdI1ASiqKJwXTgjRfDdxuN7Xr1qNKdlXy9u7F5XZTt0ETUtPSUEoHEkQirP71Z3744Sc+WZ7PsvULaVzNzdldjqVF43qEQmEiagSfz4fbchEK2QMRhLC4rH9/mrdowUMPPMD2bduZPWsWeXl5XNznEqpUqYJpCkzTjE84Le1Uk0tAZaAmSQf1xPgpPDH1E1pd/Qy6Xx5QJqtlP/9K76tvY/BTL9C8jZy+laxCoQgd+t9Knz6XMujmG50uR6pEyY9vUjfT4ran3sHl9jhdjpTA+z/9xmMfLmHW4L40qJrpdDlSArk7dzD8wtMY2r4lZzau7XQ5UgJCCG7o05PWdbIYO/ax+MbbkiT9uyzrr3UeHuowx7Isdu/ezbDBQ/hy4UIsy6Jq1arc/9CDHN+2HcFgSTxQCvj9RKJRCguLsCyLrbsKeO/L1Sxaupqo5cJV7ShE0Q50jx+XvwlabHWGFshE1VwoKrhTskBVSPN7qVMzG03XKTEgKlQ0BTQMdJcbr8/Drrxi1ufsAVRWbtxNmzatadW0Ppaq4lW9tD76WEzVw7Klv5AvqvLthr0sXb+Q9kc1pWeXVtSvmU4wGMSlu/D7fYTDEUzTJBQKc1zbtjz/4ovcNXQoq1au4vPPPiMcDnNp376YVQWmMKFcoFaqNFT7o7/bvyKZAzsZqEnSfm58cAzTvvmNllc/jeaSB5TJ6qPPvuT6ex7jgZfepE5DedY/We3Zl0fngXcwfNhQLr7wAqfLkSoR+vYlWreoxzX3P4X6J94cSf+uCV/+yJvfreTDuwaQnRZwuhwpgU1rV/HogJ6MOuU4jq8tu9yTVSQS4fIep9Gra3vuvfFyp8tJWoYAJYmWWRrC6Qqkv6p8oJLo8z8rUdhS/vt/NJCxLIu9ubkVwrTaderw+MgnaNykaXzwgMfjxuvxUlRcHN/s/4MFi5kx70uKTR++qk2I5m0ivG8dqemZ1KnfiHqNmpKVXZXCoiLyCosp3LMVf936uNxuiouLCQeLcLk0mjVvSklJiF9WrSMjuwpGsABLRNDQyM5MoUXzznz1xRfs3L6VpV/vwEsHmjRviQA0NBo2qMf6lcvZtX0piuZBSa/LZ18uYcPG9fQ4rT0nHdUQBQUhTDweL6qmEY1GiUSi1Kpdh2fHjeOuIUP54fvvWfT11wBccmkfhDCxhN3Rtv/zlShU2/96hzpQ2//7TgRvMlCTpHLOu/Eevt9p0nzgEyjygDJpvfbWdB4fP4nHX59JlWrVnS5HSmDd5q10u+Eennn6KU7t2sXpcqQEhDAIL3qWk0/tykU3DU3qs4CHu0ffX8Q3m7bz4fCBpHjdv38DyRHLv/2a52+7gpfOOYlmVeQU42RVkJ/H5eedyh2XX8SVF3Z3uhxJ+k8rv1Rw/4t9hb9x5/u9bVEUpWxj/9jn5b8u/3H/GgsLCrnv3nvjYVrdevUY9dRT1KlXj3AkEl/iqWkahUVFCCHYsGkb49+YwfJV63GlVMeTUZPg9p9IS0ulYatjaNSiNXXqN0RVFbAsqlXNQtdVNK0NqhqrTYVoJEKwuBif30cgxU/XGtUQQrB7xw42b9rML8uXk101m0aNGtKmTSuMcJA9u3cjhEBVFYQQbNm0nq8++5j8vbmkpqaQkprOjm2rcaXUYfvuQr5bm8++6E5OaJZOnapphEIhXG43Ho+bSCSKYZqkpWfw5NOjGTZ4MN8s+YbFixbh9rjpff758SELB1O+u9eyrPh1D8lzvN/zvP/zW9lz/U+TgZokYbf7t+9zM7v9DWl8yQ3ygDKJPTrmBd6Zv5BRk+cQSE1zuhwpgW9+WkH/u5/kzTcmctSRbZwuR0pAREOEF43mvMsu5/SLBzpdjlSJW978mPxolPeG9MOtyxM+yerz92cy/fHhTOrZiVqpsoMwWW3fmsN1F3bj6eE3061rB6fLkaT/LMuyePPDRfyytTAWrFhYVlmnkhVLWf5M45KigEtVCOgWmQEPVVM9VM9KoXpWOpnpqfi8XpTYhvmlG+eXXvb/uvSYr3Sa55OjRvHxR/OxLIuatWrx+KhR1KlXj2jU3i/N5/NiWRZFRcWYpuCLr7/n5cmz2JdfjDerPro/HS20jdZHHUv9Js2p26AJbo8LS5gIAW6PKzYlU7HDtNKPioLu9+EP+FBQiEajbNyynRUr1hApyiMSiZCemYmiaezevZstW7awLy8fRdX46cdl1KlfH5fLzeIvP6egIB9UFVMIUtLSUbdvIbJvHa60uvy6Yg1pmdnMXpxD1+Ma0KJ2AGLDFtxuN9FoFCEEfn+Ax0eN4o5bB7F06VIWfv4FKSkpnHnWWQd9Ttas28SUr9dip15W/Pm0LOsvPccAmqrg1yxSvRrZKR6qZwSoXiWN7Mx0UgJ+dF2v+Lxi/z73f57tvzP/3LG9DNSkw14oFOKIXteiNOtCvU5yQmQyu+GuB1m2fhuPvz4Dt8frdDlSAu99+jXDnnud92ZOp1HDBk6XIyVglOQTXfIM/W4bTrvTujldjpSAEIJLX5lDnaqZvHldL/sMt5SUZrz6PIsmjWPq+V3I8MotI5LVql+WM+TqS5n05H2ccPQRTpfzf0FO+ZT+KsuymPXzXqavO7SvXYpixRKaEAohNDWfFD2H2n6TIzItTmoQ4ITmtahXqzoejwdVUVE11Q6zFDUWZKnxriohBJMnTWLaO+8ghCAzK4uHH32UBg0bEI0aKIqC3++L7zcWjkR5c/o83n1vAVHTwpfdEM3tw23kcnrPSykuzGfrpg2sXfULzVq1oXbdeqSkpqFpygFhmhoPgeyPwWCQjz5awPa9RViKSjR/DxmZGfTq0Y3iomLmzH4fRVVA1VBRyMvP56MP3qdhk2aY7jRUNQ+BSSgUYu3qX7FMExSFSP5msOqwdUceNbJTmfP5r2xpVo2ux9VHUeyBAy6XjmHYgxUCgRRGjBrJzdffwJo1a5j/4UdkZWbRrn372HOgUJpTrc3ZwahvwwhUO/FEiXWU/Z3n3ULBAgzAQFWC+LSdZHsETdNM2tVy0aFZNq0b1SUlJRALKtX4x9JLacBWWvOhJgM16bC2Ny+fI3peS0bHvmQffZrT5UiV6Hn5TYQ8qTz44pQ/tfml9O96aeocxs/8mI8/eI9qVeW+Qckqmr8dc+nL3PDQk7Q87kSny5ESiBgG3cdO58yjmzOsR0fZPZ3EXnl0OFsWfsDbvbvikxMik9aiLz5l1PBBzB0/iuaN6jtdjiT955V2pf29YOVg90v8Pi1ACNgX0dgX0fg1z2LqhiipX26gfdXVXNImnc5HNyE1NQVd19E1HU23J1SWhi2Lvvqacc8+hxGN4vV6GTpsGK1atyYS70wrC9OKi0sYN2Eqn3zxLZai4KvSAN3txyzaiqdKVRZ/Po89O7cDCi6Ph8bNW+L2eCoGZ/EwTUVVqRCoRSIRwuEIiqbbnXaaTp26dUhLSyUlJUC37meDBTNmzCESCYMQ7Mndy549i/FkN0RzeyEasn8vsd9X6W9KjeayY+tGdmwRpPtV5uasJ4Kb04+tid+jYBomuqbFp5VmVcnmkcdHcNN117N7925mz5pFVnYVmjZpWqHLLxI1QNjBHRXeq/y9594qd1vTgiJDpchQ2Vis88l2C9eyXJqkbKdXE5UebRtRt1Z1+znWdTRNQ9f1CuFaacB2KMlXfOmwtSFnK237DKLWuTeT0bSt0+VICZimScfe/Wh4ZHtuuvNeeUCZxO4bO5HPl63ik3lzSU2V03GTVXjXGtRV7zD4mQnUbdLC6XKkBApCIbo9O43rzziBy7vIKcbJbNStl+Pa8CsTz+uES5MTIpPV7Hfe4s3nn+TTSc9Rq7o84SNJ/7T4/ln/ckNhaQhTYGh8st3LZzvCHPXdD9zcLo2ORzXF6/PidrnQXfbyy927dvHYI49QVFSEoigMvOIKOnXpQtSwO9N8Xi9CCMLhMIVFQUaPe50vl/wIioo3vSYuXzpG/iYsLHJ3bUfVNBRVQ3PppGdmUa1GLdxuN4pChVDNvlCui8oO1qpWzeLii3uyZuMWvv3hF7KrN+H444+Kdbdp1KlTC8MwaNGqBatWrCIajWIp9sAAI5iLO7MW4T2bEJRFWaplIbCIRiKk+y0KQm62bd+OR4vy2ZffYSkdOOOYbPweDYRA01SEsDBNk8aNG3PXPfdw19Ch5OXlMXvmLPoPGBDv8iMWAiIEqAKsWHda6cd/8HmOCFhR4GHlUotXfl1P/+ZrufTkZmRnZeL2uHG5XBUCNsuyDlgO+nfJQE06LH330wrOuP4+Gl58Dyl1mjtdjpRAMBik/bl96Nr7Unr0v8bpcqRKXH3/U+wsivLhe7Nwu+Vm6cmqZPP3eLcsYMiLb1G1Vh2ny5ES2JZXSO/nZ/BIn9Ppdox8jUpWQgjuvexcWlnF3H/2ifKETxKb8NyTfPXBDBa+9TwZafKEz58ll3xKf5W9Mb1zj2+hYFiwdJ+Xaz8Oc8lv33LDKU3JzsrA4/GgKArPPD2G9evXY1kWnTp3pk/fvhimAdjTPFEUQiUlFAdLGD3uDRYuWgqqituXjjutFsHdK+2wTNPtME1RUVQNt8dHqyOPJatKtt0dFVsiWbrPV8W93ezvq4rdvebzeWjapAErftvEKV3akp6WWmHvOV3XaXv8MSiKws8//xrrDDMR4WJcqVVRXR6IhiEWpGFpKLFuQbfbQ7pikNXsGBSjiPqNmrI932L+slzOPDYbv0fBssoazQzTpFPnzvQb0J9XXhrPhvXr+eKLzznzzLPiXX4loRCY0dhvXcH+gYiFaoe+Q/Fgz/POsIunllvM37iKezpncFTzBni9XtxuN263Ha65dBeWZsVXOx2K120ZqEmHnbkLvuay+5+l6cDH8VWp7XQ5UgK79uTSoedlXHrzUDqd3cPpcqQEhBD0uPk+qtVpwPSXx1SY8CMll5I1n5BW9AvDJkwnNTPL6XKkBH7dupvLJ77P+Gt60r5pXafLkRKIhEIM7t2V7rXSuP74Y5wuR6rEiLvvYOea5Xw2eSw+r9x/VZL+LRUmPDrMQiEsFN5YH2DtvjXcf0YtalXL5pslS/h4ftkQgltvvw1VU+39xHQdl+6iqLiYSCTK8y+/zRdffw+Kgq578FVtSmjPGvveVS0Wjqn2Mk1VoWadujRu3hKwEMJEQQFFtQMmRQUR22sMC0VR0eKhm4KiQkDVOe+Mk0gN+MACwxTs2rOXXbv3IkzBjh27KC4uQdNdCNOIL+80S/JxpVcjsncriqXZUZZloVgawjRY/esyEIJa9YOkVW2IprswTYNNuwWfLt/LmcdWwa1o8UBNURRMYTLwiiv4cekyvvv2WxZ/vYhGjRrRQm+JoiiEQmEwI7FgT40FaYdu6ecfJVBYXuDl+g8LuX/fL3Q6qjF+vx+f14vX68VyC1yxE/+HKlSTRz7SYeXFt2fT76EXaHHVaBmmJbE16zbQvnsfrr9/lAzTkphhGHTsfxtHtz2BF8c+I8O0JBb6dSbVxEbunfCuDNOS2JdrNnPVGx/wzm2XyDAtiRUV5HHrOSdwZdPqXH98S6fLkSpx59V9Mfds5oMJo2WYJkkOsf7tNZ+VsIBF+1J44KMcflm5ijcmTiQam3J53Q03UL16DXtZoKLi9XkJlgQRQvDW9PeZ98mXdueWquHLbkI4fwvCjJR1msWCNBQVTXdRu24DdE3HMAWGYWCYBlEjan8eNeyPsUsoHMayLIIlISLRKKqqousqGWkBVFXFME3WrNtMfkEh3y1bwedff48hLNq0aUW1atVQVB1V01FUDREqRA9koGguFFW1hzDEgj5VVTENg2g0wtaNq0n167jcbixLUFJcwIoNuXz16z7EQYJQj8fD7YPvJD09nVAoxIJPPiE3N5eCggKCJSVghO0uNWGAZYIl7N+4A0t+c6Mu7lts8dWPa8jbt4+CggKKiospCYWIhCMYhoFpmock9JUdatJh454xr/Di+9/Q8pox6F45yj5ZLf5uKZfeehfDn32VRs1bO12OlEBRcZAO/QZx7bXXcs2VlztdjlSJkh8m0qhWgJsffxPd5XK6HCmB6d+t5LnPl/Le0P7UzkpzuhwpgZ1bc7j3kjN5oEMbOjeo6XQ5UgJCCK4+/2xOat2IUcNukstx/ya55FP6q8qGEiQPy4LF+1IpmDiH0NatWJZFx86d6HJKV4QQKCj4fF5CoRCWsPhy0fdMmToXUwhUVcOdUhUQGMG9sSWesc602EdV02h15LG0OaYtbreOrqm4dPui66Wf23uoBaOWHXRp9nJPV8BHQVExPp8bRVERQhCNGkSiUbKrZBAOR6hWLRtN1/ltwxbWbchBxaJ+rWx0FcLhCKFQGOFVcWVVIVSwl2gkgoEdHFmKCopAUVSMaJRliz7mpNPT8PrtpfChkmK+X63gEfmcfGwTFAWEsNBiXXvNmjenb79+vDBuHJs3beanZT/S/oT2lJSUgBGKdeBp9kdUUK1/deln/DlGIc90MfL7ICN8W6hfu7odoAkRv07p60LpstW/SgZq0mFhwLDH+XDFDlpc9SSqLvd3SlYz581n8GPP8sgr71CjTj2ny5ES2LZrD6deOYRHHn6QHud2c7ocKQEhBOFvnufY445gwLBHZQdhEntuwffM/WUd84YPIDPgc7ocKYG1vy5n5DUX8czpbTmyRhWny5ESKCkpYWD3rlze43Ruu6KP0+VIkpQ0SrulLPSinRSt+QbNskhPT+eKq65GU+0lgLqu2xvyGyY5W7bz3ItTCIUjdnimu/Fm1CG4e5UdwigKbrebJg3rUjU7iypZGdStW5tjj2tLasCL16Xi1hXcmoqmKWgq8b3UhAXfbSmhxLRjptL91SJRg6hhYCkKhmESDkXI2b6HX1dvRlGguDhIflEIVBXTNPF73Qy//Byy0wOYQmCYgqgpiERNgsFiioMhCouC5BUUsTevgNy9eezJzSN3bx47cvNZv2oZR7Y7jZLiQnZv20RW9brM+Wwvtar4adKwNopiIYRAU+1N/S+65GI+mT+f3377jSVLltCocSOCxQKiUVB0UPXYHmoHW/r5bz7bCpvDfqYs28W1Xh0R60grpSiguD0V9rL7K2SgJv2nCSE446qhrCpJoVm/R1HkAWXSGvvqZJ5/cyZPTJpJRla20+VICaxYu4Fegx5i/PNj6XDSiU6XIyUgDIPw4tGc2v08el51q9PlSJW4e+bnrNmdx7y7BuBzyw7CZPXdFwt49e4befXcDjTMlJvaJ6t9uXu4vOfpPHDjAPp0P8PpciTpsFV+KV2SbKNWxrJI3/wFarQYgHN79KBBw4b2nykKLpeLkpISwpEIY8dPYcfOPSiaBoqCL7Me4YLt9rLGWGdTo/q1ee7RO/B53WiajqbrsbuqGNAo+32iWhaZfo2SorJELRI1Ynt7Kfg9biKqwY7d+Sxato5gScjusLJMUEuHDCjUr1WFqpkp6KqKjoan/CNmBio0CJYFShamEHy/eg8LV+wjakRZs/IXwgU7KCkuIC2rBm/M+JzhN12E1+PCNEwsxV4Km5qaxhVXXcXdw4ezNzeXH3/8kZRaTbEisUBNi4VqpZ1qFaZ+/ntdavZPqfDp3ixO3rCDIxrEfiux5bmaWrbfXelJ578SqslATfrPMgyDYy+4nqLqR9HwnCtku38SGz5iNB8t+ZGRk2bjC8jluMnq829/5LpHnmP621No2aKF0+VICYhIkPCi0Vxw9U107nGx0+VIlbjq9Q/Q3Toz7uiLrskTPslq/rTJfPDcY7zVqzNVZQdh0tq0fh039+3Jiw/dyakntXW6nP8UIaykWmYpkqgWqXJ/ZX8qBQuPCOIWRsXvx/bwN1ExFBcGOqZid5VZvxvSlHWnuYp3kLLjRxSgatWq9Dr//PhxotvtJhqNYgHzFyxi0bfLUVQNVdXRPH50bzrB3dtRVR1Ftfcsq1mjGj6vB01T0XTdruQPHndm+zW2F5mxnxs8bhc+rxtFgYKSMJ//8BtrN+zAEqC6PSAEljCxTANFVbFMgyOb1ImFcBbKAVuWxUIsq/R3aA9BAAVd08gIuFAU++dW3akEgxswzW1EwmGCKel8vyaPjm2qoWkahmniVu2lrZ06d+aoo45i2dKl/LL8Z5r7q0JUBc0FUReoLlA1+2KpB/196JaBTxShWGV/VvocC0vBUDQMxYWp6Fgof+A5PriQpfPpJoPaGftQVMUOPTUNXdPQdPtzVVXjQwr+LBmoSf9JRUVBWve6Gu/R51LnhJ5OlyNVYsCtw9i4N8ijE6bFp65IyWfqB5/y6KvvMG/OLOrWkQM9kpVRnEv023FcMewhjul4qtPlSAkIIej9wkyOaliLEX1Olyd8kthbz41k+axJvH1+F1I98jUqWf30w3fcd9MVTH/uEY5q2dTpciRJ+puOK/meeuxGVRVUVUPTVFTFDj4UVcVEJWzp7DN9bKAqG5Q6hBX374culkXalsWoRhCAc849l+rVq1dY9meYJrt27+Ot93/AU6UJmqaj6i486TUQRohAjVbxAQCqrtGw6RGosVDG9sde0xVFIc2j4dYUopY9vEGJTf3cF4zw5Yad7PN4Sa1XEywLS1hYpsAyTXv5omGiITi6Vf3Y+wgl9ujlx0CodougUr5NrbQ+C69bpTC/gIwq1UjPyKSgsBmRYC5FYZWSaBFzv/iVlvUyqJrhQRHCXvqpaXh9Pi69rC/Lf/qJvLw81q5dD0YdiLpBc4PutsM1kbhLrWpkG52i36LFOsQ0TbO7xhQVVVMRioqBRpHwsFWk8xt12Ktm/OlgzULhp2AGZ+buxqXruF1u3G4XbrcHl9sdD9T+apeaDNSk/5wdu3M56vzrqXraVWS17uh0OVICQgjOvuwavNXqc89z4+T+Tkls9OvvMPXjJXzywftkZWU6XY6UQGTfZqyfJnLr42Np0uYYp8uREghFDM557h0uOulIbj1bLptOZmPvHkTBsi+Z0qsLHv2vnbmW/nmffjiXcY/ezUevPU3DurWcLkeSpENAswQezULXNfvi0nG5XOiajqbHgjUU6iM40trB7vAuFgQbsonqBwlcyrrTtHA+KTvt7rSMzEzO7tYtfgyi6zqGYWBZFlOnv8e6X5bYQZmmoXkCqLqL4K6V9l5qmoaq6WiaTqb3SHuipaL96R4qtwbpHpXdJQLLKl0qq7BzVz5bN9lLTUP77KWplmWHaZaIYhkGlhmlVoaPWmnecutqrYpLbBUAsV/bWtkXfpfFz4vnEsioyfbNa6nT5HhSA3XYmfMbG1f9wOLtK3knI8oNl52JS9cJRyLosSWt7U88keYtWrDi11/ZkbMBJSsLS42FaUYsUFN1UM2DdqlpioVHFbFOMQWXS8Olu9BdOrrussNURcUCmlHAicZKfijKZLFoSvRPxlh7LT9b9gZJ8xfi8XjweD14vF48Hg9utxvLsuJh4Z8lAzXpP2Xl2o10GHAn9XoNJq3hkU6XIyUQiUTo0KMvbTqdQd8b73S6HKkSg598kWXrtvHxB3Pw+/1OlyMlENr2K/q6WQweN4ma9Rs5XY6UQG5RkPPGTmdwj05cfGIbp8uRKvHw1RdTNTeHV87tgCZP+CStqa+/wnuTXuLzKeOoViXL6XL+s0xhoSTRMstkWn4q/TNKO5Z0Xcfj8eD1enF7PHg8Hlx6WagG9kn6TFNQI7STyTtdbBZZCbuYArt/QQvnA9Dh5JOpVbt2rDON2ERLQc6W7bz3wYJyt1LwpNW0906Lfyf2UVGonh37t8eysBQlYaiWaPewbL/G7qDAsiwiUZM1m3fz/apt4HGjaDqK7rZvLUTsHizQLLAErRtUw+euGOmULuqMP2glAn4vLtUiWFzA8Z27k5ZVA1V14Quks3nNTwQL9zHtnXc4s+ORNGtUG1VRME0TXdcI+AOc17MHK379FT1cgCe0hxLdC4YHjAjoUdAMEDoooqxLrVyFiqKi6Zodcnk8eD12yOVyu+KhmqIoscDLIjsjin/XeuaHmiD446/NpqWSUyCoV1yM1+fD5/fj9/uJ+HwYUQ+my4Wm2UMXZIeadNha+O2PnHfbozTp+xD+6g2dLkdKoKCgiPbdL6Fbv2s4+6J+TpcjVaLv0MeI6j7mzpwePxslJZ/ghkUEdn3NsFfeIbNqDafLkRLYsHsfl7w8m9H9u3HKETL0TFaGYXDXxWdwYgCGnN5WLsdNYmOfeIifv/6Ez6c8T2qK3H9Vkv5LFAU0TcPtduP1+UgJBPAH/Hh9vniopsa6iYQQCFOQaUQ5T8nj+W2ZmJQta4x/NKOkbF8KgMvt5syzzoq/v9VUDdO09zKbPnMe+/IKUGNTPxVVQ/emEdy1tdyAOzs403SNKpnpWICwBGp8aWP5n2W/15FyyZqiKGR4VXQFcguCLFq+nk07C0Bz4fX5UDQd1WV3UGEa9o0tAcJE0TSOaVLjd/Zriz3YgZurAeB2uchMT8Vf8wiya9SnuKgAj9dFWpXqtD7hTH778UuEaTJ9zgLuurUfbrebUDiErvtQVYWOnTozYfzL7Ny1i5SSnZR4q0I0BKXBmuaOdamV20uttCRFQdNUXC4XHo8Hvz9AIODH7w/g9XpwudxouhYP1CwhiBoGZ6aE+XVNITlGemV/hQ6wJ6pTUlJCSTBoD50Ih4lEIkQNA49pIoRAVdU/HarJIyTpP+GdDxZwzYhXaH7FKDwZ1Z0uR0pgy/YddD5/IFcOfYD2Xc90uhwpASEEZ14zjBZtjmL0yMflAWUSK1k1j6zwOoa++i6B1D/3xkL69yzbtJ3rJn/Eqzecz7EN5ZK0ZBUKBhncszOXNK7GwKPkPlzJ7P7bbyC8azOfvP4cbjkdV5L+c1RFRdd13G43fr+PlNQUUlJTSUlJwefz4Xa70TU7yrAsC8M0iEajHOMppsquMLuMcgNkYkGSu3gnnsIcFKBRo0Y0b9ky/h5XURQsAbt25TL/4y/LhU8Kui8DI5SPZQmU8l1RioLbpZOWGiiL74RAUbUKrWiWVdYvV277stjSTAuPphDQLaZ/t4ptewpRdDeKYu8Vh6qB5kKxhH0DYWIpKigqmSlemtbK/Atb9ZclfpqmkpGeSgQVIxpBVTUsQNNc1GzQioyqddm3ayvbgm527SuhRhV//GdSVZVq1apywkknMWfWLHyRfajRYoTuBSMMZgTMqB2qldZvWRUCQE3TcekuvB4vgYCf1LQ0UlNTCfgDeLwedF1HVezfuSlMjKhBejjMMTv3sSU37Q/vp2ahUGDoRMJFhMMRwqFQPFAzDANTiL80QANkoCb9Bzz12ts8OmkeLa9+GldAHlAmq+UrVtHjylu44/FxtDzmeKfLkRIIhSJ0HDCI8y+4gDtvG+R0OVIlSpZPpXZKmNvHTcPt8TpdjpTA/F/W8eDcr5lxZ18aVZdL0pJVXu5uhvY+hTvbNuecJnWcLkdKQAjBLf0voGEVP+NfeFzuv/ovsWIboieLv3rgK/3/UFUFTdNwue2wxe/3k5aWRnp6OoFAAI/Hg67bEyotC8xYoOYPBMl272aXceB9+nNXoZgRAE7qcDKBQMBe6omCiP2dWvDZ1+zes7dcJxq4UqoSKdh60Dp9Xg8Bv9duuirtPIvdpz0aILanWWy6pjCEfd1Y11WpLJ9KsCR84AOUrkW1DgyOWtTJItX3R4bl7NcyV+HuFapkprGpIEiopBhfIB0R23LN5faiubz407IxogZLf8ulW3YAt9tNJBLB5/Oh6y66nnIK77/3HpYZwRvJI+hOsQO10mWfwgBh2uEgql2LZXf4qWrFDrXU1FQy0jNITUvF6/XhdrtiXWP2HnLRaJRwOEzjrCLI/QM/ejlRoRKJRolE7CAtHqYZBqZpxvav+/P/tvzpV6HyEzAWL16c8HrvvPNO/HoNGjT404X9lboOxeM88MADKIrCxIkT/9Rj739xu93UrVuXvn378vPPP//h2+1/OeWUU/72z/RfNuix53h86hcyTEtyn3y5iJ5XD+L+FybLMC2J7c0r4LiLb+DGm26SYVqSC333Mi3q+Bny3BsyTEtiE79ezuMffcMHdw2QYVoSy9mwliE9OvFYxzYyTEtihmEwoPspnNyqPi8/epcM0yTpP8xeDqih6y7cHg8+v5+UlJR4qJaRmUlmZgaZWVlkVckiK6vs4tL2/7fBAmHg37MSAI/HQ7sT2sc2oC/boyscjjDvw88rhCqKqqGoOiJacmCRloXf68Hjdts5mqJgWWL/nyR+/4Zh2sGdZR1wvSp+Da+u2iGcZS/rtIS9tBMjai/3tGJdVJZAweLYxlUrX+2ZUMXQqGHjpmRm14qHiKUzQhVNxxIWiqKhqBo/rd1HSTiKS9cxDHt5rKoqtD7iCKpXt1eI+cN7Y/VGwIx1qZUGaqVdauVomoam63Zw6vUQ8AdITUslLS2NjIx0MjL2e46rVCGrShVSfH/+va9lWZiGiWEYRA0DI2rYgZpp/67FX+xS+1sdalOmTOHEEw8+oWry5Ml/567/Lw0YMCD+eX5+Pj/88ANvvvkm06dP58MPP6Rr164Jr7+/999/nz179tCxo5xSmcj5tz7Aopwgza8YiarJZstkNeXd2Tz43ARGTJxBdvWaTpcjJbBp6w7OuPYunn5yJGecdqrT5UgJCCEIL36GEzt1oM+ge+Ry3CQ2ct5iFq7byrzhA0nzeZwuR0rg16Xf8uzN/Xnx7BNonp3hdDlSAkUFBQw47xRu7debay/p6XQ5kiT9C1RNRdc1XC576afX68Xn8xEIBPD5/bhdrngIJEyTqGEQNQV5ZulxYdm+YXq4AHfRdhSgVu3a1G/QAFVVYteyr7Ru3UZWr1lXdltA86ZihgoqLNssLxDw4XLpdi9aaYeTZcW71FCIT5Ck3D3vf19eXaFWpo8deUE7bBN2yGOZBpYRsWs07YDNEoI0r06L2n9lued+FIVGjZvx644NWEIQLC7A7U2J515CCBTV7pLbV2yxcVs+rRtXQ1UVTGFP6MzMzOCII49k27ZteKOFKGYIy/DGgrXYRTftoQrlhhMoihIbPqHawanbjcfrwev14ff7CaSk4PV4UTU1tiRXYJgmkUiEvZE/P41TtexONMMwMQ0Dw7S/NoUddJaGaf/KHmoej4fGjRszdepUxowZc8Bm1bm5uXz44Ycce+yxLF269K88xP+l/bvaotEoV155JZMmTeLWW29l+fLllV6/VF5eHm+//TYAl1122T9R6v81IQQn9xvEVr0WTS4dIg8ok9gTY8cz6b0FjJw0m9T0DKfLkRJYumINfYY8zuuvvsLxxx7jdDlSAsIIE/56NN0u6ctZfa92uhypEre/8wk7i0PMHdoPj0ue8ElWX334Hm89Mpg3zjuZOukpTpcjJbBrx3auPv8sRg25nh6ndXK6nMOSEPaEvWSRTLVI/xw7cNHi+2y5XC7cbg9ud2zaZ2wyI9jHh7phsGnnenaEDlwG6S7YghoNAtCyVWtSUlIqLLtUFPjy6+8IhcKoemlYY6H7MogG95XdUWnoEovhUvz+eLdsaTAnhEDVtAph1wGdTxXWiNr/bde0Oj9u2INp2utVLWFgmSCMqH3vQsS6vQya1KhCZsrBu7TiYxjK/WyJKEDA50bTddyeAAV5ewmXFKO7fXYuqKqEgoVoLh9C0fh1YwGtG1eLL/vU/X5cbjfHHX888z/8EJcIoxtBomagbA+1WM1Ygv2748DeL0/TVLtTrTRYi0399Hg88Ymu9vAJk2jU4Ntdyh/eP83+OS38hO3nTQiEsMq60mKdaf/aks9Sffv2Zc+ePXz00UcH/NnUqVOJRqOHfRjkcrl44IEHAPj555/Jy8v7Q7ebNm0a4XCYE044gaZN5aa45UUiEVp2v4JdWUdS/7xbZZiWxG6551FmfP4tIyfNlGFaEvvwy2+4bPgoZr87VYZpScwMFRL+aiSX3nibDNOSmBCCy16Zg1A13rn1EhmmJbE5b4xnxuPDePv8rjJMS2JrVq3gql6n89qIu2SYJkmHGaU0bFIUlNieanbwoqFrOrqux5aF2kFMJBJl7Fc7iFRYTWl3jHnzN9mfKwptjmyDa7/XZ8MwWLToe2LbnpUuekRz+TAjxWUb6kPsc/tjwO9FLZ1cWa7DCYv4HUUNg41bd1cMa5T4fwB76eQR9bMJuJVYkFbuYkQQRgTLjCBMA0sYHN+4KuoBh8FW+TSNjVt3x/eGO7AnroxbtydbWoDucqPpbkIlQbteRcU0DIKF+xCmwaqNuUQME5fLRTRqxJfmtmzVCq/Xi4LAEy2yAzSzXIeaMME6+LLP0udYVVRUzQ7XVE2LLQfVKjzPqqbxwZKV/Jj35zv/UwnGltuWXqjQlfZX/a1ATVGUgy7tnDx5MikpKfTo0aPS+/jggw84/fTTyczMxOv10rx5c4YNG5YweCouLmbo0KHUq1cPr9dLixYtGD169O/+Ar766it69epFtWrV8Hg8NGjQgFtuuYXdu3f/4Z/3rypdTwz2/6h/ROnvtF+/fv9ITf+v8goKaHjmANSjzqNWl75OlyNV4sJrbmXlznweHv8WHq/v928gOWLCjA+4a+wk5r8/hyaNGztdjpRAtGAn0SVPc919j3PiWZW/rkrOMQxBt+emcUTDWjx/Zff4UhIp+bw28n6+mzSOt3t3IUsux01a33z9BUOuuJjZLzxBh+OOdLocSZKSlGVZ7MvP4+4pi/h4myfWuVQuH7AEnsItKIDH7aZR48ZomhbrULPDnD179rF27cZY51ksmFI1u3tJ2PuFxUYMYMW/gpRA2XHO/olE6fWEEHz27S+YsWWfmqaiqlqFWwhhkZXq5ax2LfC5NBRhoAgTy4jaYZoRwTKiWEaEgEuhdd2sSvuzoqbJZ9+tiHVzJs5KLEBXLXTdZS91VDUsFHTdTTQcwoiEcXkDeAPpmIbB3mLYWxBG1bR4MKUoCjVr1iQ7OxsAj1EEorQzrVyHmigN0/5keBW7umEYzPniB4YvDGLsv1Xd79Ask0ylGKBCQ075Ka9/1V8+dVq/fn06dOjAnDlzKCoqIiXFPru3YcMGFi9eTP/+/fH7/QlvP2LECIYPH46u63Tu3Jns7Gy+/vprnnjiCWbOnMnChQsrhFHhcJgzzjiDRYsWkZ2dTffu3SksLGTYsGGsW7cu4eM8++yzDBo0CFVVadeuHbVr1+aXX37hueeeY+7cuXz99dfUrPnP7ev0ww8/AJCdnR3/S1aZzZs38+WXX+Jyubj44ov/sbr+32zaup3jL76FGufcSGbz9k6XIyUghKDLBQOo2eJohg59UHYQJrFHXpzEvCU/s2DeXNLS0pwuR0ogvHstyoq3uGP0y9Rv1srpcqQEikIRznn2Ha48tS3XnCoHrySzp26/Bn5bxqSenQ+ycbWULD6YOY2JYx5jwRvPUadmNafLOez91aVQ/5RkqkX655RfQmkJy97vyhSYhknUiCIswb68Aj7/aR3PLsln+V6N/ZvTABQzjCu4B4CMzEyqVa9e7hjFQlFU1q7dSEF+YWyipv19VffawwhKu9P2u1iWhc978JMyFhaKpcSGc6qs37qH3LwiamRnoCgqwjQPuuyzZqqGGQ3TqFYNatauzq8bthLFJGqY8b3VmtbPpEra/g0LVoUGtS0797J64/aD/L9iVfzMArdLx+X2Eo0KFFXDiERQdQ8YJqFgPt6UTEzDRNXdRI0oW3YHqVU1FU3TME27Wy0lNZU6deuyZcsW3GaJPUTBjA1TMI0DO9T2q8uyLIQlEKbANO2lnaZpEo1GKS4O8uu6zUz4Kod3N6iEzT+33BMgzcwnTY2gKD6I7d2mqGWDIOGvh2p/ay3CZZddxldffcWMGTPo378/UNZd1bdv4g6i7777jnvuuYfU1FQ++eQT2rVrB9ihWb9+/Zg2bRo333wz77zzTvw2o0ePZtGiRbRr14758+eTnm5PdFy6dOkBm/2XWrJkCbfddhv16tVjzpw5HHmkfXbLsiweeeQR7rvvPm655RamTZv2d34NB5Wfn8+3337LTTfdBMDw4cP/0O2mTJmCZVmcffbZVKlS5ZDX9f9o6a+rOPWae2hwwXBS67V0uhwpgVAoRPtzL6HDuRdy/hU3OF2OVIkbHhrDpr3FzH9/Nh6P7M5IViU5y/Bs/pAhL75Jtdr1nC5HSmBHfhG9nn+X+y88lR7Hy9eoZCWE4P4BPWkS2cdD55xkL9GRktLEF57h81lv88Wbz5OVISe4S9LhKIdsRFERPsNFSjBC6t49pGwqwufdRlRxk1tisa4AVuXr7CpRsdAShixapAg1UgRAtWrV7P3TUMqmWaKwYsVvmKaJomnxsExz+TDCRfGuNaXcMkHLAmX/QC2WjVmAaRiomoaKiqZpHHnsSeRHXNRSVXvvrtL9zfYL1VrUySLFrbJ5yzZ2FhVjWKAIw+5OsywUS9C2aY39lntWDNMsy+Kb5WspDob+UPisKcR/ZkWx9ypTLMveu87ji61wje3hpmhs3lVCu1bgcukYhoHb7cbtdlOvfn2WLF6MLiJ2zcIs15124KTPQiXAL+EsfHkuAmGLlPwC0rZFCfhyQXdTGFXIKYTV+SobinQiIvFzXBkFi3rmFtwu0NTYsmHNfl5UVbOfJ0WNh2t/Nlj7W4HaRRddxC233MKUKVPigdqUKVOoUaMGp556asIllWPHjkUIwaBBg+JhGtjDDsaOHcvcuXN599132bp1K7Vr1wbghRdeAODpp5+Oh2kAxx57LDfeeCMjRow44HEef/xxhBCMHz8+HqaBnT7ec889zJw5kxkzZrBnz54/1D32ew72y69WrRpvvvkmffr0+UP38WeWe4bDYcLhcPzrgoKCP1jp/4+PFi7h4uGjadrvMXxV6zpdjpTAnty9nNTzMi667na6dj/f6XKkSvS65X7SqtZkxtSX4xu5SsknuPYLUvOWcter75KWKU+uJKtV23MZ8Np7jLvqPDo0q+90OVICkUiEoeefwhlV/dzS4Tiny5EqMeq+oWz+9Xs+mzIOv+/gG25LkvTft8rVjFUA4dil3FwAYks1/xDLQg8XoIoIANVr1MDjdsemRlrxfdrWrFkXm+RZGpaBqnsxQnnxPdPs2Ku0Q01gWSpejyvBpv8WwjSxFEE4YvDL8mW4lDY0r52CQmxfOMrttxYL1dJTvLSsk8nC5ZsQbjeKphEKFsd/lhSfmzYNqlbosCsfpgGEI1EW/bgGYRqUK/xgJQKgqRAKFhKOCoyogSkEqubBMAxELDgUpsCIRlA0F9tyQ1gWuHQXJaEQCqBrGrXr1LHvzzLRRBSjQqBWGqbFBhNYFvu0DL7geDCBotilQnmH5sSXxwrRhG2oqhKbHOuKXXR0XUNTVVT1zwdppf5Wr3tmZibnnHMOCxYsYMeOHXz33XesXr2aPn36VHqg9uWXXwIH72KrVq0aZ5xxBkIIFi1aBNjLIHNycqhduzYnnXTSAbc5WFglhGDBggWkpqZy6qmnHvDniqLQoUMHhBDxZZl/14ABA+KXSy65hBNPPJE9e/YwZMgQvvjii9+9/dKlS1mxYgUZGRl07979d68/YsQI0tPT45e6df9bgdNr0+dyyb3P0eLK0TJMS2LrNmyi3bl9uHr4ozJMS2KGYdCx/yCaH3kMr7w4ToZpSazk19lUi6zivgnTZZiWxBat3cLAiXN565aLZZiWxIoK8xl0dnv6NcjilnZy2XQyG3rdAIq3/saHrz4tw7QkYwkr6S7Sf5cda1Vy+cNPv31FLZwfC3KgWrXq6LrdUyQsC0VVMU2TnM3b4gMJSpckKqrL3r+sdMlzhQ3t7a/LhhuU31mt7PEty8IwomzeuI4PF3zBrr1F8Y38y6aDlv1HAU5sWRdFGAgjijCisb3TolhmlGa1M6mS7i97TGv/x4R1OTvZtG2XvVQ2we+q/LfV2BLISDiM5vbg9gYoKS6gOD8XRVExjajdtaZqWBbk5kcwTIGua5ixPeI1TaVatWqxoFDYAaZl2kFa+Ut8uacV/9RK8JwfCgoWzcKrydQj6OWmxZZeSgcdKKr6l7rT4G92qIG97HPWrFm8/fbbbNiwIf69ymzbtg1FUahf/+BvQBs0aBC/XvmP9eodfMnLwb6fm5tLUZEdc5b+T5PInj17Kv3zP2rixIkHfG/ZsmV07tyZM888k5UrV9KwYcOEty/tTrvwwgv/0DKsu+66i9tvvz3+dUFBwX8mVHtw7ESenfUlLa8Zg+6V07eS1bdLf+LiG4cw7OmXadKqjdPlSAkEgyFO6ncrV1xxBTdcKydEJrOSZZNoUNXNraPeRncdOPZdSg6zlq3mqY+/Y86QftStIpekJatd27dyz8VncO+JrTmlYS2ny5ESEEJw7UXnclyT2oy552G5/6okSYeUFi6Mf56ZlWUHKIpiL+tUVYLhCHv27K0QlpVOuLTMqN2BVrokssJ1BK7fyRlKoyshTDZuWM/nS5Zx4VkdyoYilLuWYtmNZC3qV6VmVgq7wnaHmWVGAfvP2reoE5sqerAAz+54+/KHVUTCkdgwhfLXsA7yqYVpRtm7awtC9aHqHlRVQQiB259KsKgABTBME1Vzo7q8FIUMwhETt99lB22xQC4jMxNd1xHRKFppV1o8SNuvQ+1foGBRJbqbI9iEpmm4XC7cHg9ujxuPx4Pb7cHlcuOKTREtXfb5Z/3tQO3cc88lIyODN954g23bttGyZUuOPfbYv3u3QNkSytK1v4l+wIN93zTtaRypqan07t270sdJFOwdCscccwzXXnstTz75JGPHjuWpp5466PVM0+Ttt98Gfj+QLOXxeP6T+x9dec8o3vsxh5ZXjUaVB5RJ672PP2XQg0/y8CtvU7NuA6fLkRLYsXsvp1w5mPvvvZvze/V0uhwpASEE4e9e5Mg2zbjynifiZy2l5PPCZz8w86ffmDd8AFVSEg9fkpy1ftUKHr+yN0+ddjzH1vz723pI/4xQKMTA7qfQt1sXhlz9x97/SpIkVc6qEBhp0bLpjqlpqfaG9LFATVEUQiVhioqK9+s+ww7RhABVwbIEiqXGQ7XSgK3iRG8LLOXA5ZUW9r5pwuSr71dw3ilt8brdKPu917OwZyKkBbx0PLopv+wIsXnbdqKxOlN9bto0qrH/DNMK91BYXMK3y9fEppMKKiR2B34KgBENYRoG/qwMwiVBuztMUSkpykdVPXj8qZhCUJSXi1v3EooIQlFBuqrGcxpVVQkEAui6TiQaRbWMsgAtNkyhbCBBucEE/9AJFAULv1nMScZSUtzgdrnweDx4vV58Xh8+nw+v1w7XdJfLDjhVB/ZQAzvUueCCC3jllVcAuOWWW373NrVq1WLDhg1s2rSJ5s2bH/DnmzZtAohP36xVq1aF7ye6fnnZ2dl4PB5cLtdBO8f+TaVdaatXr054nQULFrB9+3bq169Px44d/63Sks5Z19zFz/k6zQc8hqLKJWnJavyktxn92lSeeGMmmdly+layWr0hh/Nuvo8Xxj5Lp5M7OF2OlIAQBuGvn6bL2WfT+9rbZXdGEntgzkKWb8vlg7sGEPDIEz7JaulXn/Py0OuY0O0kGmXJKcbJKm/fXi4/71Tuvu4y+vU82+lypEoIYSGSaJllMtUiJTkLVDMcz7h8Ph+qotjDPGNBVTgSJhIO2wGPsEAtv8RTQCxIs0M1BSt2USw1vgfb/o9ZYduy2GNZpmD95u2sz9lBy8Z1D7qoUY0d/3Y6phnGijzqVatOSUkQFIXqKVAlLZAwTLMsWL5mEzt278USph2olRYUe7Sy5aVln1imyeY1S6nbKgWXx084WIwRjaK5vAjspbFC2EtX1WgYFA+hsFnu/ar9+/R4PGixjj3VMsuFZ/td9v9lHaLlnaXsMK2ITpHFVHOV4HLbQZrX58Pv9+EP+PH5fXi9XtxuN3pph5r61zrUDskp8P79+1OlShWys7Mrne5ZqjQwmjJlygF/tnv3bubPn4+qqvH90urXr0+dOnXYunUrixcvPuA2pZ1d5em6TpcuXdi7dy8LFy78sz/SIbV+/XoAAoFAwuuULve87LLLDsuDKSEEx5x/HautGjS68C4ZpiWx+0Y9ywtT5zJq8hwZpiWxRct+psctDzB1yiQZpiUxEQkSXvgEvfpfzvnX3XFY/vv//+L6yR+RUxBk1p19ZZiWxD6ZNZWJd13P5F6dZJiWxHI2baT/OZ0ZM/xmGaZJkvQPslBEbMmkqqLreoUN/RUFTFMgRKw7jbLVcZYQsc9Lw7X49l/xaZ9/ZPmiArGlooJQOMKX363AsiyEMFEU4l1RWrl9vKqme0n1arhcbtLSMkhPTaddqwZoeuJjZGEJPl/yM6ZhYJlmfFnoH1GQu4NwSTHF+Xtx+9PwpmSiam77viwQpomiahjRiB0oUtbJVfozlN8XTokPHygfppX75f1Dyz4VLKoZOzkt8hW1XcW43W67K83vIyUlQEpKCimBFPz+AD6fzw7UNP0vL/eEQxSodezYkT179rB79+4/tHzyxhtvRFVVnnnmGb7//vv49yORCDfffDPBYJDevXvHJ3wCXHvttQDccccdFaZZ/vjjj4wbN+6gjzN8+HBUVWXAgAF89dVXB/z5tm3bEt72UFm2bBnjx48H4JxzzjnodYLBIDNnzgT++HLP/5JgMETjswZQ3OBk6p51jTygTGJX3nEPny5bzWMTp+NPkXvbJasZHy/k2ofH8v7sGRzRWm7CnayM4F4iXz/JwDvu5pTzf/9klOQMIQQXvDCDzPQAk268AFclb2YlZ019YTQfP/swU8/vSg25HDdp/fLTMm665FzeGvMgZ3U6welyJEn6j1PKfVRi3WmKooAV+6ho+DLrk5LdmJQqjQhkNyFQpQkufxUCVZsRqNocf7UWBKq1IFC9JYEarQjUaE2gRmt0fxVQ1P0uCgplXyuajq96Y3w1m+Or1Ywft0cpDhn2Hm323NDYcs9YpRa4VIWaGW5KwhFMIXDrCjXS3bEBAlr8cewlkwqgsjuvhFW5Cu7sxrizG+PJqoeq6UDsuqj2dUtvp6j29zQXempdNq5bi6J7URQtPhigKG8v0UiYcCiE5vKheQKx/eXKdbyV+0VXnD6a4PIPhGkKFl4R5OjQMk4zllDdHcHj8eDz+QgEAqSmppKalkZqWhopqakEAn48pR1qLh1Vc3AowV/Rrl07Hn74Ye6++25OPPFEunTpQnZ2Nl9//TU5OTk0bdqUsWPHVrjN4MGDmTt3LosXL6Zx48Z07dqVwsJCPv30U6688kpeeOGFAx6nU6dOPPPMMwwaNIiOHTty5JFH0rRpU0KhEJs2bWLlypWkpKRw4403HpKfa+DAgfHPI5EImzZtYsmSJQgh6N69O/369Tvo7WbNmkVRURFt27alRYsWh6SW/xe79uzlyPOvp0qXAVQ5sqvT5UgJCCE4d8D1KOnVuf/5N+T+TknsuckzeOODL/h43lyyq8gJkckqsm8L1k+vctOjY2h2dFuny5ESiBgG5zw7jR7tWnHnuSc7XY5UiRcfHMzuxZ/wZu8ueH93k2jJKQs/+YgxDwzhgwlP0aT+f2OQ1uHgoCu1HJRMtUjJSVFAEQZVjF1kWXkY2B1lpfuslw9OFAQl+zZTXBxEUVVUXUPVXSiqQnDfehRNQ1U1VE2LTYSMfdRUQgXtDvgLaaHF/qfBzrqESWjnbxRv34OiaqzbpvPzyiaceEyL0gJit1PtT2ObpDWo6uObVbswFZWqVTx4PWp8nzUtFggKy8LCnua56Luf2LXuJ4QpsIRJ1F3DHgaAvUR1/73lSgkjQknuWvLzC1DNIjKrN6Bw706K8/ewd/deGrZpR2bNJoCKEQlhxKZ12r9DKx5E2Y9b+rso3enNKjeIwCqr4RBkaqVRZEAU0yi6gebkkOGKxid5ejwe/AE/KSkppKenk56WTlpaGqmpqfj9AXvJp8uFpv715Z7gUKAGdvfYUUcdxdNPP813331HSUkJ9erVY8iQIQwbNozMzMwK1/d4PHzyySc8+OCDvPXWW8yePZsGDRrwyCOPcMcddxw0UAO46aabOPHEE3n66adZuHAhc+bMITU1lTp16nDddddx4YUXHrKf6fXXX49/rqoqGRkZdOrUiX79+jFw4MCEIUT55Z6Hk9825HDCZbdRp8ftpDc+xulypAQMw6BDj760aN+ZfrcOkx2ESeyupyewZOV6Ppk3t9Il5pKzQjtWov32LoOfnUjtRk2dLkdKIC8Yottz7zCo28lcdvJRTpcjVeKx6/uStn0dr57XEV2e8ElaM6a8zrQJz/HZ5LHUqCpP+EiSdGiUn5XpFhHSrXxqGTuoxy6ylGI2akVsiQ0hCIfDFW+rKOi6Fmu4suy9x0ywYpMuLVPE7tnCUiwEFqr9FaARjUQOUlG858zew021l0CaRhRVFUSFyaeLl9H+qGZ2PnCwcEmBrBQX2Sku9gZNaqYdZKcxRUFVFEzTIhKJ8NninzCjUSzLDtQURMUBBhU3UCur1rIwDQMhTLas/o6cld9iGhGEMHGn1GHlV+/S7ISepFVvhGmauDyeWFBXOlfAriwcicQDS0FsEmn8qbEqfv0XErXS51mzTAKimKrmbupb26mpFeB3mei6hq7Zyzg9Hg8+v9/uTktLJSM9g/SMdNLT00lJTcHn9+HxeNBdrr/VnQZ/IVCz/sRa3Bo1alR6/W7dutGtW7c/fH8pKSmMGjWKUaNG/am6jjvuuHho9XseeOABHnjggT9c0+899h/xwQcf/K3b/z9avOxnzrnpIRr1eYBAzcZOlyMlUFRUTPvul3DGxZdz7qWXO12OVIkBw5+g0FD5YPYMXC6X0+VICQQ3foN/xxcMHf8OVarXdLocKYGc3HwufGkmT1x2Nmcc2cTpcqQEhBAMv/gsjvVEGX5me3nCJ4m9OPpxfvj0fRa++QJpqfKEjyRJiSlY+MxiXFakQohU+k+8AmgIXETxWhECVjHpFJNJkHQ1hF8z0F2gKioo9rK/0uP14uLi+DCC0sEEXq8Hj8dNUZE9DdSyLCxhle2hJgSWqmAJCxSBJVQsRYBQCEcileyrb/+BoiioCljCRMR+vqXLV7N7bz7Vs+0mogMmfloWmqpQr4qLkqhBw5oZB/y5/TuxQ7XfNm5l7cYtsWEEJkKI2PAF5Xf3/TcNE9M07J/XNBGxgQb278AiGiqmaN9OXIFMfBk1cHvc+L3u+OCG0npKgkEMw7C/Vsr/PPtnJfbXuhUlIIpjpZXtx1b6lYJAR+C1wvitEKkUk0ERmWoJASWKx2OhaSqa6kLVPLh0VzxM8/p88e60tLS0CpeA34/X68UVm+6padrfev8ge+Klf92Mjz7niodfpNnAJ/BmyQPKZLVtx0469u7PgDvupcPpfzz4lv5dQgjOuX44DZu14uWnRsrluEksuHo+mSWrGTphOinpmb9/A8kRy3N2cvWkebxybS+Ob1zH6XKkBCKhEHf27Mz59atw1TFyr8hk9vDgm8nfspYFbzyHRw70+L9kTzhMnsmayVSL9M84puQH6rMTVdPQVDX+UStdehkLqRSs+HtfVVVQFBeq4rGXZCoKqqqQnl42oKYgPx9TCHufM8We3unz+wgE/OTm7gNiwwhiyyYtC4gFS6UhmqUILKGAIgiHwlhYKJZSaaima2rZoABLY29ePt8sW0n3005EVdX4z7D/3+0W9apQHM7F56kY25QPgCwLPlvyE6FQKBaE2Rdd3b+mg/x/Y0EkGkWYph0cWrHbW2VhIpZCIKsWntQqoOhoisDr0SrspSaEoKCgIB6omfGt+hP/v1o1uoPO0W9RVSUebGlq2fOtxsJCVSlbVmpv/6aiKj57CIKmoms6ukuPL/P0er34fH4CpUMIUlJITU2zvw4E8Ppi3Wmx6Z5/pzsNZKAm/cuefWMa97/2Hi2vfhpXSobT5UgJrFj9G90G3sigx57hiOPkhsHJKhKJ0HHA7XTr3p3hQwY7XY5UiZKfp1HLW8QdL0/D4/M5XY6UwGcrN3L37IVMu/1SmiTh5bsAAJleSURBVNaQS9KSVf7eXIb27sqtxzThvOb1nC5HqsStAy6iVqrOnJdGomlyoIckSX+MhsCtKei6PZnT5XLhcrtw6S40XbOnMqpqhSmZpd9TVcWe3KipqKpGYWFh/H5z9+RiRA0UH6iaiikEXq+XKtlZbN68NX49SwiEYYClYFkmllCw7I3ZsISCUBRUAcFgSVnRCTrBFBQ0VbGnemLFJn6qLPj6B87u0haP15vw95CZ5qXTkTXjAxXKKw26i8NRdha5adjsaDav/YVIqNgO1DT14AWVrxeIhCMYUcNe4hoP1ezlmcI00HQ3gazaCFOg6BZqOA+fW8MwzXgQaApB7p5c+z5QMJXSf+9LhyYcSLUsXKqFS9fQdS3+/Lpc9nMc39tMVcoFamXPs6apaJqOq1yY5o4NIvD7/PgDfvyxZZ9+vx9frDPN7XbHw7S/s3daKRmoSf+aIaNe5NVPltHq6jFoHnlAmay+WPwNA++4n3vGvk6DpofXkIz/J3kFRXQccBu33nwzA/sfXvsv/r8Jff8qzepnccOjk9HkZulJa8qSX3j5q594f1h/amSkOl2OlMC2TRt4oG83Hul0FB3qVXe6HCkBwzC4stcZnNa2NY/efp1cjitJ0p+iqiqapsWDEo/Xi9frwe1243K50fXYoIDyoZqqoip20KKW63iKRqPouo5hGOzatctepqko6LpOJBIlEPBQr24tli39Of74liUQ0TCKoiHMKJYZC9EUsFBKt0ejuDgYC9ISr6tUVHDpGsI0USwLVbWXSq5cs4ENOdtp3qQ+inLwEw6qouB1xf6sXLNX6aeKorBhRxBPeh2aVWuGxxsgb892igr24fV6KZvFmejfYItQKIxhGGXdaUIghEBRdIQRxYhGKN63E29GdTAtqlZLxe3SKSkJ4na5UBQFIxplx47tWJaFQEOgVXxMRSmXrdmfKKqCioau67g9brweL16v1w7GSqdvlhsYoMaDtViYpuvomoauu3B73GV7p3l9eH1efD4fXp8PX6wjzePxVFjm+XeXepaS7+ylf8UldzzM5+vyaHHFKFRd7u+UrKbOnss9T73EiNemU7VmbafLkRLI2b6T068ZxsgRj3HOWWc6XY6UgBCC8JLnaHdiWy678wF5QJnERs//hk9Wb+bD4QNJ9yc+Uyw5a9VPP/D0DZcy7swTaFVNLptOVsHiYgZ078r1F53Ljf0ucLoc6RAQwkKI5FlmmUy1SP8MRcEOWtxuvD4vgUAKgYAfn89vByPu2HTGchvKq4qKqqllSwc1DUVVcLtdeLxejKIidu3aSXFxMdWqVUXTdEyzBFVVad6iCcz+qEINRiSIormxokG7O820u9SEIlCFvfF+YVERZYGVBdaB7/UUFNy6hhWbjiksC0WxCAZL+HzRMpo3rh/f1630fizLQik3biHBbwlTWKzIKUZ3+7GERZ2GR1C74REIU9Aw2yp773nQbczsbxYVB+N7qJXvUlMUHdMIYUZCFOftwlelHqYRpUGtqqiqQigUxuPxYFl2KLd1yxYATEVDKFosN1PKNkYrTdTK/Yo0TUV36Xg9XvwBP4FACn6/HYS5XS50vWxogKqqdrdfaWCqauguPdbB6Laf51iXWmkoV/rR5bI730qD2kPRmVZKBmrSP0oIQZeBd7BBVKFp34cO2HBRSh6jX3qVCe/OY9Tk2aRmyAOVZPXjqrVcfOejvPryS7Rve7zT5UgJCCNCeNFozux9EecOvN7pcqRKDJn+KZvzinh/aH+8bvm2KFkt/mQekx64jYnndaReeorT5UgJ7Nm1k6t6n8Gjt13NBWed4nQ5kiT9n1IVFU23O9R8Pp+9D1ZaKikpKfhjoZqml3YZ2V1qpXuR2V1NSvxjamoqWZmZFBcVkbtnD7m5uTRs2ABd1zBjUzzbtGmJqqoIIeI1mOEg7kAmlrDsEE1RSodbxqIohYKCIoSw0LTScMZi//RKUcDrcdkdaqq95FNRBYql8vnipfQ9/yxSUwIQm4wZG2Xwh35PuQVhNu8MIkxhP3J8mKZCekYWmu5CmNGymg4yZbOwqAhhmrE942LDCEyBorkwIvZ9R0qKsQDTiNCkrn2cWBIKkZpqd/QXFRWxJccO1AzFFRtKUBqmKXabXjwwVOJ/pGkabpcdfAX8AdLS0khNTcUfqDg4QI3d3n5e7edY09T40k1dL10SbIdrLrcdoLl0HS12nfJB2qE8yS3fOUr/mEgkwtHnX0+4XjsantLf6XKkStzx4OMs/HE1IyfNwuvzO12OlMDHi77nlideZOa0t2nWtKnT5UgJiFAR4SVjuOT62+jQrbfT5UiVGPjaXFL8Pqbf3gdNnvBJWu+/+RqfvDSKt3p3IVt2ECat9b+tZlD/85nw2F10aneM0+VIkvR/TFUVNFWzlwJ67aEB6WnppKWlkZKaEl++p8bCltJln/aeWypaaQdSbDli/Qb1ycnJoaiwkJzNWzjmmKPRNR1Fsad3tmjRhLS0FPLyCuI1mEYYRXPHJmYS606zl2EKBVQUCgoK7T/X1AMjsHIrLb0ed3zJp6LYYZqiCDbnbOfHX1ZxcvtjD7zp74Q+lmWxYlMBwbARC9KsWKhmYQlwaUq5rjcOGqZZFuTnFyJMM96dVvpRUV2YkRKwLAJV6hINh3C79XigFo1GcbnsOGnX7t32kk8grHgoC9H2v5R1qynYoaeu63g8HnsqZ2oK6enppKam4vP74vud2aFp2bLP0ue9dOmnppYtAdXiIZteLmQtG/xwqFeMyEBN+kcUFBbRuuc1BNr2pnZbOSEymfW54Q52llg88spUdJdcjpus3pg1n9FTZvHR3NnUqimn4yYro2g3xncvcM09j9HmxM5OlyMlYBiCni+8ywnN6/PQhafI5bhJ7I2nHmb1vGm8fX5XUtzyNSpZ/fDN1zx023XMfH4ERzRr7HQ50iFmxaYcJotkqkX6ZyixPc7Kln368Af8pKalkZqWis/riy37LB1OsN+m9aXLDS0LwzRp1bo1X335FdFolN9+W0M4FMaVouN2uYhEItSsWY0mTRry/fc/xWuwhBFvOCvrUovtoYb9saCgkIgRRXfpZfmZVX4JqH1ffq8nvuTTUpVYsKYQFYL5ny3hxOPaoLsqTkHef+Ln/u9VwlHBLxsLMIVlP6RllQVrloVbV7AsQUXWfp9Z7N27z+5OK51uapr2//OWgjAiKJobd0oWqDrVsgLUzE4hEo3G9yAzDIONGzZQkJ9v16V6y5Z62q2Dsc/Vsu/FlAZibk9pl5qf1NRU0tLTCQT8uD0ee8CEqhzkeVZiwwlKh1Qo8S60sn3X/rkgrZQM1KRDbsv2XRx70Y1UP/M6Mlue5HQ5UgJCCE676HIyG7Zk+IjH5AFlEnv8lbeYvfB7Ppn3PhkZ6U6XIyUQyV2P9fNkBo16gUatjnS6HCmBYCTCOc+8Q7/Ox3LDGe2dLkeqxDNDbiD067dM7tUZt5wQmbQ+mjuLV0Y+yMcTx1C/tjzhI0nSoVEakLhcdgeTz+fD7/cRiE1sdLlcdvcSSjxAKx+4gH28YxgGRx11dHwwwaoVK8gvKCA1NQWfz0tRUTFZWZl06nRChUANwIyGUDU3phFCxQ6hBAoqIICiwmLC4Qj+2AR3O0qz4mFeqYDfa3eoxbrTUARWrOvqmx9+ZufuXGrVrI6ilO+WTzxMwLIsNu8KsisvhDCtit1psWDNrSkIs3SNasWlqOWjuj25++yuNFPYS0eFAHRENIwwTbyBFHRfKoYRpXXDTHwenb379pESCKAoCiUlJfz6yy8YhoFAJaJ6ygVoWrlLrEsttt6zNPAqndbpdrtxezx4fV77eQ4E8Hg86C5XhZBU+Z3L/ss6/+ljXLm+QTqklq9aS5sLbqB2ryEyTEti4XCY4866gCbtu3LDvSNkmJbEbnnsOT77cQ3z358jw7QkVrLlJ7SVbzP8hSkyTEtiuwuDnPrUWwzu0VmGaUlMCMEDA3uRsuEnxp/bQYZpSWzyy+OY/OwIPp8yToZpkiQdUnY4oqFpOrpWfvN5u5updCKkx2tPAfWU24y+dBP60kuTpk3IysoCYMO6dWzbth0Al8tFNBoF4LTTO+HxVOwSM8KFaO4Uu/urdDlkueWRxUXFFBeX/O7PkuL3xZZVmvZH07TvxzTJzd3H19/+iDCN+M8d+w2UuwcrHpbZX8GytfuIRk3MWF0iFoiZpkW4JEi0ZG+FUK/snsoIC3bv2VsuTDMRpkBRvURDRVjCIqNuSyxVx4yGad+iCgAFBYWkpKZgWRZ79+Xxy/LlAEQUN4aiV1zmqcYuB+lQA6XC0k23yx1/jt1uT/w53X/YwP7PcWk3Y/llnod6r7REZKAmHTKfLvqeTlcOp/Flj5Ja/winy5ES2JeXz5Gn9eLMvldz8bW3Ol2OVImLbn+Y3LDCnHffwRc78yUln+D6hQS2f8J9E6ZTvW4Dp8uREli7cy/nPvsOYwaeS+92rZwuR0rAMAwG9+rK8RQx4pTjUeUJn6T19MP3sPiDd/liyvNkZ2U4XY70T4ot+UyWC3LJ52HB7jyLbUQfm96p61p8r6zSkK38pXTvLL3cRvSappGZlUWr1q1RFIXc3D2s+PVXIpEIqqriii37POKI5jRvXnHJuhkJorli3WelXVyloZopKCkpIT+voEJKtf/fTgVIDfjtZZWmiTCNWKhmYJkGphHlowWLiESiscEAFge+9JUNPbAsi/yiKGty7OWepSGaKYT9tRDs3bGRaOHOctVY5f5b7ucTJnt258aHEgjT/hlVzYsRLsLlT6X6EV0QwqJm9QyOaFINw7Cv53a5sCyLjRs2sH79eiygRPWVC9I0UHVQ9IodauX3dYOKS3VVpew5dunosaEEuks/IDgrfY73n9z5bzeKyEBNOiQmzfqQ84eNpvmVT+KvVs/pcqQENuVs5bizL+KKoQ9zeq9LnC5HSkAIQdfL76BukxZMfOUlNNmdkbRKVswlO/gr9014l/QqVZ0uR0rgm/VbuezV95h880V0atnA6XKkBIqLCrj1rPZcXCeN205oLbunk9jwm64kd93PfPTaGAJ+ecJHkqR/lh2UlE56jO2bpioHbDqv7rfPVunFpet0OPlkFEXBNE2+//Zb9uXloSgKKSkpFBYVEQj46dXr7AqPa1l20KSo9h6e+4dqkXCEPXv2Un6/tNgNK8z7TE3xgxBlnWnCvpixjrVfV65h/cYt8e+X3z+twithbK+0lZvyyS+OYpp2iGaKWKhmWpjCoqQ4n5TSIT771VL+viLhKLl79sa72yxT2HunmQJLRMlqdCx6IBPTEnRoU5O0gIf8/HzS0uzpnsFgCT98/wOFBQVYQFAN7Beo7XeJL/nc/weruFxTVcqex4Ptibb/8+skGahJf9uIlyYz6Ll3aHn1GDzp8oAyWS37+Ve6XHQ5dz75PMd3kqPsk1UoFKHtxTdybo9ePPHYw46/SEiJlfz4JnUD+dw9fir+lFSny5ESeP+n37hj2qfMuvMyjqhX3elypARyd+7g9nNO4s5jGtG3jdzUPlkJIbju4u7U8lpMH/tYfMKbJEnSP6X8e2FFUeKda78XqFS4jqpyfNvjSU+3t0/5eflPbNiwCcuy8Ho9RCNRLMuiR8+zyM7OqnA/0VABuqfsfZ5Vbr8xM2qwffvOsv4vyzowWyMWqGHZXWDxDreycK24OMgnny+2l3WaJqYRtfcysyys/aKwqClY+tteTFMgRFmIJmLdacK0CBflEQj4E/9SLfs/xcXF7NuXFxtGYAd+qubHjBSiaDopNZoQ3LcTlxXizHZ208y+vLz4NjQ7d+1iyaJFWJZFVHHH9k8rH6bpoOmxTrVyy0APTNT2e94qPtf7/z1IJjJQk/6WGx4cw9Ozl9Di6tHofnlAmazmffoFF1x3Jw++9BbN2xz7+zeQHLFnXx7HXnw9t99+G7fedIPT5UgJCCEo+fZFjmicxZ3PTMTl9jhdkpTAKwuX8fSn3zPvrgHUr5rhdDlSApvWruKu3l0Y2fkozmhc2+lypAQikQiXndOZ049rwbgH70zagxvp0BOWlXQXSfojync11ahRk6OPOQZFUdi9axfffvMNwWBJWZdaYSH169ehe/fTK9yHGS1G1X2UD4DKh2pbtmzbb6+y0n6wsusHUvyoCvGlnuX3ULNME2EYLPhiMUVFQUoTOSHM0oY0+15jnWZbdxWxaUdRuTDNDtFKu9WCRXnsyVlBSsCHhVIxkIvfof29ffvyKSooju/tZgkLVfdhRIpxBzIJ1GyC6g1wQuvqNKyVTnEwGF96aZgmv/7yK6tXr8ICitUAllIuSFNdZWFavEPt4EHa//PriQzUpL+s+w33MOuXXTQf+DiaSx5QJqsJb05j0MOjGfH6TGo3aOR0OVICv23KoUO/23hm9FNcfOEFTpcjJSCEQXjR05zcqT3XPDA6PopbSj6PzP2aub+sZ95dA8lOCzhdjpTAT998xYgBPRh/zokcX1t2uSergvw8Ljn9RG7p0517brzc6XIkSZL+sNJQzeV2cfqZZ6BpGkIIvvziC3K2bAEgNTWFgsIiAK64sg/p6Wlld2AJhBFG1b0V7tey7I6uzZu2Hvig+2W+KQE/uqaWC9KMChfLNNm4MYflv6yOBWhWhZCu/BLQ71fuoDgYinWoxcK02P5ppinIWbmYaDCv4v7L8QytYmE7d+wiVBLCMuwhC4rmxhJRsEx8VeqAquPz6JzfqSGqqrBz5y6qVc0GYO/evXz+2acEi4tBUQlqqbHhA6WBmg6aq+xzRQMONpjg/5s8EpD+NCEEx190Az+HMml88T0ocn+npPXI08/z7OQZjJo8hyrV5FKnZPXNTyvoftP9vPnGRLp27uR0OVICIhIi/OVIzutzKRffNOz/+mzaf93Nb85n7d58Zg/pR4rX/fs3kBzx+fszeeXOq5nUsxNNq8gpxslq+9YcLju7E08NuY7LLzjX6XIkSZL+sPLLPjVV5bjjjqd+/foArFm1ku++/Y5wOIyqqvj9PgoKC2nZsikXXNCtwv0Y4Xx0d9qBD2BZ5GzeimEYFUKveHAVC7J8Xi8etys+4XP/iyns/dg+/Ph/7N13eFNlG8fxb3bSvdlQ9lQBFdl7763sLSAgooLsISAgIBtkyt4bZMgQQRDZS5bsUVYLnWmbcc77R9pShFQFfM8Bns915SqkGb80kObceZ77/gVZlp66j2TxiQ5OX4/DGhtFYkI8DknGIUnY7Q7u3zjHhd83cfPsfsxGPRZL6gUvT3ZRcy1Uk7lx4zZOuz3lPvUGHxyJ0aDR4JO1IAClCgRQIGd64uPjkWUZi8WCJMmcP3+R3/bvB8Cu90ja7qkDnc61Mk1nSFqlZkhaoZa85fOfPnuvBtH4QPhXEhISKFi/M9p8FclapqnScYQ0dOkzhFM37jF6wVqMJvPfX0FQxIbdvzJgykI2rVtD9tBsSscR3HDER2E/OJlWn/enWKWaSscR3JAkiWazN5I1nT/ft26IVvuavWt7jaydM5XflnzP8kbl8TOLVe5qdf7MKfp0as7icUP4oHBBpeMICpHlpOmaKiGLLZ/Cv6TVatHqdPj6+lC9Zg1mTJtOYmIiO7Zvp2TJEuTKlRN/Pz9u3rqNt5c3n3zSjq1bD3Hv3kPQugYiaHWeGD0zABKapKb5aLXcuGfjm3FLyZgxhJBgXwL9vfHz9cTb2wtPDzMmkx6NVkdASE5i7F4pwxU0SSu1NMnbIDVw9Hw4D6PiCPL3ebyKK9UHuH/eiuL2vSj0Zh8S42Mx6YxITgfX//id+9fO4HTY0BgC8AtOj1arJ84aT2JCIvHxCcTGxhMZbeXhoxjCw6O4ey+C/b8exuCRzlVQkzVotRa0Oidm/3R4pc+NhyaBZpXeQ6fTEhZ2lwwZ0gPwKPIRu3bsIPzBAzQaDUHBIdx6lNw7LamIlnJK3UPNfe+0V5UoqAn/WPjDSN5q2JmAMi0JLFxZ6ThCGuq2/QS7xZ+hMxaLCZEqNnPFRmat28lPWzYREiy2OqmVPeoOzmOz+WT4ePIXLa50HMENm8NB7SmrqVE0L33riZWeajZ7RD9u79vKsoblsYim9qp1YM8uxg7oxY+zx5Enu5jgLgjC/8/jlWUv77Z0Oh06vZ5KlSuzds1a7t65w8njxzhw4DcyZ8mM2WTCx8ebh48ekT1HFrr3+IhBA8fgtLtWbzkdMehN/jgSwlPfOA9i7jJ3xp8p96XV6dAb9Hh4+eLlH4zWEYWHp4WwO+Ek2uxJBbrUBTVwFdQ0XH90gzk/LCV/npwYTB4EZMqNTqdDozMgyzK//RFOYmIikIgtPhad2QtrVDg3Tm7Dabe5it+yzJX4e7T9eDAxUQ+JiQwnPj6BxAQbdrsDp8M1GME1QOFxhzWDMQBb4l2QHQTkqYZWp6N+8RByZQshOjoGrc61ik+SJM6cOcvunTsA8Pb2dg17iEp8PIRAZwS9MdWWT51r9VrKds9UgwZShg68+HOtBPEuRvhHLl+/xQctepGxzqf45XpP6TiCGw6HgzINWpGjaAnafT5QbElTscFTfuCXkxfZuXUT3t5ioIdaJd67iPbCCnpPmkuWXPmUjiO4EZ2QQM1Jq+hWvQRtyxVROo6Qhm97tMV04xwL6pVFL3oQqtaGlUtZNmM8uxdNIWM68YGPIAjK+Os0z+c9tkm+rl6nIzgkhFq1azFvzlzi4+P5cdNGir77LoUK5sfP15cbN2/h4+NNq1YN2b5tF7/8chAAWUpEdiai0RqQnAmuG/7LYkkZkJx2HHYNtsRE7BpPrA9vu+pI2scr01KKadqkr66QODUw74cVoAGzdzrylWmKV0A6V7/ypPvSmTxJiIvE5B2EJEHk3avY4+OQJWdKQS02MYELf94mPvKmq0ebLCFLcsrk0Kd+PlojkuRAlhIwePrjGRJK7hANH9UsiizL3A67Q66c2QG4f/8BWzZv5t7duwBkzpyZBL0etI7HxbSUU9K2T60u1Qq1vz4vj4cSvIrHruKdjPC3Dp88y3vNe5G16SBRTFMxq9VKkaqNeL96A9p/MeiVfEF6U3QcPJ6T1+6xdeM6UUxTsfgbhzFeWceAmStEMU3FwiJjqDphBV83rSyKaSomSRL9m9Ugc/hVplQvLoppKjZn8lg2zJ/GL0uni2KaAJB0IK6uk/BqeN7jEa3WtdJLm1yE4vlXq6We9KnT6zEaDFSvUYNMmTOj0Wg4ffIkO3fsICo6Go1GQ0hwMHfv3sPT04PhI74iJCQo5bYctii0Om/+dtuiLCM57DgS49FojEhOCcnhdJ1S909zuCZ8PuurIzGRxPh4ZHQp0zydkoTdZsMWb8VuS+TupWNcPrgJp93uWnmWdELW4bQn4rTbUu5HdjqfWUxz/by9cdqjAQgqUBpvi47uDd/Gx9uTsLA7BAb6YzAYsNvt/P77IXbvcK1O8/PzIyAwAG3qIQTJq9OSTzr949VpyT+3pD8mPy8ajQZN0teUFWupfsZqPq4V72aENG3etZ8q3YaRq80ovDLlVjqO4Ma9B+G8XaUhDTv3ol6rTkrHEdyQJIk6nwwAD19WLVuM0SiapatV/MWd+ITvZ/DcNQRlyKR0HMGNP24/oMH0NczsVI8aRfIoHUdww5aQQK/apajkJTOkXBFVvzF+043q/zln9+/k58VT8fMRH/gIgvB8Uq8mM+m1WPQyZp2ESSth1DgxaZwYcWB4xkmPA4NOi16nQ6fTo9fp0eqSCi88uaLp39Bqtei0OvQGA8HBwTRp2gSdTofdbmfT+vUcPXocSZKwWMyYTSYiIh5SqFA+Bg/+PNV7dgnJEZdUVPt7triH6E3eTxbTHKlPDrfnO+12Eq0x2BLjcTolnE4Zh1PG4XBg9ArEGh3BtSNbSYiN/Mt1nWj1nthiH6VMFXVXSHP9XLyQpQTAiUdwNnzShdK8VCCFC+UkJiYWa3w8wUGuouKly1dZvXIlkZGP0Ol0ZMmaBYPBiMGgd301mjCYTK6vBj0GnQ6DDgxaGYNWwqBxuk440SOj1+vR6fTo9Dp0Wp2rNx2pViI+53P9/yS2fApuzVi6jv6z1pCv43cYvQOUjiO4ceHSFaq36kKPr7/jnQ9KKR1HcMPhcFC2zedUrlqNQf3FhEg1SzizlnSGh3w5dw1mi4fScQQ3frlwnb5r97Dys2bkyRj091cQFBEd+YivGlbgk7dDaZgvVOk4Qho+79CcQIODzbPHif6rgiC8FBqNhqENCtEt/BHRsXFERUXz8FEkj6KiiI6JJS7OSmKiDYfDgSRLaNCg1WnxN2sxmz0wGo0YDAYMeoOrB5pO+1zbA1O2jGpdvdSMRhMVKlZkz897OH7sGDdvXGfNqlVky5aVnDmyExwcxPXrN/DwsND0wzqcPXeR72csQJJkJMmKTuePRmNElm1p3q/TZsXoEQgajavxvxNk/tI/LWULKJBqJZ7kcOCwO0iMj0OjtwCufmd2m417Fw4SF3Gb2Ig7SQMFkoZ1yDLIGjRaI/bEe2kW0lz0gAFJepR8p5TJYqVZo0o4nE5u3LxJ7ly50Gg0PHz4iB83b+bI4UOulXzpQvD3D0Cv15MtnSft0+sxmc0YjCa0Oh0OZzx2Rww2uwO7I6l3W1IerUaLxaLFYjFjMpkwGo3oDXr0+qTnWKt9ZXqqiYKa8EwDvpvNzG2HyP/xJPQmcUCpVgcOH6VFz/4MmPwD2fMWUDqO4EZ0bCxlWn9Oly5d6NS+rdJxhDTEH/uBnBm86T56KXqDQek4ghsrD59l2p7jbP6qNRkDnjHGXlCFOzevM6R5DYaVfpuy2dIrHUdwQ5IkOjSsRtm3czOmTzfxgY/wFEkCjYq2WUqS0gmEf0qj0ZAxXTB+3p5ER0fzMMKIn0XLQxNEmWRiTTIJiRocdh2SJCU19ddiNJrw8LDg4emBxWLBaDKi1+vR6nSPtwY+RxatVoter8dgNODt7UPrNm249OefREdHs/fnn8mXPz+tWrXEz8+XTJkycv3GLUKzZaFv3x7cvnWHDRu2A+B0RqHT+eF0RgJp/YOUcSTGoDd64UiMTjnPVfxKaov21wJhUk81yeFAZ/RA1uhxSFJKDzWtyZP46Ifc//No0m3ITxTOdAZPHIlx/6CYpkGr9UGSolLOKVU0JwO++hiDQc/Fi5fInCkTRqMBm83Gr/sPsG71KpwOBxaLhSxZsqLX6zGajHhYLHh6eeHp6YnJZESr1eJ0OklMtGFLTMRmt+GwO1J6vGm1WvQGPWazBU9PDzwsHpjNZgwGAzq93vWhzgv0zPt/EgU14Smt+45i+9n75OswDq1ebElTqzU/buer0ZMZMWcl6TOL6VtqFXY/nIod+vDN8K+pW7um0nEENyRJIvH3aRR97x3a9h3xSvwCf1NN2XmYH/+4wtb+bfD3tCgdR3Dj4pkTjOv8EZOrFOOt9GKVu1pZrVba1q1Ih/rV+Kzdh0rHEQThNZNSxNLpMOj1mM1mPCwe2LxtyElb/hITbTidDtfkSUCr1WE0GjCbLXh5e+Hp5YnZbMFkMiVtEdSh1TxfUS25Z5der8dkMpGvQH7qN2zAkkWLSUiIZ9XyZWTNlo0a1atiNBrJkD6EGzduERqalXHjhxATE8vu3fsBCUmKTSpIRaZ5n/aEaEye6VIV1P4iqfAlpyqAyYDkcJIQ8wiPoIw4nTIgE3nzHA8u/s6jG2ddAwaeQWf0xm4Nf+b3nvxZ+CDLcYATgBIl3mXS5BH4+vpw9dp1fHx98PX1QZIkTp46w6L587l/7x46nY5soaF4eHpgMBowmUxYPDzw8vLCy8sLk9mETusqkNpsNmw2G3a7HYfDNRwh+XnQ6fSYTEY8PD3x8vbCkrqolqq3mtqJgpqQQpIkqnT8igsJXuRpNcI1iURQpSlzFjJj+Qa+XbwBX/9ApeMIbpy5eIVGnw9nzvfTKVH8A6XjCG5IDgeJB76jcr161OvwqdJxhDT0X7uHP8Oj2NKvDRajWEGoVof27OCHgT2YV7sU2f1FHy61ehgeTvv6lRnWox0f1a6idBxBEF4zydssU4YBGI1YLBZXYSWpmGYxW7DbbTiczpSCkqvPmevynh6eeHp54unp4doaaDAkrV769721ki+fepWaxWymdp26/HHmD44dPcqdsDAWzZ9PUFAQJYoXw9PTE39/Ozdu3iJb1ixMmz6Krl36smfPgaTtnvqkopqbYhmA7MRpT0Cr90ByWP9xXlmWeHj9D8yBGfAwehL/6C43j2wh9v41t9fR6sxIDjuy7EjztrVab2TZgSwnAlC8eFFmzhpL+vTB3Lp1G61GQ4b06QC4fOUqSxYt4tSJ42g0GtKnT09wcDAGfVIxzWxxFdO8vfD29sbD4oFWp0WWXf3eHHY7DqczacunlPJc6JO23prMJrw8vfD29sJssSRt/3Q9zy8y2fX/RRTUBMDV36lI467EpS9M9prtVP8P903Wd+Q4dhw6zbeLNmDx9FQ6juDGnkMn6DpiCquXLyF/PjEhUq0km5XEA9/RuFN3ytUTqzPUrOOCLeiNBtZ+0Ry9Tnzgo1bbVi5i29RRLGtQjmCxglC1rl+5TI+W9Zn5dR8qlnhX6TiCyiVv01ILNWUR0pZ6m6XJZMKZtKpKp9NhMpmx2Ww4HHbX6rSkpzW5z5nBYMBsNmO2WPCwWDBbLCmFFm3Swo+XsUrN39+P9h07cPv2be7dvcupE8dZOH8+3t7evFWoAP7+fjidTm7cvEXWLJmZNWssn/UazNYtu5FlKxqNF1qtF5IU6/Y+HbYoDKbAf1lQk4m9f51HN89h8EmH0+kkPvJe2o9N743D9ijNy2g0nkm3H4dGo6FChZJMmTqSDBnScTssjESbjRzZQwG4HXaHlStWsmPbVmRZxsfHh2yhoRgMBleB1GzB08sTLy8vfHx88PHxweLhgUHvKjNJsozD7np+nU5nynOcPOFTn1SUM5tNeHi4tn6ajMZXppgGoqAmALGxVgo26IS5SB0yf1BP6ThCGlp9+hU3IhP4Zt4q0d9JxZb/uItvfljN1k3ryZxJTIhUK0dsOI7D0+nQbziFS1dUOo7ghiRJNJixliI5MvHNR1VeiTdXb6qlU8Zwev1iljeqgLdJ/I5Sq5NHDzG4RwdWTx7BO/nFBHdBEP4bqVeo6fV65KRJmTqtFoPBgMViwe5wuCZcptq+qNFokgYQ6DAYjRiNxpTG9QaDIaUg9rx91CD1KjUjJrOZ0NBQOnTsyMQJE7DGxbH35934+PjQ6eOPyZMnF0FBgcgPZK5dv0G2rFmYPn00X3/9HYsWrsJuj0Wr9U67qCY7cTri0eo8kZxx/yirLEnIGglZo8XhsBF16xxOe6L7x6a1IDkTIY3VaRqNJxqNDkmKRqfT0fTDuowY8RX+/r7cvHkLm81GjhzZ0Wg03H/wgDVr1rJ6+TLsdjtms5lcuXNjMplStnqaLRY8PFxbNn28XQU1T09PDAaDa7ebLON0SkiS86nCfPK2T9eEUNftpQwoSNra+yoU1URB7Q0Xdu8BhZt0I6RyRwIKllE6juCGJElUa94Jz/ShDJw8PeVTGUF9xs9fycpdB9m5ZTMBAf5KxxHcsEVcRz69gE/HTCNXocJKxxHcSLA5qDl5JR+WeptPa5RQOo6Qhin9PyX2xH6WNCiPSS8mRKrVrm2bmD5yINvnTSB7loxKxxEE4TWXXFADwGgEjQatTofBYMThTCqmPWMFpFajRavTotPpXIUvvQG9QZ9SaHuRQkvKNtSkVXAmkwmHhwfvvf8ezZo3Y+H8BdhsNrZs3oTJbKZtu7bkypmD4OAgtDotl69cI3toVr75ph95cudg9OgpREZGo9V6odV6I0kxz7xfyRGLThcAxJP2IIMksozkdJDwMAyQMfqkNdFci1ZjwelwvzpNq/UCNEhSNN7eXnz+eWc6d2mF0Wjk8pWr6LTalGLag/AI1q5Zz6If5hEXF4feYCBnrlx4enq6BhEYXIVIDw9LSu80L28vvLy98fTwwGg0oUvaTSDL8jOfY41Gg1ajQafXo9fpUlYfGl6h7Z4gCmpvtLN/XqF02z5kbdAbn+xvKx1HcMNms1GyXnPeKVuN5t2+VDqOkIYvvp3Bqat32PHjRjw8xHRctYoPO4PhygZ6T1tEhmw5lI4juBERa6Xu1NX0qVeWpiXeUjqOkIYRnZoS/PAWs2uXQic+8FGtZT/M4sclc/hlyXSCA8UHPsI/J0uuk1qoKYuQticKakl/1+v1OA1OZElKKbSkLrakrCLTaNFoHxe/klemvYxm9amLakajEafTicPTk8pVqhD5KJIN69djS0xk/ZrVIEPrNq3JnTsngQEBGPQG/rx0hdBsWej0cQvefic//fuN4uTJs4AFrdYvqafaX/+hykhSzD8aZACuQpRGlom6cRq92RNb7COQJTQ6PaBBdtpTLusq5MXxeE/lE482aQCBA1mOo0CBPIz8pi9lyxbH4XBw7vx5Avz9yZDBNY373r37rFm7jgVz5hAVGYlWqyVTpozodDpiY2ORZRmPpJ52ZosFs8WMxWLBYvHAYra4prIajej0+qeep78+z8lFNc0znmMxlEBQtV9+P079z78hZ4uv8UiXXek4ghtR0TF8UOcj6rTuQvUmLZWOI6SheZ+ROI2ebFq3Gr1evLSqlfXqfjwf/Ebf2avwD06ndBzBjasPHvHR7I1MaFOTCgVF0VOtHA4H/ZpWpaS3ht6V338l3vi+qaaMHsaZA7v5Zel0vDzFBz6CIPz/JBevkr9KkoROp3MVV5LqK/JfCkEaNMl/SLlu6uLcyyyoGQwGJEnC4XDg4+ND3fr1iI+PZ/u2bSQmJLB+zSoSbYm0at2aQgXz4+Pjjclk5Oq1GwQFBlC8+LusXjOHqVPmMW/eCmJiEtHpApCk6KeGA8iyA41GQqPxRJbj0w4pa0ADkt1O+LkDmHyC0Jm8cSYmbxlN/lmYAQ2ybE857zEdOp0vkmTF01NLy1at+OyzToSEBBEVFc3Va9cJDc2Kn68vADdv3mLV6jUsXbiAqMjIlJ/z7dth3Lh+w3WLOh1eXl7kzpOHD4oXx2Q0YjQYMRj0rlWEej16g+GJbZvPfA5SPcepn9/kP78q7ynEUd8baMWPu+g8eg5524/D5BeidBzBjZu3wyjXuB0d+w7jgwrVlI4juCFJElU7fUWBd4owfsyoV+bF/01kPbeFAPtV+s5dg4e3j9JxBDeOXrvDJ0u3s6BbY97JlkHpOIIbCVYrX9YvS/Oc6WnzTi6l4whpGPxZV+wRN9m5YDJGMR1XEAQFJBdIZFlGq9WmrFT6uwETye+r//r1ZUleCZU8AMHpdOLn50/Dxo2RZIkd238iMTGRzevXER0VRas2bSj2/ruYTCby5M7JzVu3ibwcRbasWRg0uBe1alVjwoTF/PzzQWw2XzQaI5Jk5a8rx7RaC+CZNCnU3WM3JF3W9dUWHQ/o0Gp9Ul3GiKuYZnvifNeqNAuybMdg0FK6dAV69WrJB8XfRpZlrl69RnxCAgXy58VoNCJJEucvXGTF8hVsWLOauDhX0U6WZZxOp2ugQBJJknj06BGHDx3i2tWrNG7ahICAQNdKQ0lKeU5Trz5LqxD6Xz/H/zVRUHvDjJu7jG+WbCd/pwkYPH2VjiO4cfKPc9Tv0JMvxkwjf5H3lI4juJGQYKN0689o3KQJX/bqqXQcIQ3xJ5eTxcdOr+krMZrMSscR3Nh+5jJfb97Pui9bkj1EbElTq4cP7tOvcSW+fD8vNXNlVjqO4IYkSfRo2YicIV7MnD5a9F8VnpskyWgk9UzWlFSURfh3/lo0+acFtf8yS/JqOaPRiJRUPApw+tOwUSMMBgPbtmzFbrezZ9dOIsLDad6qFRUrlsfP15dsWbMQHR3NxT8vExQUSNF3CzDvh6/Zt+8g06cv4OBvZ7DZdEhSPLKckPre0Wr9kOV4ZPnZgwZcK8+0yPKzJ4O6imnapO2j0hPX02o90OvjeO+9fHT9pDUVK5bGZDLy8NEjbty4Rfp0IYSGZkOj0ZCQkMDhI0dZungJv+zejd3uvsiXmizLPHjwgEULFmI0Gnm/WDHXQAGDAZ1W5+qDp9WigzRXnb1qBbS/EgW1N8inI6ew7Ndz5O80AZ1RHFCq1Y69++ncbwRDvl9ClhziU3+1ehgZTZk2vejT+0tafPSh0nGENMQfmk3+3Bno8vVEtDrRLF2t5u8/xcKDZ9jSrw0hvl5KxxHcuHnlT4a3rsvo8kX4ILNY5a5WDoeDtnUrUbfsewzu3v6VP2ARBOH1pPRrU+qtn8ggm5L6ukkSTqeT2nXq4OXlzYZ167BarZw8fowH9+9z4/oNatepRY7sofj4+JA/nxd37tzl7LkLZMyYnooVS1OmTHGOHDnJ4kVr+fnnY0RExOJ0WpMKazKSFIlW64eraPY32z+fym1Go7GkKqZp0GhMaLUe+Pt7Uq58YVq1bMgHxYtgMpmIjY3j0uXLGAyGlFVpAHfv3WfXrt2sXLqUc2f/AMBgMOBwOP622AmuolpsbCwLfpiPxcODt956K+X8FMYnt/0q/Zy/bKKg9oZo1HMoB24lkLfdGLQ68bSr1aJV6xg+7QdGzV9DUDqx1Umtrt68Q/Wu/Zk47luqVK6kdBzBDUmSsP02iRLlStOs54DX7hf462T01gPsvxLGtv5t8baYlI4juHH6yEGm9mzD9zVKkDfIT+k4ghux0dG0rVuRnq0a8vFH9ZWOIwiCoHparRZZJ2MwGpFkGUmSkCQZSZIpX6E8vr4+rF65ivDwcMJu32Le7JlcvHiB+g0aUPyDYvj4eJMpU0aCg4O4HXaHsLC7pE8fQvES71K8+LuEhd1l9679bN16gBMn/iQiIhyHIw5JikKr9USj8UWSYgHn3yVNmtYJkhQF6NDrvfH3D+Ltt3NSvUZJKlcuRZYsGdFqtURHx3D5ylUAQrNlxdPTE4CEhETO/PEHmzZuYtvmzTx8GIFGo8Hb2xs/f39u3rjxj392siwTGRnJ3Fmz6dLtE3LnzoNTklzbP1NtddWnGlLwOr0nF5WV15wkSZRq2ZMwQ2ZyNevzWv3jfd2MnjKTJT/u5ttFG/D29VM6juDG0TMXaN5vDAvmzeG9okWUjiO4ITkSSdz/HbU+akn1Fh2VjiOk4bPlO4iIt7GpTytMBvG2RK32btvAypF9WVSvLJl8PJWOI7hx785tPm5cg7F9PqFe5bJKxxFeE7IkI6tom6WasgivvtRbPwGMRmPK5NHkglDRd9/F3z+AdWvXcvHCBRLi49m5bSsXz52jeq1aVKlahXx582Aymcgemg2bzcadu/cIC7uDn68vwSHBtG7TmBYtG3D/fgSnTv3JoUNnOXbsHFevXOXRoyhsNk8cjngkKS5pwEDycbsGjcaAVuuBTueB0Sjj7+9DaGgoRYrmp1ixArzzTm7SpQtGr9dht9u5d/8+Dx6EYzKZyJY1K55Jw2icTic3btxkzy972bxhA3+cPo0kOdHpdISkS0e2bNm4fevWv/4ZJm//nDd7Dp06dyY0e2hKP7XUPdVS/5xfl7qEeOf6GrPZbLxV/2OcOUuTrXwLpeMIaegxYDiHLtxgzMJ1mMwWpeMIbmz55SBfTpjLxtUryZlTTB5UK0dCNPaDk2jR4yuKV6ujdBzBDUmSaDl3E+kCfFn+cQO02tfjjdXraP2Cmez7YTLLGpYnQKwgVK2L58/yZbumLPh2ICWLvq10HEEQhFdGcnEneftn8pbI1AWh7Dmy07ptG37Zs4d9e/dhjYvj+rWr/DB7Nod//50q1atTpkxpsodmw2g0ki1rFpxJDfyvXr3mGnjg60tAgD9Vq5akWrVSOJ1OYmLiCQ+P5N69CO7fjyQiIpLYmFgkSUrKpMHT04vAIH9Cgn1Jlz6QoCA/vL090Ot1yDLYbImEh4cT8fAhTqeTwMAA8ufLi8HgGmjgdDq5c/cehw4dZvu2bRz67QCxMTFoNBrMZjOhoaEEBQej1+txOJ3/aLvnX8myzO3bt1nwww+069CBzFkyP1VMM5pcP9fXqagmCmqvqcjoaArW64xvyQ9JV1RMiFSzxp0+JRoLw2ctRacX/yXVau7aLUxdvoWfftxI+nTplI4juGGPvofz6Ey6DPmWgsVKKR1HcMPhkKgzbRUV38rFgAblXos3VK+reaMHc233BpY3qoCHWEGoWr//uofRX/Vg4/ffkj9XqNJxBEEQXjnJfb7cfc81tVJLlapVyZEjBzt+2sGVy5ex220cO3KY8+fOsmf3bspXqMgHxYuRI0d2PD08CAoMJCgwELvdQVRUFDdv3SYhIRGDQY+npyfeXl5kyhRIaGh6dDqd2/dEctJWVIfDQXxCAnfv3SM2NpbExET0egP+/r7kzJEDs/nxB182m41bt8M4duw4e37+mcMHf+NhhGt7p06nIzgkhGzZsuHh4YFOp0Nv0GMyGVMmsv5brgmiV1m6eDEt27R+4menQYNGAxqj6Ymf9av+HlC8M3oN3bhzl/Lt+5KhZjf8836gdBzBDUmSKNeoNZnyF6HPV8Ne+ReT19nwGYvY9vtpdm3bjI+Pz99fQVBE4oNLaM4u44sJs8mWp4DScQQ3YhNs1Jy8ko6V36dTRTHFWM3G9eqA5tIpFtYrh0EnJkSq1ea1K1k4aRQ7F0whcwYxKEJ4+cSWT+FN4a6ollwQ0mo1aHVa9PnyERQczMkTJzjw637Cw8OJt1r5/cB+Tp04Tu68+figRAmKFXufPHnyEBwUiMFgICgokKCgQGRZxmazExcXR2xsLPcfPMButyetSksqPKWahOqqbcloNFr0ej0WswkvLy8Cs2bBZDKlNPyXZRmn00lkVBRXrlzj2LFjHDxwgDMnTxIZ+Sjl8fn4+JA1W1b8/QMwGAzoDXqMBiNGo5Hg4BCuX7v+3D9DWZY5f/48q1aspFnz5o8HEWhcjwuNBiNG0JOS+1UmCmqvoTJtehP64SC8s+RTOorghixLFK7akDJ1mtKwXVel4whpGL9gNRqzFz/9uAGTSWx1Uit7zAM8Lq6iz/dLCcmUVek4ghsOh5PK3y1j2IeVqfuu+B2lVrIsM6hlHXLboxhWsyTaV/zN7uvs0L5f+H37evYum4G/r/jARxAE4UU9q6j2eBqoa0uoXq/HoDdgKVGCnLlycfzYMU4cP0Hko0ckxMdz6vgxzp4+xY8bN5C/YCEKFylCgQIFCM2WlcCgQCxmMyaTEZPJSGBgwBOrwVL6tz2xXVLzRGuMJ4ttMomJiTx6FMmt27c5f/4CJ44f58ypU9y6cZ3ExMSU/J6enmTOnJmg4GCMRmNKIc1kMmEymzCZTOTOnZszp08TH//vJo+mJssyp06exGw207hpk1QFtcePIbmo9qpP/tTIz7OWT1Cl6OhofH198cmcG0tARqXjCG7IkpPYayfw9PQm39uiqb2anTx8gASrlWpVKqPV6pSOI7hx4cIFrl6/Ru6338PTx0/pOIIbdlsiF48dJKOvB/kzi23TaiXLMrvOXEYrSZQMFe8l1Ozk3QjCY61UK1scU1K/H0F9wu4/4ODxM0RFRb1yq9yTjy3yfrIcnclD6TgpnIlWLkz/6JX8mQqvjuQtlk6nE6fTid1mJzExEWu8FWvSyrKYmJiUU/iDB/zxxx/8cfoMERERSElN+cE16CA4JIRs2XOQK08ecubMSZYsWQgJCcbX1xdPDw9XgUuve2orZPJtSJKEU5Kw2+zEx8cTFR1NeHgEt2/f5uqVK/z5559cvXyJu2FhWK3WlNtIXpGWIWNGAgMDUwppBr3BVUgzmTBbzHh4eGA2WzAYDez8aQdHDh9+rm2fqel0OsqWL0fduvUICg7C19cPL28vPD08MJnNrtVxev0rvVJNrFB7DS2dOz1lJK6gLjdu3GBwn88xelroa/SB85eVjiQ8g1OWmGaPwajXU7RtH7zyiObOanXmx0XERtyn9PvvMurrgUrHEdw4fPQEI6fMwi8ohAUrlikdR3AjJjqajh93QWMw0XjEPKXjCGn4df54HKZ42pZ9j061KygdR3Bj7d4j7Dh6XukYL0ySZTQqWoMhqSiL8PpKLkal9ADTaNBoXSvF9DodeoMBg8GA0WhMKUz5+flTsGAhbly/zvnz57l96xZWqxW73c7tW7e4fesWv/26D7PFgp+fH4FBwQSHhBAYFExAQAA+vj54enpiMpnQJ/XWlpwSibZErFYrMTExPHr0iIjwcB7cv0/4g/tEPnyE1RqH0+l8IrfJZCIwMJCQdOnw8fFBr9enFNJSMpvNeHhY8PDwxMPTA4vZgslkonadOoTdvk1YWNgLFdWcTif7ftmLxWyhWo3qJG9n1Wq0kKqA9ioX1URB7TVUsGAhvMWnNapz9PBhhvfrzZrJw2nXZwS5NOr5pE94zCo56Bv/kC59enP02HHiMmQlOIfYmqZGe6f0I2vsdYZ2qs/S03coXKig0pGEZ1ixbiNjZy5g1Pw1DPn4I9555x2lIwnPcPPmTT5s1oKP+33NkhkTSSde91RJkiTWD+nEe0XfIWOdWnheO8I7ubIpHUt4huGLNrLw8FXK9xjJpiHtlY4jCMJzSC7wJA8LcDXW16DV6dDp9Rj0egxJWybNZjMWswWzxYy3jzfZc+Tg0aOH3LxxkxvXr3P//n2sViuSJBFvtRJvtXInLCzlvrRaret2dTp0Wl3SFk8Nkiw9sVJOSiqcPSuj2WLBz9eXwKBAfH39MJlMKVtU9QZXVqPRiNlswmKxYLF44OHpgWdyQc1iwWg0EhQcRKcunZk6aTIREREvVFRzOBzs2LEDDw8PylesgFabXKDkiWJl8s/gVSuqiYKaIPwfbPlxIyMH9GXL7LHkyCK20KjVQ4eNgYmRjBj7LQ0aNKBjp4+VjiQ8gyRJ7PqmM8UDNEz+ogU/n7mkdCTBjQnfz2Huyo2MWbQebz9/peMIbpw8eZImHzaj99hpFCj8HktmTFQ6kvAMDlsCq/q1pn6jJrT9pCdL58xQOpLgRueJi/j5ppXqg2YTfeem0nEEQXgBqYs96ElapaZFp9W5VqrpDRhNyavUzFg8LHh4eGD1iMPT05PAwEDy5stHbGwMERER3L93n/DwcKKjorBarTgcjpTtoU6HA4fdnmaO5BVoer0es9mMp5cnvj6+ePu4VrcZDAa0Olc+nV6HQZ+0ki51RovFldPigaeXp2uFmocFs9mMXq9HluGtt96ic9euTJ08mejo6BcrqtntbN60CYuHhRIlSj5z9V+yV62oJgpqgvAf+2HObBbPnsbPCycTEigOKNXqRmI8o6Q4Zv4wjzJlyigdR3DDYbOxfVALPiqSjf6NKykdR0jDF4NHsO/4WUYvXIfZIlbkqtXOXbvo1qMnX89cQpYcuZSOI7hhjXrEmgFt6dqrN7UaNVU6jpCGOoOmckMXTJWvRr82/VfFlE/hTffEts+kk06rcxWu9HoMRkPSyi8zlqR+ZFZPT6xxVqzxVhLi4/H29sbfP4AsWbJis9lITEhI6sdmxWqNw2qNJyEhAbvdjsNhR5IeDyVwbTFNvRrOhNFoSimeaTXapBVuWvQ6PTq9Dr3OlctgeNwrzZS0ii6loJZUSLNYLJiTVqfptFokWcaWaOPd996lfccOzPp+Jlar9bmLasmDE9auWYvZbKbou++6CpRJwwr+6lUqqomCmiD8h0Z9PZSDP//EnkVT8PYUB5Rqddoaw/cGB6vWrKVgQbFtUK0SrbFs6/chX9b8gPaV3lc6jpCGZh9/yv14J1/PXo7eYFA6juDG4sWLGT32O75duI6gdOmVjiO4EXn3JhuHd2XgN+MoUa6i0nEENxwOByV7jUOX+wPKNPv0lTkYFAThn/vryio0rq2W+qRtlUajEaMpeTulq2CVkBBPfHw8iQkJxMcnkJiYSGJiIjabDbvdllRAc+J0OnA6nDglp2vFmiQ/NfEzdQ5XTzctWq3GVVDT6dDrk7Z36g0YDPqUXmnGpIKaxWJJ2ZaaXEQzm82uyxiNKb3MJElK6eFWomRJYmJiWLRgIYmJic/9s5NlGWtcHKtWrMRstlDorULPLFSmXrH2KryOioKaIPxHPuvWmchbV9kx7zuMRnFAqVZ7YyNZ621gy6btZM2SRek4ghsx4ffYNbQV41tVp+a7+ZWOI7ghSRKVG7UkIDQvfb8Z+Uq8EXpTffvtt6xYu4HxSzfi5eOrdBzBjbCLp9k1qR9jZ8ylgJgMrlrWhASKdhtNpvKNKVCjudJxBEH4D/218CNJElpN0ko1vQFj0oows8WCR2IiiQkJJCS4CmnJX23JRTW7HZvNhsPuwOFwFdYkyYnTKSFJzpRJo6nraal7jyUX0nQ6LTqdPqWgltwrzWgyphTVXIUzc6o/m5KKbcaUaZs6nWtVrSRJaLVa1zZUp4PyFSoSExPD2jVrsdtsz/2zk2WZqKgoli1ZQtv27cmTNw8azbO3fiZnUft7SVFQE4SXTJIkWjSuT2ZfM2unjkwZfSyoz/qYCA6G+LNj8yaCgoKUjiO48eDaRQ6M/YQfPmlM8byi+bZaJSQkULJmI96rUoemH/dUOo6Qhp6f9eLYmXOMXbQOk9midBzBjT8P7eHIkonMWLKGLKHZlY4juHH/UTTFen5LoSbdyV6iqtJx/hOyrLItn2LKp6ACyavVtFotklZCKyX1VTPoMdgdKYUsu8WCLdGGzeZalZa8Os21Qs2O3WZ3rVRzuFaoOZwOJGdSUU2WkJP6q6W+35TVaRotOl3S6rSkfm6uraGuLahGgzGld5rRaHziZDAYMRgNKUMLkh8LuI5nk4uFTqcTH4eT6jVqEBcbx9YtW1Imij4PWZaJiIhg6ZLFtG3Xnhw5tUnFQc0Tgx/g1SiqiYKaILxEDoeD2pXLU634Owzt3k7V//nfdD/ERhCWKys7163D09NT6TiCGzdOHeTUrIGs69OKvJlClI4juPEoMpISNRtTv0N3Ktf/UOk4QhqafvgRVknHyDnLU96oCupzctsqLu1axdzVmwkMFq99anXx5h0q9pvKB50Gk7GgaEUgCG+av06n1Gq1TxTWjEYjDocDh8mB3WHH4XA8LqDZ7djtDux2m+sydgeOZ2z7lORnFNRSpo1q0el0KUMKdDpdymozg8GQUjQzGgyubaBGQ9KWUP1ThbRnbbU0Go3IKRNG/ajXoD5xcXH8smcPkiQ9989NlmXuhN1h2dIltGnXDo1Gm2r6Z9J9m4yA+otqoqAmCC9JdHQ0tSuVpVuzenT+sK7ScYQ0jI8Lx/JeYX5cvBiD6O+kWhd+2cz1dVPYPrgjGQPEljS1un7zJhUbtaJTv5G8V1b0d1IrSZKoVLkKWfO/w6dfDVXtG1MBDiybTtT5w8xbuwUvbx+l4whuHDhzkcajF1Lh8+8IyJpb6TiCICgouRAky/KThbWkApfT6EwplDkdjidWozkcDtd0z6Ttng6HE1l2FbFcWz7lZ6xQS71CLnnLp8510uvRJ3016PVP/D3lMlpdyrTSZw0ASF6pZjQYQZaRZBlZkpAkiaYffojVauXQ77+/0GpRWZa5dvUaK5Yuo0XrVk9sZXUVDEFjNKU8zuTHrjaioCYIL8GdO3doUL0Soz//mPqVxYRItZIkiWHxEbxdszqTJk1S5Yuy4HJi3TziDq5l59CP8fcSAz3U6vipMzTq0J0vx04nz1uiv5NaWa1WypQtT7m6jWnUrqvScYQ07Jw+FHNCJLNWbsRoMikdR3Bj3d7DdJ/7I1X7f493SEal4/znZElOmTioBmrafioIqaUurCX3IJMkCZ1Oh6R3FaQkScLkdOJMWvklOaWUFWnJf04uXskyyDxdUAMeb5FMKorptMn91HSP/5xUOEtexZZ6Jd3fNf/XarXIOhmD0ejKkTQcQZJkWrRqSXy8ldOnTr9wUe38+fOsXrmKZs2bP952mlQ0RKPBiBH0qHZVvSioCcILOnfuD9o2bcS8b/pSquhbSscR3LBJEv3iw2nSqSNfffWV0nGENBycOwrPm0fZPuRjLGKgh2pt3fUz3fsPZ8j3S8gUmkPpOIIb4eHhlCtfkWaffEGFOg2VjiO4IUkSm0f1JGfmdAz9frnov6piU9ft4Nsfj1BjyBwsPgFKxxEEQYX+2lw/dYEtuciGDJL8uMgmSzIycsrfSZnw+azbT7qP5OEE2lR9yJKKUlqt1rXSS6v5x0W0v+ZPLmIZjIaUwp4ky8hyBtq0a8f302dw6c8/X7iodurkSTw8PGjYuNETjyuZkcfbP9W2IEIU1AThBfy67xd6d+vCuqkjKZArVOk4ghuxDgd9Ex/y5aCBtGnTRuk4Qhp+HteLPJqHzOvXFr1KP4kSYN6S5Xz7/QJGLVhHgOjvpFqXLl2iVp169Bg2liIlxOpptZIcDlYPaEuFihXo1meg6g4WhMf6zl7NqpO3qTl0LgazWD0tCELaUhevUhed5JRi2ZMnUq1IS768u2LVX4tjGjSQetvkX06p8/yb7KlXhqXOmjlzZtp37MCMadO5eePGCxXVJEni94MHMVvM1K1bz1UQ1Pylt1uqba5q+j0pCmqC8JzWrFrBpFHD+WneeLJkSKd0HMGN+3YbQ+yRfDd1CtWrV1c6juCGJEn8NLQNVUN9Gd3qQ1X9ohSeNGL8JFZt38uYRevxFP2dVOvwoUO0aNOWgZPmkjN/IaXjCG7YrHGs6t+KFm078mHbjkrHEdLQcvRcjkVqqT7we7T6N2v1dOqDezVQUxbhzSTLMnfv3ScuPiHlPK1Gg5+PN/7+fk+9j42Li+PcpevcfRiFl8VMnmwZSZ8uOGXV2vWbt7ly+wHpA33JkyNryvVlWebu/QdcuHaHID8v8ucKRavVkpiYyL0HEUhJ/xd0Wi1enh74+fo8sYrLZrNx6vwVsqQPJH26f/8BaOqhCzqdzjWkIKmfmizLZM+enQ4dOzJ92jTu37v3Qv83nU4n+37Zi4fFg6rVq6HRaF0r8XhyxVryIAW1HCuIgpogPIdpkyeycfki9iyaQoCfOKBUq0sJVsYTz4KlS3n/fTF9S60ctgS29vuIjqUL8FkdsYpGzTp/2Z/TV+8wav5qjCaz0nEENzZu2sRX/QbwzdyVZMiSTek4ghsxEfdZP7gDvQYMoVJNMcxIrSRJokq/STzyy0nFz/ujEdtxBeGN99vR0zRecpUY++PVWxokAowSLfLr6d+8Ap6engDsO3Sc3huucvKRAZvTVaQKsdyi8zt6ejcthyTLNJ11jOMRWtJbbrG3p4Hs2bIAEB8fT7s5h9lzR4u/SeZATxM5smXh66V7mXXagVN2vR5pceJr0lA6o4Z+tfLzVr5cAGz65Qgt1z+iWsY/WNuvwXP1IUs9FAB4YuWcLEnkzpOb9h07MHPG9zx6+PCFimoOh4Oftm/HbLFQvkL5VIMKeGqVnVqKaqKgJgj/0qC+vTl/7Hd+XjgZD4s4oFSro3FR/GCGDes2kTu3mL6lVtboR/w0oDlDGpXlo9KFlY4jpKF+649JNPkwZMYi1TaGFWDmzJlMnzWXsYvX4x8YrHQcwY3wm5fZOronX4+fStHiJZWOI7hhszko1nMM3oUrUqpRZ6XjCIKgEn/efsDdeB1+Bpna2UGnhdtRDvbd0zPmKKT3/pUeH1Xj1NkLNFt6jTvxBooGOqieXUdYjJ1VVwyMOOjAy/AL7WuW4H6iDqcsE2bVsP3IRbqGZnXdz7WbHHygxSlDjA1iYq1IksShW1ai7Sbye9vI6W3HLmk4E21i+UUNB+/8wbauOvLkzM7dqARsEoRF2XA6nc/9/i11Ue2JglrSsIKCBQvRpm0b5syaTWxs7HMX1WRZxmaz8eOmTXh4WChevMQTwxRSb18FdRTVREFNEP6FTm1aoo2PZOvssej14r+PWu2IecRWfwvbN20iY8bXf/rWqyryzk32jGjP1A51qPS2KHqqlcPhoFzdDwktXJxunw9Q/I2L4N6QoUPZtusXxi3egIeXl9JxBDdunD7C3pnDmDRvCTnz5lc6juBGdKyVoj1Gk7NGG/JUbKB0HEXJkqyqyZpqyiK8mZLrRRktdr7vUh0PDw/sdjufTN3M3Atatv0ZR1eHg++2/EFYvIFS6Rys61mWoMBAJEmi6OpdfLU3gQv3n9wyqtNq2HQ2kk4OB3q9nu1Hr5Dg0GDQue7U6XSi0WhwOJyAhrbvePBli2rIsszV6zep//0x/ojUM33bKSZ2y/5SH3NyUS35GDilnxquyZ9F332X5i3jWbRgAfHx8S+0Ui0hIYE1q9dgNpkp8m7RlEEFfx1WAMoX1URFQBD+AUmSaFS7Om+HpmfiyKHigFLFVsQ85EymIHZt2oSfn5/ScQQ37v55mkMTe7Hss48onD2T0nEEN6xWKx9Ub0iFhi2o20r0d1KzTh9/zJXb9xkzfzUGo1HpOIIb5/Zt49TaWcxasZ4MmbIoHUdw49b9h5T8YjxFWn5J1nfLKR1HEIRXhFNyfdVqICoqmr13dGiALsX8CQoMdH1Pq6VzwwqULHCJLOmDU44rvY0a8gXqOBJh5Padu2RMn45tl6xk8TZj1Gu5Hik9dX+Pt0RqyJk9G60KnaXvrw5+vS2RmJj40h9fclHtiZVuqQYVlCxVkvh4KyuWLcdms73QSjVrXBwrV6zAZDZT6K1Cz5xS+rwDF14mUVAThL+RkJBArUpl+bBqGfp0bK50HCEN38dGEFswNz+tWoXZLLbjqtXVI3s4v+AbNvdvS/Z0gUrHEdy4/yCcUnWa0qLHV5SuLvo7qZUkSdRr0BCjbyBDpy94os+JoC5HNy7k5m/bmLd2C37+AUrHEdw4dfkGNQbPpHS3EYTkeUfpOIIgqFhYvI6ukzeh08rcSdCz564JvUambgFfYuPieJQgYdDpyJPlyRYMer2eIoXyARAZGQWAFpm6uQwM2efg15OX+MDu4MRDAw1zOjgVDvD3BaOcIZ5oiOKRTUNCQsLfXv55aDSaxwU1GWST/MRqtQoVK2KNs7Jh/Xrsdvtz348sy0RFRbF86VLatm9Pnrx5AA1ajfaprZ/JeZQoqomCmiCk4eHDh9SpXI6+HZvRql41peMIaRgdF066MiVYPHeu6O+kYn/8tIp72+axfUhH0vl5Kx1HcOPCpcvUaNaB7l+P4+1ipZSOI7jhcDgoW74ChYqXo/WnfcTqaRXbN388ibcvMm/Nj1g8PJWOI7ix+9gftJywnEp9JuOX6eVul3qVSZIMKtpmKakoi/Bmi7brWHvLM2kapUxGDyftCxloVbMU4REP0Wk1SA6ZRNvThSVZlp/6vV02bwgBh66y+exD4u1O4pxa6rwVwOnd94C/P76xJjqRAYNG/k+Ph1J6qunBiDFpldrjQQU1atbEarWyfds2nE7nc9+PLMuEh4ezdPFi2rZvT/Yc2dFqHw8p0PC4sKZUUU0U1ATBjevXrtK0bk0m9/+UamWKKR1HcEOSJAbER1CuSSO++eYbcUCpYkeWT0U6tYOdwzrjbTEpHUdw48ChI7Ts3pt+k+eRPY/o76RW0dHRlC5bjtotOlDrozZKxxHSsO27fgQYJSYsXYvBYFA6juDG4h376bt0N9UHzcYzIETpOIIgvAJyeNtZ3a4gFrMRrUZLgJ8Pfn6+aDQa/Hx9SGeReRgF+8+HUfr9winX+/PyVQauOk69twKoUfLx+ZmC/fkg+CL77ui5HRdLJgsUK5gTdt/72yxOp5Pdf0aCxkAuXxmLxfLyH3AqqYtqBlytJmTklC2g9Rs2wGq1sveXX5Ckp7er/lOyLBMWFsayJUto064dGs3TAwqMJuMTgxP+n8eDoqAmCM9w4vgxOrduzpJxg3gvaTmuoD4JkkTf+HDaf9qDHp9+qnQcIQ2/Th9EyKM/WTqoIyaD+NWjVms3b+WrkeMZPmc56TJlVTqO4EZYWBiVqlSl/ZeDKVm5htJxBDckSWL9sM4ULpSfviPGig98VGz00h/5fu9Zag6Zi8nLR+k4giC8IowamdzZszyzeOXl5UWdnDrOHYMpx+wUDj1MiXfy8iDiEV0WneDnOwa8TY+okWrQs16vpW4BP37cbuWOFdrkdRDg7+f2/h/ESVy4dBWb3c6WI5dZdUWPQQstiwb+5zt2kn+npfRUMxpTVqlJkoQkSXz40UdYrVYOHzr0QkMKZFnm6tWrLF+2jJatW6HRPB5G4FqlBho0oOf/XlQTRzWC8Bc7ftrKkD5f8OPMMeTKllnpOIIbkQ47/RMfMXTUNzRp0kTpOIIbkiSxe3Q3inrbmNG7lejvpGJTZs/n+6WrGb1oPb7+oredWp05c4aGjZvyxegpFHpXrJ5WK4fNxur+raldrz4denyudBwhDd2nLGP7lUfUGDwbvVH0X30WWXIiS8+/betlU1MW4c2k1WpAltH+Tc3ms/rF2X97Pwfu6Wmw/C4Z1t8ixqEjPEFPZk+ZLhXzuopCsoQG13vkCkVy4bfnFNE2DXXfDvlL431Xocioc1124gmJaaf+QJIh0SFj0kP3Qg4aVCruunzSijHXNsmXX2D663ZLg9GATHJPNdc01BatWhIfH8+Z06dfuKh2/tw5Vq9cRbPmzR9P99S4CmpoNK7tp3r+r+1/REFNEFJZvGA+c6dOYPf8iaQPFgeUanXblsAIZwzT586hfDkxfUutJIeDrYNa0rBQBoY0rSNWZ6hY3+Gj2XnwBGMWrscs+jup1p49e+j8STeGzlhEtlx5lI4juJEQG83qfq3p2L0n9T5soXQcIQ0Nh83gT6cvVftORasTh0WCIPwz7+fLRtkjxykfasZkct/GJH26ENb2KMOcrYfY+mc89xL0ZLTYaZRDR/fqhSiYNxd2u52aWSWiEhwE+vthNBppnfcEVx7ZKVu0AAaDgZo59AQYE8iWKQM6nY5eFbMQfPAWdqeERqNBr9OSzU9PzcJZKP3e2+j1rtezkvkyUerkH9TJ55dy3sv216JasuQhBRnkDLRt347vp8/g0p9/vnBR7eSJE1gsFho1aez6oD5pq2fK9s+k7ac6ne7/cuwhfnMIQpKxo0byy9aN/LJ4Cj5e4oBSrc7GxzJVZ2P56tW8/fbbSscR3LDFW9nW/0N6VinKx1WLKx1HSEPrT3pxIzKeEXNXohf9nVRrxYoVDBs5ijEL1hKcPqPScQQ3Iu/dZuOwzvQbMYbSFasoHUdwQ5IkSn8+Dme2IpRr+bn4wEcQhH8lf56c7BiUFZ1O97e7L4KDg+jXuia9HQ4SExPR6XSYTKaU1x2DwcCU7nWQZTml6DW+ax0kSUr5+8C2NZ/4e50Kxald/snC1LNex4q8lZ/d+XKh1+v/09e5v27/NBqNjyd/yjKZM2emfccOTJ86jVs3b75QUU2SJH4/eBCLxUKdunVdvdM02lSr+DSgedzj7b9+fRcFNUEAvujxCfeuXmDXgomYjEal4whu7I+JZIWXjh83biU0NFTpOIIbsQ/vs3NwS0Y3q0q9DwoqHUdwQ5Ikqn/YBo/02eg/aarYjqtiEyZMYNHyVYxfshEfP3+l4whu3L10lh3f9WHMtFkUKvKe0nEENxJsNt7tNpqQ0vUoVKuV0nFeCWLLpyA87d8OmdHr9W5Xif11dZdWq33ifdlf/w7/vEfY/2sYTuqhAEBKUY2kyZ+hoaF06NSRGVOncf/+/RcqqjmdTvb+8gseHh5UqVY1aVBBUh+1VFtk9Xr9f15UEwU14Y3X6sPGBBplNkwf9X/dby38O5tjH7Ev2JudmzcTHBysdBzBjYgbl9g3pgtzujSkVP7sSscR3LDZbJSs1Zh3ylal2SdfKB1HSMOXX/bm4LGTjF20DrPFQ+k4ghtXju3n4PxvmbZ4Jdly5FI6juBGeGQ07/f8lgINu5KjlBjoIQiC8DL9taiWepWaLMvkyZOHdh07MHPG90Q+evRCRTW73c72bdswW8yUK18+1aACnlytBv9pUU0U1IQ3lsPhoF61SpQrnI+RvTqJ5f4qtjD2IddCM7Jzwwa8vLyUjiO4ceuPwxyf3o81X7agQJb0SscR3IiMjKJErcbUbtWZak1Efyc1a96iJY/i7XwzdwW6/6j3ifDiTu1Yy8Xty5izahPB6cRrn1pdvn2Pcl9NoliHQWR66wOl4wiCILyWUhfVUhfMkieAFir0Fm3atmHu7DnExsa+cFFt88ZNeFg8+KB48ceTP1Odkv1XRTXx7kx4I1mtVmpUKM3HjWrQrUVDpeMIaZgYF4GuSCG2Ll2KUWzHVa0/92/jysrv2DaoA5mD/JSOI7hx8/ZtKjRoQfuvhlGsfFWl4whuSJJEteo1CMmehwGjRooPfFTs4MqZRJw+wLy1W/D28VU6juDGoXOXqD/yB8r1HEdQ9nxKx3nlyJKkqm2WsiQpHUFQicTERKzWeDw8LE8NB7Db7cTGxmE2m7BYLC/9vuPi4tBoNHh4vPzV47Is43Q+/j/3svuBORwOEhIS/rOFCsl5k7e4pqxSQ0aSZN597z2s1ngWL1xIfHz8cxfVZFkmISGBNWvWYLZYKFykcMrkTzRPTzb9L4pqoqAmvHHu3btH/WoVGdGzPY2qllc6juCGJEkMj39I3ioVmTZtmujvpGInNy4gat9Kdgz9mEBvMdBDrU79cZb6bbvSa/RU8hcW/Z3UKiEhgTJly1Gyen2aduqudBwhDbtnjkAXfY/ZqzZhMpuVjiO4sfnAMTp/v4Eq/Wbgky6z0nEEQXhJ4uPjaTlhK0fCtbzl72Rpzyr4+PgArmLaJ1M389NNDTm8JVb1KEdQUODf3qbD4eC3Y6fJnC6I7NmyuL1cdHQ0DSfswqCRWf1FNTw9X97734iIh/Sav5crUalWV2lkMlgkPno3HXUqfPDCEzu/WfQTq88nsrz9WxTI+9+0KUguqj3RUinV9s9SpUsRH29lxbLl2Gy2574fWZaJi41l5fLlmEwmChYq+MRKteQsf/37yyKOUIU3ysUL56lTuRzfD/1cFNNUzCFJ9LGGU7FtK6ZPny6KaSr2+/yxSEc2iGKayu3Ys48G7bszeMZiUUxTsYcPH/Le+x9Qu/XHopimcptH9yRQ72DKwpWimKZiszb9TNe5W6k+eI4opgnCayY6Jpb99w3ciNPzU5iR306eT/nepas3WHlZx404PYfu67gXHvGPbvPMhcvUWnibHvN/e2KF2F/JMiQ6IVHSvNCWxWf548+rLP9Tx8H7Om5E2bkVZeN8pJbVVw20WP+QhZt+eaHbl2WZQzdiORtl4Nqd8JeU+tk0Gg06nQ6dTodBb8BoMuFhseDl7YWvry8VK1WiTt26Lzw4QZZlIiMjWb50KZcvXSIqKoqY6BiscVYSEhKw2Ww4HA6cTmdKQe9lESvUhDfGwQP7+axLR9ZOHk6hPDmUjiO4Eed00DfhIT37fUWHjh2VjiOk4ZeJvQm13WFB//YY9GKgh1otXrmW4ZNnMfKH1QSly6B0HMGNq1evUr1mLboOGsX7ZSoqHUdwQ5Ik1gxsS+mSJek5YJjYjqtig35Yx5Ij16g5dB5Gi/jA50XITidyGsWF/zc1ZRGUJWs0aDTgkDX8eCKMamVc5+8+cZlYu+t7fxUTE8O5S9eIjE0gU7AfeXKGYjAYsNlsXLz5gHgH3IlzcvnqdXKEZiUqKpqL125RuEBuzly4grenmby5cjCydnZ0Wk3KtklZlgm7c5eL18MAyBuaiYwZXH017XY7f165zq37j/A0G8gdmokQN0PWnE4nMhBogd093ickyJ+Y2Dj6LjnIkssG5h2NpGUtG3q9nus3b3P19n0kWSZziD85Q7M+UZyKjIzi3OVrxMbbyJo+kFzZsz01NADg3v373Ai7T9G38qPT6ZAkKem27yFJMpnTBTx122F37nLuyi38vD0omCcHp89fJl2QH1kzZwJcq/2uXL9F2INHWEwGcmXLiJeHZ9IqtcfbQGvWqoU13spP27anWcT8O7Is8+DBA5YuXkLb9u0Jza5Bq9WkDCnQ8HiFWvKquZfxO1wU1IQ3wsb1axn79WC2zxlHtkyiYbBaPbDbGGyPZMyE76hTp47ScQQ3JElix9cdKJfRzHfdmosDShUbPWkaSzftYMyi9XiJ/k6qdfToUZq1bE2/72aSp1BhpeMIbtgSrKzu15qmLVrRomNXpeMIaWg39gd+e+Ck+qBZ6PQvtvJBEAR18zFp8TPr2HXTTkxMDB4eHmy5GEuQhxmtFqKtj4s0vx8/Q/fVf3I20oAW0GjuUjPrH0xuV5pTF67S5cdHOGU4Fe1Fual/sKFtPJsPXWTCSQPtC91k7hmJLJ4ONnfV03b5VQw6Lb/nCsXb24spK3fx7aF4whNcBat0lluMKO9L40rv89msn1hxWY9N0oAM6S3XGF0lgI9qlHX7uDSyjI+3Jz4+Pvj4+FAyuxdLLicSmShjtcYzesUvzPkDYh1aZMCkvUOj7KeY1rUaHh4e/PzbUXquv8GFaAOSDJ76u7TOd5rR7as8cT9hd+7SdNp+zkYZ2N/DQs7QLAye/xNzzkipbvsuDbOfYlqXqnh6erJh534+3fKAMKsek06metY/2ROmpUpGO0v7NCA6OobeP+xi1WUddglkNOT2vsx39bJQskgBAGTklC2g9Rs0wBpnZd/evUgv0CNRlmVu377NsiVLaNOuHRqNFq1Wm2oLKGiMppSVcy+D2EclvPZmTpvKlDEj+HnhJFFMU7GrifEMdkQzb9EiUUxTMYfNxo9fNaZZoRAmtK8rimkq9mm/Iaz/+XdGLVgrimkqtnXbNlq0bsuI2UtFMU3F4iIjWPHlR3zS6wtRTFMxSZKo3n8yJxJ9qfzlRFFME4Q3gEUnUz2Lkyuxek5fuELYnXsceaCjbAYnAQZHyuUePYqk84qLXIjU8V0FEwe656FTQZk1V3QMW/YrxQvnZ1wVH3QaDQW8E9jYLgfv5M9FlNVGnENmyQUoExxPiwJGDHo9cXaZGIdr9dmeg8cZsC8RLTC9ioWplUw4JA3j9z/i50OnWXBRT1ZPB1taZWBOLS/MOpm9fz5Kc+thogTbDpxkw097mbduF5MPxYEs83aInvsRD5nzB7wT4GD9RyGsahyAn8HJsksGTp27xP0HD+iy5gZ/xujoXVRmeUNf8vs7mXtOx7lL11Lu42FcIh3nHOBguInaWe1ky5yBG7fuMPcPmbcDnEm3HYSfUWb5ZQOnL1zhwYNwvtz2gPsJOvq9Dwvr+REW4+RhAkQnOJEkiQlr9jLvvIEaWRzs65KbhfUDuJ8Indfc5EHEQwxGI2aTGYuHB15eXgQGBvLhRx/x7nvvvfCxhSzLXLlyhU0bN/Lo0UOioqKIjY0lPj6exMRE7A47Tqcr58vY+ilWqAmvtWGD+nPywF72LJyMp8fLn+4ivBwn4mKYbXKydv0G8uUT07fUKiE2mu39P6J/vVK0LFdU6ThCGhq360q0bGTYzCXoXrBxrfDfmTdvHhOmTGfsonUEBKdTOo7gxsNb19j8TXeGjJ1IsVLuVxMIynI4HHzQ81vMBctSqklX8YHPSyTLTnVN+ZTVk0VQAVmmVqEQFp67x7YTNymQMZJHNi11C/hwft8jwPVacPjMRf6INFDQz46fp5EL1++RP70n3metbLsB3zgl8mb0Q6OJwVvvoEjBvI+b/2s01MmcwOzPGqLX67l7797j+9fAmqO3iHfqGFLUQMeGlQEokO0k8Yk2TAY9WuBOvJ51R25SJncgS1vnJ1e2jGm+TkUlQrstVtdDBLQaPUUC7Qyq9zY5smVhU5tYfLwsJCTauHrnIRoNOGWZ+EQbv528yOVYA5Uy2hjStg4Gg4F3893ktzNXyJsjK3AGWZYZti+Oq5EGKmVyMOXjynh4eBCaNRMb20Tj7WEm0WZ33TYyTgniE+2cuniNa7E6SobYGNiqDkajEZ3uAI1WPQTAarWy5oIdg1bP+1m8uHw7HDRQ0F9m920tJy7epHrpYAxGAzLJQwpcfelatm5FfHw8f5w580LFLlmWOXHiBG+/8zZvv/0OOp0OrVaHTqtDq9M9c2jB8xLvsoXXVteObbE/us/2ueMxGMQ/dbX6OeYRm/zMbNu4hcyZRcNgtYq+f5vdw9oyqW0tqhbJq3QcwQ1Jkihf/yMy5i9M795DxQGlio0YPoIN235i/NKNeHp5Kx1HcOPW2ePsmT6YCXMWkqdAIaXjCG7EWhMo2n0U2ao0J1+VJkrHEQTh/6xQ9gzk8rrJtqsazoZHEmKSKVUoO+x7lHKZOxFROGW4bdUzfn9kyvk5fWRy+mn+9njxrYxez5yuKctwO8qGBgt5MzzeEVD6/XcASExMpP/7t5n/h4NZZ3VMPxNJsPkRfT64zmcfVXE7fM3bpGFAcRM+Jh0aZDIHeFDinXwEBPgTducuE3Zc4pc7Wpxo8dU7ibY/vp3bj6xIskxOHzml71lo1iyEZs2SsqVS1mi4Gun6861YiIqJxdfXl/sPwpnw05/8ckfnum2Dk2jb4/eTD6NicMoQbNGk3HaQjydajWvwQ5w1nohEDU4ZFp6Ox6CNT7qmhuLpZEL8vZ7abinLMjIyGeQMtG3fju+nz+DypUsvVFRz2O0cOXSYrFmzodPr0Ov16A16dHpdUoFNiyRJL7z1U1QZhNeOJEl8WL82edL7M3XS1+KAUsXWxERwLEMgOzdvxt/fX+k4ghv3r5zlt/GfsqhHU97L5X6EuKCshIQEPqjegNK1m9CgndiSpmaffNKNc1du8O3CtRiNJqXjCG5cOLCT4yunMXPZOjJmyap0HMGNO+GPKP75eN756DOyFRMDPQThTeTpYaZaqJbJp/WcidRQJ6uNkKCAJy6TPsAHnSaBQv52NnxRBbPZjM1mY9+xcxTInhEPD48078Ogf3bhS6OBYE89MvDnvRjAdTy6YOMe7kfH077mB4QGe7O1a1YiIqPZfvIG445pGPt7As0rhZM+Xcgzb9eilWlbpQghwUFPfW/WlsOsvqrnoxx2BtYvSFCAH/Um/sKRCCMAwd4mNCRwNUaL3W7HYDBw8Nhplh24Qr8mJVw3Isv0eEfmWqSTjdf1fLH4EAs+rcbMLYdZfdXAhznsDEq+7Qk/c+Sha7dX+kA/DNo4TkfAnbv3SBcSzK/nw3AktT7zsJgJMMlE22VmNM5J0UJ5ADh/+ToPo2N5/+38KT3MZNlV8LNYLMiyjCRJZMmShfYdOzBj6jRu3br13EU1WZa5eesWDx8+xGA0YDQaXSeDAYNej16vf6K/2vMSBTXhtWKz2ahdqRz1yhVjQNfWSscR0jA7NpyHeXOwY+1aLBaxHVetrh3bxx/zvmZj3zbkyvD0L3RBHcIjHlKqdmOadP2C8rUaKh1HcEOSJBo1boJs9mbY94teWkNc4eU7tnkJ1/dtZu6aHwkIFK99anX22m2qDJxOyS5fkz5fEaXjvLZkSWVbPlWURVAHjUZD7aLZmHL6NokOiboF/Z/6HfvB2/kovP0OB+6b6DFrJyWy+/Lr5SjWXDPQ7907DO6QCb1Oh0YDNxPMLPpxHw3+SYsTGRoWzcTSyw8YfyiRRMcW4mxOpp6E/D423sp2hY+3xlDyyHE+L5+BrAEe6IjDxwgmo/G5Hm9sogRoMWgl4hNtLN5xhD+iTMhAvM1BybdzE7rzED+H6ek5Ywu5gy1MOxLHA5uOjg8jAdBqNFTJH0S+0Iycnnac9dcMzNn0K3GJTkCHQcfj2442IwPWhETKvf8WpdNdY88dA9UmHiCLFxwOf9yv0tPTk2b5jQw5KPPJ6ku0uRhGZLydOacd+BlkfiuYO2UyaurnSE7e9ynLZM+enfadOvL9tOncv3//uYtq8VYrkY8e4enpidlsTjkZTSaMDid6nT5l2ujzFtXEUALhtREZGUmlku/TtWlNUUxTuTGx4WhKFmPjpk2imKZi53at5dKikWwf0lEU01Ts8tXrFKvegE4DRolimoo5HA7Kla9IQLbcfDl6siimqdiviybx4NjPzFsrimlqtufEOSoPnEGFLyeKYprwn9i9ezft27cnX758eHp6kilTJurVq8fRo0eVjiYAFrOJTBYHGS12zCYjRfLn5G1/G9m9nZQv6up/lsVLIsQi4+vthZ+fLz+0KkiFDDY2XtfTa2cc227pqJHZTqvKhQHIlTUjxYNshCfq+Wp3LH9ev0UGPw88dTLpfB4fs5iMJoI9NISYwWQyUrX0u4wua8Kih5G/O5l8QiaPj42x9XJQ/v1CfFlE5nK0hmarw/lsp5VQLycTamfA39/vqceVLiiAYItMqJcTD4v5mY+9Q+VCFA10sPqqkVrzLnPwRjyt8rkGNERa7WTKmIHZjTLzlr+DRRe0DNiXiAMtI8t6kD93DjL7GvA1ygT5eZMzezbGVg3CxyBx6YGV9pUKUTTQyZorBmrNu8xvNxJonU923XZcAl5eXizo9AGfF5YJMDjx0Mv0/cCEXgs6rasv2WdNytOnqMyDBA399iYy7ohEgMHJ4IqBeHp6Aq4iqFarRadzbcc0Go2YLRY8PV2TTfPmzUu7Du3x8/d/7mKXLMvEJ8RjtcZhjYvDarWSkJCA3W7H4XQgyS8+mECsUBNeC7du3qRRrapM6NuNmuVKKB1HcEOSJAbFR1C8fl3GjhsrtuOq2LHV35N4ZAs7h32MrxjooVq/Hz1Osy69+GrCHHLmF/2d1Co2NpYyZctTpUlL6rXsoHQcIQ0/TR6At5zIzOXrMTznygHhv7fy54N8vmA71QbOwitITHAX/hszZswgIiKCnj17UqBAAR48eMD48eMpXrw427dvp2JFscVYST4+PmzsVgyNRoO3t6sX6YYepbDZXAUlgAVdyhJrtab8vVC+3Gzsm41bYXeJibUS4OdDhvQhKb3RAgMD2PRlZa7fuoOnh4XQrJkpkCuUuiXCyJU9W8p9+/v7saW76759fHwA+PTDKrSo/JCwew/Q63VkyZg+ZSXW8I61+SziIXcfRKDVasicIV3K9f4qf56c/NrTiIfFnHL9Z11m11dB3Lh9F08PC1kyZcDhcND95m2yZ3X1pK5Y4l32vJ2Pm2F3sdnsZEwXTGBgABqNhtFtK/J5+ENyZne1M6hfuSRv57pBSFAAPj4+7Poq+Knb7pZ027Iss+/kn+TL4M3Q1pUwm83MWrcbpwTZ/FzbKD09PRnZqTafhYdz934ERqOBzBnSPfV4kotqQEphK3lIgSRJFHrrbVq3acPcOXOIi43918Uvo9GI5JRISEgkISGRxMREbDYbDrsDp+PxpM8XWaEmCmrCK+/0qZN0bPEhC0f354PCBZWOI7hhkyS+ig+n5Sdd+fzzz5WOI6Rh/8xh+N//gw2DO2I2Gv7+CoIiNmz9iS+Gfcuw2cvJkCXb319BUMTdu3epUKkKbXr1o0y1OkrHEdyQJImNIz6hQK7sDBg9wW2TaEF5363azqQdJ6gxdB5mL9+/v4Lwwt7ULZ/Tpk0jJOTJ/lbVq1cnV65cfPPNN6KgpgKZM2V84u8Z0j85MTs4OIjgv1zHaDSSI9R9X0wfHx/eKvC42OXp6UmBvLmfulzWLJmeOi8wMIDAwICnztdoNAQFBRIUFOj2flNfNq18yXx9fXnL9/FroMFgIH+eXE9cxtPTk3y5cz51XT8/X/z8Hl9Xq9WSK0foP7pth8PBomOP2BWmZ/WprYRYYOstI/5mDS1LP75/jUZDSHAwIcF/fQaefrxarTalqGkyuXrLuiaAwnvvv0e81cqihQuJj49P66aeul3/gABkWcZutyWd7DidTpxJq9OSC2ov4l+/W0geL6rRaPjtt9/cXm7lypUplwsNDX2RjP8418u4n6FDXVPR5s+f/6/u+68no9FIlixZaNGiBadPn3Z73du3b9O1a1dy5MiByWTCy8uLokWLMnbsWBITE1/48bzuft69k86tPmLTjNGimKZi0Q4HveLD+XL416KYpnK7xnQnd8I11n7VRhTTVOz7+YvpO3oioxeuFcU0FTt//jxlK1Tk0+HjRDFNxRw2G6v6tqR8mVIM+naSKKap2OczVjB9/5/UHDJHFNOE/9xfi2kAXl5eFChQgJs3byqQSBCUp9frmdS8MO3zOblr1XHkvpaSIQ5WfpSJYs95PJ56+6fBYMBkMuFhseDl7YWPjy+lypSmYeNGGI3Gf7ySTG8wkClTJiRJQpJkJElGlqQnVqW9DC+0Qm3JkiWUKPHs7XWLFy9+kZt+JbVp0yblz1FRURw9epSlS5eyevVqtm3bRoUKFZ64/MWLFylVqhTh4eHkyJGDOnXqEBcXx6+//kqfPn3YuHEju3fvThlHKzxp+dIlfP/dGHbOn0jGENHjRK3CbAkMd0Qz+fsZVK5cWek4ghuSw8G2Ia2pkzeY4c2aiO24KjZo1Dg2//I7YxZtwMPz2VsBBOXt37+fdh0/ZvDU+WTPk1/pOIIbCdZYVvdrRbuPP6FhizZ/fwVBMU1HzOKPeAvV+k1HqxObbASIjo5+4u8mkylldct/JSoqimPHjonVacIbLXeOUKb3yEZiYiKSJGE2m1/4w6jkyZ8AyCCb5JTClyxLVKpcGZvNxsb1G7DZbG4LYsnFuezZs+Pt4500xdM1jZWkxU8v03P9NjKZTOTMmZMVK1YwceLElOV5ySIiIti2bRtFixbl2LFjLyXoq+Cvq9rsdjsdOnRg0aJF9OzZk1OnTj3x/b59+xIeHk737t2ZOHFiyj+g+/fvU7p0aX799VcWL15Mu3bt/l8P4ZUxady3bF+/ij2LJuPn4610HMGNC/FxTNQmsGjFCt4t+g+m5AiKsCVY2dbvIz6p+A7dapRUOo6Qhg49v+LinQi++WG16O+kYmvWrmXQ0K8Z/cMq0mXKonQcwY3o8LtsGNKJL4cMp3zVmkrHEdyQJInyvb8jPkNBKnTuIz7wUYBat3xmyfLk6+uQIUMYOnTof3rf3bp1Iy4ujgEDBvyn9yMIaqfRaDCbnz004UVotVrQgxEjJPVTSy6sVa9RE38/fzZu3Mj9e/eeWmmm0WgwmUxkz56dTJkzYzQYMRgMGAxG10lvQKfTodXpkgptL/775LnLiC1atCA8PJzt27c/9b0VK1Zgt9tp2bLlC4V71RkMhpQX9dOnTxMZGfnE9/fu3QvAwIEDn5j2FRISwieffALA4cOH/y9ZXyV9v+jFwV1b2b1wkiimqdjvcVFMMznZvGWLKKapmDUygi29G/B1w1KimKZikiRR86N23LdpGDRtgSimqdiUyVMYPmoM4xatF8U0Fbt39TwbBndk5KTpopimYgk2G+90HYmmQHmKt/1KFNOEJ9y8eZOoqKiUU79+/dxeds+ePc9s0/Os04kTJ555G4MGDWLJkiVMmDCBd9999z96VILw5kr+P6jVatHpdRiMRsxmEx4eHnh5eeHv70fxkiXo0fNTGjZuTIGCBUmXPj2BQYFkyJiRfPnzU7xECUJzZMfDwwOzxYzZYsHDw4LZbMZkNiUV1vTotNqUotqL/G557vXSLVq0YODAgSxevJhatWo98b3Fixfj5eVFvXr10uyXtGXLFiZMmMCRI0eIj48nW7ZsNGjQgL59++Ln5/fU5ePi4vj6669ZtmwZ9+/fJzQ0lI8//phevXqlmfXXX39l/Pjx7N+/n6ioKDJkyEDdunUZNGgQwX/TJO9FpUv3uDGiw+F44nv/ZElyQMDTTQ3fZO2af4gniWyeOeaJIqSgLltjHrIr0IufNm9+4v+AoC6Pbl/ll5GdmNm5PmULPt2wVFAHh8NBqVqNyV+8PC16iNUZata3Xz/2/naYcYs3YPHwVDqO4Ma1E7+xf+4opixcTvZceZSOI7jxMDqW9z8dQ566nchVtrbScQQV8vHxcTsp8a/y5s3L7Nmz/9Fls2Z9uiH8sGHDGDFiBCNHjqR79+7/KqcgCP9c8vvc5J5qGI0pq9Tg8VePUh4UKFiA6KgoYmNjSUhIcE3vTFrBatAbMJldk1K9vLzw9PJ0FdnMZgwGAzq9/qWsUnvuglq2bNkoVaoUGzduJDY2NmUE6tWrV/ntt99o3bo1Hh4ebq8/atQo+vfvj16vp1y5cgQFBbF//37GjBnDunXr2Lt37xMH4omJiVStWpUDBw4QFBREnTp1iImJoW/fvly+fNnt/UyePJnPPvsMrVZLsWLFyJQpE2fOnGHKlCls3ryZ/fv3kyFDhuf9Mfyto0ePAhAUFERQ0JN9vqpUqcKCBQsYOXIkEydOTNl3fP/+faZPn45er6dFixb/WbZXiSRJ1K9emQ/yZ+fb3r3FAaWKLYl9yJ9Z07Fr48Z//CZH+P8LO3eco1N7s+rL5hTK+t+9BgovJjo6hhK1GlHto/bU/Ej0d1Kz1m3aci8yllHzVqIXvU9V64/dmzj74wJmr9hAuoxPT2gT1OH63QeU/nIi77btS5bCpZSO88aTJUllWz6lf32dDBky0LFjx+e6v2HDhjF06FCGDh1K//79n+s2BEH455KP9ZMX0BhSDUqTZTllFZtWp8VoMGC2WIi3xmOzJeJ0OEGjwWDQuwpqnp74+Pri4+ODp6cnFosZo9GEXq9PuZ0X8UIdPVu2bMmvv/7K2rVrad26NfB4GEFahaDDhw8zcOBAvL292blzJ8WKFQNcRbNWrVqxatUqevTowcqVK1Ou891333HgwAGKFSvGTz/9hG/SGNdjx4491ew/2cGDB+nVqxdZs2Zl48aNvP3224DrSRgxYgSDBw/m008/ZdWqVS/yY3imqKgoDh06lPIJxrNefEeNGsWRI0eYMmUKP/74I0WLFiUuLo59+/YRGBjI+vXryZ9fNDK2Wq3UqlSONnUq8VmbJkrHEdIwNS4Cx1v52LZixX/eFFZ4fpd/28Gfy8by44B2ZAsRq2DVKuzuPcrW+4g2nw+iROUaSscR3JAkiZq1auOXMRuDpkwWH/io2KHVc7l3fA/z1mzB5xk7IQR1OHbxKnW+nkOZHqMJzikmuAvKGj58OEOHDmXgwIEMGTJE6TiC8Mb4a1ENQMa1Ok2btF1Tp9NjNBoxWywkxMdjs9lwOJyuAQd6HWazGQ+LB97e3nj7+ODp5YXZYsFoMqLX69HpdMpt+QRo2rQpn376KUuWLEkpqC1ZsoT06dNTqVIlHjx48MzrTZ06FUmS+Oyzz1KKaeDaAjl16lQ2b97MmjVruH37NpkyuT49nDFjBgATJkxIKaYBFC1alG7dujFq1Kin7mf06NFIksSsWbNSimngenIGDhzIunXrWLt2LeHh4U+tHnsez3oiQkJCWLp0Kc2aNXvqexkyZOCXX36hWbNm7NixgytXrqTcTqNGjShQoECa95eYmEhiYmLK3/866eZ1EBEeTp0q5Rn8SWs+qllJ6TiCG5Ik8Y01gtAK5Zg5a+YLV/qF/87pH5cQsXsJPw3tRLCPmBCpVmfPX6R2q0589s1kChQt9vdXEBRhs9koU7Yc71esQbOuabefEJS1Z85opPAbzFm9GbPFonQcwY1tv5+kw9Q1VPpqKr4ZsikdR3jDjR8/nsGDB1O9enVq1arFwYMHn/h+8eLFFUomCG+GZxXVNCT1O9Rq0ev1mEyuHmsJCQnY7fakFWqu6xiNRiwWCx4eHnh6eeFhcfVS07+k7Z7wggU1f39/atasyaZNm7h79y43b97kwoUL9OrVK83+Vvv27QOevYotJCSEqlWrsmHDBg4cOECTJk24ceMGN2/eJFOmTJQs+XTT7GbNmj1VUJMkiV27duHt7U2lSk8XYjQaDaVKleL48eMcPXqUatWq/duH/5Q2bR5vx0lMTOT69ev8/vvv9OnTh4wZM1KuXLknLn/q1Clq1aqFTqdjw4YNlC1blri4OFavXk2/fv3Ytm0b+/fvJ2fOZ/c2GjVqFMOGDXvh3Gp1+dIlmjeozfQhn1OphGj8qVYOSaK/NZzqLZszdOhQsTpDxQ4tnoDu/F52DPsYL7NYQahWe/YfpH2vfgycuoCsufIqHUdwIzIykjLlytOgXVeqNWqudBwhDVvGfUkGbzMjFq9+ajK9oB5zt+5l6Kq9VB8yBw+/F/+gW3h5JMkJKtryKf2fsmzatAmAbdu2sW3btqe+n3q6oCAI/42/FtVcK8pAp9ViSCqoWSzmpNVpDpzOpBVqOl1Kwc1kNruGEphMGI2PV6cp2kMtWcuWLVm/fj3Lly/n6tWrKeelJSwsDI1GQ7Zsz/7kKTQ0NOVyqb8+q0Gku/MjIiKIjY0F+Ns3T+Hh4Wl+/5+aP3/+U+cdP36ccuXKUa1aNc6dO0f27NkBsNvtNGnShLCwMI4cOUKRIkUA8PPzo2fPnjidTr744gsGDRrE0qVLn3l//fr1e2LoQ3R09FPjo19VRw4donuH1qycOIx38uVSOo7ghlVy0Nf6kK69v6BLly5KxxHSsHdyXzLF3WDJwA4Y9GKgh1otW7uBIeOnMXLeKoIziP5OanX9+nWq1qhJ534j+KB8ZaXjCG5IksS6wR0o9t67fDFkpPjAR8W+XrSRHw78Sc2hczF6iAnugjrs2bNH6QiCIPCMoprRhFanQ28wYDQaMZnNrmKaw4EkJW8L1aDT6zEYDBgNBgwGI3qDq5D2sopp8BIKarVr18bPz4+FCxcSFhZG/vz5KVq06AsHg8c/uOTqv7sH/KzznU7XJxfe3t40bNgwzftxV9h7GYoUKULnzp0ZN24cU6dOZfz48YCrv9vFixfJlStXSjEttaZNm/LFF1+k+UJuMpleyz5VW37cyMgB/dg6ZyzZM2dUOo7gxkOHjQGJkYwc9y0NGjRQOo7ghiRJ7Bz5MSWCtEz+ooXYjqti302fxbzVmxmzcD3efv5KxxHcOHnyJE0+bMZX46aT7x2xelqtHLYEVvZtRaOmH9G6Sw+l4whp+HjCIn65HU+NwbPRGYxKxxEEQRBUKHVRzbXlU5NSHDMaTTglJ5LT+UTtSKvTodPq0OkfF9GSv74sL1xQM5lMNG7cmDlz5gDw6aef/u11MmbMyNWrV7l+/Tp58z69neX69esAKdM3M2bM+MT57i6fWlBQECaTCYPB8MyVY/9PyavSLly4kHLerVu3ANxOQUw+/+HDh/9xOnWZN3sWS+fOYM+iyQQH+CkdR3DjemI8o6U4Zs3/gdKlSysdR3DDYbOxbWBzmhUNpX9j0YNQzT4fNJxfT5xj9MJ1mC3uJ2QLytqxYwc9Pvuc4bOWkjn7s9sxCMqzRj1izcC2fNKrDzUbimFGalZ70FRu6kOo3Gc0Wq1YPa1WsuRU2ZRP9WQRBOH/J3mAQPJ0TkmS0Gq1yJKMjIwkSU8W1LRaV881rSZlkMGLDiH4q5dSmmvdujWBgYEEBQWlOd0zWZkyZQDXAIO/evDgAT/99BNarTalX1q2bNnInDkzt2/f5rfffnvqOsuXL3/qPL1eT/ny5Xn48CF79+79tw/ppUoeNuDp6ZlyXvr06QFXkS0mJuap6xw+fBh4vP31TfDN10PZsHQ+Py8UxTQ1O22NYYw2gdXr14limoolWmP5sXcDPq/8tiimqdyHnbpz6vo9hs9ZIYppKrZw4UJ69f6KMQvWimKaij26c4M1/VsxcOS3opimYg6Hg2I9RhMRXIgynwwXxTRBEAThH0suiiX3SdMb9Oj1romfqU+pv/cyJno+y0spqJUpU4bw8HAePHjwj7ZPduvWDa1Wy6RJkzhy5EjK+TabjR49emC1WmnYsGHKhE+Azp07A/DFF188Mc3yxIkTTJs27Zn3079/f7RaLW3atOHXX3996vthYWFur/uyHD9+nFmzZgFQs2bNlPNLlChBSEgIcXFxdO/e/YlpnWFhYfTq5ZoW1rhx4/80n1r07PoxF4/9xo553+HtKQ4o1Wpv7CPmemrZun37306hFZQTE36PrX0aMq55JdpVfF/pOIIbkiRRoX4znN7BfPXdLHSiWbpqjRkzhskzZjF+yUaC0qVXOo7gRtjF02we8QnjZs6neNkKSscR3LAmJFCo8wi8P6jDe80/Fb3tBEEQhH8t9Uq15K2cKQU2/ZO90v6LlWnJFHn3XqxYMYYPH86AAQMoUaIE5cuXJygoiP3793Pz5k1y587N1KlTn7hO79692bx5M7/99hs5c+akQoUKxMTEsHv3bjp06MCMGTOeup+yZcsyadIkPvvsM8qUKcPbb79N7ty5SUhI4Pr165w7dw4vLy+6dev2Uh5X27ZtU/5ss9m4fv06Bw8eRJIk6tSpQ6tWrVK+bzabmTlzJk2aNGHhwoXs2rWL9957j/j4eH777TdiYmIoWrQoffv2fSnZ1EqSJJo3rk9WPwtrp4wQ/Z1UbH1MBAfT+bNz82YCAwOVjiO48eDaRQ58+wnzuzfmgzz/XX9I4cUkJCRQsmYj3q9WnyYduysdR0hDz549OXHuT8YuWofRZFY6juDGn4f2cGTJRGYsWUOW0OxKxxHcuPcwig8+G0uhpj3IXryK0nGEf0hs+RQEQc2U/GBGsY/D+/fvzzvvvMOECRM4fPgw8fHxZM2alT59+tC3b1/8/Z9syGwymdi5cyfDhg1j2bJlbNiwgdDQUEaMGMEXX3zxzIIaQPfu3SlRogQTJkxg7969bNy4EW9vbzJnzkyXLl1o0uTlbQdYsGBByp+1Wi1+fn6ULVuWVq1a0bZt26eKRfXr1+fQoUOMGzeOvXv3smXLFoxGI7lz56Zp06Z89tlnWCyWl5ZPbRwOB7Url6d6icIM6dZWfEKpYvNiI7ibKys71617YuuyoC43Tv7GqdmDWN+3NXkyBisdR3Dj4aNIStZqRIOOn1KpXlOl4whpaNL0QxI0BobPWpoyWUpQn5PbVnFp92rmrt5MYHCI0nEENy7evEPFflP5oNNgMhYUq6cFQRCEV9+/LqglN3n7J9KnT5/m5WvVqkWtWrX+8e15eXkxduxYxo4d+69yvfvuuyxevPgf3cfQoUMZOnToP870d/f9d4oUKfLMXnKvu+joaGpXKkf35nX5uGldpeMIaRgfF47H+0XYvGgRBoNB6TiCGxf2bOTG+mlsH9yRjAG+SscR3Lh6/SaVm7Ti4wHf8G5psSVNrSRJomKlyoQWKkrP3oPFBz4qdmDZNKIvHOWHtVvw9PJWOo7gxoEzF2kyeiHlP/+OgKy5lY4jCIIgCC+FaNgi/N+FhYXRsEZlxnzRmXqVRFN7tZIkiaHWCArXrsHEiRPFAaWKnVg3h7iD69kx9GP8vUQPQrU6euIUTTp9ypfjZpCnUGGl4whuWK1WSpctR4V6H9KwbWel4whp2DFtCBZbNDNXbMBoMikdR3Bj3d7DdJ/7I1UGfI93cEal4wjPw+lE1qpom6VTRVkEQXijiYZVwv/V2bNnaFCtIvNG9BHFNBWzSRJfxodTq3NHJk2aJIppKvbb3JHoTm5j+xBRTFOzLTt382GXXgz5fokopqnY/fv3ea/YBzTs2EMU01RMkiQ2juxOJm8jk35YJoppKjZ13Q56LtpJzSFzRTFNEARBeO2IFWrC/82+X/bQ59OurJ82kvw5Q5WOI7gR63DQNzGC3oMH07p1a6XjCGn4edxn5NU8Ym6/tuhFfyfVmrt4GWNnLuSb+WsJEP2dVOvSpUvUrFOXnsPGUbhEGaXjCG5IDgerB7alYsWKfNJ7gPjAR8X6zFrNmtNh1BwyD4P59e0JLAiCILy5REFN+L9Ys2oFk0ePYMe878icXhxQqtU9WyJDHVF8N3Uq1atXVzqO4IYkSfw0pA1Vs/syutWH4oBSxb4eO5G1O/bx7eINeIj+Tqr1+8GDtGrXgYGT5pIzfyGl4whu2KxxrOrXipbtO9G0TQel4whpaDl6Dsei9FQfMAOtXvRffdXJshNUNFlTltWTRRCEN5soqAn/uWmTJrBp5RL2LJqCv684oFSrSwlWxmNlwdKlvP++mL6lVg5bAlv7fUSnMgXoWVusolGzjz/vxx837jFqwRoMRrElTa02bNhA3wGD+GbuCjJkyaZ0HMGNmIj7rB/cgc8HDqVijTpKxxHckCSJyn0nERWQk4q9+qPRiu4ygiAIwutLFNSE/9TAr77kwvFD7F4wCQ+LWek4ghtH4qJYYIYN6zaTO7eYvqVW1uhH/DSgGcMal6NpqcJKxxHSUK9VR+xmPwZPX4hObMdVre+//54Zs+cxbskG/AKClI4juPHg+iW2junJ8O+mUbR4SaXjCG7YbA6K9RyNd5EqlGzYSek4giAIgvCfEwU14T/TqXUL9InRbJ09Fr1e/FNTq59iHrLd34PtmzeTIUMGpeMIbkTeucmeEe2Z1rEuFd/KpXQcwQ2Hw0HZuh+So0gJ2vTqL7bjqtigIUP4afdexi/ZgMXTS+k4ghs3Th9h38xhTP5hKTnz5lc6juBGZGwc7/UYQ86abclTob7ScYSXTJYkdW35lCSlIwiCIACioCb8ByRJolHt6hTOnpHvvhkiDihVbHlMBH9kDmHXpk34+voqHUdw487FUxye9DnLPvuIwtkzKR1HcMNqtVKsekMqNWpBnZYdlY4jpKFjx05cuxvOmPmrMRiNSscR3Di7dwun181h1soNpM+YWek4ghs370VQ6svvKNKqN1mLllU6jiAIgiD834iCmvBSJSQkUKtiWT6qVobeHZsrHUdIw4zYCGIL5OKn1asxm8V2XLW6cvhnzi/8hs3925I9XaDScQQ37t67T5m6H9GiZ19KVxP9ndRKkiTq1q2HOTAdQ6bNRyv6O6nWkQ0Luf37duat3YKff4DScQQ3Tl2+To0hsyj9yQhC8ryjdBxBEARB+L8SBTXhpXn48CF1Kpejf6fmtKhbVek4QhpGxT0gfZmSLJk7V/R3UrE/flrFvW3z+GlIJ9L5iYEeanX+4iVqNu9A9xHf8fb7or+TWjkcDsqWK8/bpSrSsvuXYvW0iv0ybxyOu5eYu+ZHLBYPpeMIbuw8coY2k1ZQsc8U/DKGKh1H+A/JksqmfKooiyAIbzZRUBNeiuvXrtK0Tg2mDOpJ1VLFlI4juOGUJAbEh1OhaRNGjhwpDihV7MjSKUhndrJzWGe8LWJCpFrtO3iINj360G/KD2TPI/o7qVV0dDRlypandssO1PywtdJxhDRs/a4vQWYNo5aswWAwKB1HcGPRT7/Sb9nPVBs0G8+AEKXjCIIgCIIiREFNeGEnjh+jS+vmLB0/mHcL5lU6juBGgiTxlfUBHXt9Rvfu3ZWOI6Th12kDCYm8xLLBHTGKgR6qtXrjj/QbPZHhc1eSLlMWpeMIbty+fZtKVarSofcQSlauoXQcwQ1Jklg/rDNF3irAV8O/FR/4qNiopT8yc+9Zag6dh8lTrJ4WBEEQ3lziSE14ITt+2srQPl/w46xvyZlVNEtXq0iHnf6Jjxg6ZjRNGjdWOo7ghiRJ7B7VlaI+Tmb0biX6O6nY5JnzmLl8HaMXrsPXX/S2U6szZ87QsHFTvhwzlYJF31c6juCGw2ZjVf9W1KnXgA49Plc6jpCGblOWsuNKFDUGz0ZvFP1X3xSuKZ/qmawppnwKgqAWoqAmPLdF839g3vSJ7F4wiXRBomGwWt2yJTDCGcOMuXMoX66c0nEENySHg22DWtCwUCYGN60sVmeo2FfDRrHr0EnGLFyH2cNT6TiCG3v27KHzJ90ZOmMR2XLlUTqO4EZCbDSr+7fm4x6fUaeJGGakZvWHTuey5E+VflPRakX/VUEQBEEQBTXhuYz9ZgR7t2/ml0VT8PESB5RqdTY+lqk6GyvXrOGtt95SOo7ghi3eytZ+TelV7T06VflA6ThCGlp1/YybUQmMmLsSvejvpFpLly1j5KhvGbNgDcHpMyodR3Aj8t5tNg7rTL8RYyhdsYrScQQ3JEmi1OfjkLMVpVzLXuIDH0EQBEFIIgpqwr/2RY9u3L92gZ3zJ2AyGpWOI7ixPzaSlZ56fty4ldDQUKXjCG7EPrzPzsEtGd28KvWKFVQ6juCGJElUa9oGr4zZ6T9pmtiOq2LfjR/P4pVrGL90I96+fkrHEdy4e+ksOyb04dvpsylY+F2l4whuJNhsFO02ivSl61OwViul4wgKEVM+BUEQnk0U1IR/TJZlWn/YiGCLlg3TR4kDShXbFPuQX4N82LF5M8HBwUrHEdyIuHGJX8d0YW6XRpTMH6p0HMENm81GyVqNKVy+Oh916aV0HCENX37Zm4PHTzFu8XpMZovScQQ3Lh/Zx+8LxzFt0Sqy5cipdBzBjfDIaN7v+S0FGnYlRykx0EMQBEEQ/koU1IR/xOFwUK9aRSoULcDwnh3Fcn8VWxDzkOvZM7Jzwwa8vLyUjiO4cevMIY7P6M+a3i3Jnzmd0nEENyIjoyhRqzF12nShaiPR30nNmjVvQbRN4ps5y9GJ6biqdWrHOi5uX8acVZsITpde6TiCG5du3aV83ykU6ziQTIVEKwJBEARBeBbxjlP4W7GxsdSqVJaPG9WgW4uGSscR0jAhLhx90bfYunQpRrEdV7X+/HUrV1ZNYNugDmQO8lM6juDGzdu3Kd+gBR2+Gkax8lWVjiO4IUkSVapWI0Ou/PTrP0J84KNiB1d8T8QfB5m39ke8fXyVjiO4cejcJeqPnE/5z8YRGJpX6TiCCogtn4IgCM8mCmpCmu7du0f9ahUZ2bMDDauKCZFqJUkSX1sjyF+tMlOnThXbcVXs5Mb5RO1bxc5hnQnw8lA6juDGiTN/0LBdNz4fM5V874j+TmqVkJBA6bLlKF2jPk06dlc6jpCGXd8PxxD7gNkrN2Iym5WOI7ixaf8xuszcQJX+M/AJyaR0HEEQBEFQNXHULbh18cJ56lYux8yhn4timorZJIk+1nAqt2/D9OnTRTFNxQ7+MAb56CZ2DP1YFNNUbMeefTTq0IPBMxaJYpqKhYeH8+77xajburMopqncplE9CTFKTF6wQhTTVGzmpt10+2EbNYbMFcU0QRAEQfgHxAo14ZkOHthPry4dWTN5OIXy5FA6juBGnNPBVwkR9BrQn/bt2ysdR0jDnglfkN1+j4X926HX6ZSOI7ixcMVqRk6Zwzfz1xAYIvo7qdXly5epWbsunwwexXulKygdR3BDcjhYM6g9ZcqU4tN+Q8V2XBUbMHcty47foMaQuRgtnkrHEVRGkpxoVLTNUmz5FARBLURBTXjKxvVrGfv1YLbPHU/WjKJZulo9sNsYbI/k20mTqF2rltJxBDckSWLHsPaUz2xhfNvm4oBSxUZPmsbSTTsYs2g9XqK/k2odOXyY5q3a0H/ibHIXfFvpOIIbtgQrq/q24qNWbWjeoYvScYQ0tB37A7+HS1QfOBOd3qB0HEEQBEF4ZYiCmvCE76dNZc3ieexZOJlAf3FAqVZXEqyMxcq8RYsoXry40nEENxy2BLb2b06bEnnoXU9sm1az7n0Hc/TCNUYvXIfRJLakqdWWLVv4vHdfRs5dTsas2ZWOI7gRFxnB2oHt+PSrAVSt00DpOIIbkiRRY8BU7ntlodIXg9CIlhGCIAiC8K+IgpqQYuiAfpz+fR97Fk7G08OidBzBjeNxMcwxOlm3biN584rpW2qVEBPJtgHNGFi/NC3KFlU6jpCGhu26EKcxM3TmUnRiO65qzZkzh0nTvmfc4nX4B4UoHUdwI+LWVbaM6sGQsZN4v2QZpeMIbjgcDj7o+S2WQuUo1biLWD0tpEl2SqBRzzZL2SkpHUEQBAEQBTUhSdcObXFE3WfbnPEYDOKfhVrtjnnEJl8T2zZtIXPmzErHEdyIvn+b3cPaMqltLaoWEUVPtZIkiXJ1PyRzwXfp2nuwOKBUsa+Hfc3mn3YxfulGPL28lY4juHHr7HH2TB/MhDmLyJ2/oNJxBDdi4uJ5t8doslVtQb7KjZWOIwiCIAivLFE5ecNJksSH9WqTJ2MAUyd+LQ4oVWx1TATHMwSxa/Mm/P39lY4juHH/yll+G/cpi3s25d2cWZSOI7hhtVopUbMRpes0pUFb0d9Jzbp0/YQL124yZuEajEaT0nEEN87v38HJ1TOYuWwdGbNkVTqO4Mad8EcU7zWed5p/Rrb3KyodRxAEQRBeaaKg9gaz2WzUqlSOBhU+oH/nVkrHEdIwMzaCyLw52LF2DRaL2I6rVteO7eOPeV+zsV8bcmUIUjqO4EZ4xENK1W5Mk65fUL5WQ6XjCG5IkkSjRo3Bw4evv1+MVvR3Uq1jm5dwfd9m5qzeTECgeO1Tqz+u3qLqwBmU7Dqc9PkKKx1HeIXIshNUNFlTltWTRRCEN5soqL2hIiMjqV2pHF+0bUy7hjWVjiOkYUxsOIEli7Hxhx/Q68V/WbU6t2stYZtn8dPQTqT3E1vS1OrPy1ep/lE7ug75lsIlRH8ntXI4HJSvUIl875ek7Wf9xOppFdu3cALx188yb+2PeHh6KR1HcGPPiXM0H7+Uir0n4p85p9JxBEEQBOG1II7O30C3bt6kca1qTOjXjRplxYRItZIkiYHWcEo2rM+3334rDihV7OiqGdiObmXHsI/xFQM9VOv3o8dp1qUXfSfOIUc+0d9JrWJjYyldthzVmraibosOSscR0rB90gD8tHYmLF+PwWBQOo7gxordB/li4U9UGzgLr8B0SscRBEEQhNeGKKi9YU6fOknHFh+y6NsBFHu7gNJxBDdskkQfazitunXl888/VzqOkIb9M4cScP8sGwd3xGwUB5RqtWHrT3wx7Fu+nrOc9JmzKR1HcOPOnTtUrFyVtr36U7pabaXjCG5IksSG4V0plCcX/UeNF9txVWzcym1M2XWSGkPnYvbyVTqO8IqSJae6pnyqaPupIAhvNlFQe4Ps3rmDgV98yqYZo8mTXTRLV6soh4N+iREMGDGc5s2aKR1HSMOu0Z/wliWeWV+1QScOKFVrxrxFTFm4nNEL1+IXGKx0HMGNs2fP0qBRY3p9M5G33iuhdBzBDYfNxuoBbahesyade32ldBwhDT2nLWfLnxHUHDwHvUmsnhYEQRCEl00U1N4QyxYvYtakceyaP5EMIaJhsFqF2RIY7ohhysyZVKpUSek4ghuSw8G2wa2oky+E4c1qi+24Kjbom3H8uO8QoxeuF/2dVGzfr7/SsVNnBk9bQGjufErHEdxIsMayum9L2nfpToPmrZWOI6ShyYhZnEvwoGq/aWh14u2+IAiCIPwXxG/YN8DEsWPYsXENexZNxtdbHFCq1fn4OCZpE1i6aiWFCxdWOo7ghi3ByrZ+H9Gt4jt8UqOk0nGENLT/tDeX7kUyct4qDEaj0nEEN1avXs3gr0cwev5qQjJmVjqO4EbUgztsGNqJPkO/oVyV6krHEdyQJInyvb8jIUMhynfuLT7wEV4KseVTEATh2URB7TX31eefcfP8KXYtmIjZZFI6juDGwdgolnpo2LxhCzly5FA6juBG3KNwdgxuwcimlWhY4i2l4whuSJJErWbt0QekZ+DU+aK/k4pNnjSZeYuXMn7xBnz8A5SOI7hx7+p5fhr3Jd9M/p533i2mdBzBjQSbjfe6jyaoeG0+qNtW6TiCIAiC8NoTBbXXWNvmTfHR2Nn0/Wh0Op3ScQQ3tsY8ZFegFz9t3ky6dGL6llo9un2VX0Z2Ymbn+pQtmFPpOIIbDoeDkrUaUbBkJZp3+1KszlCxr77qy77fjzBu8XosHp5KxxHcuHbiNw7MHcXUhSsIzZlb6TiCGw+jY3mvxxjy1u9ErjJioIcgCIIg/D+IgtprSJIk6lStSMmCORj9RRdxQKlii2MjuJQ1Pbs2bsTHx0fpOIIbYeeOc3Rqb1Z92ZxCWTMoHUdwIzo6huI1G1GjeQdqfCj6O6lZ6zZtuRcVx6h5K9EbxHRctfpj90bO/riQ2Ss3EpIho9JxBDeu3X1Amd4Tea9NXzIXLqV0HOE1JLZ8CoIgPJsoqL2G6lSpQIcG1ejZponSUYQ0TIkNx/l2AbYtX45JbMdVrdtnDhH1+2Z+HNCObCFiS5paxcbFUbRKXdp8MZgSlUR/J7WSZZlq1WsQkDk7gyZPFh/4qNjvq+dw/8QvzFuzBR8/P6XjCG5cvxdOqS8nUqbHaIJzFlQ6jiAIgiC8UURB7TUiyzIAWdL9r707Vm0qDMAw/LnEImZwK6UZOrq4Obi5qINQnLwFd1G8GwcH7dJFEXUQOggWp2pxsBBDbQix0GqNwWNKG0cLbYYz/Y0+zxW8cOBw+A4//4U0z5/Lw+XnhYuYZO/X73w828j9xcU8WVoqncME6x/W8uVzO/duXc3KejtJu3QSJ1jrdLP6/lOuXLuZH99382r5cekkJhj+HGb7214u37iUl57TqbWz3U//6aPcufsgr188K53DBKtvVvLu7VouXr+dnc2N7GxulE7iBMPdr0n+fqdPpYP9nKr6g/3SBQBJkjPjqX67c1S3202r1SqdAQAAHLG1tZX5+em6ybiqqiwsLKTf75dOOWZ2djadTiczMzOlU4D/mEHtH3J4eJher5dms+kYDQAAFDYejzMYDDI3NzeVt05XVZXRaFQ645hGo2FMA4ozqAEAAABADdP3mwQAAAAACjKoAQAAAEANBjUAAAAAqMGgBgAAAAA1GNQAAAAAoAaDGgAAAADUYFADAAAAgBr+AIiLgkDsEErQAAAAAElFTkSuQmCC",
       "text/plain": [
-       "<Figure size 864x720 with 3 Axes>"
+       "<Figure size 1200x1000 with 3 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -251,9 +247,9 @@
     {
      "data": {
       "text/plain": [
-       "(<Figure size 864x720 with 3 Axes>,\n",
+       "(<Figure size 1200x1000 with 3 Axes>,\n",
        " <AxesSubplot:>,\n",
-       " <matplotlib.colorbar.Colorbar at 0x16915b100>)"
+       " <matplotlib.colorbar.Colorbar at 0x16f3dbb20>)"
       ]
      },
      "execution_count": 6,
@@ -262,14 +258,12 @@
     },
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
-       "<Figure size 864x720 with 3 Axes>"
+       "<Figure size 1200x1000 with 3 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -297,9 +291,9 @@
     {
      "data": {
       "text/plain": [
-       "(<Figure size 864x720 with 3 Axes>,\n",
+       "(<Figure size 1200x1000 with 3 Axes>,\n",
        " <AxesSubplot:>,\n",
-       " <matplotlib.colorbar.Colorbar at 0x16938e160>)"
+       " <matplotlib.colorbar.Colorbar at 0x16f5415b0>)"
       ]
      },
      "execution_count": 7,
@@ -308,14 +302,12 @@
     },
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
-       "<Figure size 864x720 with 3 Axes>"
+       "<Figure size 1200x1000 with 3 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -344,9 +336,9 @@
     {
      "data": {
       "text/plain": [
-       "(<Figure size 864x720 with 3 Axes>,\n",
+       "(<Figure size 1200x1000 with 3 Axes>,\n",
        " <AxesSubplot:>,\n",
-       " <matplotlib.colorbar.Colorbar at 0x169679c70>)"
+       " <matplotlib.colorbar.Colorbar at 0x16f212430>)"
       ]
      },
      "execution_count": 8,
@@ -355,14 +347,12 @@
     },
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
-       "<Figure size 864x720 with 3 Axes>"
+       "<Figure size 1200x1000 with 3 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -373,6 +363,8 @@
     "              annotate_data = annotate_data,\n",
     "              xaxis_labels=xaxis_labels, \n",
     "              yaxis_labels=yaxis_labels, \n",
+    "              annotate_textcolors=(\"green\", \"yellow\"),\n",
+    "              annotate_textcolors_threshold=(-1.5, 1.5),\n",
     "              annotate=True, \n",
     "              cbar_label='Metric Name')"
    ]
@@ -394,14 +386,12 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": 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",
       "text/plain": [
-       "<Figure size 360x360 with 3 Axes>"
+       "<Figure size 500x500 with 3 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -435,9 +425,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": 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",
       "text/plain": [
-       "<Figure size 360x360 with 3 Axes>"
+       "<Figure size 500x500 with 3 Axes>"
       ]
      },
      "execution_count": 10,
@@ -468,14 +458,12 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
-       "<Figure size 864x864 with 9 Axes>"
+       "<Figure size 1200x1200 with 9 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -531,9 +519,9 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python [conda env:pcmdi_metrics_dev_3] *",
+   "display_name": "pmp_devel_20230223",
    "language": "python",
-   "name": "conda-env-pcmdi_metrics_dev_3-py"
+   "name": "python3"
   },
   "language_info": {
    "codemirror_mode": {
diff --git a/pcmdi_metrics/graphics/portrait_plot/portrait_plot_lib.py b/pcmdi_metrics/graphics/portrait_plot/portrait_plot_lib.py
index bc04665fa..f791ab99d 100644
--- a/pcmdi_metrics/graphics/portrait_plot/portrait_plot_lib.py
+++ b/pcmdi_metrics/graphics/portrait_plot/portrait_plot_lib.py
@@ -17,6 +17,7 @@ def portrait_plot(
     annotate=False,
     annotate_data=None,
     annotate_textcolors=("black", "white"),
+    annotate_textcolors_threshold=(-2, 2),
     annotate_fontsize=15,
     annotate_format="{x:.2f}",
     figsize=(12, 10),
@@ -63,6 +64,7 @@ def portrait_plot(
                   but work only for heatmap style map (i.e., no triangles)
     - `annotate_data`: 2d numpy array, default=None. If None, the image's data is used.  Optional.
     - `annotate_textcolors`: Tuple. A pair of colors for annotation text. Default is ("black", "white")
+    - `annotate_textcolors_threshold`: Tuple or float. Value in data units according to which the colors from textcolors are applied. Default=(-2, 2)
     - `annotate_fontsize`: number (int/float), default=15. Font size for annotation
     - `annotate_format`: format for annotate value, default="{x:.2f}"
     - `figsize`: tuple of two numbers (width, height), default=(12, 10), figure size in inches
@@ -171,14 +173,14 @@ def portrait_plot(
                     sys.exit("Error: annotate_data has different size than data")
             else:
                 annotate_data = data
-            annotate_heatmap(
+            ax = annotate_heatmap(
                 im,
                 ax=ax,
                 data=data,
                 annotate_data=annotate_data,
                 valfmt=annotate_format,
                 textcolors=annotate_textcolors,
-                threshold=(2, -2),
+                threshold=annotate_textcolors_threshold,
                 fontsize=annotate_fontsize,
             )
 
@@ -503,6 +505,8 @@ def annotate_heatmap(
                 kw.update(color=textcolors[int(data[i, j] > threshold)])
             text = ax.text(j + 0.5, i + 0.5, valfmt(annotate_data[i, j], None), **kw)
             texts.append(text)
+            
+    return ax
 
 
 # ======================================================================

From d233ad591a0f3cc38d3e4b3fe27a6dc355c5f826 Mon Sep 17 00:00:00 2001
From: Jiwoo Lee <lee1043@llnl.gov>
Date: Wed, 8 Nov 2023 15:46:56 -0800
Subject: [PATCH 112/133] clean up -- pre-commit fix

---
 pcmdi_metrics/graphics/portrait_plot/portrait_plot_lib.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/pcmdi_metrics/graphics/portrait_plot/portrait_plot_lib.py b/pcmdi_metrics/graphics/portrait_plot/portrait_plot_lib.py
index f791ab99d..8209bdfbd 100644
--- a/pcmdi_metrics/graphics/portrait_plot/portrait_plot_lib.py
+++ b/pcmdi_metrics/graphics/portrait_plot/portrait_plot_lib.py
@@ -505,7 +505,7 @@ def annotate_heatmap(
                 kw.update(color=textcolors[int(data[i, j] > threshold)])
             text = ax.text(j + 0.5, i + 0.5, valfmt(annotate_data[i, j], None), **kw)
             texts.append(text)
-            
+
     return ax
 
 

From 4e91e19c459d922e2e80d61fc8ace08f837bdd1d Mon Sep 17 00:00:00 2001
From: Jiwoo Lee <lee1043@llnl.gov>
Date: Thu, 9 Nov 2023 14:32:04 -0800
Subject: [PATCH 113/133] make time_dim_sync as option and try it if not
 successful

---
 .../mean_climate/lib/compute_metrics.py       | 24 +++++++++----------
 .../mean_climate/mean_climate_driver.py       | 24 ++++++++++++-------
 2 files changed, 26 insertions(+), 22 deletions(-)

diff --git a/pcmdi_metrics/mean_climate/lib/compute_metrics.py b/pcmdi_metrics/mean_climate/lib/compute_metrics.py
index 67d80a5ee..617599f50 100644
--- a/pcmdi_metrics/mean_climate/lib/compute_metrics.py
+++ b/pcmdi_metrics/mean_climate/lib/compute_metrics.py
@@ -3,7 +3,7 @@
 import pcmdi_metrics
 
 
-def compute_metrics(Var, dm, do, debug=False):
+def compute_metrics(Var, dm, do, debug=False, time_dim_sync=False):
     # Var is sometimes sent with level associated
     var = Var.split("_")[0]
     # Did we send data? Or do we just want the info?
@@ -35,23 +35,21 @@ def compute_metrics(Var, dm, do, debug=False):
 
     # unify time and time bounds between observation and model
     if debug:
-        print("before time and time bounds unifying")
         print("dm.time: ", dm["time"])
         print("do.time: ", do["time"])
 
-    """
-    # Below is temporary...
-    dm['time'] = do['time']
-    dm[dm.time.encoding['bounds']] = do[do.time.attrs['bounds']]
-    """
+    if time_dim_sync:
+        # Below is temporary...
+        dm['time'] = do['time']
+        dm[dm.time.encoding['bounds']] = do[do.time.attrs['bounds']]
 
-    if debug:
-        print("after time and time bounds unifying")
-        print("dm.time: ", dm["time"])
-        print("do.time: ", do["time"])
+        if debug:
+            print("time and time bounds synced")
+            print("dm.time: ", dm["time"])
+            print("do.time: ", do["time"])
 
-        dm.to_netcdf("dm.nc")
-        do.to_netcdf("do.nc")
+            dm.to_netcdf("dm.nc")
+            do.to_netcdf("do.nc")
 
     metrics_dictionary = OrderedDict()
 
diff --git a/pcmdi_metrics/mean_climate/mean_climate_driver.py b/pcmdi_metrics/mean_climate/mean_climate_driver.py
index 6b61fdcfd..ce7a248b2 100755
--- a/pcmdi_metrics/mean_climate/mean_climate_driver.py
+++ b/pcmdi_metrics/mean_climate/mean_climate_driver.py
@@ -425,16 +425,22 @@
                                     )
 
                             # compute metrics
-
                             print("compute metrics start")
-                            result_dict["RESULTS"][model][ref][run][
-                                region
-                            ] = compute_metrics(
-                                varname,
-                                ds_test_dict[region],
-                                ds_ref_dict[region],
-                                debug=debug,
-                            )
+                            try:
+                                result_dict["RESULTS"][model][ref][run][region] = compute_metrics(
+                                    varname,
+                                    ds_test_dict[region],
+                                    ds_ref_dict[region],
+                                    debug=debug,
+                                )
+                            except Exception:
+                                result_dict["RESULTS"][model][ref][run][region] = compute_metrics(
+                                    varname,
+                                    ds_test_dict[region],
+                                    ds_ref_dict[region],
+                                    debug=debug,
+                                    time_dim_sync=True
+                                )                                
 
                             # write individual JSON
                             # --- single simulation, obs (need to accumulate later) / single variable

From eb919aa1350879aec10c127d828acb4271930c75 Mon Sep 17 00:00:00 2001
From: Jiwoo Lee <lee1043@llnl.gov>
Date: Thu, 9 Nov 2023 14:33:08 -0800
Subject: [PATCH 114/133] pre-commit fix

---
 pcmdi_metrics/mean_climate/mean_climate_driver.py | 12 ++++++++----
 1 file changed, 8 insertions(+), 4 deletions(-)

diff --git a/pcmdi_metrics/mean_climate/mean_climate_driver.py b/pcmdi_metrics/mean_climate/mean_climate_driver.py
index ce7a248b2..5e6f1719f 100755
--- a/pcmdi_metrics/mean_climate/mean_climate_driver.py
+++ b/pcmdi_metrics/mean_climate/mean_climate_driver.py
@@ -427,20 +427,24 @@
                             # compute metrics
                             print("compute metrics start")
                             try:
-                                result_dict["RESULTS"][model][ref][run][region] = compute_metrics(
+                                result_dict["RESULTS"][model][ref][run][
+                                    region
+                                ] = compute_metrics(
                                     varname,
                                     ds_test_dict[region],
                                     ds_ref_dict[region],
                                     debug=debug,
                                 )
                             except Exception:
-                                result_dict["RESULTS"][model][ref][run][region] = compute_metrics(
+                                result_dict["RESULTS"][model][ref][run][
+                                    region
+                                ] = compute_metrics(
                                     varname,
                                     ds_test_dict[region],
                                     ds_ref_dict[region],
                                     debug=debug,
-                                    time_dim_sync=True
-                                )                                
+                                    time_dim_sync=True,
+                                )
 
                             # write individual JSON
                             # --- single simulation, obs (need to accumulate later) / single variable

From f619758263dfc4cab57a12f2c32cb1dcf2619b20 Mon Sep 17 00:00:00 2001
From: Jiwoo Lee <lee1043@llnl.gov>
Date: Thu, 9 Nov 2023 16:31:19 -0800
Subject: [PATCH 115/133] When open ref dataset, try with decode_times True
 first, and if that is not successful, open with decode_times False

---
 .../mean_climate/mean_climate_driver.py       | 32 +++++++++++++------
 1 file changed, 22 insertions(+), 10 deletions(-)

diff --git a/pcmdi_metrics/mean_climate/mean_climate_driver.py b/pcmdi_metrics/mean_climate/mean_climate_driver.py
index 5e6f1719f..bfacf02a3 100755
--- a/pcmdi_metrics/mean_climate/mean_climate_driver.py
+++ b/pcmdi_metrics/mean_climate/mean_climate_driver.py
@@ -250,16 +250,28 @@
         print("ref_data_full_path:", ref_data_full_path)
 
         # load data and regrid
-        ds_ref = load_and_regrid(
-            data_path=ref_data_full_path,
-            varname=varname,
-            level=level,
-            t_grid=t_grid,
-            # decode_times=False,
-            decode_times=True,
-            regrid_tool=regrid_tool,
-            debug=debug,
-        )
+        try:
+            ds_ref = load_and_regrid(
+                data_path=ref_data_full_path,
+                varname=varname,
+                level=level,
+                t_grid=t_grid,
+                decode_times=True,
+                regrid_tool=regrid_tool,
+                debug=debug,
+            )
+        except Exception:
+            ds_ref = load_and_regrid(
+                data_path=ref_data_full_path,
+                varname=varname,
+                level=level,
+                t_grid=t_grid,
+                decode_times=False,
+                regrid_tool=regrid_tool,
+                debug=debug,
+            )
+
+        print("ref_data load_and_regrid done")
 
         ds_ref_dict = OrderedDict()
 

From c254d5393d9f45780d2fc4c2c6ad4be978cd2644 Mon Sep 17 00:00:00 2001
From: Jiwoo Lee <lee1043@llnl.gov>
Date: Thu, 9 Nov 2023 16:35:29 -0800
Subject: [PATCH 116/133] pre-commit fix

---
 pcmdi_metrics/mean_climate/lib/compute_metrics.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/pcmdi_metrics/mean_climate/lib/compute_metrics.py b/pcmdi_metrics/mean_climate/lib/compute_metrics.py
index 617599f50..33080f379 100644
--- a/pcmdi_metrics/mean_climate/lib/compute_metrics.py
+++ b/pcmdi_metrics/mean_climate/lib/compute_metrics.py
@@ -40,8 +40,8 @@ def compute_metrics(Var, dm, do, debug=False, time_dim_sync=False):
 
     if time_dim_sync:
         # Below is temporary...
-        dm['time'] = do['time']
-        dm[dm.time.encoding['bounds']] = do[do.time.attrs['bounds']]
+        dm["time"] = do["time"]
+        dm[dm.time.encoding["bounds"]] = do[do.time.attrs["bounds"]]
 
         if debug:
             print("time and time bounds synced")

From 847a1a2b1a446a06bb7c2f13fb7c7f588d54043e Mon Sep 17 00:00:00 2001
From: Jiwoo Lee <lee1043@llnl.gov>
Date: Thu, 9 Nov 2023 17:14:49 -0800
Subject: [PATCH 117/133] update json file name because now case_id is included
 as a part of json file name

---
 doc/jupyter/Demo/Demo_1b_mean_climate.ipynb | 1178 ++++++++++++-------
 1 file changed, 739 insertions(+), 439 deletions(-)

diff --git a/doc/jupyter/Demo/Demo_1b_mean_climate.ipynb b/doc/jupyter/Demo/Demo_1b_mean_climate.ipynb
index 76c1b948a..eb515380c 100644
--- a/doc/jupyter/Demo/Demo_1b_mean_climate.ipynb
+++ b/doc/jupyter/Demo/Demo_1b_mean_climate.ipynb
@@ -135,20 +135,29 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "2023-02-21 14:17:32,585 [WARNING]: dataset.py(_is_decodable:474) >> 'time' does not have a 'units' attribute set so it could not be decoded. Try setting the 'units' attribute (`ds.{coords.name}.attrs['units']`) and try decoding again.\n",
-      "INFO::2023-02-21 14:18::pcmdi_metrics:: Results saved to a json file: /Users/lee1043/Documents/Research/git/pcmdi_metrics_20221013_pcmdi/pcmdi_metrics/doc/jupyter/Demo/demo_output/basicTest/rlut/ACCESS1-0_rlut_2.5x2.5_regrid2_metrics_alternate1.json\n",
-      "2023-02-21 14:18:04,318 [INFO]: base.py(write:245) >> Results saved to a json file: /Users/lee1043/Documents/Research/git/pcmdi_metrics_20221013_pcmdi/pcmdi_metrics/doc/jupyter/Demo/demo_output/basicTest/rlut/ACCESS1-0_rlut_2.5x2.5_regrid2_metrics_alternate1.json\n",
-      "2023-02-21 14:18:04,348 [WARNING]: dataset.py(_is_decodable:474) >> 'time' does not have a 'units' attribute set so it could not be decoded. Try setting the 'units' attribute (`ds.{coords.name}.attrs['units']`) and try decoding again.\n",
-      "INFO::2023-02-21 14:18::pcmdi_metrics:: Results saved to a json file: /Users/lee1043/Documents/Research/git/pcmdi_metrics_20221013_pcmdi/pcmdi_metrics/doc/jupyter/Demo/demo_output/basicTest/rlut/CanCM4_rlut_2.5x2.5_regrid2_metrics_alternate1.json\n",
-      "2023-02-21 14:18:32,211 [INFO]: base.py(write:245) >> Results saved to a json file: /Users/lee1043/Documents/Research/git/pcmdi_metrics_20221013_pcmdi/pcmdi_metrics/doc/jupyter/Demo/demo_output/basicTest/rlut/CanCM4_rlut_2.5x2.5_regrid2_metrics_alternate1.json\n",
-      "2023-02-21 14:18:34,137 [WARNING]: dataset.py(_is_decodable:474) >> 'time' does not have a 'units' attribute set so it could not be decoded. Try setting the 'units' attribute (`ds.{coords.name}.attrs['units']`) and try decoding again.\n",
-      "INFO::2023-02-21 14:19::pcmdi_metrics:: Results saved to a json file: /Users/lee1043/Documents/Research/git/pcmdi_metrics_20221013_pcmdi/pcmdi_metrics/doc/jupyter/Demo/demo_output/basicTest/rlut/ACCESS1-0_rlut_2.5x2.5_regrid2_metrics_default.json\n",
-      "2023-02-21 14:19:02,987 [INFO]: base.py(write:245) >> Results saved to a json file: /Users/lee1043/Documents/Research/git/pcmdi_metrics_20221013_pcmdi/pcmdi_metrics/doc/jupyter/Demo/demo_output/basicTest/rlut/ACCESS1-0_rlut_2.5x2.5_regrid2_metrics_default.json\n",
-      "2023-02-21 14:19:03,017 [WARNING]: dataset.py(_is_decodable:474) >> 'time' does not have a 'units' attribute set so it could not be decoded. Try setting the 'units' attribute (`ds.{coords.name}.attrs['units']`) and try decoding again.\n",
-      "INFO::2023-02-21 14:19::pcmdi_metrics:: Results saved to a json file: /Users/lee1043/Documents/Research/git/pcmdi_metrics_20221013_pcmdi/pcmdi_metrics/doc/jupyter/Demo/demo_output/basicTest/rlut/CanCM4_rlut_2.5x2.5_regrid2_metrics_default.json\n",
-      "2023-02-21 14:19:34,352 [INFO]: base.py(write:245) >> Results saved to a json file: /Users/lee1043/Documents/Research/git/pcmdi_metrics_20221013_pcmdi/pcmdi_metrics/doc/jupyter/Demo/demo_output/basicTest/rlut/CanCM4_rlut_2.5x2.5_regrid2_metrics_default.json\n",
-      "INFO::2023-02-21 14:19::pcmdi_metrics:: Results saved to a json file: /Users/lee1043/Documents/Research/git/pcmdi_metrics_20221013_pcmdi/pcmdi_metrics/doc/jupyter/Demo/demo_output/basicTest/rlut_2.5x2.5_regrid2_metrics.json\n",
-      "2023-02-21 14:19:34,357 [INFO]: base.py(write:245) >> Results saved to a json file: /Users/lee1043/Documents/Research/git/pcmdi_metrics_20221013_pcmdi/pcmdi_metrics/doc/jupyter/Demo/demo_output/basicTest/rlut_2.5x2.5_regrid2_metrics.json\n"
+      "2023-11-09 16:38:41,260 [WARNING]: dataset.py(_is_decodable:670) >> 'time' does not have a 'units' attribute set so it could not be decoded. Try setting the 'units' attribute (`ds.{coords.name}.attrs['units']`) and try decoding again.\n",
+      "2023-11-09 16:38:41,260 [WARNING]: dataset.py(_is_decodable:670) >> 'time' does not have a 'units' attribute set so it could not be decoded. Try setting the 'units' attribute (`ds.{coords.name}.attrs['units']`) and try decoding again.\n",
+      "INFO::2023-11-09 16:39::pcmdi_metrics:: Results saved to a json file: /Users/lee1043/Documents/Research/git/pcmdi_metrics_20230620_pcmdi/pcmdi_metrics/doc/jupyter/Demo/demo_output/basicTest/rlut/rlut_ACCESS1-0__2.5x2.5_regrid2_metrics_alternate1_basicTest.json\n",
+      "2023-11-09 16:39:14,932 [INFO]: base.py(write:250) >> Results saved to a json file: /Users/lee1043/Documents/Research/git/pcmdi_metrics_20230620_pcmdi/pcmdi_metrics/doc/jupyter/Demo/demo_output/basicTest/rlut/rlut_ACCESS1-0__2.5x2.5_regrid2_metrics_alternate1_basicTest.json\n",
+      "2023-11-09 16:39:14,932 [INFO]: base.py(write:250) >> Results saved to a json file: /Users/lee1043/Documents/Research/git/pcmdi_metrics_20230620_pcmdi/pcmdi_metrics/doc/jupyter/Demo/demo_output/basicTest/rlut/rlut_ACCESS1-0__2.5x2.5_regrid2_metrics_alternate1_basicTest.json\n",
+      "2023-11-09 16:39:14,958 [WARNING]: dataset.py(_is_decodable:670) >> 'time' does not have a 'units' attribute set so it could not be decoded. Try setting the 'units' attribute (`ds.{coords.name}.attrs['units']`) and try decoding again.\n",
+      "2023-11-09 16:39:14,958 [WARNING]: dataset.py(_is_decodable:670) >> 'time' does not have a 'units' attribute set so it could not be decoded. Try setting the 'units' attribute (`ds.{coords.name}.attrs['units']`) and try decoding again.\n",
+      "INFO::2023-11-09 16:39::pcmdi_metrics:: Results saved to a json file: /Users/lee1043/Documents/Research/git/pcmdi_metrics_20230620_pcmdi/pcmdi_metrics/doc/jupyter/Demo/demo_output/basicTest/rlut/rlut_CanCM4__2.5x2.5_regrid2_metrics_alternate1_basicTest.json\n",
+      "2023-11-09 16:39:50,007 [INFO]: base.py(write:250) >> Results saved to a json file: /Users/lee1043/Documents/Research/git/pcmdi_metrics_20230620_pcmdi/pcmdi_metrics/doc/jupyter/Demo/demo_output/basicTest/rlut/rlut_CanCM4__2.5x2.5_regrid2_metrics_alternate1_basicTest.json\n",
+      "2023-11-09 16:39:50,007 [INFO]: base.py(write:250) >> Results saved to a json file: /Users/lee1043/Documents/Research/git/pcmdi_metrics_20230620_pcmdi/pcmdi_metrics/doc/jupyter/Demo/demo_output/basicTest/rlut/rlut_CanCM4__2.5x2.5_regrid2_metrics_alternate1_basicTest.json\n",
+      "2023-11-09 16:39:55,027 [WARNING]: dataset.py(_is_decodable:670) >> 'time' does not have a 'units' attribute set so it could not be decoded. Try setting the 'units' attribute (`ds.{coords.name}.attrs['units']`) and try decoding again.\n",
+      "2023-11-09 16:39:55,027 [WARNING]: dataset.py(_is_decodable:670) >> 'time' does not have a 'units' attribute set so it could not be decoded. Try setting the 'units' attribute (`ds.{coords.name}.attrs['units']`) and try decoding again.\n",
+      "INFO::2023-11-09 16:40::pcmdi_metrics:: Results saved to a json file: /Users/lee1043/Documents/Research/git/pcmdi_metrics_20230620_pcmdi/pcmdi_metrics/doc/jupyter/Demo/demo_output/basicTest/rlut/rlut_ACCESS1-0__2.5x2.5_regrid2_metrics_default_basicTest.json\n",
+      "2023-11-09 16:40:29,591 [INFO]: base.py(write:250) >> Results saved to a json file: /Users/lee1043/Documents/Research/git/pcmdi_metrics_20230620_pcmdi/pcmdi_metrics/doc/jupyter/Demo/demo_output/basicTest/rlut/rlut_ACCESS1-0__2.5x2.5_regrid2_metrics_default_basicTest.json\n",
+      "2023-11-09 16:40:29,591 [INFO]: base.py(write:250) >> Results saved to a json file: /Users/lee1043/Documents/Research/git/pcmdi_metrics_20230620_pcmdi/pcmdi_metrics/doc/jupyter/Demo/demo_output/basicTest/rlut/rlut_ACCESS1-0__2.5x2.5_regrid2_metrics_default_basicTest.json\n",
+      "2023-11-09 16:40:29,627 [WARNING]: dataset.py(_is_decodable:670) >> 'time' does not have a 'units' attribute set so it could not be decoded. Try setting the 'units' attribute (`ds.{coords.name}.attrs['units']`) and try decoding again.\n",
+      "2023-11-09 16:40:29,627 [WARNING]: dataset.py(_is_decodable:670) >> 'time' does not have a 'units' attribute set so it could not be decoded. Try setting the 'units' attribute (`ds.{coords.name}.attrs['units']`) and try decoding again.\n",
+      "INFO::2023-11-09 16:41::pcmdi_metrics:: Results saved to a json file: /Users/lee1043/Documents/Research/git/pcmdi_metrics_20230620_pcmdi/pcmdi_metrics/doc/jupyter/Demo/demo_output/basicTest/rlut/rlut_CanCM4__2.5x2.5_regrid2_metrics_default_basicTest.json\n",
+      "2023-11-09 16:41:05,136 [INFO]: base.py(write:250) >> Results saved to a json file: /Users/lee1043/Documents/Research/git/pcmdi_metrics_20230620_pcmdi/pcmdi_metrics/doc/jupyter/Demo/demo_output/basicTest/rlut/rlut_CanCM4__2.5x2.5_regrid2_metrics_default_basicTest.json\n",
+      "2023-11-09 16:41:05,136 [INFO]: base.py(write:250) >> Results saved to a json file: /Users/lee1043/Documents/Research/git/pcmdi_metrics_20230620_pcmdi/pcmdi_metrics/doc/jupyter/Demo/demo_output/basicTest/rlut/rlut_CanCM4__2.5x2.5_regrid2_metrics_default_basicTest.json\n",
+      "INFO::2023-11-09 16:41::pcmdi_metrics:: Results saved to a json file: /Users/lee1043/Documents/Research/git/pcmdi_metrics_20230620_pcmdi/pcmdi_metrics/doc/jupyter/Demo/demo_output/basicTest/rlut_2.5x2.5_regrid2_metrics_basicTest.json\n",
+      "2023-11-09 16:41:05,142 [INFO]: base.py(write:250) >> Results saved to a json file: /Users/lee1043/Documents/Research/git/pcmdi_metrics_20230620_pcmdi/pcmdi_metrics/doc/jupyter/Demo/demo_output/basicTest/rlut_2.5x2.5_regrid2_metrics_basicTest.json\n",
+      "2023-11-09 16:41:05,142 [INFO]: base.py(write:250) >> Results saved to a json file: /Users/lee1043/Documents/Research/git/pcmdi_metrics_20230620_pcmdi/pcmdi_metrics/doc/jupyter/Demo/demo_output/basicTest/rlut_2.5x2.5_regrid2_metrics_basicTest.json\n"
      ]
     },
     {
@@ -159,6 +168,7 @@
       " test_data_set: ['ACCESS1-0', 'CanCM4'] \n",
       " realization:  \n",
       " vars: ['rlut'] \n",
+      " varname_in_test_data: None \n",
       " reference_data_set: ['all'] \n",
       " target_grid: 2.5x2.5 \n",
       " regrid_tool: regrid2 \n",
@@ -168,11 +178,12 @@
       " filename_template: cmip5.historical.%(model_version).r1i1p1.mon.%(variable).198101-200512.AC.v20200426.nc \n",
       " sftlf_filename_template: sftlf_%(model_version).nc \n",
       " generate_sftlf: True \n",
-      " regions_specs: {'NHEX': {'domain': {'latitude': (30.0, 90)}}, 'SHEX': {'domain': {'latitude': (-90.0, -30)}}, 'TROPICS': {'domain': {'latitude': (-30.0, 30)}}, 'global': {}, '90S50S': {'domain': {'latitude': (-90.0, -50)}}, '50S20S': {'domain': {'latitude': (-50.0, -20)}}, '20S20N': {'domain': {'latitude': (-20.0, 20)}}, '20N50N': {'domain': {'latitude': (20.0, 50)}}, '50N90N': {'domain': {'latitude': (50.0, 90)}}, 'land_NHEX': {'value': 100, 'domain': {'latitude': (30.0, 90)}}, 'land_SHEX': {'value': 100, 'domain': {'latitude': (-90.0, -30)}}, 'land_TROPICS': {'value': 100, 'domain': {'latitude': (-30.0, 30)}}, 'land': {'value': 100}, 'ocean_NHEX': {'value': 0, 'domain': {'latitude': (30.0, 90)}}, 'ocean_SHEX': {'value': 0, 'domain': {'latitude': (-90.0, -30)}}, 'ocean_TROPICS': {'value': 0, 'domain': {'latitude': (30.0, 30)}}, 'ocean': {'value': 0}, 'CONUS': {'domain': {'latitude': (24.7, 49.4), 'longitude': (-124.78, -66.92)}}, 'land_CONUS': {'value': 100, 'domain': {'latitude': (24.7, 49.4), 'longitude': (-124.78, -66.92)}}, 'NAM': {'domain': {'latitude': (20.0, 90), 'longitude': (-180, 180)}}, 'NAO': {'domain': {'latitude': (20.0, 80), 'longitude': (-90, 40)}}, 'SAM': {'domain': {'latitude': (-20.0, -90), 'longitude': (0, 360)}}, 'PNA': {'domain': {'latitude': (20.0, 85), 'longitude': (120, 240)}}, 'PDO': {'domain': {'latitude': (20.0, 70), 'longitude': (110, 260)}}, 'AllMW': {'domain': {'latitude': (-40.0, 45.0), 'longitude': (0.0, 360.0)}}, 'AllM': {'domain': {'latitude': (-45.0, 45.0), 'longitude': (0.0, 360.0)}}, 'NAMM': {'domain': {'latitude': (0.0, 45.0), 'longitude': (210.0, 310.0)}}, 'SAMM': {'domain': {'latitude': (-45.0, 0.0), 'longitude': (240.0, 330.0)}}, 'NAFM': {'domain': {'latitude': (0.0, 45.0), 'longitude': (310.0, 60.0)}}, 'SAFM': {'domain': {'latitude': (-45.0, 0.0), 'longitude': (0.0, 90.0)}}, 'ASM': {'domain': {'latitude': (0.0, 45.0), 'longitude': (60.0, 180.0)}}, 'AUSM': {'domain': {'latitude': (-45.0, 0.0), 'longitude': (90.0, 160.0)}}, 'AIR': {'domain': {'latitude': (7.0, 25.0), 'longitude': (65.0, 85.0)}}, 'AUS': {'domain': {'latitude': (-20.0, -10.0), 'longitude': (120.0, 150.0)}}, 'Sahel': {'domain': {'latitude': (13.0, 18.0), 'longitude': (-10.0, 10.0)}}, 'GoG': {'domain': {'latitude': (0.0, 5.0), 'longitude': (-10.0, 10.0)}}, 'NAmo': {'domain': {'latitude': (20.0, 37.0), 'longitude': (-112.0, -103.0)}}, 'SAmo': {'domain': {'latitude': (-20.0, 2.5), 'longitude': (-65.0, -40.0)}}} \n",
+      " regions_specs: {'global': {}, 'NHEX': {'domain': {'latitude': (30.0, 90)}}, 'SHEX': {'domain': {'latitude': (-90.0, -30)}}, 'TROPICS': {'domain': {'latitude': (-30.0, 30)}}, '90S50S': {'domain': {'latitude': (-90.0, -50)}}, '50S20S': {'domain': {'latitude': (-50.0, -20)}}, '20S20N': {'domain': {'latitude': (-20.0, 20)}}, '20N50N': {'domain': {'latitude': (20.0, 50)}}, '50N90N': {'domain': {'latitude': (50.0, 90)}}, 'CONUS': {'domain': {'latitude': (24.7, 49.4), 'longitude': (-124.78, -66.92)}}, 'land': {'value': 100}, 'land_NHEX': {'value': 100, 'domain': {'latitude': (30.0, 90)}}, 'land_SHEX': {'value': 100, 'domain': {'latitude': (-90.0, -30)}}, 'land_TROPICS': {'value': 100, 'domain': {'latitude': (-30.0, 30)}}, 'land_CONUS': {'value': 100, 'domain': {'latitude': (24.7, 49.4), 'longitude': (-124.78, -66.92)}}, 'ocean': {'value': 0}, 'ocean_NHEX': {'value': 0, 'domain': {'latitude': (30.0, 90)}}, 'ocean_SHEX': {'value': 0, 'domain': {'latitude': (-90.0, -30)}}, 'ocean_TROPICS': {'value': 0, 'domain': {'latitude': (30.0, 30)}}, 'ocean_50S50N': {'value': 0.0, 'domain': {'latitude': (-50.0, 50)}}, 'ocean_50S20S': {'value': 0.0, 'domain': {'latitude': (-50.0, -20)}}, 'ocean_20S20N': {'value': 0.0, 'domain': {'latitude': (-20.0, 20)}}, 'ocean_20N50N': {'value': 0.0, 'domain': {'latitude': (20.0, 50)}}, 'NAM': {'domain': {'latitude': (20.0, 90), 'longitude': (-180, 180)}}, 'NAO': {'domain': {'latitude': (20.0, 80), 'longitude': (-90, 40)}}, 'SAM': {'domain': {'latitude': (-20.0, -90), 'longitude': (0, 360)}}, 'PNA': {'domain': {'latitude': (20.0, 85), 'longitude': (120, 240)}}, 'PDO': {'domain': {'latitude': (20.0, 70), 'longitude': (110, 260)}}, 'AllMW': {'domain': {'latitude': (-40.0, 45.0), 'longitude': (0.0, 360.0)}}, 'AllM': {'domain': {'latitude': (-45.0, 45.0), 'longitude': (0.0, 360.0)}}, 'NAMM': {'domain': {'latitude': (0.0, 45.0), 'longitude': (210.0, 310.0)}}, 'SAMM': {'domain': {'latitude': (-45.0, 0.0), 'longitude': (240.0, 330.0)}}, 'NAFM': {'domain': {'latitude': (0.0, 45.0), 'longitude': (310.0, 60.0)}}, 'SAFM': {'domain': {'latitude': (-45.0, 0.0), 'longitude': (0.0, 90.0)}}, 'ASM': {'domain': {'latitude': (0.0, 45.0), 'longitude': (60.0, 180.0)}}, 'AUSM': {'domain': {'latitude': (-45.0, 0.0), 'longitude': (90.0, 160.0)}}, 'AIR': {'domain': {'latitude': (7.0, 25.0), 'longitude': (65.0, 85.0)}}, 'AUS': {'domain': {'latitude': (-20.0, -10.0), 'longitude': (120.0, 150.0)}}, 'Sahel': {'domain': {'latitude': (13.0, 18.0), 'longitude': (-10.0, 10.0)}}, 'GoG': {'domain': {'latitude': (0.0, 5.0), 'longitude': (-10.0, 10.0)}}, 'NAmo': {'domain': {'latitude': (20.0, 37.0), 'longitude': (-112.0, -103.0)}}, 'SAmo': {'domain': {'latitude': (-20.0, 2.5), 'longitude': (-65.0, -40.0)}}} \n",
       " regions: {'rlut': ['Global']} \n",
       " test_data_path: demo_data/CMIP5_demo_clims/ \n",
       " reference_data_path: demo_data/obs4MIPs_PCMDI_clims \n",
       " metrics_output_path: demo_output/basicTest \n",
+      " diagnostics_output_path: demo_output/basicTest \n",
       " debug: False \n",
       "\n",
       "--- prepare mean climate metrics calculation ---\n",
@@ -182,46 +193,67 @@
       "reference_data_set (all):  ['alternate1', 'default']\n",
       "ref: alternate1\n",
       "ref_data_full_path: demo_data/obs4MIPs_PCMDI_clims/rlut/CERES-EBAF-4-0/v20210804/rlut_mon_CERES-EBAF-4-0_PCMDI_gn.200301-201812.AC.v20210804.nc\n",
+      "'DataArray' object has no attribute 'units'\n",
+      "units: \n",
+      "ref_data load_and_regrid done\n",
+      "=================================\n",
+      "model, runs, find_all_realizations: ACCESS1-0 [''] False\n",
       "-----------------------\n",
       "model, run: ACCESS1-0 \n",
       "test_data (model in this case) full_path: demo_data/CMIP5_demo_clims/cmip5.historical.ACCESS1-0.r1i1p1.mon.rlut.198101-200512.AC.v20200426.nc\n",
-      "[WARNING]: calendar info mismatch. ds.time.attrs[\"calendar\"] is adjusted to ds.calendar\n",
+      "units: W m-2\n",
       "load and regrid done\n",
       "region: Global\n",
       "compute metrics start\n",
       "var:  rlut\n",
       "compute_metrics-CALCULATE ANNUAL CYCLE SPACE-TIME RMS, CORRELATIONS and STD\n",
       "compute_metrics, rms_xyt\n",
+      "compute_metrics, rms_xyt: nan\n",
       "compute_metrics, stdObs_xyt\n",
+      "compute_metrics, stdObs_xyt: 33.46854119190699\n",
       "compute_metrics, std_xyt\n",
+      "compute_metrics, std_xyt: 36.438416968613225\n",
       "compute_metrics-CALCULATE ANNUAL MEANS\n",
       "compute_metrics-CALCULATE ANNUAL MEAN BIAS\n",
+      "compute_metrics-CALCULATE ANNUAL MEAN BIAS, bias_xy: 1.1384451737888808\n",
       "compute_metrics-CALCULATE MSE\n",
+      "compute_metrics-CALCULATE MSE, mae_xy: 5.770012023689812\n",
       "compute_metrics-CALCULATE MEAN RMS\n",
+      "compute_metrics-CALCULATE MEAN RMS: rms_xy, rmsc_xy:  8.044682445768467 7.9637213813349925\n",
       "compute_metrics-CALCULATE MEAN CORR\n",
+      "compute_metrics-CALCULATE MEAN CORR: cor_xy: 0.9693780836555635\n",
       "compute_metrics-CALCULATE ANNUAL OBS AND MOD STD\n",
       "compute_metrics-CALCULATE ANNUAL OBS AND MOD MEAN\n",
       "compute_metrics-CALCULATE ANNUAL MEANS\n",
       "compute_metrics-CALCULATE ANNUAL AND ZONAL MEAN RMS\n",
       "compute_metrics-CALCULATE ANNUAL MEAN DEVIATION FROM ZONAL MEAN RMS\n",
       "compute_metrics-CALCULATE ANNUAL MEAN DEVIATION FROM ZONAL MEAN STD\n",
+      "=================================\n",
+      "model, runs, find_all_realizations: CanCM4 [''] False\n",
       "-----------------------\n",
       "model, run: CanCM4 \n",
       "test_data (model in this case) full_path: demo_data/CMIP5_demo_clims/cmip5.historical.CanCM4.r1i1p1.mon.rlut.198101-200512.AC.v20200426.nc\n",
-      "[WARNING]: calendar info mismatch. ds.time.attrs[\"calendar\"] is adjusted to ds.calendar\n",
+      "units: W m-2\n",
       "load and regrid done\n",
       "region: Global\n",
       "compute metrics start\n",
       "var:  rlut\n",
       "compute_metrics-CALCULATE ANNUAL CYCLE SPACE-TIME RMS, CORRELATIONS and STD\n",
       "compute_metrics, rms_xyt\n",
+      "compute_metrics, rms_xyt: nan\n",
       "compute_metrics, stdObs_xyt\n",
+      "compute_metrics, stdObs_xyt: 33.46854119190699\n",
       "compute_metrics, std_xyt\n",
+      "compute_metrics, std_xyt: 35.67665337064905\n",
       "compute_metrics-CALCULATE ANNUAL MEANS\n",
       "compute_metrics-CALCULATE ANNUAL MEAN BIAS\n",
+      "compute_metrics-CALCULATE ANNUAL MEAN BIAS, bias_xy: -1.1635463134324031\n",
       "compute_metrics-CALCULATE MSE\n",
+      "compute_metrics-CALCULATE MSE, mae_xy: 6.331106438822097\n",
       "compute_metrics-CALCULATE MEAN RMS\n",
+      "compute_metrics-CALCULATE MEAN RMS: rms_xy, rmsc_xy:  9.610248337339145 9.539550989529207\n",
       "compute_metrics-CALCULATE MEAN CORR\n",
+      "compute_metrics-CALCULATE MEAN CORR: cor_xy: 0.9518659641205703\n",
       "compute_metrics-CALCULATE ANNUAL OBS AND MOD STD\n",
       "compute_metrics-CALCULATE ANNUAL OBS AND MOD MEAN\n",
       "compute_metrics-CALCULATE ANNUAL MEANS\n",
@@ -230,46 +262,67 @@
       "compute_metrics-CALCULATE ANNUAL MEAN DEVIATION FROM ZONAL MEAN STD\n",
       "ref: default\n",
       "ref_data_full_path: demo_data/obs4MIPs_PCMDI_clims/rlut/CERES-EBAF-4-1/v20210804/rlut_mon_CERES-EBAF-4-1_PCMDI_gn.200301-201812.AC.v20210804.nc\n",
+      "'DataArray' object has no attribute 'units'\n",
+      "units: \n",
+      "ref_data load_and_regrid done\n",
+      "=================================\n",
+      "model, runs, find_all_realizations: ACCESS1-0 [''] False\n",
       "-----------------------\n",
       "model, run: ACCESS1-0 \n",
       "test_data (model in this case) full_path: demo_data/CMIP5_demo_clims/cmip5.historical.ACCESS1-0.r1i1p1.mon.rlut.198101-200512.AC.v20200426.nc\n",
-      "[WARNING]: calendar info mismatch. ds.time.attrs[\"calendar\"] is adjusted to ds.calendar\n",
+      "units: W m-2\n",
       "load and regrid done\n",
       "region: Global\n",
       "compute metrics start\n",
       "var:  rlut\n",
       "compute_metrics-CALCULATE ANNUAL CYCLE SPACE-TIME RMS, CORRELATIONS and STD\n",
       "compute_metrics, rms_xyt\n",
+      "compute_metrics, rms_xyt: nan\n",
       "compute_metrics, stdObs_xyt\n",
+      "compute_metrics, stdObs_xyt: 33.41842190556023\n",
       "compute_metrics, std_xyt\n",
+      "compute_metrics, std_xyt: 36.438416968613225\n",
       "compute_metrics-CALCULATE ANNUAL MEANS\n",
       "compute_metrics-CALCULATE ANNUAL MEAN BIAS\n",
+      "compute_metrics-CALCULATE ANNUAL MEAN BIAS, bias_xy: 1.1365878854524063\n",
       "compute_metrics-CALCULATE MSE\n",
+      "compute_metrics-CALCULATE MSE, mae_xy: 5.763493973471603\n",
       "compute_metrics-CALCULATE MEAN RMS\n",
+      "compute_metrics-CALCULATE MEAN RMS: rms_xy, rmsc_xy:  8.033820554038556 7.953014565127818\n",
       "compute_metrics-CALCULATE MEAN CORR\n",
+      "compute_metrics-CALCULATE MEAN CORR: cor_xy: 0.9694728084795681\n",
       "compute_metrics-CALCULATE ANNUAL OBS AND MOD STD\n",
       "compute_metrics-CALCULATE ANNUAL OBS AND MOD MEAN\n",
       "compute_metrics-CALCULATE ANNUAL MEANS\n",
       "compute_metrics-CALCULATE ANNUAL AND ZONAL MEAN RMS\n",
       "compute_metrics-CALCULATE ANNUAL MEAN DEVIATION FROM ZONAL MEAN RMS\n",
       "compute_metrics-CALCULATE ANNUAL MEAN DEVIATION FROM ZONAL MEAN STD\n",
+      "=================================\n",
+      "model, runs, find_all_realizations: CanCM4 [''] False\n",
       "-----------------------\n",
       "model, run: CanCM4 \n",
       "test_data (model in this case) full_path: demo_data/CMIP5_demo_clims/cmip5.historical.CanCM4.r1i1p1.mon.rlut.198101-200512.AC.v20200426.nc\n",
-      "[WARNING]: calendar info mismatch. ds.time.attrs[\"calendar\"] is adjusted to ds.calendar\n",
+      "units: W m-2\n",
       "load and regrid done\n",
       "region: Global\n",
       "compute metrics start\n",
       "var:  rlut\n",
       "compute_metrics-CALCULATE ANNUAL CYCLE SPACE-TIME RMS, CORRELATIONS and STD\n",
       "compute_metrics, rms_xyt\n",
+      "compute_metrics, rms_xyt: nan\n",
       "compute_metrics, stdObs_xyt\n",
+      "compute_metrics, stdObs_xyt: 33.41842190556023\n",
       "compute_metrics, std_xyt\n",
+      "compute_metrics, std_xyt: 35.67665337064905\n",
       "compute_metrics-CALCULATE ANNUAL MEANS\n",
       "compute_metrics-CALCULATE ANNUAL MEAN BIAS\n",
+      "compute_metrics-CALCULATE ANNUAL MEAN BIAS, bias_xy: -1.1654036017688774\n",
       "compute_metrics-CALCULATE MSE\n",
+      "compute_metrics-CALCULATE MSE, mae_xy: 6.333692053819536\n",
       "compute_metrics-CALCULATE MEAN RMS\n",
+      "compute_metrics-CALCULATE MEAN RMS: rms_xy, rmsc_xy:  9.608637289548378 9.537701243333409\n",
       "compute_metrics-CALCULATE MEAN CORR\n",
+      "compute_metrics-CALCULATE MEAN CORR: cor_xy: 0.9518850115349531\n",
       "compute_metrics-CALCULATE ANNUAL OBS AND MOD STD\n",
       "compute_metrics-CALCULATE ANNUAL OBS AND MOD MEAN\n",
       "compute_metrics-CALCULATE ANNUAL MEANS\n",
@@ -307,7 +360,9 @@
       "{\n",
       "  \"ACCESS1-0\": {\n",
       "    \"alternate1\": {\n",
+      "      \"source\": \"CERES-EBAF-4-0\",\n",
       "      \"\": {\n",
+      "        \"InputClimatologyFileName\": \"cmip5.historical.ACCESS1-0.r1i1p1.mon.rlut.198101-200512.AC.v20200426.nc\",\n",
       "        \"Global\": {\n",
       "          \"bias_xy\": {\n",
       "            \"ann\": \"1.138\",\n",
@@ -342,7 +397,7 @@
       "              \"0.95\",\n",
       "              \"0.95\",\n",
       "              \"0.93\",\n",
-      "              \"0.95\",\n",
+      "              \"0.94\",\n",
       "              \"0.95\",\n",
       "              \"0.96\",\n",
       "              \"0.96\",\n",
@@ -353,27 +408,27 @@
       "          },\n",
       "          \"mae_xy\": {\n",
       "            \"ann\": \"5.770\",\n",
-      "            \"djf\": \"7.158\",\n",
-      "            \"mam\": \"7.246\",\n",
-      "            \"jja\": \"7.512\",\n",
-      "            \"son\": \"6.449\",\n",
+      "            \"djf\": \"7.157\",\n",
+      "            \"mam\": \"7.247\",\n",
+      "            \"jja\": \"7.517\",\n",
+      "            \"son\": \"6.448\",\n",
       "            \"CalendarMonths\": [\n",
-      "              \"7.775\",\n",
-      "              \"7.644\",\n",
-      "              \"8.019\",\n",
-      "              \"7.775\",\n",
-      "              \"7.819\",\n",
-      "              \"7.990\",\n",
-      "              \"8.503\",\n",
-      "              \"8.005\",\n",
+      "              \"7.774\",\n",
+      "              \"7.641\",\n",
+      "              \"8.017\",\n",
+      "              \"7.776\",\n",
+      "              \"7.820\",\n",
+      "              \"7.989\",\n",
+      "              \"8.508\",\n",
+      "              \"8.006\",\n",
       "              \"7.362\",\n",
-      "              \"7.295\",\n",
-      "              \"7.277\",\n",
-      "              \"7.721\"\n",
+      "              \"7.296\",\n",
+      "              \"7.280\",\n",
+      "              \"7.719\"\n",
       "            ]\n",
       "          },\n",
       "          \"mean-obs_xy\": {\n",
-      "            \"ann\": \"240.317\",\n",
+      "            \"ann\": \"240.316\",\n",
       "            \"djf\": \"237.540\",\n",
       "            \"mam\": \"239.327\",\n",
       "            \"jja\": \"243.879\",\n",
@@ -388,7 +443,7 @@
       "              \"244.361\",\n",
       "              \"244.378\",\n",
       "              \"242.922\",\n",
-      "              \"240.569\",\n",
+      "              \"240.568\",\n",
       "              \"238.119\",\n",
       "              \"237.120\"\n",
       "            ]\n",
@@ -418,112 +473,115 @@
       "            \"ann\": \"5.808\"\n",
       "          },\n",
       "          \"rms_xy\": {\n",
-      "            \"ann\": \"8.043\",\n",
-      "            \"djf\": \"10.231\",\n",
-      "            \"mam\": \"10.774\",\n",
-      "            \"jja\": \"10.439\",\n",
-      "            \"son\": \"9.258\",\n",
+      "            \"ann\": \"8.045\",\n",
+      "            \"djf\": \"10.232\",\n",
+      "            \"mam\": \"10.775\",\n",
+      "            \"jja\": \"10.443\",\n",
+      "            \"son\": \"9.260\",\n",
       "            \"CalendarMonths\": [\n",
       "              \"11.121\",\n",
-      "              \"10.900\",\n",
-      "              \"11.601\",\n",
+      "              \"10.901\",\n",
+      "              \"11.602\",\n",
       "              \"11.489\",\n",
-      "              \"12.611\",\n",
-      "              \"11.978\",\n",
-      "              \"11.848\",\n",
-      "              \"11.342\",\n",
-      "              \"10.655\",\n",
-      "              \"10.751\",\n",
-      "              \"11.131\",\n",
+      "              \"12.612\",\n",
+      "              \"11.981\",\n",
+      "              \"11.852\",\n",
+      "              \"11.344\",\n",
+      "              \"10.657\",\n",
+      "              \"10.753\",\n",
+      "              \"11.132\",\n",
       "              \"11.892\"\n",
       "            ]\n",
       "          },\n",
       "          \"rms_xyt\": {\n",
-      "            \"ann\": \"11.443\"\n",
+      "            \"ann\": \"nan\"\n",
       "          },\n",
       "          \"rms_y\": {\n",
-      "            \"ann\": \"5.565\"\n",
+      "            \"ann\": \"5.566\"\n",
       "          },\n",
       "          \"rmsc_xy\": {\n",
-      "            \"ann\": \"7.962\",\n",
-      "            \"djf\": \"10.093\",\n",
+      "            \"ann\": \"7.964\",\n",
+      "            \"djf\": \"10.094\",\n",
       "            \"mam\": \"10.684\",\n",
-      "            \"jja\": \"10.404\",\n",
-      "            \"son\": \"9.237\",\n",
+      "            \"jja\": \"10.407\",\n",
+      "            \"son\": \"9.238\",\n",
       "            \"CalendarMonths\": [\n",
       "              \"10.989\",\n",
-      "              \"10.833\",\n",
+      "              \"10.834\",\n",
       "              \"11.539\",\n",
-      "              \"11.408\",\n",
-      "              \"12.508\",\n",
-      "              \"11.909\",\n",
-      "              \"11.819\",\n",
-      "              \"11.332\",\n",
-      "              \"10.653\",\n",
-      "              \"10.744\",\n",
-      "              \"11.055\",\n",
+      "              \"11.409\",\n",
+      "              \"12.509\",\n",
+      "              \"11.912\",\n",
+      "              \"11.823\",\n",
+      "              \"11.334\",\n",
+      "              \"10.655\",\n",
+      "              \"10.745\",\n",
+      "              \"11.056\",\n",
       "              \"11.701\"\n",
       "            ]\n",
       "          },\n",
       "          \"std-obs_xy\": {\n",
-      "            \"ann\": \"29.642\",\n",
-      "            \"djf\": \"32.679\",\n",
-      "            \"mam\": \"30.811\",\n",
-      "            \"jja\": \"35.368\",\n",
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-      "              \"33.043\"\n",
+      "              \"38.696\",\n",
+      "              \"37.492\",\n",
+      "              \"34.881\",\n",
+      "              \"32.548\",\n",
+      "              \"33.053\"\n",
       "            ]\n",
       "          },\n",
       "          \"std_xy_devzm\": {\n",
-      "            \"ann\": \"14.247\"\n",
+      "            \"ann\": \"14.246\"\n",
       "          },\n",
       "          \"std_xyt\": {\n",
-      "            \"ann\": \"35.673\"\n",
+      "            \"ann\": \"35.677\"\n",
       "          }\n",
       "        }\n",
       "      }\n",
       "    },\n",
+      "    \"units\": \"W m-2\",\n",
       "    \"default\": {\n",
+      "      \"source\": \"CERES-EBAF-4-1\",\n",
       "      \"\": {\n",
+      "        \"InputClimatologyFileName\": \"cmip5.historical.CanCM4.r1i1p1.mon.rlut.198101-200512.AC.v20200426.nc\",\n",
       "        \"Global\": {\n",
       "          \"bias_xy\": {\n",
       "            \"ann\": \"-1.165\",\n",
@@ -1002,24 +1065,24 @@
       "            ]\n",
       "          },\n",
       "          \"mae_xy\": {\n",
-      "            \"ann\": \"6.332\",\n",
-      "            \"djf\": \"7.484\",\n",
-      "            \"mam\": \"8.026\",\n",
-      "            \"jja\": \"7.644\",\n",
-      "            \"son\": \"7.994\",\n",
+      "            \"ann\": \"6.334\",\n",
+      "            \"djf\": \"7.487\",\n",
+      "            \"mam\": \"8.034\",\n",
+      "            \"jja\": \"7.646\",\n",
+      "            \"son\": \"7.998\",\n",
       "            \"CalendarMonths\": [\n",
-      "              \"8.023\",\n",
-      "              \"8.686\",\n",
-      "              \"8.548\",\n",
-      "              \"8.887\",\n",
-      "              \"8.653\",\n",
-      "              \"7.949\",\n",
-      "              \"8.216\",\n",
-      "              \"8.267\",\n",
-      "              \"8.651\",\n",
-      "              \"8.920\",\n",
-      "              \"8.357\",\n",
-      "              \"7.868\"\n",
+      "              \"8.026\",\n",
+      "              \"8.696\",\n",
+      "              \"8.557\",\n",
+      "              \"8.897\",\n",
+      "              \"8.659\",\n",
+      "              \"7.953\",\n",
+      "              \"8.219\",\n",
+      "              \"8.271\",\n",
+      "              \"8.655\",\n",
+      "              \"8.928\",\n",
+      "              \"8.362\",\n",
+      "              \"7.874\"\n",
       "            ]\n",
       "          },\n",
       "          \"mean-obs_xy\": {\n",
@@ -1030,7 +1093,7 @@
       "            \"son\": \"240.454\",\n",
       "            \"CalendarMonths\": [\n",
       "              \"237.534\",\n",
-      "              \"237.975\",\n",
+      "              \"237.974\",\n",
       "              \"238.321\",\n",
       "              \"239.051\",\n",
       "              \"240.758\",\n",
@@ -1065,109 +1128,109 @@
       "            ]\n",
       "          },\n",
       "          \"rms_devzm\": {\n",
-      "            \"ann\": \"9.364\"\n",
+      "            \"ann\": \"9.363\"\n",
       "          },\n",
       "          \"rms_xy\": {\n",
-      "            \"ann\": \"9.608\",\n",
-      "            \"djf\": \"10.915\",\n",
+      "            \"ann\": \"9.609\",\n",
+      "            \"djf\": \"10.919\",\n",
       "            \"mam\": \"11.801\",\n",
-      "            \"jja\": \"11.246\",\n",
-      "            \"son\": \"12.593\",\n",
+      "            \"jja\": \"11.248\",\n",
+      "            \"son\": \"12.596\",\n",
       "            \"CalendarMonths\": [\n",
-      "              \"11.408\",\n",
-      "              \"12.619\",\n",
-      "              \"12.472\",\n",
-      "              \"13.174\",\n",
-      "              \"13.056\",\n",
+      "              \"11.412\",\n",
+      "              \"12.621\",\n",
+      "              \"12.476\",\n",
+      "              \"13.176\",\n",
+      "              \"13.057\",\n",
       "              \"12.083\",\n",
-      "              \"12.149\",\n",
-      "              \"12.263\",\n",
-      "              \"13.010\",\n",
-      "              \"14.094\",\n",
-      "              \"13.144\",\n",
-      "              \"11.572\"\n",
+      "              \"12.152\",\n",
+      "              \"12.268\",\n",
+      "              \"13.012\",\n",
+      "              \"14.098\",\n",
+      "              \"13.152\",\n",
+      "              \"11.583\"\n",
       "            ]\n",
       "          },\n",
       "          \"rms_xyt\": {\n",
-      "            \"ann\": \"12.587\"\n",
+      "            \"ann\": \"nan\"\n",
       "          },\n",
       "          \"rms_y\": {\n",
-      "            \"ann\": \"2.152\"\n",
+      "            \"ann\": \"2.159\"\n",
       "          },\n",
       "          \"rmsc_xy\": {\n",
-      "            \"ann\": \"9.537\",\n",
-      "            \"djf\": \"10.875\",\n",
-      "            \"mam\": \"11.756\",\n",
-      "            \"jja\": \"11.159\",\n",
-      "            \"son\": \"12.524\",\n",
+      "            \"ann\": \"9.538\",\n",
+      "            \"djf\": \"10.878\",\n",
+      "            \"mam\": \"11.757\",\n",
+      "            \"jja\": \"11.161\",\n",
+      "            \"son\": \"12.527\",\n",
       "            \"CalendarMonths\": [\n",
-      "              \"11.357\",\n",
-      "              \"12.597\",\n",
-      "              \"12.456\",\n",
-      "              \"13.135\",\n",
-      "              \"12.979\",\n",
+      "              \"11.361\",\n",
+      "              \"12.600\",\n",
+      "              \"12.461\",\n",
+      "              \"13.137\",\n",
+      "              \"12.980\",\n",
       "              \"12.005\",\n",
-      "              \"12.054\",\n",
-      "              \"12.193\",\n",
-      "              \"12.923\",\n",
-      "              \"14.029\",\n",
-      "              \"13.099\",\n",
-      "              \"11.529\"\n",
+      "              \"12.057\",\n",
+      "              \"12.199\",\n",
+      "              \"12.925\",\n",
+      "              \"14.034\",\n",
+      "              \"13.107\",\n",
+      "              \"11.540\"\n",
       "            ]\n",
       "          },\n",
       "          \"std-obs_xy\": {\n",
-      "            \"ann\": \"29.638\",\n",
-      "            \"djf\": \"32.730\",\n",
-      "            \"mam\": \"30.769\",\n",
-      "            \"jja\": \"35.354\",\n",
-      "            \"son\": \"31.187\",\n",
+      "            \"ann\": \"29.644\",\n",
+      "            \"djf\": \"32.736\",\n",
+      "            \"mam\": \"30.775\",\n",
+      "            \"jja\": \"35.361\",\n",
+      "            \"son\": \"31.194\",\n",
       "            \"CalendarMonths\": [\n",
-      "              \"33.135\",\n",
-      "              \"33.720\",\n",
-      "              \"32.905\",\n",
-      "              \"31.195\",\n",
-      "              \"31.759\",\n",
-      "              \"34.475\",\n",
-      "              \"36.335\",\n",
-      "              \"36.150\",\n",
-      "              \"34.657\",\n",
-      "              \"31.891\",\n",
-      "              \"30.957\",\n",
-      "              \"32.007\"\n",
+      "              \"33.142\",\n",
+      "              \"33.727\",\n",
+      "              \"32.912\",\n",
+      "              \"31.203\",\n",
+      "              \"31.767\",\n",
+      "              \"34.483\",\n",
+      "              \"36.342\",\n",
+      "              \"36.157\",\n",
+      "              \"34.665\",\n",
+      "              \"31.899\",\n",
+      "              \"30.965\",\n",
+      "              \"32.014\"\n",
       "            ]\n",
       "          },\n",
       "          \"std-obs_xy_devzm\": {\n",
-      "            \"ann\": \"12.970\"\n",
+      "            \"ann\": \"12.976\"\n",
       "          },\n",
       "          \"std-obs_xyt\": {\n",
-      "            \"ann\": \"33.411\"\n",
+      "            \"ann\": \"33.418\"\n",
       "          },\n",
       "          \"std_xy\": {\n",
-      "            \"ann\": \"31.121\",\n",
-      "            \"djf\": \"34.345\",\n",
-      "            \"mam\": \"33.079\",\n",
+      "            \"ann\": \"31.123\",\n",
+      "            \"djf\": \"34.351\",\n",
+      "            \"mam\": \"33.081\",\n",
       "            \"jja\": \"37.357\",\n",
-      "            \"son\": \"33.756\",\n",
+      "            \"son\": \"33.759\",\n",
       "            \"CalendarMonths\": [\n",
-      "              \"35.078\",\n",
-      "              \"36.311\",\n",
-      "              \"35.853\",\n",
-      "              \"33.871\",\n",
-      "              \"34.140\",\n",
-      "              \"36.391\",\n",
+      "              \"35.083\",\n",
+      "              \"36.318\",\n",
+      "              \"35.860\",\n",
+      "              \"33.875\",\n",
+      "              \"34.141\",\n",
+      "              \"36.390\",\n",
       "              \"38.237\",\n",
-      "              \"38.695\",\n",
-      "              \"37.491\",\n",
-      "              \"34.878\",\n",
-      "              \"32.540\",\n",
-      "              \"33.043\"\n",
+      "              \"38.696\",\n",
+      "              \"37.492\",\n",
+      "              \"34.881\",\n",
+      "              \"32.548\",\n",
+      "              \"33.053\"\n",
       "            ]\n",
       "          },\n",
       "          \"std_xy_devzm\": {\n",
-      "            \"ann\": \"14.247\"\n",
+      "            \"ann\": \"14.246\"\n",
       "          },\n",
       "          \"std_xyt\": {\n",
-      "            \"ann\": \"35.673\"\n",
+      "            \"ann\": \"35.677\"\n",
       "          }\n",
       "        }\n",
       "      }\n",
@@ -1180,7 +1243,7 @@
    "source": [
     "import json\n",
     "import os\n",
-    "output_path = os.path.join(demo_output_directory,\"basicTest/rlut_2.5x2.5_regrid2_metrics.json\")\n",
+    "output_path = os.path.join(demo_output_directory,\"basicTest/rlut_2.5x2.5_regrid2_metrics_basicTest.json\")\n",
     "with open(output_path) as f:\n",
     "    metric = json.load(f)[\"RESULTS\"]\n",
     "print(json.dumps(metric, indent=2))"
@@ -1272,20 +1335,29 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "2023-02-21 14:19:44,197 [WARNING]: dataset.py(_is_decodable:474) >> 'time' does not have a 'units' attribute set so it could not be decoded. Try setting the 'units' attribute (`ds.{coords.name}.attrs['units']`) and try decoding again.\n",
-      "INFO::2023-02-21 14:20::pcmdi_metrics:: Results saved to a json file: /Users/lee1043/Documents/Research/git/pcmdi_metrics_20221013_pcmdi/pcmdi_metrics/doc/jupyter/Demo/demo_output/Ex2/rlut/ACCESS1-0_rlut_2.5x2.5_xesmf_metrics_alternate1.json\n",
-      "2023-02-21 14:20:31,985 [INFO]: base.py(write:245) >> Results saved to a json file: /Users/lee1043/Documents/Research/git/pcmdi_metrics_20221013_pcmdi/pcmdi_metrics/doc/jupyter/Demo/demo_output/Ex2/rlut/ACCESS1-0_rlut_2.5x2.5_xesmf_metrics_alternate1.json\n",
-      "2023-02-21 14:20:32,017 [WARNING]: dataset.py(_is_decodable:474) >> 'time' does not have a 'units' attribute set so it could not be decoded. Try setting the 'units' attribute (`ds.{coords.name}.attrs['units']`) and try decoding again.\n",
-      "INFO::2023-02-21 14:21::pcmdi_metrics:: Results saved to a json file: /Users/lee1043/Documents/Research/git/pcmdi_metrics_20221013_pcmdi/pcmdi_metrics/doc/jupyter/Demo/demo_output/Ex2/rlut/CanCM4_rlut_2.5x2.5_xesmf_metrics_alternate1.json\n",
-      "2023-02-21 14:21:11,672 [INFO]: base.py(write:245) >> Results saved to a json file: /Users/lee1043/Documents/Research/git/pcmdi_metrics_20221013_pcmdi/pcmdi_metrics/doc/jupyter/Demo/demo_output/Ex2/rlut/CanCM4_rlut_2.5x2.5_xesmf_metrics_alternate1.json\n",
-      "2023-02-21 14:21:14,865 [WARNING]: dataset.py(_is_decodable:474) >> 'time' does not have a 'units' attribute set so it could not be decoded. Try setting the 'units' attribute (`ds.{coords.name}.attrs['units']`) and try decoding again.\n",
-      "INFO::2023-02-21 14:21::pcmdi_metrics:: Results saved to a json file: /Users/lee1043/Documents/Research/git/pcmdi_metrics_20221013_pcmdi/pcmdi_metrics/doc/jupyter/Demo/demo_output/Ex2/rlut/ACCESS1-0_rlut_2.5x2.5_xesmf_metrics_default.json\n",
-      "2023-02-21 14:21:57,643 [INFO]: base.py(write:245) >> Results saved to a json file: /Users/lee1043/Documents/Research/git/pcmdi_metrics_20221013_pcmdi/pcmdi_metrics/doc/jupyter/Demo/demo_output/Ex2/rlut/ACCESS1-0_rlut_2.5x2.5_xesmf_metrics_default.json\n",
-      "2023-02-21 14:21:57,670 [WARNING]: dataset.py(_is_decodable:474) >> 'time' does not have a 'units' attribute set so it could not be decoded. Try setting the 'units' attribute (`ds.{coords.name}.attrs['units']`) and try decoding again.\n",
-      "INFO::2023-02-21 14:22::pcmdi_metrics:: Results saved to a json file: /Users/lee1043/Documents/Research/git/pcmdi_metrics_20221013_pcmdi/pcmdi_metrics/doc/jupyter/Demo/demo_output/Ex2/rlut/CanCM4_rlut_2.5x2.5_xesmf_metrics_default.json\n",
-      "2023-02-21 14:22:36,898 [INFO]: base.py(write:245) >> Results saved to a json file: /Users/lee1043/Documents/Research/git/pcmdi_metrics_20221013_pcmdi/pcmdi_metrics/doc/jupyter/Demo/demo_output/Ex2/rlut/CanCM4_rlut_2.5x2.5_xesmf_metrics_default.json\n",
-      "INFO::2023-02-21 14:22::pcmdi_metrics:: Results saved to a json file: /Users/lee1043/Documents/Research/git/pcmdi_metrics_20221013_pcmdi/pcmdi_metrics/doc/jupyter/Demo/demo_output/Ex2/rlut_2.5x2.5_xesmf_metrics.json\n",
-      "2023-02-21 14:22:47,436 [INFO]: base.py(write:245) >> Results saved to a json file: /Users/lee1043/Documents/Research/git/pcmdi_metrics_20221013_pcmdi/pcmdi_metrics/doc/jupyter/Demo/demo_output/Ex2/rlut_2.5x2.5_xesmf_metrics.json\n"
+      "2023-11-09 16:41:28,762 [WARNING]: dataset.py(_is_decodable:670) >> 'time' does not have a 'units' attribute set so it could not be decoded. Try setting the 'units' attribute (`ds.{coords.name}.attrs['units']`) and try decoding again.\n",
+      "2023-11-09 16:41:28,762 [WARNING]: dataset.py(_is_decodable:670) >> 'time' does not have a 'units' attribute set so it could not be decoded. Try setting the 'units' attribute (`ds.{coords.name}.attrs['units']`) and try decoding again.\n",
+      "INFO::2023-11-09 16:42::pcmdi_metrics:: Results saved to a json file: /Users/lee1043/Documents/Research/git/pcmdi_metrics_20230620_pcmdi/pcmdi_metrics/doc/jupyter/Demo/demo_output/Ex2/rlut/rlut_ACCESS1-0__2.5x2.5_xesmf_metrics_alternate1_Ex2.json\n",
+      "2023-11-09 16:42:31,697 [INFO]: base.py(write:250) >> Results saved to a json file: /Users/lee1043/Documents/Research/git/pcmdi_metrics_20230620_pcmdi/pcmdi_metrics/doc/jupyter/Demo/demo_output/Ex2/rlut/rlut_ACCESS1-0__2.5x2.5_xesmf_metrics_alternate1_Ex2.json\n",
+      "2023-11-09 16:42:31,697 [INFO]: base.py(write:250) >> Results saved to a json file: /Users/lee1043/Documents/Research/git/pcmdi_metrics_20230620_pcmdi/pcmdi_metrics/doc/jupyter/Demo/demo_output/Ex2/rlut/rlut_ACCESS1-0__2.5x2.5_xesmf_metrics_alternate1_Ex2.json\n",
+      "2023-11-09 16:42:31,736 [WARNING]: dataset.py(_is_decodable:670) >> 'time' does not have a 'units' attribute set so it could not be decoded. Try setting the 'units' attribute (`ds.{coords.name}.attrs['units']`) and try decoding again.\n",
+      "2023-11-09 16:42:31,736 [WARNING]: dataset.py(_is_decodable:670) >> 'time' does not have a 'units' attribute set so it could not be decoded. Try setting the 'units' attribute (`ds.{coords.name}.attrs['units']`) and try decoding again.\n",
+      "INFO::2023-11-09 16:43::pcmdi_metrics:: Results saved to a json file: /Users/lee1043/Documents/Research/git/pcmdi_metrics_20230620_pcmdi/pcmdi_metrics/doc/jupyter/Demo/demo_output/Ex2/rlut/rlut_CanCM4__2.5x2.5_xesmf_metrics_alternate1_Ex2.json\n",
+      "2023-11-09 16:43:43,018 [INFO]: base.py(write:250) >> Results saved to a json file: /Users/lee1043/Documents/Research/git/pcmdi_metrics_20230620_pcmdi/pcmdi_metrics/doc/jupyter/Demo/demo_output/Ex2/rlut/rlut_CanCM4__2.5x2.5_xesmf_metrics_alternate1_Ex2.json\n",
+      "2023-11-09 16:43:43,018 [INFO]: base.py(write:250) >> Results saved to a json file: /Users/lee1043/Documents/Research/git/pcmdi_metrics_20230620_pcmdi/pcmdi_metrics/doc/jupyter/Demo/demo_output/Ex2/rlut/rlut_CanCM4__2.5x2.5_xesmf_metrics_alternate1_Ex2.json\n",
+      "2023-11-09 16:43:47,139 [WARNING]: dataset.py(_is_decodable:670) >> 'time' does not have a 'units' attribute set so it could not be decoded. Try setting the 'units' attribute (`ds.{coords.name}.attrs['units']`) and try decoding again.\n",
+      "2023-11-09 16:43:47,139 [WARNING]: dataset.py(_is_decodable:670) >> 'time' does not have a 'units' attribute set so it could not be decoded. Try setting the 'units' attribute (`ds.{coords.name}.attrs['units']`) and try decoding again.\n",
+      "INFO::2023-11-09 16:45::pcmdi_metrics:: Results saved to a json file: /Users/lee1043/Documents/Research/git/pcmdi_metrics_20230620_pcmdi/pcmdi_metrics/doc/jupyter/Demo/demo_output/Ex2/rlut/rlut_ACCESS1-0__2.5x2.5_xesmf_metrics_default_Ex2.json\n",
+      "2023-11-09 16:45:02,114 [INFO]: base.py(write:250) >> Results saved to a json file: /Users/lee1043/Documents/Research/git/pcmdi_metrics_20230620_pcmdi/pcmdi_metrics/doc/jupyter/Demo/demo_output/Ex2/rlut/rlut_ACCESS1-0__2.5x2.5_xesmf_metrics_default_Ex2.json\n",
+      "2023-11-09 16:45:02,114 [INFO]: base.py(write:250) >> Results saved to a json file: /Users/lee1043/Documents/Research/git/pcmdi_metrics_20230620_pcmdi/pcmdi_metrics/doc/jupyter/Demo/demo_output/Ex2/rlut/rlut_ACCESS1-0__2.5x2.5_xesmf_metrics_default_Ex2.json\n",
+      "2023-11-09 16:45:02,157 [WARNING]: dataset.py(_is_decodable:670) >> 'time' does not have a 'units' attribute set so it could not be decoded. Try setting the 'units' attribute (`ds.{coords.name}.attrs['units']`) and try decoding again.\n",
+      "2023-11-09 16:45:02,157 [WARNING]: dataset.py(_is_decodable:670) >> 'time' does not have a 'units' attribute set so it could not be decoded. Try setting the 'units' attribute (`ds.{coords.name}.attrs['units']`) and try decoding again.\n",
+      "INFO::2023-11-09 16:46::pcmdi_metrics:: Results saved to a json file: /Users/lee1043/Documents/Research/git/pcmdi_metrics_20230620_pcmdi/pcmdi_metrics/doc/jupyter/Demo/demo_output/Ex2/rlut/rlut_CanCM4__2.5x2.5_xesmf_metrics_default_Ex2.json\n",
+      "2023-11-09 16:46:12,238 [INFO]: base.py(write:250) >> Results saved to a json file: /Users/lee1043/Documents/Research/git/pcmdi_metrics_20230620_pcmdi/pcmdi_metrics/doc/jupyter/Demo/demo_output/Ex2/rlut/rlut_CanCM4__2.5x2.5_xesmf_metrics_default_Ex2.json\n",
+      "2023-11-09 16:46:12,238 [INFO]: base.py(write:250) >> Results saved to a json file: /Users/lee1043/Documents/Research/git/pcmdi_metrics_20230620_pcmdi/pcmdi_metrics/doc/jupyter/Demo/demo_output/Ex2/rlut/rlut_CanCM4__2.5x2.5_xesmf_metrics_default_Ex2.json\n",
+      "INFO::2023-11-09 16:46::pcmdi_metrics:: Results saved to a json file: /Users/lee1043/Documents/Research/git/pcmdi_metrics_20230620_pcmdi/pcmdi_metrics/doc/jupyter/Demo/demo_output/Ex2/rlut_2.5x2.5_xesmf_metrics_Ex2.json\n",
+      "2023-11-09 16:46:33,802 [INFO]: base.py(write:250) >> Results saved to a json file: /Users/lee1043/Documents/Research/git/pcmdi_metrics_20230620_pcmdi/pcmdi_metrics/doc/jupyter/Demo/demo_output/Ex2/rlut_2.5x2.5_xesmf_metrics_Ex2.json\n",
+      "2023-11-09 16:46:33,802 [INFO]: base.py(write:250) >> Results saved to a json file: /Users/lee1043/Documents/Research/git/pcmdi_metrics_20230620_pcmdi/pcmdi_metrics/doc/jupyter/Demo/demo_output/Ex2/rlut_2.5x2.5_xesmf_metrics_Ex2.json\n"
      ]
     },
     {
@@ -1296,6 +1368,7 @@
       " test_data_set: ['ACCESS1-0', 'CanCM4'] \n",
       " realization:  \n",
       " vars: ['rlut'] \n",
+      " varname_in_test_data: None \n",
       " reference_data_set: ['all'] \n",
       " target_grid: 2.5x2.5 \n",
       " regrid_tool: xesmf \n",
@@ -1305,11 +1378,12 @@
       " filename_template: cmip5.historical.%(model_version).r1i1p1.mon.%(variable).198101-200512.AC.v20200426.nc \n",
       " sftlf_filename_template: sftlf_%(model_version).nc \n",
       " generate_sftlf: True \n",
-      " regions_specs: {'NHEX': {'domain': {'latitude': (30.0, 90)}}, 'SHEX': {'domain': {'latitude': (-90.0, -30)}}, 'TROPICS': {'domain': {'latitude': (-30.0, 30)}}, 'global': {}, '90S50S': {'domain': {'latitude': (-90.0, -50)}}, '50S20S': {'domain': {'latitude': (-50.0, -20)}}, '20S20N': {'domain': {'latitude': (-20.0, 20)}}, '20N50N': {'domain': {'latitude': (20.0, 50)}}, '50N90N': {'domain': {'latitude': (50.0, 90)}}, 'land_NHEX': {'value': 100, 'domain': {'latitude': (30.0, 90)}}, 'land_SHEX': {'value': 100, 'domain': {'latitude': (-90.0, -30)}}, 'land_TROPICS': {'value': 100, 'domain': {'latitude': (-30.0, 30)}}, 'land': {'value': 100}, 'ocean_NHEX': {'value': 0, 'domain': {'latitude': (30.0, 90)}}, 'ocean_SHEX': {'value': 0, 'domain': {'latitude': (-90.0, -30)}}, 'ocean_TROPICS': {'value': 0, 'domain': {'latitude': (30.0, 30)}}, 'ocean': {'value': 0}, 'CONUS': {'domain': {'latitude': (24.7, 49.4), 'longitude': (-124.78, -66.92)}}, 'land_CONUS': {'value': 100, 'domain': {'latitude': (24.7, 49.4), 'longitude': (-124.78, -66.92)}}, 'NAM': {'domain': {'latitude': (20.0, 90), 'longitude': (-180, 180)}}, 'NAO': {'domain': {'latitude': (20.0, 80), 'longitude': (-90, 40)}}, 'SAM': {'domain': {'latitude': (-20.0, -90), 'longitude': (0, 360)}}, 'PNA': {'domain': {'latitude': (20.0, 85), 'longitude': (120, 240)}}, 'PDO': {'domain': {'latitude': (20.0, 70), 'longitude': (110, 260)}}, 'AllMW': {'domain': {'latitude': (-40.0, 45.0), 'longitude': (0.0, 360.0)}}, 'AllM': {'domain': {'latitude': (-45.0, 45.0), 'longitude': (0.0, 360.0)}}, 'NAMM': {'domain': {'latitude': (0.0, 45.0), 'longitude': (210.0, 310.0)}}, 'SAMM': {'domain': {'latitude': (-45.0, 0.0), 'longitude': (240.0, 330.0)}}, 'NAFM': {'domain': {'latitude': (0.0, 45.0), 'longitude': (310.0, 60.0)}}, 'SAFM': {'domain': {'latitude': (-45.0, 0.0), 'longitude': (0.0, 90.0)}}, 'ASM': {'domain': {'latitude': (0.0, 45.0), 'longitude': (60.0, 180.0)}}, 'AUSM': {'domain': {'latitude': (-45.0, 0.0), 'longitude': (90.0, 160.0)}}, 'AIR': {'domain': {'latitude': (7.0, 25.0), 'longitude': (65.0, 85.0)}}, 'AUS': {'domain': {'latitude': (-20.0, -10.0), 'longitude': (120.0, 150.0)}}, 'Sahel': {'domain': {'latitude': (13.0, 18.0), 'longitude': (-10.0, 10.0)}}, 'GoG': {'domain': {'latitude': (0.0, 5.0), 'longitude': (-10.0, 10.0)}}, 'NAmo': {'domain': {'latitude': (20.0, 37.0), 'longitude': (-112.0, -103.0)}}, 'SAmo': {'domain': {'latitude': (-20.0, 2.5), 'longitude': (-65.0, -40.0)}}} \n",
+      " regions_specs: {'global': {}, 'NHEX': {'domain': {'latitude': (30.0, 90)}}, 'SHEX': {'domain': {'latitude': (-90.0, -30)}}, 'TROPICS': {'domain': {'latitude': (-30.0, 30)}}, '90S50S': {'domain': {'latitude': (-90.0, -50)}}, '50S20S': {'domain': {'latitude': (-50.0, -20)}}, '20S20N': {'domain': {'latitude': (-20.0, 20)}}, '20N50N': {'domain': {'latitude': (20.0, 50)}}, '50N90N': {'domain': {'latitude': (50.0, 90)}}, 'CONUS': {'domain': {'latitude': (24.7, 49.4), 'longitude': (-124.78, -66.92)}}, 'land': {'value': 100}, 'land_NHEX': {'value': 100, 'domain': {'latitude': (30.0, 90)}}, 'land_SHEX': {'value': 100, 'domain': {'latitude': (-90.0, -30)}}, 'land_TROPICS': {'value': 100, 'domain': {'latitude': (-30.0, 30)}}, 'land_CONUS': {'value': 100, 'domain': {'latitude': (24.7, 49.4), 'longitude': (-124.78, -66.92)}}, 'ocean': {'value': 0}, 'ocean_NHEX': {'value': 0, 'domain': {'latitude': (30.0, 90)}}, 'ocean_SHEX': {'value': 0, 'domain': {'latitude': (-90.0, -30)}}, 'ocean_TROPICS': {'value': 0, 'domain': {'latitude': (30.0, 30)}}, 'ocean_50S50N': {'value': 0.0, 'domain': {'latitude': (-50.0, 50)}}, 'ocean_50S20S': {'value': 0.0, 'domain': {'latitude': (-50.0, -20)}}, 'ocean_20S20N': {'value': 0.0, 'domain': {'latitude': (-20.0, 20)}}, 'ocean_20N50N': {'value': 0.0, 'domain': {'latitude': (20.0, 50)}}, 'NAM': {'domain': {'latitude': (20.0, 90), 'longitude': (-180, 180)}}, 'NAO': {'domain': {'latitude': (20.0, 80), 'longitude': (-90, 40)}}, 'SAM': {'domain': {'latitude': (-20.0, -90), 'longitude': (0, 360)}}, 'PNA': {'domain': {'latitude': (20.0, 85), 'longitude': (120, 240)}}, 'PDO': {'domain': {'latitude': (20.0, 70), 'longitude': (110, 260)}}, 'AllMW': {'domain': {'latitude': (-40.0, 45.0), 'longitude': (0.0, 360.0)}}, 'AllM': {'domain': {'latitude': (-45.0, 45.0), 'longitude': (0.0, 360.0)}}, 'NAMM': {'domain': {'latitude': (0.0, 45.0), 'longitude': (210.0, 310.0)}}, 'SAMM': {'domain': {'latitude': (-45.0, 0.0), 'longitude': (240.0, 330.0)}}, 'NAFM': {'domain': {'latitude': (0.0, 45.0), 'longitude': (310.0, 60.0)}}, 'SAFM': {'domain': {'latitude': (-45.0, 0.0), 'longitude': (0.0, 90.0)}}, 'ASM': {'domain': {'latitude': (0.0, 45.0), 'longitude': (60.0, 180.0)}}, 'AUSM': {'domain': {'latitude': (-45.0, 0.0), 'longitude': (90.0, 160.0)}}, 'AIR': {'domain': {'latitude': (7.0, 25.0), 'longitude': (65.0, 85.0)}}, 'AUS': {'domain': {'latitude': (-20.0, -10.0), 'longitude': (120.0, 150.0)}}, 'Sahel': {'domain': {'latitude': (13.0, 18.0), 'longitude': (-10.0, 10.0)}}, 'GoG': {'domain': {'latitude': (0.0, 5.0), 'longitude': (-10.0, 10.0)}}, 'NAmo': {'domain': {'latitude': (20.0, 37.0), 'longitude': (-112.0, -103.0)}}, 'SAmo': {'domain': {'latitude': (-20.0, 2.5), 'longitude': (-65.0, -40.0)}}} \n",
       " regions: {'rlut': ['Global']} \n",
       " test_data_path: demo_data/CMIP5_demo_clims/ \n",
       " reference_data_path: demo_data/obs4MIPs_PCMDI_clims \n",
       " metrics_output_path: demo_output/Ex2 \n",
+      " diagnostics_output_path: demo_output/Ex2 \n",
       " debug: False \n",
       "\n",
       "--- prepare mean climate metrics calculation ---\n",
@@ -1319,46 +1393,67 @@
       "reference_data_set (all):  ['alternate1', 'default']\n",
       "ref: alternate1\n",
       "ref_data_full_path: demo_data/obs4MIPs_PCMDI_clims/rlut/CERES-EBAF-4-0/v20210804/rlut_mon_CERES-EBAF-4-0_PCMDI_gn.200301-201812.AC.v20210804.nc\n",
+      "'DataArray' object has no attribute 'units'\n",
+      "units: \n",
+      "ref_data load_and_regrid done\n",
+      "=================================\n",
+      "model, runs, find_all_realizations: ACCESS1-0 [''] False\n",
       "-----------------------\n",
       "model, run: ACCESS1-0 \n",
       "test_data (model in this case) full_path: demo_data/CMIP5_demo_clims/cmip5.historical.ACCESS1-0.r1i1p1.mon.rlut.198101-200512.AC.v20200426.nc\n",
-      "[WARNING]: calendar info mismatch. ds.time.attrs[\"calendar\"] is adjusted to ds.calendar\n",
+      "units: W m-2\n",
       "load and regrid done\n",
       "region: Global\n",
       "compute metrics start\n",
       "var:  rlut\n",
       "compute_metrics-CALCULATE ANNUAL CYCLE SPACE-TIME RMS, CORRELATIONS and STD\n",
       "compute_metrics, rms_xyt\n",
+      "compute_metrics, rms_xyt: nan\n",
       "compute_metrics, stdObs_xyt\n",
+      "compute_metrics, stdObs_xyt: 38.93927790599767\n",
       "compute_metrics, std_xyt\n",
+      "compute_metrics, std_xyt: 36.60527090825452\n",
       "compute_metrics-CALCULATE ANNUAL MEANS\n",
       "compute_metrics-CALCULATE ANNUAL MEAN BIAS\n",
+      "compute_metrics-CALCULATE ANNUAL MEAN BIAS, bias_xy: 2.849440160911837\n",
       "compute_metrics-CALCULATE MSE\n",
+      "compute_metrics-CALCULATE MSE, mae_xy: 7.557470849444263\n",
       "compute_metrics-CALCULATE MEAN RMS\n",
+      "compute_metrics-CALCULATE MEAN RMS: rms_xy, rmsc_xy:  22.607395028695382 22.427104154412426\n",
       "compute_metrics-CALCULATE MEAN CORR\n",
+      "compute_metrics-CALCULATE MEAN CORR: cor_xy: 0.7861025676947934\n",
       "compute_metrics-CALCULATE ANNUAL OBS AND MOD STD\n",
       "compute_metrics-CALCULATE ANNUAL OBS AND MOD MEAN\n",
       "compute_metrics-CALCULATE ANNUAL MEANS\n",
       "compute_metrics-CALCULATE ANNUAL AND ZONAL MEAN RMS\n",
       "compute_metrics-CALCULATE ANNUAL MEAN DEVIATION FROM ZONAL MEAN RMS\n",
       "compute_metrics-CALCULATE ANNUAL MEAN DEVIATION FROM ZONAL MEAN STD\n",
+      "=================================\n",
+      "model, runs, find_all_realizations: CanCM4 [''] False\n",
       "-----------------------\n",
       "model, run: CanCM4 \n",
       "test_data (model in this case) full_path: demo_data/CMIP5_demo_clims/cmip5.historical.CanCM4.r1i1p1.mon.rlut.198101-200512.AC.v20200426.nc\n",
-      "[WARNING]: calendar info mismatch. ds.time.attrs[\"calendar\"] is adjusted to ds.calendar\n",
+      "units: W m-2\n",
       "load and regrid done\n",
       "region: Global\n",
       "compute metrics start\n",
       "var:  rlut\n",
       "compute_metrics-CALCULATE ANNUAL CYCLE SPACE-TIME RMS, CORRELATIONS and STD\n",
       "compute_metrics, rms_xyt\n",
+      "compute_metrics, rms_xyt: nan\n",
       "compute_metrics, stdObs_xyt\n",
+      "compute_metrics, stdObs_xyt: 38.93927790599767\n",
       "compute_metrics, std_xyt\n",
+      "compute_metrics, std_xyt: 41.277546088282016\n",
       "compute_metrics-CALCULATE ANNUAL MEANS\n",
       "compute_metrics-CALCULATE ANNUAL MEAN BIAS\n",
+      "compute_metrics-CALCULATE ANNUAL MEAN BIAS, bias_xy: -1.3070924524925915\n",
       "compute_metrics-CALCULATE MSE\n",
+      "compute_metrics-CALCULATE MSE, mae_xy: 9.860459737057527\n",
       "compute_metrics-CALCULATE MEAN RMS\n",
+      "compute_metrics-CALCULATE MEAN RMS: rms_xy, rmsc_xy:  31.29959559143669 31.27229114580701\n",
       "compute_metrics-CALCULATE MEAN CORR\n",
+      "compute_metrics-CALCULATE MEAN CORR: cor_xy: 0.6355158166285978\n",
       "compute_metrics-CALCULATE ANNUAL OBS AND MOD STD\n",
       "compute_metrics-CALCULATE ANNUAL OBS AND MOD MEAN\n",
       "compute_metrics-CALCULATE ANNUAL MEANS\n",
@@ -1367,46 +1462,67 @@
       "compute_metrics-CALCULATE ANNUAL MEAN DEVIATION FROM ZONAL MEAN STD\n",
       "ref: default\n",
       "ref_data_full_path: demo_data/obs4MIPs_PCMDI_clims/rlut/CERES-EBAF-4-1/v20210804/rlut_mon_CERES-EBAF-4-1_PCMDI_gn.200301-201812.AC.v20210804.nc\n",
+      "'DataArray' object has no attribute 'units'\n",
+      "units: \n",
+      "ref_data load_and_regrid done\n",
+      "=================================\n",
+      "model, runs, find_all_realizations: ACCESS1-0 [''] False\n",
       "-----------------------\n",
       "model, run: ACCESS1-0 \n",
       "test_data (model in this case) full_path: demo_data/CMIP5_demo_clims/cmip5.historical.ACCESS1-0.r1i1p1.mon.rlut.198101-200512.AC.v20200426.nc\n",
-      "[WARNING]: calendar info mismatch. ds.time.attrs[\"calendar\"] is adjusted to ds.calendar\n",
+      "units: W m-2\n",
       "load and regrid done\n",
       "region: Global\n",
       "compute metrics start\n",
       "var:  rlut\n",
       "compute_metrics-CALCULATE ANNUAL CYCLE SPACE-TIME RMS, CORRELATIONS and STD\n",
       "compute_metrics, rms_xyt\n",
+      "compute_metrics, rms_xyt: nan\n",
       "compute_metrics, stdObs_xyt\n",
+      "compute_metrics, stdObs_xyt: 38.896082465222186\n",
       "compute_metrics, std_xyt\n",
+      "compute_metrics, std_xyt: 36.60527090825452\n",
       "compute_metrics-CALCULATE ANNUAL MEANS\n",
       "compute_metrics-CALCULATE ANNUAL MEAN BIAS\n",
+      "compute_metrics-CALCULATE ANNUAL MEAN BIAS, bias_xy: 2.847343450867303\n",
       "compute_metrics-CALCULATE MSE\n",
+      "compute_metrics-CALCULATE MSE, mae_xy: 7.551058394040502\n",
       "compute_metrics-CALCULATE MEAN RMS\n",
+      "compute_metrics-CALCULATE MEAN RMS: rms_xy, rmsc_xy:  22.603314702394204 22.42325736391287\n",
       "compute_metrics-CALCULATE MEAN CORR\n",
+      "compute_metrics-CALCULATE MEAN CORR: cor_xy: 0.7861552158165768\n",
       "compute_metrics-CALCULATE ANNUAL OBS AND MOD STD\n",
       "compute_metrics-CALCULATE ANNUAL OBS AND MOD MEAN\n",
       "compute_metrics-CALCULATE ANNUAL MEANS\n",
       "compute_metrics-CALCULATE ANNUAL AND ZONAL MEAN RMS\n",
       "compute_metrics-CALCULATE ANNUAL MEAN DEVIATION FROM ZONAL MEAN RMS\n",
       "compute_metrics-CALCULATE ANNUAL MEAN DEVIATION FROM ZONAL MEAN STD\n",
+      "=================================\n",
+      "model, runs, find_all_realizations: CanCM4 [''] False\n",
       "-----------------------\n",
       "model, run: CanCM4 \n",
       "test_data (model in this case) full_path: demo_data/CMIP5_demo_clims/cmip5.historical.CanCM4.r1i1p1.mon.rlut.198101-200512.AC.v20200426.nc\n",
-      "[WARNING]: calendar info mismatch. ds.time.attrs[\"calendar\"] is adjusted to ds.calendar\n",
+      "units: W m-2\n",
       "load and regrid done\n",
       "region: Global\n",
       "compute metrics start\n",
       "var:  rlut\n",
       "compute_metrics-CALCULATE ANNUAL CYCLE SPACE-TIME RMS, CORRELATIONS and STD\n",
       "compute_metrics, rms_xyt\n",
+      "compute_metrics, rms_xyt: nan\n",
       "compute_metrics, stdObs_xyt\n",
+      "compute_metrics, stdObs_xyt: 38.896082465222186\n",
       "compute_metrics, std_xyt\n",
+      "compute_metrics, std_xyt: 41.277546088282016\n",
       "compute_metrics-CALCULATE ANNUAL MEANS\n",
       "compute_metrics-CALCULATE ANNUAL MEAN BIAS\n",
+      "compute_metrics-CALCULATE ANNUAL MEAN BIAS, bias_xy: -1.3091891625371264\n",
       "compute_metrics-CALCULATE MSE\n",
+      "compute_metrics-CALCULATE MSE, mae_xy: 9.862415553120186\n",
       "compute_metrics-CALCULATE MEAN RMS\n",
+      "compute_metrics-CALCULATE MEAN RMS: rms_xy, rmsc_xy:  31.29710758581451 31.269713253797885\n",
       "compute_metrics-CALCULATE MEAN CORR\n",
+      "compute_metrics-CALCULATE MEAN CORR: cor_xy: 0.6355540591449562\n",
       "compute_metrics-CALCULATE ANNUAL OBS AND MOD STD\n",
       "compute_metrics-CALCULATE ANNUAL OBS AND MOD MEAN\n",
       "compute_metrics-CALCULATE ANNUAL MEANS\n",
@@ -1441,14 +1557,19 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "2023-02-21 14:22:54,655 [WARNING]: dataset.py(_is_decodable:474) >> 'time' does not have a 'units' attribute set so it could not be decoded. Try setting the 'units' attribute (`ds.{coords.name}.attrs['units']`) and try decoding again.\n",
-      "INFO::2023-02-21 14:23::pcmdi_metrics:: Results saved to a json file: /Users/lee1043/Documents/Research/git/pcmdi_metrics_20221013_pcmdi/pcmdi_metrics/doc/jupyter/Demo/demo_output/Ex3/rlut/ACCESS1-0_rlut_2.5x2.5_regrid2_metrics_alternate1.json\n",
-      "2023-02-21 14:23:18,559 [INFO]: base.py(write:245) >> Results saved to a json file: /Users/lee1043/Documents/Research/git/pcmdi_metrics_20221013_pcmdi/pcmdi_metrics/doc/jupyter/Demo/demo_output/Ex3/rlut/ACCESS1-0_rlut_2.5x2.5_regrid2_metrics_alternate1.json\n",
-      "2023-02-21 14:23:20,370 [WARNING]: dataset.py(_is_decodable:474) >> 'time' does not have a 'units' attribute set so it could not be decoded. Try setting the 'units' attribute (`ds.{coords.name}.attrs['units']`) and try decoding again.\n",
-      "INFO::2023-02-21 14:23::pcmdi_metrics:: Results saved to a json file: /Users/lee1043/Documents/Research/git/pcmdi_metrics_20221013_pcmdi/pcmdi_metrics/doc/jupyter/Demo/demo_output/Ex3/rlut/ACCESS1-0_rlut_2.5x2.5_regrid2_metrics_default.json\n",
-      "2023-02-21 14:23:44,270 [INFO]: base.py(write:245) >> Results saved to a json file: /Users/lee1043/Documents/Research/git/pcmdi_metrics_20221013_pcmdi/pcmdi_metrics/doc/jupyter/Demo/demo_output/Ex3/rlut/ACCESS1-0_rlut_2.5x2.5_regrid2_metrics_default.json\n",
-      "INFO::2023-02-21 14:23::pcmdi_metrics:: Results saved to a json file: /Users/lee1043/Documents/Research/git/pcmdi_metrics_20221013_pcmdi/pcmdi_metrics/doc/jupyter/Demo/demo_output/Ex3/rlut_2.5x2.5_regrid2_metrics.json\n",
-      "2023-02-21 14:23:44,273 [INFO]: base.py(write:245) >> Results saved to a json file: /Users/lee1043/Documents/Research/git/pcmdi_metrics_20221013_pcmdi/pcmdi_metrics/doc/jupyter/Demo/demo_output/Ex3/rlut_2.5x2.5_regrid2_metrics.json\n"
+      "2023-11-09 16:46:57,258 [WARNING]: dataset.py(_is_decodable:670) >> 'time' does not have a 'units' attribute set so it could not be decoded. Try setting the 'units' attribute (`ds.{coords.name}.attrs['units']`) and try decoding again.\n",
+      "2023-11-09 16:46:57,258 [WARNING]: dataset.py(_is_decodable:670) >> 'time' does not have a 'units' attribute set so it could not be decoded. Try setting the 'units' attribute (`ds.{coords.name}.attrs['units']`) and try decoding again.\n",
+      "INFO::2023-11-09 16:47::pcmdi_metrics:: Results saved to a json file: /Users/lee1043/Documents/Research/git/pcmdi_metrics_20230620_pcmdi/pcmdi_metrics/doc/jupyter/Demo/demo_output/Ex3/rlut/rlut_ACCESS1-0__2.5x2.5_regrid2_metrics_alternate1_Ex3.json\n",
+      "2023-11-09 16:47:57,629 [INFO]: base.py(write:250) >> Results saved to a json file: /Users/lee1043/Documents/Research/git/pcmdi_metrics_20230620_pcmdi/pcmdi_metrics/doc/jupyter/Demo/demo_output/Ex3/rlut/rlut_ACCESS1-0__2.5x2.5_regrid2_metrics_alternate1_Ex3.json\n",
+      "2023-11-09 16:47:57,629 [INFO]: base.py(write:250) >> Results saved to a json file: /Users/lee1043/Documents/Research/git/pcmdi_metrics_20230620_pcmdi/pcmdi_metrics/doc/jupyter/Demo/demo_output/Ex3/rlut/rlut_ACCESS1-0__2.5x2.5_regrid2_metrics_alternate1_Ex3.json\n",
+      "2023-11-09 16:48:03,578 [WARNING]: dataset.py(_is_decodable:670) >> 'time' does not have a 'units' attribute set so it could not be decoded. Try setting the 'units' attribute (`ds.{coords.name}.attrs['units']`) and try decoding again.\n",
+      "2023-11-09 16:48:03,578 [WARNING]: dataset.py(_is_decodable:670) >> 'time' does not have a 'units' attribute set so it could not be decoded. Try setting the 'units' attribute (`ds.{coords.name}.attrs['units']`) and try decoding again.\n",
+      "INFO::2023-11-09 16:49::pcmdi_metrics:: Results saved to a json file: /Users/lee1043/Documents/Research/git/pcmdi_metrics_20230620_pcmdi/pcmdi_metrics/doc/jupyter/Demo/demo_output/Ex3/rlut/rlut_ACCESS1-0__2.5x2.5_regrid2_metrics_default_Ex3.json\n",
+      "2023-11-09 16:49:02,702 [INFO]: base.py(write:250) >> Results saved to a json file: /Users/lee1043/Documents/Research/git/pcmdi_metrics_20230620_pcmdi/pcmdi_metrics/doc/jupyter/Demo/demo_output/Ex3/rlut/rlut_ACCESS1-0__2.5x2.5_regrid2_metrics_default_Ex3.json\n",
+      "2023-11-09 16:49:02,702 [INFO]: base.py(write:250) >> Results saved to a json file: /Users/lee1043/Documents/Research/git/pcmdi_metrics_20230620_pcmdi/pcmdi_metrics/doc/jupyter/Demo/demo_output/Ex3/rlut/rlut_ACCESS1-0__2.5x2.5_regrid2_metrics_default_Ex3.json\n",
+      "INFO::2023-11-09 16:49::pcmdi_metrics:: Results saved to a json file: /Users/lee1043/Documents/Research/git/pcmdi_metrics_20230620_pcmdi/pcmdi_metrics/doc/jupyter/Demo/demo_output/Ex3/rlut_2.5x2.5_regrid2_metrics_Ex3.json\n",
+      "2023-11-09 16:49:21,917 [INFO]: base.py(write:250) >> Results saved to a json file: /Users/lee1043/Documents/Research/git/pcmdi_metrics_20230620_pcmdi/pcmdi_metrics/doc/jupyter/Demo/demo_output/Ex3/rlut_2.5x2.5_regrid2_metrics_Ex3.json\n",
+      "2023-11-09 16:49:21,917 [INFO]: base.py(write:250) >> Results saved to a json file: /Users/lee1043/Documents/Research/git/pcmdi_metrics_20230620_pcmdi/pcmdi_metrics/doc/jupyter/Demo/demo_output/Ex3/rlut_2.5x2.5_regrid2_metrics_Ex3.json\n"
      ]
     },
     {
@@ -1459,6 +1580,7 @@
       " test_data_set: ['ACCESS1-0'] \n",
       " realization:  \n",
       " vars: ['rlut'] \n",
+      " varname_in_test_data: None \n",
       " reference_data_set: ['all'] \n",
       " target_grid: 2.5x2.5 \n",
       " regrid_tool: regrid2 \n",
@@ -1468,11 +1590,12 @@
       " filename_template: cmip5.historical.%(model_version).r1i1p1.mon.%(variable).198101-200512.AC.v20200426.nc \n",
       " sftlf_filename_template: sftlf_%(model_version).nc \n",
       " generate_sftlf: True \n",
-      " regions_specs: {'NHEX': {'domain': {'latitude': (30.0, 90)}}, 'SHEX': {'domain': {'latitude': (-90.0, -30)}}, 'TROPICS': {'domain': {'latitude': (-30.0, 30)}}, 'global': {}, '90S50S': {'domain': {'latitude': (-90.0, -50)}}, '50S20S': {'domain': {'latitude': (-50.0, -20)}}, '20S20N': {'domain': {'latitude': (-20.0, 20)}}, '20N50N': {'domain': {'latitude': (20.0, 50)}}, '50N90N': {'domain': {'latitude': (50.0, 90)}}, 'land_NHEX': {'value': 100, 'domain': {'latitude': (30.0, 90)}}, 'land_SHEX': {'value': 100, 'domain': {'latitude': (-90.0, -30)}}, 'land_TROPICS': {'value': 100, 'domain': {'latitude': (-30.0, 30)}}, 'land': {'value': 100}, 'ocean_NHEX': {'value': 0, 'domain': {'latitude': (30.0, 90)}}, 'ocean_SHEX': {'value': 0, 'domain': {'latitude': (-90.0, -30)}}, 'ocean_TROPICS': {'value': 0, 'domain': {'latitude': (30.0, 30)}}, 'ocean': {'value': 0}, 'CONUS': {'domain': {'latitude': (24.7, 49.4), 'longitude': (-124.78, -66.92)}}, 'land_CONUS': {'value': 100, 'domain': {'latitude': (24.7, 49.4), 'longitude': (-124.78, -66.92)}}, 'NAM': {'domain': {'latitude': (20.0, 90), 'longitude': (-180, 180)}}, 'NAO': {'domain': {'latitude': (20.0, 80), 'longitude': (-90, 40)}}, 'SAM': {'domain': {'latitude': (-20.0, -90), 'longitude': (0, 360)}}, 'PNA': {'domain': {'latitude': (20.0, 85), 'longitude': (120, 240)}}, 'PDO': {'domain': {'latitude': (20.0, 70), 'longitude': (110, 260)}}, 'AllMW': {'domain': {'latitude': (-40.0, 45.0), 'longitude': (0.0, 360.0)}}, 'AllM': {'domain': {'latitude': (-45.0, 45.0), 'longitude': (0.0, 360.0)}}, 'NAMM': {'domain': {'latitude': (0.0, 45.0), 'longitude': (210.0, 310.0)}}, 'SAMM': {'domain': {'latitude': (-45.0, 0.0), 'longitude': (240.0, 330.0)}}, 'NAFM': {'domain': {'latitude': (0.0, 45.0), 'longitude': (310.0, 60.0)}}, 'SAFM': {'domain': {'latitude': (-45.0, 0.0), 'longitude': (0.0, 90.0)}}, 'ASM': {'domain': {'latitude': (0.0, 45.0), 'longitude': (60.0, 180.0)}}, 'AUSM': {'domain': {'latitude': (-45.0, 0.0), 'longitude': (90.0, 160.0)}}, 'AIR': {'domain': {'latitude': (7.0, 25.0), 'longitude': (65.0, 85.0)}}, 'AUS': {'domain': {'latitude': (-20.0, -10.0), 'longitude': (120.0, 150.0)}}, 'Sahel': {'domain': {'latitude': (13.0, 18.0), 'longitude': (-10.0, 10.0)}}, 'GoG': {'domain': {'latitude': (0.0, 5.0), 'longitude': (-10.0, 10.0)}}, 'NAmo': {'domain': {'latitude': (20.0, 37.0), 'longitude': (-112.0, -103.0)}}, 'SAmo': {'domain': {'latitude': (-20.0, 2.5), 'longitude': (-65.0, -40.0)}}} \n",
+      " regions_specs: {'global': {}, 'NHEX': {'domain': {'latitude': (30.0, 90)}}, 'SHEX': {'domain': {'latitude': (-90.0, -30)}}, 'TROPICS': {'domain': {'latitude': (-30.0, 30)}}, '90S50S': {'domain': {'latitude': (-90.0, -50)}}, '50S20S': {'domain': {'latitude': (-50.0, -20)}}, '20S20N': {'domain': {'latitude': (-20.0, 20)}}, '20N50N': {'domain': {'latitude': (20.0, 50)}}, '50N90N': {'domain': {'latitude': (50.0, 90)}}, 'CONUS': {'domain': {'latitude': (24.7, 49.4), 'longitude': (-124.78, -66.92)}}, 'land': {'value': 100}, 'land_NHEX': {'value': 100, 'domain': {'latitude': (30.0, 90)}}, 'land_SHEX': {'value': 100, 'domain': {'latitude': (-90.0, -30)}}, 'land_TROPICS': {'value': 100, 'domain': {'latitude': (-30.0, 30)}}, 'land_CONUS': {'value': 100, 'domain': {'latitude': (24.7, 49.4), 'longitude': (-124.78, -66.92)}}, 'ocean': {'value': 0}, 'ocean_NHEX': {'value': 0, 'domain': {'latitude': (30.0, 90)}}, 'ocean_SHEX': {'value': 0, 'domain': {'latitude': (-90.0, -30)}}, 'ocean_TROPICS': {'value': 0, 'domain': {'latitude': (30.0, 30)}}, 'ocean_50S50N': {'value': 0.0, 'domain': {'latitude': (-50.0, 50)}}, 'ocean_50S20S': {'value': 0.0, 'domain': {'latitude': (-50.0, -20)}}, 'ocean_20S20N': {'value': 0.0, 'domain': {'latitude': (-20.0, 20)}}, 'ocean_20N50N': {'value': 0.0, 'domain': {'latitude': (20.0, 50)}}, 'NAM': {'domain': {'latitude': (20.0, 90), 'longitude': (-180, 180)}}, 'NAO': {'domain': {'latitude': (20.0, 80), 'longitude': (-90, 40)}}, 'SAM': {'domain': {'latitude': (-20.0, -90), 'longitude': (0, 360)}}, 'PNA': {'domain': {'latitude': (20.0, 85), 'longitude': (120, 240)}}, 'PDO': {'domain': {'latitude': (20.0, 70), 'longitude': (110, 260)}}, 'AllMW': {'domain': {'latitude': (-40.0, 45.0), 'longitude': (0.0, 360.0)}}, 'AllM': {'domain': {'latitude': (-45.0, 45.0), 'longitude': (0.0, 360.0)}}, 'NAMM': {'domain': {'latitude': (0.0, 45.0), 'longitude': (210.0, 310.0)}}, 'SAMM': {'domain': {'latitude': (-45.0, 0.0), 'longitude': (240.0, 330.0)}}, 'NAFM': {'domain': {'latitude': (0.0, 45.0), 'longitude': (310.0, 60.0)}}, 'SAFM': {'domain': {'latitude': (-45.0, 0.0), 'longitude': (0.0, 90.0)}}, 'ASM': {'domain': {'latitude': (0.0, 45.0), 'longitude': (60.0, 180.0)}}, 'AUSM': {'domain': {'latitude': (-45.0, 0.0), 'longitude': (90.0, 160.0)}}, 'AIR': {'domain': {'latitude': (7.0, 25.0), 'longitude': (65.0, 85.0)}}, 'AUS': {'domain': {'latitude': (-20.0, -10.0), 'longitude': (120.0, 150.0)}}, 'Sahel': {'domain': {'latitude': (13.0, 18.0), 'longitude': (-10.0, 10.0)}}, 'GoG': {'domain': {'latitude': (0.0, 5.0), 'longitude': (-10.0, 10.0)}}, 'NAmo': {'domain': {'latitude': (20.0, 37.0), 'longitude': (-112.0, -103.0)}}, 'SAmo': {'domain': {'latitude': (-20.0, 2.5), 'longitude': (-65.0, -40.0)}}} \n",
       " regions: {'rlut': ['Global']} \n",
       " test_data_path: demo_data/CMIP5_demo_clims/ \n",
       " reference_data_path: demo_data/obs4MIPs_PCMDI_clims \n",
       " metrics_output_path: demo_output/Ex3 \n",
+      " diagnostics_output_path: demo_output/Ex3 \n",
       " debug: False \n",
       "\n",
       "--- prepare mean climate metrics calculation ---\n",
@@ -1482,23 +1605,35 @@
       "reference_data_set (all):  ['alternate1', 'default']\n",
       "ref: alternate1\n",
       "ref_data_full_path: demo_data/obs4MIPs_PCMDI_clims/rlut/CERES-EBAF-4-0/v20210804/rlut_mon_CERES-EBAF-4-0_PCMDI_gn.200301-201812.AC.v20210804.nc\n",
+      "'DataArray' object has no attribute 'units'\n",
+      "units: \n",
+      "ref_data load_and_regrid done\n",
+      "=================================\n",
+      "model, runs, find_all_realizations: ACCESS1-0 [''] False\n",
       "-----------------------\n",
       "model, run: ACCESS1-0 \n",
       "test_data (model in this case) full_path: demo_data/CMIP5_demo_clims/cmip5.historical.ACCESS1-0.r1i1p1.mon.rlut.198101-200512.AC.v20200426.nc\n",
-      "[WARNING]: calendar info mismatch. ds.time.attrs[\"calendar\"] is adjusted to ds.calendar\n",
+      "units: W m-2\n",
       "load and regrid done\n",
       "region: Global\n",
       "compute metrics start\n",
       "var:  rlut\n",
       "compute_metrics-CALCULATE ANNUAL CYCLE SPACE-TIME RMS, CORRELATIONS and STD\n",
       "compute_metrics, rms_xyt\n",
+      "compute_metrics, rms_xyt: nan\n",
       "compute_metrics, stdObs_xyt\n",
+      "compute_metrics, stdObs_xyt: 33.46854119190699\n",
       "compute_metrics, std_xyt\n",
+      "compute_metrics, std_xyt: 36.438416968613225\n",
       "compute_metrics-CALCULATE ANNUAL MEANS\n",
       "compute_metrics-CALCULATE ANNUAL MEAN BIAS\n",
+      "compute_metrics-CALCULATE ANNUAL MEAN BIAS, bias_xy: 1.1384451737888808\n",
       "compute_metrics-CALCULATE MSE\n",
+      "compute_metrics-CALCULATE MSE, mae_xy: 5.770012023689812\n",
       "compute_metrics-CALCULATE MEAN RMS\n",
+      "compute_metrics-CALCULATE MEAN RMS: rms_xy, rmsc_xy:  8.044682445768467 7.9637213813349925\n",
       "compute_metrics-CALCULATE MEAN CORR\n",
+      "compute_metrics-CALCULATE MEAN CORR: cor_xy: 0.9693780836555635\n",
       "compute_metrics-CALCULATE ANNUAL OBS AND MOD STD\n",
       "compute_metrics-CALCULATE ANNUAL OBS AND MOD MEAN\n",
       "compute_metrics-CALCULATE ANNUAL MEANS\n",
@@ -1507,23 +1642,35 @@
       "compute_metrics-CALCULATE ANNUAL MEAN DEVIATION FROM ZONAL MEAN STD\n",
       "ref: default\n",
       "ref_data_full_path: demo_data/obs4MIPs_PCMDI_clims/rlut/CERES-EBAF-4-1/v20210804/rlut_mon_CERES-EBAF-4-1_PCMDI_gn.200301-201812.AC.v20210804.nc\n",
+      "'DataArray' object has no attribute 'units'\n",
+      "units: \n",
+      "ref_data load_and_regrid done\n",
+      "=================================\n",
+      "model, runs, find_all_realizations: ACCESS1-0 [''] False\n",
       "-----------------------\n",
       "model, run: ACCESS1-0 \n",
       "test_data (model in this case) full_path: demo_data/CMIP5_demo_clims/cmip5.historical.ACCESS1-0.r1i1p1.mon.rlut.198101-200512.AC.v20200426.nc\n",
-      "[WARNING]: calendar info mismatch. ds.time.attrs[\"calendar\"] is adjusted to ds.calendar\n",
+      "units: W m-2\n",
       "load and regrid done\n",
       "region: Global\n",
       "compute metrics start\n",
       "var:  rlut\n",
       "compute_metrics-CALCULATE ANNUAL CYCLE SPACE-TIME RMS, CORRELATIONS and STD\n",
       "compute_metrics, rms_xyt\n",
+      "compute_metrics, rms_xyt: nan\n",
       "compute_metrics, stdObs_xyt\n",
+      "compute_metrics, stdObs_xyt: 33.41842190556023\n",
       "compute_metrics, std_xyt\n",
+      "compute_metrics, std_xyt: 36.438416968613225\n",
       "compute_metrics-CALCULATE ANNUAL MEANS\n",
       "compute_metrics-CALCULATE ANNUAL MEAN BIAS\n",
+      "compute_metrics-CALCULATE ANNUAL MEAN BIAS, bias_xy: 1.1365878854524063\n",
       "compute_metrics-CALCULATE MSE\n",
+      "compute_metrics-CALCULATE MSE, mae_xy: 5.763493973471603\n",
       "compute_metrics-CALCULATE MEAN RMS\n",
+      "compute_metrics-CALCULATE MEAN RMS: rms_xy, rmsc_xy:  8.033820554038556 7.953014565127818\n",
       "compute_metrics-CALCULATE MEAN CORR\n",
+      "compute_metrics-CALCULATE MEAN CORR: cor_xy: 0.9694728084795681\n",
       "compute_metrics-CALCULATE ANNUAL OBS AND MOD STD\n",
       "compute_metrics-CALCULATE ANNUAL OBS AND MOD MEAN\n",
       "compute_metrics-CALCULATE ANNUAL MEANS\n",
@@ -1559,20 +1706,29 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "2023-02-21 14:23:50,755 [WARNING]: dataset.py(_is_decodable:474) >> 'time' does not have a 'units' attribute set so it could not be decoded. Try setting the 'units' attribute (`ds.{coords.name}.attrs['units']`) and try decoding again.\n",
-      "INFO::2023-02-21 14:24::pcmdi_metrics:: Results saved to a json file: /Users/lee1043/Documents/Research/git/pcmdi_metrics_20221013_pcmdi/pcmdi_metrics/doc/jupyter/Demo/demo_output/Ex4/rlut/ACCESS1-0_rlut_2.5x2.5_regrid2_metrics_alternate1.json\n",
-      "2023-02-21 14:24:14,525 [INFO]: base.py(write:245) >> Results saved to a json file: /Users/lee1043/Documents/Research/git/pcmdi_metrics_20221013_pcmdi/pcmdi_metrics/doc/jupyter/Demo/demo_output/Ex4/rlut/ACCESS1-0_rlut_2.5x2.5_regrid2_metrics_alternate1.json\n",
-      "2023-02-21 14:24:14,547 [WARNING]: dataset.py(_is_decodable:474) >> 'time' does not have a 'units' attribute set so it could not be decoded. Try setting the 'units' attribute (`ds.{coords.name}.attrs['units']`) and try decoding again.\n",
-      "INFO::2023-02-21 14:24::pcmdi_metrics:: Results saved to a json file: /Users/lee1043/Documents/Research/git/pcmdi_metrics_20221013_pcmdi/pcmdi_metrics/doc/jupyter/Demo/demo_output/Ex4/rlut/CanCM4_rlut_2.5x2.5_regrid2_metrics_alternate1.json\n",
-      "2023-02-21 14:24:35,676 [INFO]: base.py(write:245) >> Results saved to a json file: /Users/lee1043/Documents/Research/git/pcmdi_metrics_20221013_pcmdi/pcmdi_metrics/doc/jupyter/Demo/demo_output/Ex4/rlut/CanCM4_rlut_2.5x2.5_regrid2_metrics_alternate1.json\n",
-      "2023-02-21 14:24:37,325 [WARNING]: dataset.py(_is_decodable:474) >> 'time' does not have a 'units' attribute set so it could not be decoded. Try setting the 'units' attribute (`ds.{coords.name}.attrs['units']`) and try decoding again.\n",
-      "INFO::2023-02-21 14:25::pcmdi_metrics:: Results saved to a json file: /Users/lee1043/Documents/Research/git/pcmdi_metrics_20221013_pcmdi/pcmdi_metrics/doc/jupyter/Demo/demo_output/Ex4/rlut/ACCESS1-0_rlut_2.5x2.5_regrid2_metrics_default.json\n",
-      "2023-02-21 14:25:02,327 [INFO]: base.py(write:245) >> Results saved to a json file: /Users/lee1043/Documents/Research/git/pcmdi_metrics_20221013_pcmdi/pcmdi_metrics/doc/jupyter/Demo/demo_output/Ex4/rlut/ACCESS1-0_rlut_2.5x2.5_regrid2_metrics_default.json\n",
-      "2023-02-21 14:25:02,348 [WARNING]: dataset.py(_is_decodable:474) >> 'time' does not have a 'units' attribute set so it could not be decoded. Try setting the 'units' attribute (`ds.{coords.name}.attrs['units']`) and try decoding again.\n",
-      "INFO::2023-02-21 14:25::pcmdi_metrics:: Results saved to a json file: /Users/lee1043/Documents/Research/git/pcmdi_metrics_20221013_pcmdi/pcmdi_metrics/doc/jupyter/Demo/demo_output/Ex4/rlut/CanCM4_rlut_2.5x2.5_regrid2_metrics_default.json\n",
-      "2023-02-21 14:25:26,158 [INFO]: base.py(write:245) >> Results saved to a json file: /Users/lee1043/Documents/Research/git/pcmdi_metrics_20221013_pcmdi/pcmdi_metrics/doc/jupyter/Demo/demo_output/Ex4/rlut/CanCM4_rlut_2.5x2.5_regrid2_metrics_default.json\n",
-      "INFO::2023-02-21 14:25::pcmdi_metrics:: Results saved to a json file: /Users/lee1043/Documents/Research/git/pcmdi_metrics_20221013_pcmdi/pcmdi_metrics/doc/jupyter/Demo/demo_output/Ex4/rlut_2.5x2.5_regrid2_metrics.json\n",
-      "2023-02-21 14:25:26,171 [INFO]: base.py(write:245) >> Results saved to a json file: /Users/lee1043/Documents/Research/git/pcmdi_metrics_20221013_pcmdi/pcmdi_metrics/doc/jupyter/Demo/demo_output/Ex4/rlut_2.5x2.5_regrid2_metrics.json\n"
+      "2023-11-09 16:49:45,266 [WARNING]: dataset.py(_is_decodable:670) >> 'time' does not have a 'units' attribute set so it could not be decoded. Try setting the 'units' attribute (`ds.{coords.name}.attrs['units']`) and try decoding again.\n",
+      "2023-11-09 16:49:45,266 [WARNING]: dataset.py(_is_decodable:670) >> 'time' does not have a 'units' attribute set so it could not be decoded. Try setting the 'units' attribute (`ds.{coords.name}.attrs['units']`) and try decoding again.\n",
+      "INFO::2023-11-09 16:50::pcmdi_metrics:: Results saved to a json file: /Users/lee1043/Documents/Research/git/pcmdi_metrics_20230620_pcmdi/pcmdi_metrics/doc/jupyter/Demo/demo_output/Ex4/rlut/rlut_ACCESS1-0__2.5x2.5_regrid2_metrics_alternate1_Ex4.json\n",
+      "2023-11-09 16:50:41,150 [INFO]: base.py(write:250) >> Results saved to a json file: /Users/lee1043/Documents/Research/git/pcmdi_metrics_20230620_pcmdi/pcmdi_metrics/doc/jupyter/Demo/demo_output/Ex4/rlut/rlut_ACCESS1-0__2.5x2.5_regrid2_metrics_alternate1_Ex4.json\n",
+      "2023-11-09 16:50:41,150 [INFO]: base.py(write:250) >> Results saved to a json file: /Users/lee1043/Documents/Research/git/pcmdi_metrics_20230620_pcmdi/pcmdi_metrics/doc/jupyter/Demo/demo_output/Ex4/rlut/rlut_ACCESS1-0__2.5x2.5_regrid2_metrics_alternate1_Ex4.json\n",
+      "2023-11-09 16:50:41,183 [WARNING]: dataset.py(_is_decodable:670) >> 'time' does not have a 'units' attribute set so it could not be decoded. Try setting the 'units' attribute (`ds.{coords.name}.attrs['units']`) and try decoding again.\n",
+      "2023-11-09 16:50:41,183 [WARNING]: dataset.py(_is_decodable:670) >> 'time' does not have a 'units' attribute set so it could not be decoded. Try setting the 'units' attribute (`ds.{coords.name}.attrs['units']`) and try decoding again.\n",
+      "INFO::2023-11-09 16:51::pcmdi_metrics:: Results saved to a json file: /Users/lee1043/Documents/Research/git/pcmdi_metrics_20230620_pcmdi/pcmdi_metrics/doc/jupyter/Demo/demo_output/Ex4/rlut/rlut_CanCM4__2.5x2.5_regrid2_metrics_alternate1_Ex4.json\n",
+      "2023-11-09 16:51:35,777 [INFO]: base.py(write:250) >> Results saved to a json file: /Users/lee1043/Documents/Research/git/pcmdi_metrics_20230620_pcmdi/pcmdi_metrics/doc/jupyter/Demo/demo_output/Ex4/rlut/rlut_CanCM4__2.5x2.5_regrid2_metrics_alternate1_Ex4.json\n",
+      "2023-11-09 16:51:35,777 [INFO]: base.py(write:250) >> Results saved to a json file: /Users/lee1043/Documents/Research/git/pcmdi_metrics_20230620_pcmdi/pcmdi_metrics/doc/jupyter/Demo/demo_output/Ex4/rlut/rlut_CanCM4__2.5x2.5_regrid2_metrics_alternate1_Ex4.json\n",
+      "2023-11-09 16:51:42,513 [WARNING]: dataset.py(_is_decodable:670) >> 'time' does not have a 'units' attribute set so it could not be decoded. Try setting the 'units' attribute (`ds.{coords.name}.attrs['units']`) and try decoding again.\n",
+      "2023-11-09 16:51:42,513 [WARNING]: dataset.py(_is_decodable:670) >> 'time' does not have a 'units' attribute set so it could not be decoded. Try setting the 'units' attribute (`ds.{coords.name}.attrs['units']`) and try decoding again.\n",
+      "INFO::2023-11-09 16:52::pcmdi_metrics:: Results saved to a json file: /Users/lee1043/Documents/Research/git/pcmdi_metrics_20230620_pcmdi/pcmdi_metrics/doc/jupyter/Demo/demo_output/Ex4/rlut/rlut_ACCESS1-0__2.5x2.5_regrid2_metrics_default_Ex4.json\n",
+      "2023-11-09 16:52:38,464 [INFO]: base.py(write:250) >> Results saved to a json file: /Users/lee1043/Documents/Research/git/pcmdi_metrics_20230620_pcmdi/pcmdi_metrics/doc/jupyter/Demo/demo_output/Ex4/rlut/rlut_ACCESS1-0__2.5x2.5_regrid2_metrics_default_Ex4.json\n",
+      "2023-11-09 16:52:38,464 [INFO]: base.py(write:250) >> Results saved to a json file: /Users/lee1043/Documents/Research/git/pcmdi_metrics_20230620_pcmdi/pcmdi_metrics/doc/jupyter/Demo/demo_output/Ex4/rlut/rlut_ACCESS1-0__2.5x2.5_regrid2_metrics_default_Ex4.json\n",
+      "2023-11-09 16:52:38,494 [WARNING]: dataset.py(_is_decodable:670) >> 'time' does not have a 'units' attribute set so it could not be decoded. Try setting the 'units' attribute (`ds.{coords.name}.attrs['units']`) and try decoding again.\n",
+      "2023-11-09 16:52:38,494 [WARNING]: dataset.py(_is_decodable:670) >> 'time' does not have a 'units' attribute set so it could not be decoded. Try setting the 'units' attribute (`ds.{coords.name}.attrs['units']`) and try decoding again.\n",
+      "INFO::2023-11-09 16:53::pcmdi_metrics:: Results saved to a json file: /Users/lee1043/Documents/Research/git/pcmdi_metrics_20230620_pcmdi/pcmdi_metrics/doc/jupyter/Demo/demo_output/Ex4/rlut/rlut_CanCM4__2.5x2.5_regrid2_metrics_default_Ex4.json\n",
+      "2023-11-09 16:53:33,822 [INFO]: base.py(write:250) >> Results saved to a json file: /Users/lee1043/Documents/Research/git/pcmdi_metrics_20230620_pcmdi/pcmdi_metrics/doc/jupyter/Demo/demo_output/Ex4/rlut/rlut_CanCM4__2.5x2.5_regrid2_metrics_default_Ex4.json\n",
+      "2023-11-09 16:53:33,822 [INFO]: base.py(write:250) >> Results saved to a json file: /Users/lee1043/Documents/Research/git/pcmdi_metrics_20230620_pcmdi/pcmdi_metrics/doc/jupyter/Demo/demo_output/Ex4/rlut/rlut_CanCM4__2.5x2.5_regrid2_metrics_default_Ex4.json\n",
+      "INFO::2023-11-09 16:53::pcmdi_metrics:: Results saved to a json file: /Users/lee1043/Documents/Research/git/pcmdi_metrics_20230620_pcmdi/pcmdi_metrics/doc/jupyter/Demo/demo_output/Ex4/rlut_2.5x2.5_regrid2_metrics_Ex4.json\n",
+      "2023-11-09 16:53:54,053 [INFO]: base.py(write:250) >> Results saved to a json file: /Users/lee1043/Documents/Research/git/pcmdi_metrics_20230620_pcmdi/pcmdi_metrics/doc/jupyter/Demo/demo_output/Ex4/rlut_2.5x2.5_regrid2_metrics_Ex4.json\n",
+      "2023-11-09 16:53:54,053 [INFO]: base.py(write:250) >> Results saved to a json file: /Users/lee1043/Documents/Research/git/pcmdi_metrics_20230620_pcmdi/pcmdi_metrics/doc/jupyter/Demo/demo_output/Ex4/rlut_2.5x2.5_regrid2_metrics_Ex4.json\n"
      ]
     },
     {
@@ -1583,6 +1739,7 @@
       " test_data_set: ['ACCESS1-0', 'CanCM4'] \n",
       " realization:  \n",
       " vars: ['rlut'] \n",
+      " varname_in_test_data: None \n",
       " reference_data_set: ['all'] \n",
       " target_grid: 2.5x2.5 \n",
       " regrid_tool: regrid2 \n",
@@ -1597,6 +1754,7 @@
       " test_data_path: demo_data/CMIP5_demo_clims/ \n",
       " reference_data_path: demo_data/obs4MIPs_PCMDI_clims \n",
       " metrics_output_path: demo_output/Ex4 \n",
+      " diagnostics_output_path: demo_output/Ex4 \n",
       " debug: False \n",
       "\n",
       "--- prepare mean climate metrics calculation ---\n",
@@ -1606,10 +1764,15 @@
       "reference_data_set (all):  ['alternate1', 'default']\n",
       "ref: alternate1\n",
       "ref_data_full_path: demo_data/obs4MIPs_PCMDI_clims/rlut/CERES-EBAF-4-0/v20210804/rlut_mon_CERES-EBAF-4-0_PCMDI_gn.200301-201812.AC.v20210804.nc\n",
+      "'DataArray' object has no attribute 'units'\n",
+      "units: \n",
+      "ref_data load_and_regrid done\n",
+      "=================================\n",
+      "model, runs, find_all_realizations: ACCESS1-0 [''] False\n",
       "-----------------------\n",
       "model, run: ACCESS1-0 \n",
       "test_data (model in this case) full_path: demo_data/CMIP5_demo_clims/cmip5.historical.ACCESS1-0.r1i1p1.mon.rlut.198101-200512.AC.v20200426.nc\n",
-      "[WARNING]: calendar info mismatch. ds.time.attrs[\"calendar\"] is adjusted to ds.calendar\n",
+      "units: W m-2\n",
       "load and regrid done\n",
       "region: MyDomain\n",
       "spatial subset done\n",
@@ -1617,23 +1780,32 @@
       "var:  rlut\n",
       "compute_metrics-CALCULATE ANNUAL CYCLE SPACE-TIME RMS, CORRELATIONS and STD\n",
       "compute_metrics, rms_xyt\n",
+      "compute_metrics, rms_xyt: nan\n",
       "compute_metrics, stdObs_xyt\n",
+      "compute_metrics, stdObs_xyt: 19.18138602499969\n",
       "compute_metrics, std_xyt\n",
+      "compute_metrics, std_xyt: 22.561827391039035\n",
       "compute_metrics-CALCULATE ANNUAL MEANS\n",
       "compute_metrics-CALCULATE ANNUAL MEAN BIAS\n",
+      "compute_metrics-CALCULATE ANNUAL MEAN BIAS, bias_xy: 0.5946991696333115\n",
       "compute_metrics-CALCULATE MSE\n",
+      "compute_metrics-CALCULATE MSE, mae_xy: 4.150349791215329\n",
       "compute_metrics-CALCULATE MEAN RMS\n",
+      "compute_metrics-CALCULATE MEAN RMS: rms_xy, rmsc_xy:  5.291037658821646 5.2575100955401215\n",
       "compute_metrics-CALCULATE MEAN CORR\n",
+      "compute_metrics-CALCULATE MEAN CORR: cor_xy: 0.9522270778028106\n",
       "compute_metrics-CALCULATE ANNUAL OBS AND MOD STD\n",
       "compute_metrics-CALCULATE ANNUAL OBS AND MOD MEAN\n",
       "compute_metrics-CALCULATE ANNUAL MEANS\n",
       "compute_metrics-CALCULATE ANNUAL AND ZONAL MEAN RMS\n",
       "compute_metrics-CALCULATE ANNUAL MEAN DEVIATION FROM ZONAL MEAN RMS\n",
       "compute_metrics-CALCULATE ANNUAL MEAN DEVIATION FROM ZONAL MEAN STD\n",
+      "=================================\n",
+      "model, runs, find_all_realizations: CanCM4 [''] False\n",
       "-----------------------\n",
       "model, run: CanCM4 \n",
       "test_data (model in this case) full_path: demo_data/CMIP5_demo_clims/cmip5.historical.CanCM4.r1i1p1.mon.rlut.198101-200512.AC.v20200426.nc\n",
-      "[WARNING]: calendar info mismatch. ds.time.attrs[\"calendar\"] is adjusted to ds.calendar\n",
+      "units: W m-2\n",
       "load and regrid done\n",
       "region: MyDomain\n",
       "spatial subset done\n",
@@ -1641,13 +1813,20 @@
       "var:  rlut\n",
       "compute_metrics-CALCULATE ANNUAL CYCLE SPACE-TIME RMS, CORRELATIONS and STD\n",
       "compute_metrics, rms_xyt\n",
+      "compute_metrics, rms_xyt: nan\n",
       "compute_metrics, stdObs_xyt\n",
+      "compute_metrics, stdObs_xyt: 19.18138602499969\n",
       "compute_metrics, std_xyt\n",
+      "compute_metrics, std_xyt: 22.245208810088847\n",
       "compute_metrics-CALCULATE ANNUAL MEANS\n",
       "compute_metrics-CALCULATE ANNUAL MEAN BIAS\n",
+      "compute_metrics-CALCULATE ANNUAL MEAN BIAS, bias_xy: 0.6638373054433819\n",
       "compute_metrics-CALCULATE MSE\n",
+      "compute_metrics-CALCULATE MSE, mae_xy: 5.889229301904497\n",
       "compute_metrics-CALCULATE MEAN RMS\n",
+      "compute_metrics-CALCULATE MEAN RMS: rms_xy, rmsc_xy:  7.248559470317926 7.218097701377931\n",
       "compute_metrics-CALCULATE MEAN CORR\n",
+      "compute_metrics-CALCULATE MEAN CORR: cor_xy: 0.9066905099914794\n",
       "compute_metrics-CALCULATE ANNUAL OBS AND MOD STD\n",
       "compute_metrics-CALCULATE ANNUAL OBS AND MOD MEAN\n",
       "compute_metrics-CALCULATE ANNUAL MEANS\n",
@@ -1656,10 +1835,15 @@
       "compute_metrics-CALCULATE ANNUAL MEAN DEVIATION FROM ZONAL MEAN STD\n",
       "ref: default\n",
       "ref_data_full_path: demo_data/obs4MIPs_PCMDI_clims/rlut/CERES-EBAF-4-1/v20210804/rlut_mon_CERES-EBAF-4-1_PCMDI_gn.200301-201812.AC.v20210804.nc\n",
+      "'DataArray' object has no attribute 'units'\n",
+      "units: \n",
+      "ref_data load_and_regrid done\n",
+      "=================================\n",
+      "model, runs, find_all_realizations: ACCESS1-0 [''] False\n",
       "-----------------------\n",
       "model, run: ACCESS1-0 \n",
       "test_data (model in this case) full_path: demo_data/CMIP5_demo_clims/cmip5.historical.ACCESS1-0.r1i1p1.mon.rlut.198101-200512.AC.v20200426.nc\n",
-      "[WARNING]: calendar info mismatch. ds.time.attrs[\"calendar\"] is adjusted to ds.calendar\n",
+      "units: W m-2\n",
       "load and regrid done\n",
       "region: MyDomain\n",
       "spatial subset done\n",
@@ -1667,23 +1851,32 @@
       "var:  rlut\n",
       "compute_metrics-CALCULATE ANNUAL CYCLE SPACE-TIME RMS, CORRELATIONS and STD\n",
       "compute_metrics, rms_xyt\n",
+      "compute_metrics, rms_xyt: nan\n",
       "compute_metrics, stdObs_xyt\n",
+      "compute_metrics, stdObs_xyt: 19.049296637854248\n",
       "compute_metrics, std_xyt\n",
+      "compute_metrics, std_xyt: 22.561827391039035\n",
       "compute_metrics-CALCULATE ANNUAL MEANS\n",
       "compute_metrics-CALCULATE ANNUAL MEAN BIAS\n",
+      "compute_metrics-CALCULATE ANNUAL MEAN BIAS, bias_xy: 0.5913453737090093\n",
       "compute_metrics-CALCULATE MSE\n",
+      "compute_metrics-CALCULATE MSE, mae_xy: 4.160780868714007\n",
       "compute_metrics-CALCULATE MEAN RMS\n",
+      "compute_metrics-CALCULATE MEAN RMS: rms_xy, rmsc_xy:  5.294374298855912 5.261246037335545\n",
       "compute_metrics-CALCULATE MEAN CORR\n",
+      "compute_metrics-CALCULATE MEAN CORR: cor_xy: 0.9522958639707434\n",
       "compute_metrics-CALCULATE ANNUAL OBS AND MOD STD\n",
       "compute_metrics-CALCULATE ANNUAL OBS AND MOD MEAN\n",
       "compute_metrics-CALCULATE ANNUAL MEANS\n",
       "compute_metrics-CALCULATE ANNUAL AND ZONAL MEAN RMS\n",
       "compute_metrics-CALCULATE ANNUAL MEAN DEVIATION FROM ZONAL MEAN RMS\n",
       "compute_metrics-CALCULATE ANNUAL MEAN DEVIATION FROM ZONAL MEAN STD\n",
+      "=================================\n",
+      "model, runs, find_all_realizations: CanCM4 [''] False\n",
       "-----------------------\n",
       "model, run: CanCM4 \n",
       "test_data (model in this case) full_path: demo_data/CMIP5_demo_clims/cmip5.historical.CanCM4.r1i1p1.mon.rlut.198101-200512.AC.v20200426.nc\n",
-      "[WARNING]: calendar info mismatch. ds.time.attrs[\"calendar\"] is adjusted to ds.calendar\n",
+      "units: W m-2\n",
       "load and regrid done\n",
       "region: MyDomain\n",
       "spatial subset done\n",
@@ -1691,13 +1884,20 @@
       "var:  rlut\n",
       "compute_metrics-CALCULATE ANNUAL CYCLE SPACE-TIME RMS, CORRELATIONS and STD\n",
       "compute_metrics, rms_xyt\n",
+      "compute_metrics, rms_xyt: nan\n",
       "compute_metrics, stdObs_xyt\n",
+      "compute_metrics, stdObs_xyt: 19.049296637854248\n",
       "compute_metrics, std_xyt\n",
+      "compute_metrics, std_xyt: 22.245208810088847\n",
       "compute_metrics-CALCULATE ANNUAL MEANS\n",
       "compute_metrics-CALCULATE ANNUAL MEAN BIAS\n",
+      "compute_metrics-CALCULATE ANNUAL MEAN BIAS, bias_xy: 0.6604835095190796\n",
       "compute_metrics-CALCULATE MSE\n",
+      "compute_metrics-CALCULATE MSE, mae_xy: 5.886274128348927\n",
       "compute_metrics-CALCULATE MEAN RMS\n",
+      "compute_metrics-CALCULATE MEAN RMS: rms_xy, rmsc_xy:  7.247367618574384 7.217208527707502\n",
       "compute_metrics-CALCULATE MEAN CORR\n",
+      "compute_metrics-CALCULATE MEAN CORR: cor_xy: 0.9068727312282716\n",
       "compute_metrics-CALCULATE ANNUAL OBS AND MOD STD\n",
       "compute_metrics-CALCULATE ANNUAL OBS AND MOD MEAN\n",
       "compute_metrics-CALCULATE ANNUAL MEANS\n",
@@ -1763,11 +1963,23 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "2023-02-21 14:25:33,713 [WARNING]: dataset.py(_is_decodable:474) >> 'time' does not have a 'units' attribute set so it could not be decoded. Try setting the 'units' attribute (`ds.{coords.name}.attrs['units']`) and try decoding again.\n",
-      "INFO::2023-02-21 14:27::pcmdi_metrics:: Results saved to a json file: /Users/lee1043/Documents/Research/git/pcmdi_metrics_20221013_pcmdi/pcmdi_metrics/doc/jupyter/Demo/demo_output/Ex5/zg_500/CanCM4_zg_500_2.5x2.5_regrid2_metrics_alternate1.json\n",
-      "2023-02-21 14:27:19,315 [INFO]: base.py(write:245) >> Results saved to a json file: /Users/lee1043/Documents/Research/git/pcmdi_metrics_20221013_pcmdi/pcmdi_metrics/doc/jupyter/Demo/demo_output/Ex5/zg_500/CanCM4_zg_500_2.5x2.5_regrid2_metrics_alternate1.json\n",
-      "INFO::2023-02-21 14:27::pcmdi_metrics:: Results saved to a json file: /Users/lee1043/Documents/Research/git/pcmdi_metrics_20221013_pcmdi/pcmdi_metrics/doc/jupyter/Demo/demo_output/Ex5/zg_500_2.5x2.5_regrid2_metrics.json\n",
-      "2023-02-21 14:27:29,314 [INFO]: base.py(write:245) >> Results saved to a json file: /Users/lee1043/Documents/Research/git/pcmdi_metrics_20221013_pcmdi/pcmdi_metrics/doc/jupyter/Demo/demo_output/Ex5/zg_500_2.5x2.5_regrid2_metrics.json\n"
+      "2023-11-09 16:54:19,804 [WARNING]: dataset.py(_is_decodable:670) >> 'time' does not have a 'units' attribute set so it could not be decoded. Try setting the 'units' attribute (`ds.{coords.name}.attrs['units']`) and try decoding again.\n",
+      "2023-11-09 16:54:19,804 [WARNING]: dataset.py(_is_decodable:670) >> 'time' does not have a 'units' attribute set so it could not be decoded. Try setting the 'units' attribute (`ds.{coords.name}.attrs['units']`) and try decoding again.\n",
+      "INFO::2023-11-09 16:55::pcmdi_metrics:: Results saved to a json file: /Users/lee1043/Documents/Research/git/pcmdi_metrics_20230620_pcmdi/pcmdi_metrics/doc/jupyter/Demo/demo_output/Ex5/zg_500/zg_500_CanCM4__2.5x2.5_regrid2_metrics_alternate1_Ex5.json\n",
+      "2023-11-09 16:55:21,394 [INFO]: base.py(write:250) >> Results saved to a json file: /Users/lee1043/Documents/Research/git/pcmdi_metrics_20230620_pcmdi/pcmdi_metrics/doc/jupyter/Demo/demo_output/Ex5/zg_500/zg_500_CanCM4__2.5x2.5_regrid2_metrics_alternate1_Ex5.json\n",
+      "2023-11-09 16:55:21,394 [INFO]: base.py(write:250) >> Results saved to a json file: /Users/lee1043/Documents/Research/git/pcmdi_metrics_20230620_pcmdi/pcmdi_metrics/doc/jupyter/Demo/demo_output/Ex5/zg_500/zg_500_CanCM4__2.5x2.5_regrid2_metrics_alternate1_Ex5.json\n",
+      "INFO::2023-11-09 16:55::pcmdi_metrics:: Results saved to a json file: /Users/lee1043/Documents/Research/git/pcmdi_metrics_20230620_pcmdi/pcmdi_metrics/doc/jupyter/Demo/demo_output/Ex5/zg_500/zg_500_CanCM4__2.5x2.5_regrid2_metrics_alternate1_Ex5.json\n",
+      "2023-11-09 16:55:54,625 [INFO]: base.py(write:250) >> Results saved to a json file: /Users/lee1043/Documents/Research/git/pcmdi_metrics_20230620_pcmdi/pcmdi_metrics/doc/jupyter/Demo/demo_output/Ex5/zg_500/zg_500_CanCM4__2.5x2.5_regrid2_metrics_alternate1_Ex5.json\n",
+      "2023-11-09 16:55:54,625 [INFO]: base.py(write:250) >> Results saved to a json file: /Users/lee1043/Documents/Research/git/pcmdi_metrics_20230620_pcmdi/pcmdi_metrics/doc/jupyter/Demo/demo_output/Ex5/zg_500/zg_500_CanCM4__2.5x2.5_regrid2_metrics_alternate1_Ex5.json\n",
+      "INFO::2023-11-09 16:56::pcmdi_metrics:: Results saved to a json file: /Users/lee1043/Documents/Research/git/pcmdi_metrics_20230620_pcmdi/pcmdi_metrics/doc/jupyter/Demo/demo_output/Ex5/zg_500/zg_500_CanCM4__2.5x2.5_regrid2_metrics_alternate1_Ex5.json\n",
+      "2023-11-09 16:56:38,981 [INFO]: base.py(write:250) >> Results saved to a json file: /Users/lee1043/Documents/Research/git/pcmdi_metrics_20230620_pcmdi/pcmdi_metrics/doc/jupyter/Demo/demo_output/Ex5/zg_500/zg_500_CanCM4__2.5x2.5_regrid2_metrics_alternate1_Ex5.json\n",
+      "2023-11-09 16:56:38,981 [INFO]: base.py(write:250) >> Results saved to a json file: /Users/lee1043/Documents/Research/git/pcmdi_metrics_20230620_pcmdi/pcmdi_metrics/doc/jupyter/Demo/demo_output/Ex5/zg_500/zg_500_CanCM4__2.5x2.5_regrid2_metrics_alternate1_Ex5.json\n",
+      "INFO::2023-11-09 16:57::pcmdi_metrics:: Results saved to a json file: /Users/lee1043/Documents/Research/git/pcmdi_metrics_20230620_pcmdi/pcmdi_metrics/doc/jupyter/Demo/demo_output/Ex5/zg_500/zg_500_CanCM4__2.5x2.5_regrid2_metrics_alternate1_Ex5.json\n",
+      "2023-11-09 16:57:15,946 [INFO]: base.py(write:250) >> Results saved to a json file: /Users/lee1043/Documents/Research/git/pcmdi_metrics_20230620_pcmdi/pcmdi_metrics/doc/jupyter/Demo/demo_output/Ex5/zg_500/zg_500_CanCM4__2.5x2.5_regrid2_metrics_alternate1_Ex5.json\n",
+      "2023-11-09 16:57:15,946 [INFO]: base.py(write:250) >> Results saved to a json file: /Users/lee1043/Documents/Research/git/pcmdi_metrics_20230620_pcmdi/pcmdi_metrics/doc/jupyter/Demo/demo_output/Ex5/zg_500/zg_500_CanCM4__2.5x2.5_regrid2_metrics_alternate1_Ex5.json\n",
+      "INFO::2023-11-09 16:57::pcmdi_metrics:: Results saved to a json file: /Users/lee1043/Documents/Research/git/pcmdi_metrics_20230620_pcmdi/pcmdi_metrics/doc/jupyter/Demo/demo_output/Ex5/zg_500_2.5x2.5_regrid2_metrics_Ex5.json\n",
+      "2023-11-09 16:57:35,313 [INFO]: base.py(write:250) >> Results saved to a json file: /Users/lee1043/Documents/Research/git/pcmdi_metrics_20230620_pcmdi/pcmdi_metrics/doc/jupyter/Demo/demo_output/Ex5/zg_500_2.5x2.5_regrid2_metrics_Ex5.json\n",
+      "2023-11-09 16:57:35,313 [INFO]: base.py(write:250) >> Results saved to a json file: /Users/lee1043/Documents/Research/git/pcmdi_metrics_20230620_pcmdi/pcmdi_metrics/doc/jupyter/Demo/demo_output/Ex5/zg_500_2.5x2.5_regrid2_metrics_Ex5.json\n"
      ]
     },
     {
@@ -1778,6 +1990,7 @@
       " test_data_set: ['CanCM4'] \n",
       " realization:  \n",
       " vars: ['zg_500'] \n",
+      " varname_in_test_data: None \n",
       " reference_data_set: ['alternate1'] \n",
       " target_grid: 2.5x2.5 \n",
       " regrid_tool: regrid2 \n",
@@ -1787,11 +2000,12 @@
       " filename_template: cmip5.historical.%(model_version).r1i1p1.mon.%(variable).198101-200512.AC.v20200426.nc \n",
       " sftlf_filename_template: sftlf_%(model_version).nc \n",
       " generate_sftlf: True \n",
-      " regions_specs: {'NHEX': {'domain': {'latitude': (30.0, 90)}}, 'SHEX': {'domain': {'latitude': (-90.0, -30)}}, 'TROPICS': {'domain': {'latitude': (-30.0, 30)}}, 'global': {}, '90S50S': {'domain': {'latitude': (-90.0, -50)}}, '50S20S': {'domain': {'latitude': (-50.0, -20)}}, '20S20N': {'domain': {'latitude': (-20.0, 20)}}, '20N50N': {'domain': {'latitude': (20.0, 50)}}, '50N90N': {'domain': {'latitude': (50.0, 90)}}, 'land_NHEX': {'value': 100, 'domain': {'latitude': (30.0, 90)}}, 'land_SHEX': {'value': 100, 'domain': {'latitude': (-90.0, -30)}}, 'land_TROPICS': {'value': 100, 'domain': {'latitude': (-30.0, 30)}}, 'land': {'value': 100}, 'ocean_NHEX': {'value': 0, 'domain': {'latitude': (30.0, 90)}}, 'ocean_SHEX': {'value': 0, 'domain': {'latitude': (-90.0, -30)}}, 'ocean_TROPICS': {'value': 0, 'domain': {'latitude': (30.0, 30)}}, 'ocean': {'value': 0}, 'CONUS': {'domain': {'latitude': (24.7, 49.4), 'longitude': (-124.78, -66.92)}}, 'land_CONUS': {'value': 100, 'domain': {'latitude': (24.7, 49.4), 'longitude': (-124.78, -66.92)}}, 'NAM': {'domain': {'latitude': (20.0, 90), 'longitude': (-180, 180)}}, 'NAO': {'domain': {'latitude': (20.0, 80), 'longitude': (-90, 40)}}, 'SAM': {'domain': {'latitude': (-20.0, -90), 'longitude': (0, 360)}}, 'PNA': {'domain': {'latitude': (20.0, 85), 'longitude': (120, 240)}}, 'PDO': {'domain': {'latitude': (20.0, 70), 'longitude': (110, 260)}}, 'AllMW': {'domain': {'latitude': (-40.0, 45.0), 'longitude': (0.0, 360.0)}}, 'AllM': {'domain': {'latitude': (-45.0, 45.0), 'longitude': (0.0, 360.0)}}, 'NAMM': {'domain': {'latitude': (0.0, 45.0), 'longitude': (210.0, 310.0)}}, 'SAMM': {'domain': {'latitude': (-45.0, 0.0), 'longitude': (240.0, 330.0)}}, 'NAFM': {'domain': {'latitude': (0.0, 45.0), 'longitude': (310.0, 60.0)}}, 'SAFM': {'domain': {'latitude': (-45.0, 0.0), 'longitude': (0.0, 90.0)}}, 'ASM': {'domain': {'latitude': (0.0, 45.0), 'longitude': (60.0, 180.0)}}, 'AUSM': {'domain': {'latitude': (-45.0, 0.0), 'longitude': (90.0, 160.0)}}, 'AIR': {'domain': {'latitude': (7.0, 25.0), 'longitude': (65.0, 85.0)}}, 'AUS': {'domain': {'latitude': (-20.0, -10.0), 'longitude': (120.0, 150.0)}}, 'Sahel': {'domain': {'latitude': (13.0, 18.0), 'longitude': (-10.0, 10.0)}}, 'GoG': {'domain': {'latitude': (0.0, 5.0), 'longitude': (-10.0, 10.0)}}, 'NAmo': {'domain': {'latitude': (20.0, 37.0), 'longitude': (-112.0, -103.0)}}, 'SAmo': {'domain': {'latitude': (-20.0, 2.5), 'longitude': (-65.0, -40.0)}}} \n",
+      " regions_specs: {'global': {}, 'NHEX': {'domain': {'latitude': (30.0, 90)}}, 'SHEX': {'domain': {'latitude': (-90.0, -30)}}, 'TROPICS': {'domain': {'latitude': (-30.0, 30)}}, '90S50S': {'domain': {'latitude': (-90.0, -50)}}, '50S20S': {'domain': {'latitude': (-50.0, -20)}}, '20S20N': {'domain': {'latitude': (-20.0, 20)}}, '20N50N': {'domain': {'latitude': (20.0, 50)}}, '50N90N': {'domain': {'latitude': (50.0, 90)}}, 'CONUS': {'domain': {'latitude': (24.7, 49.4), 'longitude': (-124.78, -66.92)}}, 'land': {'value': 100}, 'land_NHEX': {'value': 100, 'domain': {'latitude': (30.0, 90)}}, 'land_SHEX': {'value': 100, 'domain': {'latitude': (-90.0, -30)}}, 'land_TROPICS': {'value': 100, 'domain': {'latitude': (-30.0, 30)}}, 'land_CONUS': {'value': 100, 'domain': {'latitude': (24.7, 49.4), 'longitude': (-124.78, -66.92)}}, 'ocean': {'value': 0}, 'ocean_NHEX': {'value': 0, 'domain': {'latitude': (30.0, 90)}}, 'ocean_SHEX': {'value': 0, 'domain': {'latitude': (-90.0, -30)}}, 'ocean_TROPICS': {'value': 0, 'domain': {'latitude': (30.0, 30)}}, 'ocean_50S50N': {'value': 0.0, 'domain': {'latitude': (-50.0, 50)}}, 'ocean_50S20S': {'value': 0.0, 'domain': {'latitude': (-50.0, -20)}}, 'ocean_20S20N': {'value': 0.0, 'domain': {'latitude': (-20.0, 20)}}, 'ocean_20N50N': {'value': 0.0, 'domain': {'latitude': (20.0, 50)}}, 'NAM': {'domain': {'latitude': (20.0, 90), 'longitude': (-180, 180)}}, 'NAO': {'domain': {'latitude': (20.0, 80), 'longitude': (-90, 40)}}, 'SAM': {'domain': {'latitude': (-20.0, -90), 'longitude': (0, 360)}}, 'PNA': {'domain': {'latitude': (20.0, 85), 'longitude': (120, 240)}}, 'PDO': {'domain': {'latitude': (20.0, 70), 'longitude': (110, 260)}}, 'AllMW': {'domain': {'latitude': (-40.0, 45.0), 'longitude': (0.0, 360.0)}}, 'AllM': {'domain': {'latitude': (-45.0, 45.0), 'longitude': (0.0, 360.0)}}, 'NAMM': {'domain': {'latitude': (0.0, 45.0), 'longitude': (210.0, 310.0)}}, 'SAMM': {'domain': {'latitude': (-45.0, 0.0), 'longitude': (240.0, 330.0)}}, 'NAFM': {'domain': {'latitude': (0.0, 45.0), 'longitude': (310.0, 60.0)}}, 'SAFM': {'domain': {'latitude': (-45.0, 0.0), 'longitude': (0.0, 90.0)}}, 'ASM': {'domain': {'latitude': (0.0, 45.0), 'longitude': (60.0, 180.0)}}, 'AUSM': {'domain': {'latitude': (-45.0, 0.0), 'longitude': (90.0, 160.0)}}, 'AIR': {'domain': {'latitude': (7.0, 25.0), 'longitude': (65.0, 85.0)}}, 'AUS': {'domain': {'latitude': (-20.0, -10.0), 'longitude': (120.0, 150.0)}}, 'Sahel': {'domain': {'latitude': (13.0, 18.0), 'longitude': (-10.0, 10.0)}}, 'GoG': {'domain': {'latitude': (0.0, 5.0), 'longitude': (-10.0, 10.0)}}, 'NAmo': {'domain': {'latitude': (20.0, 37.0), 'longitude': (-112.0, -103.0)}}, 'SAmo': {'domain': {'latitude': (-20.0, 2.5), 'longitude': (-65.0, -40.0)}}} \n",
       " regions: {'rlut': ['Global']} \n",
       " test_data_path: demo_data/CMIP5_demo_clims/ \n",
       " reference_data_path: demo_data/obs4MIPs_PCMDI_clims \n",
       " metrics_output_path: demo_output/Ex5 \n",
+      " diagnostics_output_path: demo_output/Ex5 \n",
       " debug: False \n",
       "\n",
       "--- prepare mean climate metrics calculation ---\n",
@@ -1800,23 +2014,35 @@
       "level: 500.0\n",
       "ref: alternate1\n",
       "ref_data_full_path: demo_data/obs4MIPs_PCMDI_clims/zg/ERA-INT/v20210804/zg_mon_ERA-INT_PCMDI_gn.200301-201812.AC.v20210804.nc\n",
+      "'DataArray' object has no attribute 'units'\n",
+      "units: \n",
+      "ref_data load_and_regrid done\n",
+      "=================================\n",
+      "model, runs, find_all_realizations: CanCM4 [''] False\n",
       "-----------------------\n",
       "model, run: CanCM4 \n",
       "test_data (model in this case) full_path: demo_data/CMIP5_demo_clims/cmip5.historical.CanCM4.r1i1p1.mon.zg.198101-200512.AC.v20200426.nc\n",
-      "[WARNING]: calendar info mismatch. ds.time.attrs[\"calendar\"] is adjusted to ds.calendar\n",
+      "units: m\n",
       "load and regrid done\n",
       "region: global\n",
       "compute metrics start\n",
       "var:  zg\n",
       "compute_metrics-CALCULATE ANNUAL CYCLE SPACE-TIME RMS, CORRELATIONS and STD\n",
       "compute_metrics, rms_xyt\n",
+      "compute_metrics, rms_xyt: nan\n",
       "compute_metrics, stdObs_xyt\n",
+      "compute_metrics, stdObs_xyt: 266.78874424242247\n",
       "compute_metrics, std_xyt\n",
+      "compute_metrics, std_xyt: 289.171909592503\n",
       "compute_metrics-CALCULATE ANNUAL MEANS\n",
       "compute_metrics-CALCULATE ANNUAL MEAN BIAS\n",
+      "compute_metrics-CALCULATE ANNUAL MEAN BIAS, bias_xy: -26.72829436270523\n",
       "compute_metrics-CALCULATE MSE\n",
+      "compute_metrics-CALCULATE MSE, mae_xy: 27.31174888102911\n",
       "compute_metrics-CALCULATE MEAN RMS\n",
+      "compute_metrics-CALCULATE MEAN RMS: rms_xy, rmsc_xy:  36.86848787995158 25.39456002798049\n",
       "compute_metrics-CALCULATE MEAN CORR\n",
+      "compute_metrics-CALCULATE MEAN CORR: cor_xy: 0.9982646048078304\n",
       "compute_metrics-CALCULATE ANNUAL OBS AND MOD STD\n",
       "compute_metrics-CALCULATE ANNUAL OBS AND MOD MEAN\n",
       "compute_metrics-CALCULATE ANNUAL MEANS\n",
@@ -1829,13 +2055,20 @@
       "var:  zg\n",
       "compute_metrics-CALCULATE ANNUAL CYCLE SPACE-TIME RMS, CORRELATIONS and STD\n",
       "compute_metrics, rms_xyt\n",
+      "compute_metrics, rms_xyt: nan\n",
       "compute_metrics, stdObs_xyt\n",
+      "compute_metrics, stdObs_xyt: 217.77184929408025\n",
       "compute_metrics, std_xyt\n",
+      "compute_metrics, std_xyt: 243.07955688052704\n",
       "compute_metrics-CALCULATE ANNUAL MEANS\n",
       "compute_metrics-CALCULATE ANNUAL MEAN BIAS\n",
+      "compute_metrics-CALCULATE ANNUAL MEAN BIAS, bias_xy: -33.98470754086787\n",
       "compute_metrics-CALCULATE MSE\n",
+      "compute_metrics-CALCULATE MSE, mae_xy: 36.12586072486697\n",
       "compute_metrics-CALCULATE MEAN RMS\n",
+      "compute_metrics-CALCULATE MEAN RMS: rms_xy, rmsc_xy:  43.4018204400982 26.99551205063858\n",
       "compute_metrics-CALCULATE MEAN CORR\n",
+      "compute_metrics-CALCULATE MEAN CORR: cor_xy: 0.9934590261766263\n",
       "compute_metrics-CALCULATE ANNUAL OBS AND MOD STD\n",
       "compute_metrics-CALCULATE ANNUAL OBS AND MOD MEAN\n",
       "compute_metrics-CALCULATE ANNUAL MEANS\n",
@@ -1848,13 +2081,20 @@
       "var:  zg\n",
       "compute_metrics-CALCULATE ANNUAL CYCLE SPACE-TIME RMS, CORRELATIONS and STD\n",
       "compute_metrics, rms_xyt\n",
+      "compute_metrics, rms_xyt: nan\n",
       "compute_metrics, stdObs_xyt\n",
+      "compute_metrics, stdObs_xyt: 283.22012506599816\n",
       "compute_metrics, std_xyt\n",
+      "compute_metrics, std_xyt: 296.6174627856965\n",
       "compute_metrics-CALCULATE ANNUAL MEANS\n",
       "compute_metrics-CALCULATE ANNUAL MEAN BIAS\n",
+      "compute_metrics-CALCULATE ANNUAL MEAN BIAS, bias_xy: -53.00982429706322\n",
       "compute_metrics-CALCULATE MSE\n",
+      "compute_metrics-CALCULATE MSE, mae_xy: 53.00982429706322\n",
       "compute_metrics-CALCULATE MEAN RMS\n",
+      "compute_metrics-CALCULATE MEAN RMS: rms_xy, rmsc_xy:  56.8956886480462 20.665863512804005\n",
       "compute_metrics-CALCULATE MEAN CORR\n",
+      "compute_metrics-CALCULATE MEAN CORR: cor_xy: 0.9981088912216388\n",
       "compute_metrics-CALCULATE ANNUAL OBS AND MOD STD\n",
       "compute_metrics-CALCULATE ANNUAL OBS AND MOD MEAN\n",
       "compute_metrics-CALCULATE ANNUAL MEANS\n",
@@ -1867,13 +2107,20 @@
       "var:  zg\n",
       "compute_metrics-CALCULATE ANNUAL CYCLE SPACE-TIME RMS, CORRELATIONS and STD\n",
       "compute_metrics, rms_xyt\n",
+      "compute_metrics, rms_xyt: nan\n",
       "compute_metrics, stdObs_xyt\n",
+      "compute_metrics, stdObs_xyt: 28.36627012760418\n",
       "compute_metrics, std_xyt\n",
+      "compute_metrics, std_xyt: 34.43418248863642\n",
       "compute_metrics-CALCULATE ANNUAL MEANS\n",
       "compute_metrics-CALCULATE ANNUAL MEAN BIAS\n",
+      "compute_metrics-CALCULATE ANNUAL MEAN BIAS, bias_xy: -9.959322806445527\n",
       "compute_metrics-CALCULATE MSE\n",
+      "compute_metrics-CALCULATE MSE, mae_xy: 10.055655251093725\n",
       "compute_metrics-CALCULATE MEAN RMS\n",
+      "compute_metrics-CALCULATE MEAN RMS: rms_xy, rmsc_xy:  12.575853648293624 7.678800962410827\n",
       "compute_metrics-CALCULATE MEAN CORR\n",
+      "compute_metrics-CALCULATE MEAN CORR: cor_xy: 0.9609284596570256\n",
       "compute_metrics-CALCULATE ANNUAL OBS AND MOD STD\n",
       "compute_metrics-CALCULATE ANNUAL OBS AND MOD MEAN\n",
       "compute_metrics-CALCULATE ANNUAL MEANS\n",
@@ -1919,20 +2166,29 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "2023-02-21 14:27:36,115 [WARNING]: dataset.py(_is_decodable:474) >> 'time' does not have a 'units' attribute set so it could not be decoded. Try setting the 'units' attribute (`ds.{coords.name}.attrs['units']`) and try decoding again.\n",
-      "INFO::2023-02-21 14:28::pcmdi_metrics:: Results saved to a json file: /Users/lee1043/Documents/Research/git/pcmdi_metrics_20221013_pcmdi/pcmdi_metrics/doc/jupyter/Demo/demo_output/Ex6/rlut/ACCESS1-0_rlut_2.5x2.5_regrid2_metrics_alternate1.json\n",
-      "2023-02-21 14:28:06,336 [INFO]: base.py(write:245) >> Results saved to a json file: /Users/lee1043/Documents/Research/git/pcmdi_metrics_20221013_pcmdi/pcmdi_metrics/doc/jupyter/Demo/demo_output/Ex6/rlut/ACCESS1-0_rlut_2.5x2.5_regrid2_metrics_alternate1.json\n",
-      "2023-02-21 14:28:06,360 [WARNING]: dataset.py(_is_decodable:474) >> 'time' does not have a 'units' attribute set so it could not be decoded. Try setting the 'units' attribute (`ds.{coords.name}.attrs['units']`) and try decoding again.\n",
-      "INFO::2023-02-21 14:28::pcmdi_metrics:: Results saved to a json file: /Users/lee1043/Documents/Research/git/pcmdi_metrics_20221013_pcmdi/pcmdi_metrics/doc/jupyter/Demo/demo_output/Ex6/rlut/CanCM4_rlut_2.5x2.5_regrid2_metrics_alternate1.json\n",
-      "2023-02-21 14:28:37,819 [INFO]: base.py(write:245) >> Results saved to a json file: /Users/lee1043/Documents/Research/git/pcmdi_metrics_20221013_pcmdi/pcmdi_metrics/doc/jupyter/Demo/demo_output/Ex6/rlut/CanCM4_rlut_2.5x2.5_regrid2_metrics_alternate1.json\n",
-      "2023-02-21 14:28:39,744 [WARNING]: dataset.py(_is_decodable:474) >> 'time' does not have a 'units' attribute set so it could not be decoded. Try setting the 'units' attribute (`ds.{coords.name}.attrs['units']`) and try decoding again.\n",
-      "INFO::2023-02-21 14:29::pcmdi_metrics:: Results saved to a json file: /Users/lee1043/Documents/Research/git/pcmdi_metrics_20221013_pcmdi/pcmdi_metrics/doc/jupyter/Demo/demo_output/Ex6/rlut/ACCESS1-0_rlut_2.5x2.5_regrid2_metrics_default.json\n",
-      "2023-02-21 14:29:13,750 [INFO]: base.py(write:245) >> Results saved to a json file: /Users/lee1043/Documents/Research/git/pcmdi_metrics_20221013_pcmdi/pcmdi_metrics/doc/jupyter/Demo/demo_output/Ex6/rlut/ACCESS1-0_rlut_2.5x2.5_regrid2_metrics_default.json\n",
-      "2023-02-21 14:29:13,771 [WARNING]: dataset.py(_is_decodable:474) >> 'time' does not have a 'units' attribute set so it could not be decoded. Try setting the 'units' attribute (`ds.{coords.name}.attrs['units']`) and try decoding again.\n",
-      "INFO::2023-02-21 14:29::pcmdi_metrics:: Results saved to a json file: /Users/lee1043/Documents/Research/git/pcmdi_metrics_20221013_pcmdi/pcmdi_metrics/doc/jupyter/Demo/demo_output/Ex6/rlut/CanCM4_rlut_2.5x2.5_regrid2_metrics_default.json\n",
-      "2023-02-21 14:29:47,125 [INFO]: base.py(write:245) >> Results saved to a json file: /Users/lee1043/Documents/Research/git/pcmdi_metrics_20221013_pcmdi/pcmdi_metrics/doc/jupyter/Demo/demo_output/Ex6/rlut/CanCM4_rlut_2.5x2.5_regrid2_metrics_default.json\n",
-      "INFO::2023-02-21 14:29::pcmdi_metrics:: Results saved to a json file: /Users/lee1043/Documents/Research/git/pcmdi_metrics_20221013_pcmdi/pcmdi_metrics/doc/jupyter/Demo/demo_output/Ex6/rlut_2.5x2.5_regrid2_metrics.json\n",
-      "2023-02-21 14:29:57,273 [INFO]: base.py(write:245) >> Results saved to a json file: /Users/lee1043/Documents/Research/git/pcmdi_metrics_20221013_pcmdi/pcmdi_metrics/doc/jupyter/Demo/demo_output/Ex6/rlut_2.5x2.5_regrid2_metrics.json\n"
+      "2023-11-09 16:58:00,109 [WARNING]: dataset.py(_is_decodable:670) >> 'time' does not have a 'units' attribute set so it could not be decoded. Try setting the 'units' attribute (`ds.{coords.name}.attrs['units']`) and try decoding again.\n",
+      "2023-11-09 16:58:00,109 [WARNING]: dataset.py(_is_decodable:670) >> 'time' does not have a 'units' attribute set so it could not be decoded. Try setting the 'units' attribute (`ds.{coords.name}.attrs['units']`) and try decoding again.\n",
+      "INFO::2023-11-09 16:58::pcmdi_metrics:: Results saved to a json file: /Users/lee1043/Documents/Research/git/pcmdi_metrics_20230620_pcmdi/pcmdi_metrics/doc/jupyter/Demo/demo_output/Ex6/rlut/rlut_ACCESS1-0__2.5x2.5_regrid2_metrics_alternate1_Ex6.json\n",
+      "2023-11-09 16:58:59,465 [INFO]: base.py(write:250) >> Results saved to a json file: /Users/lee1043/Documents/Research/git/pcmdi_metrics_20230620_pcmdi/pcmdi_metrics/doc/jupyter/Demo/demo_output/Ex6/rlut/rlut_ACCESS1-0__2.5x2.5_regrid2_metrics_alternate1_Ex6.json\n",
+      "2023-11-09 16:58:59,465 [INFO]: base.py(write:250) >> Results saved to a json file: /Users/lee1043/Documents/Research/git/pcmdi_metrics_20230620_pcmdi/pcmdi_metrics/doc/jupyter/Demo/demo_output/Ex6/rlut/rlut_ACCESS1-0__2.5x2.5_regrid2_metrics_alternate1_Ex6.json\n",
+      "2023-11-09 16:58:59,513 [WARNING]: dataset.py(_is_decodable:670) >> 'time' does not have a 'units' attribute set so it could not be decoded. Try setting the 'units' attribute (`ds.{coords.name}.attrs['units']`) and try decoding again.\n",
+      "2023-11-09 16:58:59,513 [WARNING]: dataset.py(_is_decodable:670) >> 'time' does not have a 'units' attribute set so it could not be decoded. Try setting the 'units' attribute (`ds.{coords.name}.attrs['units']`) and try decoding again.\n",
+      "INFO::2023-11-09 17:00::pcmdi_metrics:: Results saved to a json file: /Users/lee1043/Documents/Research/git/pcmdi_metrics_20230620_pcmdi/pcmdi_metrics/doc/jupyter/Demo/demo_output/Ex6/rlut/rlut_CanCM4__2.5x2.5_regrid2_metrics_alternate1_Ex6.json\n",
+      "2023-11-09 17:00:04,816 [INFO]: base.py(write:250) >> Results saved to a json file: /Users/lee1043/Documents/Research/git/pcmdi_metrics_20230620_pcmdi/pcmdi_metrics/doc/jupyter/Demo/demo_output/Ex6/rlut/rlut_CanCM4__2.5x2.5_regrid2_metrics_alternate1_Ex6.json\n",
+      "2023-11-09 17:00:04,816 [INFO]: base.py(write:250) >> Results saved to a json file: /Users/lee1043/Documents/Research/git/pcmdi_metrics_20230620_pcmdi/pcmdi_metrics/doc/jupyter/Demo/demo_output/Ex6/rlut/rlut_CanCM4__2.5x2.5_regrid2_metrics_alternate1_Ex6.json\n",
+      "2023-11-09 17:00:10,978 [WARNING]: dataset.py(_is_decodable:670) >> 'time' does not have a 'units' attribute set so it could not be decoded. Try setting the 'units' attribute (`ds.{coords.name}.attrs['units']`) and try decoding again.\n",
+      "2023-11-09 17:00:10,978 [WARNING]: dataset.py(_is_decodable:670) >> 'time' does not have a 'units' attribute set so it could not be decoded. Try setting the 'units' attribute (`ds.{coords.name}.attrs['units']`) and try decoding again.\n",
+      "INFO::2023-11-09 17:01::pcmdi_metrics:: Results saved to a json file: /Users/lee1043/Documents/Research/git/pcmdi_metrics_20230620_pcmdi/pcmdi_metrics/doc/jupyter/Demo/demo_output/Ex6/rlut/rlut_ACCESS1-0__2.5x2.5_regrid2_metrics_default_Ex6.json\n",
+      "2023-11-09 17:01:07,454 [INFO]: base.py(write:250) >> Results saved to a json file: /Users/lee1043/Documents/Research/git/pcmdi_metrics_20230620_pcmdi/pcmdi_metrics/doc/jupyter/Demo/demo_output/Ex6/rlut/rlut_ACCESS1-0__2.5x2.5_regrid2_metrics_default_Ex6.json\n",
+      "2023-11-09 17:01:07,454 [INFO]: base.py(write:250) >> Results saved to a json file: /Users/lee1043/Documents/Research/git/pcmdi_metrics_20230620_pcmdi/pcmdi_metrics/doc/jupyter/Demo/demo_output/Ex6/rlut/rlut_ACCESS1-0__2.5x2.5_regrid2_metrics_default_Ex6.json\n",
+      "2023-11-09 17:01:07,496 [WARNING]: dataset.py(_is_decodable:670) >> 'time' does not have a 'units' attribute set so it could not be decoded. Try setting the 'units' attribute (`ds.{coords.name}.attrs['units']`) and try decoding again.\n",
+      "2023-11-09 17:01:07,496 [WARNING]: dataset.py(_is_decodable:670) >> 'time' does not have a 'units' attribute set so it could not be decoded. Try setting the 'units' attribute (`ds.{coords.name}.attrs['units']`) and try decoding again.\n",
+      "INFO::2023-11-09 17:02::pcmdi_metrics:: Results saved to a json file: /Users/lee1043/Documents/Research/git/pcmdi_metrics_20230620_pcmdi/pcmdi_metrics/doc/jupyter/Demo/demo_output/Ex6/rlut/rlut_CanCM4__2.5x2.5_regrid2_metrics_default_Ex6.json\n",
+      "2023-11-09 17:02:06,691 [INFO]: base.py(write:250) >> Results saved to a json file: /Users/lee1043/Documents/Research/git/pcmdi_metrics_20230620_pcmdi/pcmdi_metrics/doc/jupyter/Demo/demo_output/Ex6/rlut/rlut_CanCM4__2.5x2.5_regrid2_metrics_default_Ex6.json\n",
+      "2023-11-09 17:02:06,691 [INFO]: base.py(write:250) >> Results saved to a json file: /Users/lee1043/Documents/Research/git/pcmdi_metrics_20230620_pcmdi/pcmdi_metrics/doc/jupyter/Demo/demo_output/Ex6/rlut/rlut_CanCM4__2.5x2.5_regrid2_metrics_default_Ex6.json\n",
+      "INFO::2023-11-09 17:02::pcmdi_metrics:: Results saved to a json file: /Users/lee1043/Documents/Research/git/pcmdi_metrics_20230620_pcmdi/pcmdi_metrics/doc/jupyter/Demo/demo_output/Ex6/rlut_2.5x2.5_regrid2_metrics_Ex6.json\n",
+      "2023-11-09 17:02:24,735 [INFO]: base.py(write:250) >> Results saved to a json file: /Users/lee1043/Documents/Research/git/pcmdi_metrics_20230620_pcmdi/pcmdi_metrics/doc/jupyter/Demo/demo_output/Ex6/rlut_2.5x2.5_regrid2_metrics_Ex6.json\n",
+      "2023-11-09 17:02:24,735 [INFO]: base.py(write:250) >> Results saved to a json file: /Users/lee1043/Documents/Research/git/pcmdi_metrics_20230620_pcmdi/pcmdi_metrics/doc/jupyter/Demo/demo_output/Ex6/rlut_2.5x2.5_regrid2_metrics_Ex6.json\n"
      ]
     },
     {
@@ -1943,6 +2199,7 @@
       " test_data_set: ['ACCESS1-0', 'CanCM4'] \n",
       " realization:  \n",
       " vars: ['rlut'] \n",
+      " varname_in_test_data: None \n",
       " reference_data_set: ['all'] \n",
       " target_grid: 2.5x2.5 \n",
       " regrid_tool: regrid2 \n",
@@ -1952,11 +2209,12 @@
       " filename_template: cmip5.historical.%(model_version).r1i1p1.mon.%(variable).198101-200512.AC.v20200426.nc \n",
       " sftlf_filename_template: sftlf_%(model_version).nc \n",
       " generate_sftlf: True \n",
-      " regions_specs: {'NHEX': {'domain': {'latitude': (30.0, 90)}}, 'SHEX': {'domain': {'latitude': (-90.0, -30)}}, 'TROPICS': {'domain': {'latitude': (-30.0, 30)}}, 'global': {}, '90S50S': {'domain': {'latitude': (-90.0, -50)}}, '50S20S': {'domain': {'latitude': (-50.0, -20)}}, '20S20N': {'domain': {'latitude': (-20.0, 20)}}, '20N50N': {'domain': {'latitude': (20.0, 50)}}, '50N90N': {'domain': {'latitude': (50.0, 90)}}, 'land_NHEX': {'value': 100, 'domain': {'latitude': (30.0, 90)}}, 'land_SHEX': {'value': 100, 'domain': {'latitude': (-90.0, -30)}}, 'land_TROPICS': {'value': 100, 'domain': {'latitude': (-30.0, 30)}}, 'land': {'value': 100}, 'ocean_NHEX': {'value': 0, 'domain': {'latitude': (30.0, 90)}}, 'ocean_SHEX': {'value': 0, 'domain': {'latitude': (-90.0, -30)}}, 'ocean_TROPICS': {'value': 0, 'domain': {'latitude': (30.0, 30)}}, 'ocean': {'value': 0}, 'CONUS': {'domain': {'latitude': (24.7, 49.4), 'longitude': (-124.78, -66.92)}}, 'land_CONUS': {'value': 100, 'domain': {'latitude': (24.7, 49.4), 'longitude': (-124.78, -66.92)}}, 'NAM': {'domain': {'latitude': (20.0, 90), 'longitude': (-180, 180)}}, 'NAO': {'domain': {'latitude': (20.0, 80), 'longitude': (-90, 40)}}, 'SAM': {'domain': {'latitude': (-20.0, -90), 'longitude': (0, 360)}}, 'PNA': {'domain': {'latitude': (20.0, 85), 'longitude': (120, 240)}}, 'PDO': {'domain': {'latitude': (20.0, 70), 'longitude': (110, 260)}}, 'AllMW': {'domain': {'latitude': (-40.0, 45.0), 'longitude': (0.0, 360.0)}}, 'AllM': {'domain': {'latitude': (-45.0, 45.0), 'longitude': (0.0, 360.0)}}, 'NAMM': {'domain': {'latitude': (0.0, 45.0), 'longitude': (210.0, 310.0)}}, 'SAMM': {'domain': {'latitude': (-45.0, 0.0), 'longitude': (240.0, 330.0)}}, 'NAFM': {'domain': {'latitude': (0.0, 45.0), 'longitude': (310.0, 60.0)}}, 'SAFM': {'domain': {'latitude': (-45.0, 0.0), 'longitude': (0.0, 90.0)}}, 'ASM': {'domain': {'latitude': (0.0, 45.0), 'longitude': (60.0, 180.0)}}, 'AUSM': {'domain': {'latitude': (-45.0, 0.0), 'longitude': (90.0, 160.0)}}, 'AIR': {'domain': {'latitude': (7.0, 25.0), 'longitude': (65.0, 85.0)}}, 'AUS': {'domain': {'latitude': (-20.0, -10.0), 'longitude': (120.0, 150.0)}}, 'Sahel': {'domain': {'latitude': (13.0, 18.0), 'longitude': (-10.0, 10.0)}}, 'GoG': {'domain': {'latitude': (0.0, 5.0), 'longitude': (-10.0, 10.0)}}, 'NAmo': {'domain': {'latitude': (20.0, 37.0), 'longitude': (-112.0, -103.0)}}, 'SAmo': {'domain': {'latitude': (-20.0, 2.5), 'longitude': (-65.0, -40.0)}}} \n",
+      " regions_specs: {'global': {}, 'NHEX': {'domain': {'latitude': (30.0, 90)}}, 'SHEX': {'domain': {'latitude': (-90.0, -30)}}, 'TROPICS': {'domain': {'latitude': (-30.0, 30)}}, '90S50S': {'domain': {'latitude': (-90.0, -50)}}, '50S20S': {'domain': {'latitude': (-50.0, -20)}}, '20S20N': {'domain': {'latitude': (-20.0, 20)}}, '20N50N': {'domain': {'latitude': (20.0, 50)}}, '50N90N': {'domain': {'latitude': (50.0, 90)}}, 'CONUS': {'domain': {'latitude': (24.7, 49.4), 'longitude': (-124.78, -66.92)}}, 'land': {'value': 100}, 'land_NHEX': {'value': 100, 'domain': {'latitude': (30.0, 90)}}, 'land_SHEX': {'value': 100, 'domain': {'latitude': (-90.0, -30)}}, 'land_TROPICS': {'value': 100, 'domain': {'latitude': (-30.0, 30)}}, 'land_CONUS': {'value': 100, 'domain': {'latitude': (24.7, 49.4), 'longitude': (-124.78, -66.92)}}, 'ocean': {'value': 0}, 'ocean_NHEX': {'value': 0, 'domain': {'latitude': (30.0, 90)}}, 'ocean_SHEX': {'value': 0, 'domain': {'latitude': (-90.0, -30)}}, 'ocean_TROPICS': {'value': 0, 'domain': {'latitude': (30.0, 30)}}, 'ocean_50S50N': {'value': 0.0, 'domain': {'latitude': (-50.0, 50)}}, 'ocean_50S20S': {'value': 0.0, 'domain': {'latitude': (-50.0, -20)}}, 'ocean_20S20N': {'value': 0.0, 'domain': {'latitude': (-20.0, 20)}}, 'ocean_20N50N': {'value': 0.0, 'domain': {'latitude': (20.0, 50)}}, 'NAM': {'domain': {'latitude': (20.0, 90), 'longitude': (-180, 180)}}, 'NAO': {'domain': {'latitude': (20.0, 80), 'longitude': (-90, 40)}}, 'SAM': {'domain': {'latitude': (-20.0, -90), 'longitude': (0, 360)}}, 'PNA': {'domain': {'latitude': (20.0, 85), 'longitude': (120, 240)}}, 'PDO': {'domain': {'latitude': (20.0, 70), 'longitude': (110, 260)}}, 'AllMW': {'domain': {'latitude': (-40.0, 45.0), 'longitude': (0.0, 360.0)}}, 'AllM': {'domain': {'latitude': (-45.0, 45.0), 'longitude': (0.0, 360.0)}}, 'NAMM': {'domain': {'latitude': (0.0, 45.0), 'longitude': (210.0, 310.0)}}, 'SAMM': {'domain': {'latitude': (-45.0, 0.0), 'longitude': (240.0, 330.0)}}, 'NAFM': {'domain': {'latitude': (0.0, 45.0), 'longitude': (310.0, 60.0)}}, 'SAFM': {'domain': {'latitude': (-45.0, 0.0), 'longitude': (0.0, 90.0)}}, 'ASM': {'domain': {'latitude': (0.0, 45.0), 'longitude': (60.0, 180.0)}}, 'AUSM': {'domain': {'latitude': (-45.0, 0.0), 'longitude': (90.0, 160.0)}}, 'AIR': {'domain': {'latitude': (7.0, 25.0), 'longitude': (65.0, 85.0)}}, 'AUS': {'domain': {'latitude': (-20.0, -10.0), 'longitude': (120.0, 150.0)}}, 'Sahel': {'domain': {'latitude': (13.0, 18.0), 'longitude': (-10.0, 10.0)}}, 'GoG': {'domain': {'latitude': (0.0, 5.0), 'longitude': (-10.0, 10.0)}}, 'NAmo': {'domain': {'latitude': (20.0, 37.0), 'longitude': (-112.0, -103.0)}}, 'SAmo': {'domain': {'latitude': (-20.0, 2.5), 'longitude': (-65.0, -40.0)}}} \n",
       " regions: {'rlut': ['Global']} \n",
       " test_data_path: demo_data/CMIP5_demo_clims/ \n",
       " reference_data_path: demo_data/obs4MIPs_PCMDI_clims \n",
       " metrics_output_path: demo_output/Ex6 \n",
+      " diagnostics_output_path: demo_output/Ex6 \n",
       " debug: False \n",
       "\n",
       "--- prepare mean climate metrics calculation ---\n",
@@ -1966,46 +2224,67 @@
       "reference_data_set (all):  ['alternate1', 'default']\n",
       "ref: alternate1\n",
       "ref_data_full_path: demo_data/obs4MIPs_PCMDI_clims/rlut/CERES-EBAF-4-0/v20210804/rlut_mon_CERES-EBAF-4-0_PCMDI_gn.200301-201812.AC.v20210804.nc\n",
+      "'DataArray' object has no attribute 'units'\n",
+      "units: \n",
+      "ref_data load_and_regrid done\n",
+      "=================================\n",
+      "model, runs, find_all_realizations: ACCESS1-0 [''] False\n",
       "-----------------------\n",
       "model, run: ACCESS1-0 \n",
       "test_data (model in this case) full_path: demo_data/CMIP5_demo_clims/cmip5.historical.ACCESS1-0.r1i1p1.mon.rlut.198101-200512.AC.v20200426.nc\n",
-      "[WARNING]: calendar info mismatch. ds.time.attrs[\"calendar\"] is adjusted to ds.calendar\n",
+      "units: W m-2\n",
       "load and regrid done\n",
       "region: Global\n",
       "compute metrics start\n",
       "var:  rlut\n",
       "compute_metrics-CALCULATE ANNUAL CYCLE SPACE-TIME RMS, CORRELATIONS and STD\n",
       "compute_metrics, rms_xyt\n",
+      "compute_metrics, rms_xyt: nan\n",
       "compute_metrics, stdObs_xyt\n",
+      "compute_metrics, stdObs_xyt: 33.46854119190699\n",
       "compute_metrics, std_xyt\n",
+      "compute_metrics, std_xyt: 36.438416968613225\n",
       "compute_metrics-CALCULATE ANNUAL MEANS\n",
       "compute_metrics-CALCULATE ANNUAL MEAN BIAS\n",
+      "compute_metrics-CALCULATE ANNUAL MEAN BIAS, bias_xy: 1.1384451737888808\n",
       "compute_metrics-CALCULATE MSE\n",
+      "compute_metrics-CALCULATE MSE, mae_xy: 5.770012023689812\n",
       "compute_metrics-CALCULATE MEAN RMS\n",
+      "compute_metrics-CALCULATE MEAN RMS: rms_xy, rmsc_xy:  8.044682445768467 7.9637213813349925\n",
       "compute_metrics-CALCULATE MEAN CORR\n",
+      "compute_metrics-CALCULATE MEAN CORR: cor_xy: 0.9693780836555635\n",
       "compute_metrics-CALCULATE ANNUAL OBS AND MOD STD\n",
       "compute_metrics-CALCULATE ANNUAL OBS AND MOD MEAN\n",
       "compute_metrics-CALCULATE ANNUAL MEANS\n",
       "compute_metrics-CALCULATE ANNUAL AND ZONAL MEAN RMS\n",
       "compute_metrics-CALCULATE ANNUAL MEAN DEVIATION FROM ZONAL MEAN RMS\n",
       "compute_metrics-CALCULATE ANNUAL MEAN DEVIATION FROM ZONAL MEAN STD\n",
+      "=================================\n",
+      "model, runs, find_all_realizations: CanCM4 [''] False\n",
       "-----------------------\n",
       "model, run: CanCM4 \n",
       "test_data (model in this case) full_path: demo_data/CMIP5_demo_clims/cmip5.historical.CanCM4.r1i1p1.mon.rlut.198101-200512.AC.v20200426.nc\n",
-      "[WARNING]: calendar info mismatch. ds.time.attrs[\"calendar\"] is adjusted to ds.calendar\n",
+      "units: W m-2\n",
       "load and regrid done\n",
       "region: Global\n",
       "compute metrics start\n",
       "var:  rlut\n",
       "compute_metrics-CALCULATE ANNUAL CYCLE SPACE-TIME RMS, CORRELATIONS and STD\n",
       "compute_metrics, rms_xyt\n",
+      "compute_metrics, rms_xyt: nan\n",
       "compute_metrics, stdObs_xyt\n",
+      "compute_metrics, stdObs_xyt: 33.46854119190699\n",
       "compute_metrics, std_xyt\n",
+      "compute_metrics, std_xyt: 35.67665337064905\n",
       "compute_metrics-CALCULATE ANNUAL MEANS\n",
       "compute_metrics-CALCULATE ANNUAL MEAN BIAS\n",
+      "compute_metrics-CALCULATE ANNUAL MEAN BIAS, bias_xy: -1.1635463134324031\n",
       "compute_metrics-CALCULATE MSE\n",
+      "compute_metrics-CALCULATE MSE, mae_xy: 6.331106438822097\n",
       "compute_metrics-CALCULATE MEAN RMS\n",
+      "compute_metrics-CALCULATE MEAN RMS: rms_xy, rmsc_xy:  9.610248337339145 9.539550989529207\n",
       "compute_metrics-CALCULATE MEAN CORR\n",
+      "compute_metrics-CALCULATE MEAN CORR: cor_xy: 0.9518659641205703\n",
       "compute_metrics-CALCULATE ANNUAL OBS AND MOD STD\n",
       "compute_metrics-CALCULATE ANNUAL OBS AND MOD MEAN\n",
       "compute_metrics-CALCULATE ANNUAL MEANS\n",
@@ -2014,46 +2293,67 @@
       "compute_metrics-CALCULATE ANNUAL MEAN DEVIATION FROM ZONAL MEAN STD\n",
       "ref: default\n",
       "ref_data_full_path: demo_data/obs4MIPs_PCMDI_clims/rlut/CERES-EBAF-4-1/v20210804/rlut_mon_CERES-EBAF-4-1_PCMDI_gn.200301-201812.AC.v20210804.nc\n",
+      "'DataArray' object has no attribute 'units'\n",
+      "units: \n",
+      "ref_data load_and_regrid done\n",
+      "=================================\n",
+      "model, runs, find_all_realizations: ACCESS1-0 [''] False\n",
       "-----------------------\n",
       "model, run: ACCESS1-0 \n",
       "test_data (model in this case) full_path: demo_data/CMIP5_demo_clims/cmip5.historical.ACCESS1-0.r1i1p1.mon.rlut.198101-200512.AC.v20200426.nc\n",
-      "[WARNING]: calendar info mismatch. ds.time.attrs[\"calendar\"] is adjusted to ds.calendar\n",
+      "units: W m-2\n",
       "load and regrid done\n",
       "region: Global\n",
       "compute metrics start\n",
       "var:  rlut\n",
       "compute_metrics-CALCULATE ANNUAL CYCLE SPACE-TIME RMS, CORRELATIONS and STD\n",
       "compute_metrics, rms_xyt\n",
+      "compute_metrics, rms_xyt: nan\n",
       "compute_metrics, stdObs_xyt\n",
+      "compute_metrics, stdObs_xyt: 33.41842190556023\n",
       "compute_metrics, std_xyt\n",
+      "compute_metrics, std_xyt: 36.438416968613225\n",
       "compute_metrics-CALCULATE ANNUAL MEANS\n",
       "compute_metrics-CALCULATE ANNUAL MEAN BIAS\n",
+      "compute_metrics-CALCULATE ANNUAL MEAN BIAS, bias_xy: 1.1365878854524063\n",
       "compute_metrics-CALCULATE MSE\n",
+      "compute_metrics-CALCULATE MSE, mae_xy: 5.763493973471603\n",
       "compute_metrics-CALCULATE MEAN RMS\n",
+      "compute_metrics-CALCULATE MEAN RMS: rms_xy, rmsc_xy:  8.033820554038556 7.953014565127818\n",
       "compute_metrics-CALCULATE MEAN CORR\n",
+      "compute_metrics-CALCULATE MEAN CORR: cor_xy: 0.9694728084795681\n",
       "compute_metrics-CALCULATE ANNUAL OBS AND MOD STD\n",
       "compute_metrics-CALCULATE ANNUAL OBS AND MOD MEAN\n",
       "compute_metrics-CALCULATE ANNUAL MEANS\n",
       "compute_metrics-CALCULATE ANNUAL AND ZONAL MEAN RMS\n",
       "compute_metrics-CALCULATE ANNUAL MEAN DEVIATION FROM ZONAL MEAN RMS\n",
       "compute_metrics-CALCULATE ANNUAL MEAN DEVIATION FROM ZONAL MEAN STD\n",
+      "=================================\n",
+      "model, runs, find_all_realizations: CanCM4 [''] False\n",
       "-----------------------\n",
       "model, run: CanCM4 \n",
       "test_data (model in this case) full_path: demo_data/CMIP5_demo_clims/cmip5.historical.CanCM4.r1i1p1.mon.rlut.198101-200512.AC.v20200426.nc\n",
-      "[WARNING]: calendar info mismatch. ds.time.attrs[\"calendar\"] is adjusted to ds.calendar\n",
+      "units: W m-2\n",
       "load and regrid done\n",
       "region: Global\n",
       "compute metrics start\n",
       "var:  rlut\n",
       "compute_metrics-CALCULATE ANNUAL CYCLE SPACE-TIME RMS, CORRELATIONS and STD\n",
       "compute_metrics, rms_xyt\n",
+      "compute_metrics, rms_xyt: nan\n",
       "compute_metrics, stdObs_xyt\n",
+      "compute_metrics, stdObs_xyt: 33.41842190556023\n",
       "compute_metrics, std_xyt\n",
+      "compute_metrics, std_xyt: 35.67665337064905\n",
       "compute_metrics-CALCULATE ANNUAL MEANS\n",
       "compute_metrics-CALCULATE ANNUAL MEAN BIAS\n",
+      "compute_metrics-CALCULATE ANNUAL MEAN BIAS, bias_xy: -1.1654036017688774\n",
       "compute_metrics-CALCULATE MSE\n",
+      "compute_metrics-CALCULATE MSE, mae_xy: 6.333692053819536\n",
       "compute_metrics-CALCULATE MEAN RMS\n",
+      "compute_metrics-CALCULATE MEAN RMS: rms_xy, rmsc_xy:  9.608637289548378 9.537701243333409\n",
       "compute_metrics-CALCULATE MEAN CORR\n",
+      "compute_metrics-CALCULATE MEAN CORR: cor_xy: 0.9518850115349531\n",
       "compute_metrics-CALCULATE ANNUAL OBS AND MOD STD\n",
       "compute_metrics-CALCULATE ANNUAL OBS AND MOD MEAN\n",
       "compute_metrics-CALCULATE ANNUAL MEANS\n",
@@ -2085,9 +2385,9 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python [conda env:pmp_devel_20230218] *",
+   "display_name": "Python [conda env:pmp_devel_20230223] *",
    "language": "python",
-   "name": "conda-env-pmp_devel_20230218-py"
+   "name": "conda-env-pmp_devel_20230223-py"
   },
   "language_info": {
    "codemirror_mode": {

From eff9e7a30a99cc66e3074d9ec53744f9708b36dd Mon Sep 17 00:00:00 2001
From: Jiwoo Lee <lee1043@llnl.gov>
Date: Tue, 21 Nov 2023 13:22:45 -0800
Subject: [PATCH 118/133] more options added for legend box control

---
 .../parallel_coordinate_plot_lib.py                      | 9 +++++++--
 1 file changed, 7 insertions(+), 2 deletions(-)

diff --git a/pcmdi_metrics/graphics/parallel_coordinate_plot/parallel_coordinate_plot_lib.py b/pcmdi_metrics/graphics/parallel_coordinate_plot/parallel_coordinate_plot_lib.py
index 8c37181a3..d126f678c 100644
--- a/pcmdi_metrics/graphics/parallel_coordinate_plot/parallel_coordinate_plot_lib.py
+++ b/pcmdi_metrics/graphics/parallel_coordinate_plot/parallel_coordinate_plot_lib.py
@@ -33,6 +33,8 @@ def parallel_coordinate_plot(
     colormap="viridis",
     num_color=20,
     legend_off=False,
+    legend_ncol=6,
+    legend_bbox_to_anchor=(0.5, -0.14),
     logo_rect=None,
     logo_off=False,
     model_names2=None,
@@ -77,6 +79,8 @@ def parallel_coordinate_plot(
     - `colormap`: string, default='viridis', matplotlib colormap
     - `num_color`: integer, default=20, how many color to use.
     - `legend_off`: bool, default=False, turn off legend
+    - `legend_ncol`: integer, default=6, number of columns for legend text
+    - `legend_bbox_to_anchor`: tuple, defulat=(0.5, -0.14), set legend box location
     - `logo_rect`: sequence of float. The dimensions [left, bottom, width, height] of the new Axes.
                    All quantities are in fractions of figure width and height.  Optional.
     - `logo_off`: bool, default=False, turn off PMP logo
@@ -336,7 +340,8 @@ def parallel_coordinate_plot(
     ax.set_title(title, fontsize=18)
 
     if not legend_off:
-        ax.legend(loc="upper center", ncol=6, bbox_to_anchor=(0.5, -0.14))
+        #ax.legend(loc="upper center", ncol=6, bbox_to_anchor=(0.5, -0.14))
+        ax.legend(loc="upper center", ncol=legend_ncol, bbox_to_anchor=legend_bbox_to_anchor)
 
     if not logo_off:
         fig, ax = add_logo(fig, ax, logo_rect)
@@ -396,7 +401,7 @@ def _data_transform(
     else:
         ymins = np.repeat(ymin, N)
 
-    ymeds = np.nanmedian(ys, axis=0)  # median
+    ymeds = np.nanmedian(ys, axis=0)  # median   
     ymean = np.nanmean(ys, axis=0)  # mean
 
     if vertical_center is not None:

From 24c1e5e556fe430085f3c10fcf1bb8640ccf6562 Mon Sep 17 00:00:00 2001
From: Jiwoo Lee <lee1043@llnl.gov>
Date: Tue, 21 Nov 2023 14:04:06 -0800
Subject: [PATCH 119/133] pre-commit fix

---
 .../parallel_coordinate_plot_lib.py                       | 8 +++++---
 1 file changed, 5 insertions(+), 3 deletions(-)

diff --git a/pcmdi_metrics/graphics/parallel_coordinate_plot/parallel_coordinate_plot_lib.py b/pcmdi_metrics/graphics/parallel_coordinate_plot/parallel_coordinate_plot_lib.py
index d126f678c..74f674957 100644
--- a/pcmdi_metrics/graphics/parallel_coordinate_plot/parallel_coordinate_plot_lib.py
+++ b/pcmdi_metrics/graphics/parallel_coordinate_plot/parallel_coordinate_plot_lib.py
@@ -340,8 +340,10 @@ def parallel_coordinate_plot(
     ax.set_title(title, fontsize=18)
 
     if not legend_off:
-        #ax.legend(loc="upper center", ncol=6, bbox_to_anchor=(0.5, -0.14))
-        ax.legend(loc="upper center", ncol=legend_ncol, bbox_to_anchor=legend_bbox_to_anchor)
+        # ax.legend(loc="upper center", ncol=6, bbox_to_anchor=(0.5, -0.14))
+        ax.legend(
+            loc="upper center", ncol=legend_ncol, bbox_to_anchor=legend_bbox_to_anchor
+        )
 
     if not logo_off:
         fig, ax = add_logo(fig, ax, logo_rect)
@@ -401,7 +403,7 @@ def _data_transform(
     else:
         ymins = np.repeat(ymin, N)
 
-    ymeds = np.nanmedian(ys, axis=0)  # median   
+    ymeds = np.nanmedian(ys, axis=0)  # median
     ymean = np.nanmean(ys, axis=0)  # mean
 
     if vertical_center is not None:

From f906b28cc9c38369666b920a675e5d79fbf5505d Mon Sep 17 00:00:00 2001
From: Jiwoo Lee <lee1043@llnl.gov>
Date: Wed, 22 Nov 2023 21:47:46 -0800
Subject: [PATCH 120/133] sort models case insensitive

---
 pcmdi_metrics/graphics/share/read_json_mean_clim.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/pcmdi_metrics/graphics/share/read_json_mean_clim.py b/pcmdi_metrics/graphics/share/read_json_mean_clim.py
index a67195c31..4467e99ef 100644
--- a/pcmdi_metrics/graphics/share/read_json_mean_clim.py
+++ b/pcmdi_metrics/graphics/share/read_json_mean_clim.py
@@ -148,11 +148,11 @@ def extract_data(results_dict, var_list, region, stat, season, mip, debug=False)
     Return a pandas dataframe for metric numbers at given region/stat/season.
     Rows: models, Columns: variables (i.e., 2d array)
     """
-    model_list = sorted(list(results_dict[var_list[0]]["RESULTS"].keys()))
+    model_list = sorted(list(results_dict[var_list[0]]["RESULTS"].keys()), key=str.casefold)
     # update model_list
     if "rlut" in list(results_dict.keys()):
         if "rlut" in list(results_dict["rlut"]["RESULTS"].keys()):
-            model_list = sorted(list(results_dict["rlut"]["RESULTS"].keys()))
+            model_list = sorted(list(results_dict["rlut"]["RESULTS"].keys()), key=str.casefold)
 
     print("extract_data:: model_list: ", model_list)
 

From 131fd8696d9de6583d71dea409fc34d6a7730858 Mon Sep 17 00:00:00 2001
From: Jiwoo Lee <lee1043@llnl.gov>
Date: Wed, 22 Nov 2023 21:49:48 -0800
Subject: [PATCH 121/133] pre-commit fix

---
 pcmdi_metrics/graphics/share/read_json_mean_clim.py | 8 ++++++--
 1 file changed, 6 insertions(+), 2 deletions(-)

diff --git a/pcmdi_metrics/graphics/share/read_json_mean_clim.py b/pcmdi_metrics/graphics/share/read_json_mean_clim.py
index 4467e99ef..bd72e5db3 100644
--- a/pcmdi_metrics/graphics/share/read_json_mean_clim.py
+++ b/pcmdi_metrics/graphics/share/read_json_mean_clim.py
@@ -148,11 +148,15 @@ def extract_data(results_dict, var_list, region, stat, season, mip, debug=False)
     Return a pandas dataframe for metric numbers at given region/stat/season.
     Rows: models, Columns: variables (i.e., 2d array)
     """
-    model_list = sorted(list(results_dict[var_list[0]]["RESULTS"].keys()), key=str.casefold)
+    model_list = sorted(
+        list(results_dict[var_list[0]]["RESULTS"].keys()), key=str.casefold
+    )
     # update model_list
     if "rlut" in list(results_dict.keys()):
         if "rlut" in list(results_dict["rlut"]["RESULTS"].keys()):
-            model_list = sorted(list(results_dict["rlut"]["RESULTS"].keys()), key=str.casefold)
+            model_list = sorted(
+                list(results_dict["rlut"]["RESULTS"].keys()), key=str.casefold
+            )
 
     print("extract_data:: model_list: ", model_list)
 

From 946d0cdfe900db4076a9b77ca3379efefdca3e97 Mon Sep 17 00:00:00 2001
From: Jiwoo Lee <lee1043@llnl.gov>
Date: Tue, 28 Nov 2023 13:55:33 -0800
Subject: [PATCH 122/133] new version update

---
 README.md | 2 ++
 setup.py  | 2 +-
 2 files changed, 3 insertions(+), 1 deletion(-)

diff --git a/README.md b/README.md
index 70884fcfa..dae1690c0 100755
--- a/README.md
+++ b/README.md
@@ -98,6 +98,7 @@ Release Notes and History
 
 | <div style="width:300%">[Versions]</div> | Update summary   |
 | ------------- | ------------------------------------- |
+| [v3.1.2]      | Technical update
 | [v3.1.1]      | Technical and documentation update
 | [v3.1]        | New metric added: **Precipitation Benchmarking -- distribution bimodality**
 | [v3.0.2]      | Minor patch and more documentation added
@@ -123,6 +124,7 @@ Release Notes and History
 
 
 [Versions]: https://github.com/PCMDI/pcmdi_metrics/releases
+[v3.1.2]: https://github.com/PCMDI/pcmdi_metrics/releases/tag/v3.1.2
 [v3.1.1]: https://github.com/PCMDI/pcmdi_metrics/releases/tag/v3.1.1
 [v3.1]: https://github.com/PCMDI/pcmdi_metrics/releases/tag/v3.1
 [v3.0.2]: https://github.com/PCMDI/pcmdi_metrics/releases/tag/v3.0.2
diff --git a/setup.py b/setup.py
index c200fc318..70b8075b1 100644
--- a/setup.py
+++ b/setup.py
@@ -11,7 +11,7 @@
 else:
     install_dev = False
 
-release_version = "3.1"
+release_version = "3.1.2"
 
 p = subprocess.Popen(
     ("git", "describe", "--tags"),

From ec4bb3d1acbf8c0902688f11046ba47033ae2c26 Mon Sep 17 00:00:00 2001
From: Jiwoo Lee <lee1043@llnl.gov>
Date: Tue, 28 Nov 2023 16:57:03 -0800
Subject: [PATCH 123/133] add import shapely at the top and adjust isort
 setting thus pre-commit can consider it as an exception

---
 .pre-commit-config.yaml                                    | 1 +
 pcmdi_metrics/variability_mode/lib/plot_map.py             | 1 -
 pcmdi_metrics/variability_mode/variability_modes_driver.py | 3 +--
 3 files changed, 2 insertions(+), 3 deletions(-)

diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml
index c21f665b8..fc6ef7d2d 100644
--- a/.pre-commit-config.yaml
+++ b/.pre-commit-config.yaml
@@ -28,6 +28,7 @@ repos:
       rev: 5.12.0
       hooks:
           - id: isort
+            args: ["--honor-noqa"]
 
     # =======================
     # Python linting
diff --git a/pcmdi_metrics/variability_mode/lib/plot_map.py b/pcmdi_metrics/variability_mode/lib/plot_map.py
index 71fac7b45..b1ef45db8 100644
--- a/pcmdi_metrics/variability_mode/lib/plot_map.py
+++ b/pcmdi_metrics/variability_mode/lib/plot_map.py
@@ -1,7 +1,6 @@
 import faulthandler
 import sys
 
-# import cartopy
 import cartopy.crs as ccrs
 import matplotlib.path as mpath
 import matplotlib.pyplot as plt
diff --git a/pcmdi_metrics/variability_mode/variability_modes_driver.py b/pcmdi_metrics/variability_mode/variability_modes_driver.py
index 16e5244b6..be1bff4b0 100755
--- a/pcmdi_metrics/variability_mode/variability_modes_driver.py
+++ b/pcmdi_metrics/variability_mode/variability_modes_driver.py
@@ -46,8 +46,7 @@
 for advertising or product endorsement purposes.
 """
 
-from __future__ import print_function
-
+import shapely  # noqa
 import glob
 import json
 import os

From 93239de4e6d6ad50d2a48fdc775afa49a0da1a1c Mon Sep 17 00:00:00 2001
From: Jiwoo Lee <lee1043@users.noreply.github.com>
Date: Wed, 29 Nov 2023 11:36:50 -0800
Subject: [PATCH 124/133] Update xcdat version to 0.6.1 or above

---
 conda-env/dev.yml | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/conda-env/dev.yml b/conda-env/dev.yml
index 597d568a8..a6d5756b9 100644
--- a/conda-env/dev.yml
+++ b/conda-env/dev.yml
@@ -21,7 +21,7 @@ dependencies:
     - eofs=1.4.0
     - seaborn=0.12.2
     - enso_metrics=1.1.1
-    - xcdat=0.5.0
+    - xcdat>=0.6.1
     - xmltodict=0.13.0
     - setuptools=67.7.2
     - netcdf4=1.6.3

From 327d169382f622802a723bb03be8ed7df95bf5ff Mon Sep 17 00:00:00 2001
From: Jiwoo Lee <lee1043@llnl.gov>
Date: Wed, 29 Nov 2023 16:56:14 -0800
Subject: [PATCH 125/133] use xarray data array to plot map with cartopy, and
 adjust 1e20 as nan to prevent error from shapely

---
 .../variability_mode/lib/plot_map.py          | 144 ++++++++++--------
 1 file changed, 81 insertions(+), 63 deletions(-)

diff --git a/pcmdi_metrics/variability_mode/lib/plot_map.py b/pcmdi_metrics/variability_mode/lib/plot_map.py
index b1ef45db8..344b22d1e 100644
--- a/pcmdi_metrics/variability_mode/lib/plot_map.py
+++ b/pcmdi_metrics/variability_mode/lib/plot_map.py
@@ -6,6 +6,7 @@
 import matplotlib.pyplot as plt
 import matplotlib.ticker as mticker
 import numpy as np
+import xarray as xr
 from cartopy.feature import LAND as cartopy_land
 from cartopy.feature import OCEAN as cartopy_ocean
 from cartopy.mpl.gridliner import LATITUDE_FORMATTER, LONGITUDE_FORMATTER
@@ -96,41 +97,51 @@ def plot_map(
         maskout = None
 
     if mode in ["AMO", "AMO_teleconnection"]:
-        center_lon_global = 0
+        central_longitude = 0
     else:
-        center_lon_global = 180
+        central_longitude = 180
+           
+    # Convert cdms variable to xarray
+    lons = eof_Nth.getLongitude()
+    lats = eof_Nth.getLatitude()
+    data = np.array(eof_Nth)
+    lon = np.array(lons)
+    lat = np.array(lats)
+    lon, lat = np.meshgrid(lon, lat)
+    data_array = xr.DataArray(np.array(data), coords={'lon': lon[0, :], 'lat': lat[:, 0]}, dims=('lat', 'lon'))
+    data_array = data_array.where(data_array != 1e20, np.nan)
 
     plot_map_cartopy(
-        eof_Nth,
+        data_array,
         output_file_name,
         title=plot_title,
         proj=projection,
         gridline=gridline,
         levels=levels,
         maskout=maskout,
-        center_lon_global=center_lon_global,
+        central_longitude=central_longitude,
         debug=debug,
     )
 
 
 def plot_map_cartopy(
-    data,
-    filename,
+    data_array,
+    filename=None,
     title=None,
     gridline=True,
     levels=None,
     proj="PlateCarree",
     data_area="global",
     cmap="RdBu_r",
-    center_lon_global=180,
+    central_longitude=180,
     maskout=None,
     debug=False,
 ):
     """
     Parameters
     ----------
-    data : trainsisent variable
-        2D cdms2 TransientVariable with lat/lon coordinates attached.
+    data : data_array
+        2D xarray DataArray with lat/lon coordinates attached.
     filename : str
         Output file name (it is okay to omit '.png')
     title : str, optional
@@ -153,15 +164,17 @@ def plot_map_cartopy(
 
     debug_print("plot_map_cartopy starts", debug)
 
-    lons = data.getLongitude()
-    lats = data.getLatitude()
-
-    min_lon = min(lons)
-    max_lon = max(lons)
-    min_lat = min(lats)
-    max_lat = max(lats)
+    lon = data_array.lon
+    lat = data_array.lat
+    
+    # Determine the extent based on the longitude range where data exists
+    lon_min = lon.min().item()
+    lon_max = lon.max().item()
+    lat_min = lat.min().item()
+    lat_max = lat.max().item()
+    
     if debug:
-        print(min_lon, max_lon, min_lat, max_lat)
+        print(lon_min, lon_max, lat_min, lat_max)
 
     debug_print("Central longitude setup starts", debug)
     debug_print("proj: " + proj, debug)
@@ -169,23 +182,23 @@ def plot_map_cartopy(
     # https://github.com/SciTools/cartopy-tutorial/blob/master/tutorial/projections_crs_and_terms.ipynb
 
     if proj == "PlateCarree":
-        projection = ccrs.PlateCarree(central_longitude=center_lon_global)
+        projection = ccrs.PlateCarree(central_longitude=central_longitude)
     elif proj == "Robinson":
-        projection = ccrs.Robinson(central_longitude=center_lon_global)
+        projection = ccrs.Robinson(central_longitude=central_longitude)
     elif proj == "Stereo_north":
         projection = ccrs.NorthPolarStereo()
     elif proj == "Stereo_south":
         projection = ccrs.SouthPolarStereo()
     elif proj == "Lambert":
-        max_lat = min(max_lat, 80)
+        lat_max = min(lat_max, 80)
         if debug:
-            print("revised maxlat:", max_lat)
-        central_longitude = (min_lon + max_lon) / 2.0
-        central_latitude = (min_lat + max_lat) / 2.0
+            print("revised maxlat:", lat_max)
+        central_longitude = (lon_min + lon_max) / 2.0
+        central_latitude = (lat_min + lat_max) / 2.0
         projection = ccrs.AlbersEqualArea(
-            central_longitude=central_longitude,
-            central_latitude=central_latitude,
-            standard_parallels=(20, max_lat),
+            central_longitude=central_longitude, 
+            central_latitude=central_latitude, 
+            standard_parallels=(20, lat_max),
         )
     else:
         print("Error: projection not defined!")
@@ -195,22 +208,10 @@ def plot_map_cartopy(
         print("projection:", projection)
 
     # Generate plot
-    debug_print("Generate plot starts", debug)
-    fig = plt.figure(figsize=(8, 6))
-    debug_print("fig done", debug)
-    # ax = plt.axes(projection=projection)
-    ax = plt.axes(projection=ccrs.NorthPolarStereo())
-    debug_print("ax done", debug)
-    im = ax.contourf(
-        lons,
-        lats,
-        np.array(data),
-        transform=ccrs.PlateCarree(),
-        cmap=cmap,
-        levels=levels,
-        extend="both",
-    )
-    debug_print("contourf done", debug)
+    fig, ax = plt.subplots(subplot_kw={'projection': projection}, figsize=(8, 6))
+    debug_print("fig, ax done", debug)
+
+    # Add coastlines
     ax.coastlines()
     debug_print("Generate plot completed", debug)
 
@@ -263,12 +264,15 @@ def plot_map_cartopy(
         # the bottom left and go round anticlockwise, creating a boundary point
         # every 1 degree so that the result is smooth:
         # https://stackoverflow.com/questions/43463643/cartopy-albersequalarea-limit-region-using-lon-and-lat
+        
         vertices = [
-            (lon - 180, min_lat) for lon in range(int(min_lon), int(max_lon + 1), 1)
-        ] + [(lon - 180, max_lat) for lon in range(int(max_lon), int(min_lon - 1), -1)]
+            (lon - 180, lat_min) for lon in range(int(lon_min), int(lon_max + 1), 1)
+        ] + [(lon - 180, lat_max) for lon in range(int(lon_max), int(lon_min - 1), -1)]
         boundary = mpath.Path(vertices)
-        ax.set_boundary(boundary, transform=ccrs.PlateCarree(central_longitude=180))
-        ax.set_extent([min_lon, max_lon, min_lat, max_lat], crs=ccrs.PlateCarree())
+        ax.set_boundary(boundary, transform=ccrs.PlateCarree(central_longitude=central_longitude))
+        
+        ax.set_extent([lon_min, lon_max, lat_min, lat_max], crs=ccrs.PlateCarree())
+        
         if gridline:
             gl = ax.gridlines(
                 draw_labels=True,
@@ -282,26 +286,26 @@ def plot_map_cartopy(
             gl.xlocator = mticker.FixedLocator([120, 160, 200 - 360, 240 - 360])
             gl.top_labels = False  # suppress top labels
             # suppress right labels
-            # gl.right_labels = False
+            gl.right_labels = False
             for ea in gl.ylabel_artists:
                 right_label = ea.get_position()[0] > 0
                 if right_label:
                     ea.set_visible(False)
+        
     debug_print("projection completed", debug)
 
-    # Add title
-    plt.title(title, pad=15, fontsize=15)
-
-    # Add colorbar
-    posn = ax.get_position()
-    cbar_ax = fig.add_axes([0, 0, 0.1, 0.1])
-    cbar_ax.set_position([posn.x0 + posn.width + 0.01, posn.y0, 0.01, posn.height])
-    cbar = plt.colorbar(im, cax=cbar_ax)
-    cbar.ax.tick_params(labelsize=10)
-
-    if proj == "PlateCarree":
-        ax.set_aspect("auto", adjustable=None)
-
+    # Plot contours from the data
+    im = ax.contourf(
+        lon,
+        lat,
+        data_array,
+        levels=levels,
+        cmap=cmap,
+        extend="both",
+        transform=ccrs.PlateCarree(),
+    )
+    debug_print("contourf done", debug)
+    
     # Maskout
     if maskout is not None:
         if maskout == "land":
@@ -312,8 +316,22 @@ def plot_map_cartopy(
             ax.add_feature(
                 cartopy_ocean, zorder=100, edgecolor="k", facecolor="lightgrey"
             )
+    if proj == "PlateCarree":
+        ax.set_aspect("auto", adjustable=None)
+
+    # Add title
+    ax.set_title(title, pad=15, fontsize=15)
+
+    # Add colorbar
+    posn = ax.get_position()
+    cbar_ax = fig.add_axes([0, 0, 0.1, 0.1])
+    cbar_ax.set_position([posn.x0 + posn.width + 0.01, posn.y0, 0.01, posn.height])
+    cbar = plt.colorbar(im, cax=cbar_ax)
+    cbar.ax.tick_params(labelsize=10)
 
     # Done, save figure
-    debug_print("plot done, save figure as " + filename, debug)
-    fig.savefig(filename)
-    plt.close("all")
+    if filename is not None:
+        debug_print("plot done, save figure as " + filename, debug)
+        fig.savefig(filename)
+        
+    plt.close("all")
\ No newline at end of file

From cef14df09a04a33fbce76baaa20004087f55b07e Mon Sep 17 00:00:00 2001
From: Jiwoo Lee <lee1043@llnl.gov>
Date: Wed, 29 Nov 2023 16:57:21 -0800
Subject: [PATCH 126/133] pre-commit test: style fix

---
 .../variability_mode/lib/plot_map.py          | 34 +++++++++++--------
 1 file changed, 19 insertions(+), 15 deletions(-)

diff --git a/pcmdi_metrics/variability_mode/lib/plot_map.py b/pcmdi_metrics/variability_mode/lib/plot_map.py
index 344b22d1e..e6543542b 100644
--- a/pcmdi_metrics/variability_mode/lib/plot_map.py
+++ b/pcmdi_metrics/variability_mode/lib/plot_map.py
@@ -100,7 +100,7 @@ def plot_map(
         central_longitude = 0
     else:
         central_longitude = 180
-           
+
     # Convert cdms variable to xarray
     lons = eof_Nth.getLongitude()
     lats = eof_Nth.getLatitude()
@@ -108,7 +108,9 @@ def plot_map(
     lon = np.array(lons)
     lat = np.array(lats)
     lon, lat = np.meshgrid(lon, lat)
-    data_array = xr.DataArray(np.array(data), coords={'lon': lon[0, :], 'lat': lat[:, 0]}, dims=('lat', 'lon'))
+    data_array = xr.DataArray(
+        np.array(data), coords={"lon": lon[0, :], "lat": lat[:, 0]}, dims=("lat", "lon")
+    )
     data_array = data_array.where(data_array != 1e20, np.nan)
 
     plot_map_cartopy(
@@ -166,13 +168,13 @@ def plot_map_cartopy(
 
     lon = data_array.lon
     lat = data_array.lat
-    
+
     # Determine the extent based on the longitude range where data exists
     lon_min = lon.min().item()
     lon_max = lon.max().item()
     lat_min = lat.min().item()
     lat_max = lat.max().item()
-    
+
     if debug:
         print(lon_min, lon_max, lat_min, lat_max)
 
@@ -196,8 +198,8 @@ def plot_map_cartopy(
         central_longitude = (lon_min + lon_max) / 2.0
         central_latitude = (lat_min + lat_max) / 2.0
         projection = ccrs.AlbersEqualArea(
-            central_longitude=central_longitude, 
-            central_latitude=central_latitude, 
+            central_longitude=central_longitude,
+            central_latitude=central_latitude,
             standard_parallels=(20, lat_max),
         )
     else:
@@ -208,7 +210,7 @@ def plot_map_cartopy(
         print("projection:", projection)
 
     # Generate plot
-    fig, ax = plt.subplots(subplot_kw={'projection': projection}, figsize=(8, 6))
+    fig, ax = plt.subplots(subplot_kw={"projection": projection}, figsize=(8, 6))
     debug_print("fig, ax done", debug)
 
     # Add coastlines
@@ -264,15 +266,17 @@ def plot_map_cartopy(
         # the bottom left and go round anticlockwise, creating a boundary point
         # every 1 degree so that the result is smooth:
         # https://stackoverflow.com/questions/43463643/cartopy-albersequalarea-limit-region-using-lon-and-lat
-        
+
         vertices = [
             (lon - 180, lat_min) for lon in range(int(lon_min), int(lon_max + 1), 1)
         ] + [(lon - 180, lat_max) for lon in range(int(lon_max), int(lon_min - 1), -1)]
         boundary = mpath.Path(vertices)
-        ax.set_boundary(boundary, transform=ccrs.PlateCarree(central_longitude=central_longitude))
-        
+        ax.set_boundary(
+            boundary, transform=ccrs.PlateCarree(central_longitude=central_longitude)
+        )
+
         ax.set_extent([lon_min, lon_max, lat_min, lat_max], crs=ccrs.PlateCarree())
-        
+
         if gridline:
             gl = ax.gridlines(
                 draw_labels=True,
@@ -291,7 +295,7 @@ def plot_map_cartopy(
                 right_label = ea.get_position()[0] > 0
                 if right_label:
                     ea.set_visible(False)
-        
+
     debug_print("projection completed", debug)
 
     # Plot contours from the data
@@ -305,7 +309,7 @@ def plot_map_cartopy(
         transform=ccrs.PlateCarree(),
     )
     debug_print("contourf done", debug)
-    
+
     # Maskout
     if maskout is not None:
         if maskout == "land":
@@ -333,5 +337,5 @@ def plot_map_cartopy(
     if filename is not None:
         debug_print("plot done, save figure as " + filename, debug)
         fig.savefig(filename)
-        
-    plt.close("all")
\ No newline at end of file
+
+    plt.close("all")

From b36a5f0455ff5ebd4bb015e7a8dca3aa411b4354 Mon Sep 17 00:00:00 2001
From: Jiwoo Lee <lee1043@llnl.gov>
Date: Wed, 29 Nov 2023 16:59:12 -0800
Subject: [PATCH 127/133] clean up

---
 pcmdi_metrics/variability_mode/lib/plot_map.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/pcmdi_metrics/variability_mode/lib/plot_map.py b/pcmdi_metrics/variability_mode/lib/plot_map.py
index e6543542b..e1fdbf799 100644
--- a/pcmdi_metrics/variability_mode/lib/plot_map.py
+++ b/pcmdi_metrics/variability_mode/lib/plot_map.py
@@ -43,7 +43,6 @@ def plot_map(
     """
     # Map Projection
     if "teleconnection" in mode:
-        # projection = "PlateCarree"
         projection = "Robinson"
     elif mode in ["NAO", "PNA", "NPO", "PDO", "NPGO", "AMO"]:
         projection = "Lambert"
@@ -180,6 +179,7 @@ def plot_map_cartopy(
 
     debug_print("Central longitude setup starts", debug)
     debug_print("proj: " + proj, debug)
+    
     # map types example:
     # https://github.com/SciTools/cartopy-tutorial/blob/master/tutorial/projections_crs_and_terms.ipynb
 

From 9bdba9dc78bf74636f9b93c6eef894b0f3917c13 Mon Sep 17 00:00:00 2001
From: Jiwoo Lee <lee1043@llnl.gov>
Date: Wed, 29 Nov 2023 17:31:23 -0800
Subject: [PATCH 128/133] clean up and bug fix

---
 .../variability_mode/lib/plot_map.py          | 21 +++++++++++--------
 1 file changed, 12 insertions(+), 9 deletions(-)

diff --git a/pcmdi_metrics/variability_mode/lib/plot_map.py b/pcmdi_metrics/variability_mode/lib/plot_map.py
index e1fdbf799..7a3df79b1 100644
--- a/pcmdi_metrics/variability_mode/lib/plot_map.py
+++ b/pcmdi_metrics/variability_mode/lib/plot_map.py
@@ -179,7 +179,7 @@ def plot_map_cartopy(
 
     debug_print("Central longitude setup starts", debug)
     debug_print("proj: " + proj, debug)
-    
+
     # map types example:
     # https://github.com/SciTools/cartopy-tutorial/blob/master/tutorial/projections_crs_and_terms.ipynb
 
@@ -267,13 +267,16 @@ def plot_map_cartopy(
         # every 1 degree so that the result is smooth:
         # https://stackoverflow.com/questions/43463643/cartopy-albersequalarea-limit-region-using-lon-and-lat
 
+        # vertices = [
+        #    (lon - 180, lat_min) for lon in range(int(lon_min), int(lon_max + 1), 1)
+        # ] + [(lon - 180, lat_max) for lon in range(int(lon_max), int(lon_min - 1), -1)]
         vertices = [
-            (lon - 180, lat_min) for lon in range(int(lon_min), int(lon_max + 1), 1)
-        ] + [(lon - 180, lat_max) for lon in range(int(lon_max), int(lon_min - 1), -1)]
+            (lon - 180, lat_min) for lon in range(int(lon_min), int(lon_max), 1)
+        ] + [(lon - 180, lat_max) for lon in range(int(lon_max), int(lon_min), -1)]
         boundary = mpath.Path(vertices)
         ax.set_boundary(
-            boundary, transform=ccrs.PlateCarree(central_longitude=central_longitude)
-        )
+            boundary, transform=ccrs.PlateCarree(central_longitude=180)
+        )  # Here, 180 should be hardcoded, otherwise AMO map will be at out of figure box
 
         ax.set_extent([lon_min, lon_max, lat_min, lat_max], crs=ccrs.PlateCarree())
 
@@ -299,7 +302,7 @@ def plot_map_cartopy(
     debug_print("projection completed", debug)
 
     # Plot contours from the data
-    im = ax.contourf(
+    contourf_plot = ax.contourf(
         lon,
         lat,
         data_array,
@@ -326,11 +329,11 @@ def plot_map_cartopy(
     # Add title
     ax.set_title(title, pad=15, fontsize=15)
 
-    # Add colorbar
+    # Add a colorbar
     posn = ax.get_position()
     cbar_ax = fig.add_axes([0, 0, 0.1, 0.1])
-    cbar_ax.set_position([posn.x0 + posn.width + 0.01, posn.y0, 0.01, posn.height])
-    cbar = plt.colorbar(im, cax=cbar_ax)
+    cbar_ax.set_position([posn.x0 + posn.width + 0.03, posn.y0, 0.01, posn.height])
+    cbar = plt.colorbar(contourf_plot, cax=cbar_ax)
     cbar.ax.tick_params(labelsize=10)
 
     # Done, save figure

From 8f7a10d1371a02ea9417ee837efe1c0e6e48e5fc Mon Sep 17 00:00:00 2001
From: Jiwoo Lee <lee1043@llnl.gov>
Date: Wed, 29 Nov 2023 17:34:12 -0800
Subject: [PATCH 129/133] clean up

---
 pcmdi_metrics/variability_mode/lib/plot_map.py | 7 ++-----
 1 file changed, 2 insertions(+), 5 deletions(-)

diff --git a/pcmdi_metrics/variability_mode/lib/plot_map.py b/pcmdi_metrics/variability_mode/lib/plot_map.py
index 7a3df79b1..3a70e6f05 100644
--- a/pcmdi_metrics/variability_mode/lib/plot_map.py
+++ b/pcmdi_metrics/variability_mode/lib/plot_map.py
@@ -267,12 +267,9 @@ def plot_map_cartopy(
         # every 1 degree so that the result is smooth:
         # https://stackoverflow.com/questions/43463643/cartopy-albersequalarea-limit-region-using-lon-and-lat
 
-        # vertices = [
-        #    (lon - 180, lat_min) for lon in range(int(lon_min), int(lon_max + 1), 1)
-        # ] + [(lon - 180, lat_max) for lon in range(int(lon_max), int(lon_min - 1), -1)]
         vertices = [
-            (lon - 180, lat_min) for lon in range(int(lon_min), int(lon_max), 1)
-        ] + [(lon - 180, lat_max) for lon in range(int(lon_max), int(lon_min), -1)]
+            (lon - 180, lat_min) for lon in range(int(lon_min), int(lon_max + 1), 1)
+        ] + [(lon - 180, lat_max) for lon in range(int(lon_max), int(lon_min - 1), -1)]
         boundary = mpath.Path(vertices)
         ax.set_boundary(
             boundary, transform=ccrs.PlateCarree(central_longitude=180)

From 77acd3d650e04a42556cb841f150d3bcccecd3cd Mon Sep 17 00:00:00 2001
From: Jiwoo Lee <lee1043@llnl.gov>
Date: Wed, 29 Nov 2023 17:43:43 -0800
Subject: [PATCH 130/133] clean up

---
 .../Demo/Demo_4_modes_of_variability.ipynb    | 327 +++++++++---------
 1 file changed, 166 insertions(+), 161 deletions(-)

diff --git a/doc/jupyter/Demo/Demo_4_modes_of_variability.ipynb b/doc/jupyter/Demo/Demo_4_modes_of_variability.ipynb
index 62396cc5d..1b7d7d2a9 100644
--- a/doc/jupyter/Demo/Demo_4_modes_of_variability.ipynb
+++ b/doc/jupyter/Demo/Demo_4_modes_of_variability.ipynb
@@ -228,9 +228,11 @@
       "                                   [--EofScaling EOFSCALING]\n",
       "                                   [--landmask LANDMASK] [--ConvEOF CONVEOF]\n",
       "                                   [--CBF CBF] [--nc_out NC_OUT] [--plot PLOT]\n",
-      "                                   [--parallel PARALLEL] [--no_nc_out_obs]\n",
-      "                                   [--no_plot_obs] [--update_json UPDATE_JSON]\n",
-      "                                   [--cmec] [--no_cmec]\n",
+      "                                   [--nc_out_obs NC_OUT_OBS]\n",
+      "                                   [--plot_obs PLOT_OBS] [--parallel PARALLEL]\n",
+      "                                   [--no_nc_out_obs] [--no_plot_obs]\n",
+      "                                   [--update_json UPDATE_JSON] [--cmec]\n",
+      "                                   [--no_cmec]\n",
       "\n",
       "Runs PCMDI Modes of Variability Computations\n",
       "\n",
@@ -301,8 +303,11 @@
       "  --landmask LANDMASK   Option for maskout land region: True / False (default)\n",
       "  --ConvEOF CONVEOF     Option for Calculate Conventioanl EOF for model: True / False (default)\n",
       "  --CBF CBF             Option for Calculate Common Basis Function (CBF) for model: True (default) / False\n",
-      "  --nc_out NC_OUT       Option for generate netCDF file output: True (default) / False\n",
-      "  --plot PLOT           Option for generate individual plots: True (default) / False\n",
+      "  --nc_out NC_OUT       Option for generate netCDF file output for models: True (default) / False\n",
+      "  --plot PLOT           Option for generate individual plots for models: True (default) / False\n",
+      "  --nc_out_obs NC_OUT_OBS\n",
+      "                        Option for generate netCDF file output for obs: True (default) / False\n",
+      "  --plot_obs PLOT_OBS   Option for generate individual plots for obs: True (default) / False\n",
       "  --parallel PARALLEL   Option for running code in parallel mode: True / False (default)\n",
       "  --no_nc_out_obs       Turn off netCDF generating for obs\n",
       "  --no_plot_obs         Turn off plot generating for obs\n",
@@ -344,14 +349,6 @@
    "id": "c12f429b",
    "metadata": {},
    "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "INFO::2023-06-29 16:24::pcmdi_metrics:: Results saved to a json file: /Users/lee1043/Documents/Research/git/pcmdi_metrics_20230620_pcmdi/pcmdi_metrics/doc/jupyter/Demo/demo_output/mov_1/var_mode_NAM_EOF1_stat_cmip5_historical_mo_atm_ACCESS1-0_r1i1p1_1900-2005.json\n",
-      "2023-06-29 16:24:37,726 [INFO]: base.py(write:246) >> Results saved to a json file: /Users/lee1043/Documents/Research/git/pcmdi_metrics_20230620_pcmdi/pcmdi_metrics/doc/jupyter/Demo/demo_output/mov_1/var_mode_NAM_EOF1_stat_cmip5_historical_mo_atm_ACCESS1-0_r1i1p1_1900-2005.json\n"
-     ]
-    },
     {
      "name": "stdout",
      "output_type": "stream",
@@ -378,6 +375,14 @@
       " -----  ACCESS1-0  ---------------------\n",
       " ---  r1i1p1  ---\n"
      ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "INFO::2023-11-29 17:34::pcmdi_metrics:: Results saved to a json file: /Users/lee1043/Documents/Research/git/pcmdi_metrics_20230620_pcmdi/pcmdi_metrics/doc/jupyter/Demo/demo_output/mov_1/var_mode_NAM_EOF1_stat_cmip5_historical_mo_atm_ACCESS1-0_r1i1p1_1900-2005.json\n",
+      "2023-11-29 17:34:42,500 [INFO]: base.py(write:250) >> Results saved to a json file: /Users/lee1043/Documents/Research/git/pcmdi_metrics_20230620_pcmdi/pcmdi_metrics/doc/jupyter/Demo/demo_output/mov_1/var_mode_NAM_EOF1_stat_cmip5_historical_mo_atm_ACCESS1-0_r1i1p1_1900-2005.json\n"
+     ]
     }
    ],
    "source": [
@@ -413,14 +418,6 @@
    "id": "f46eac76",
    "metadata": {},
    "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "INFO::2023-06-29 16:25::pcmdi_metrics:: Results saved to a json file: /Users/lee1043/Documents/Research/git/pcmdi_metrics_20230620_pcmdi/pcmdi_metrics/doc/jupyter/Demo/demo_output/mov_2/var_mode_NAM_EOF1_stat_cmip5_historical_mo_atm_ACCESS1-0_r1i1p1_1900-2005.json\n",
-      "2023-06-29 16:25:08,085 [INFO]: base.py(write:246) >> Results saved to a json file: /Users/lee1043/Documents/Research/git/pcmdi_metrics_20230620_pcmdi/pcmdi_metrics/doc/jupyter/Demo/demo_output/mov_2/var_mode_NAM_EOF1_stat_cmip5_historical_mo_atm_ACCESS1-0_r1i1p1_1900-2005.json\n"
-     ]
-    },
     {
      "name": "stdout",
      "output_type": "stream",
@@ -447,6 +444,14 @@
       " -----  ACCESS1-0  ---------------------\n",
       " ---  r1i1p1  ---\n"
      ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "INFO::2023-11-29 17:35::pcmdi_metrics:: Results saved to a json file: /Users/lee1043/Documents/Research/git/pcmdi_metrics_20230620_pcmdi/pcmdi_metrics/doc/jupyter/Demo/demo_output/mov_2/var_mode_NAM_EOF1_stat_cmip5_historical_mo_atm_ACCESS1-0_r1i1p1_1900-2005.json\n",
+      "2023-11-29 17:35:19,800 [INFO]: base.py(write:250) >> Results saved to a json file: /Users/lee1043/Documents/Research/git/pcmdi_metrics_20230620_pcmdi/pcmdi_metrics/doc/jupyter/Demo/demo_output/mov_2/var_mode_NAM_EOF1_stat_cmip5_historical_mo_atm_ACCESS1-0_r1i1p1_1900-2005.json\n"
+     ]
     }
    ],
    "source": [
@@ -470,14 +475,6 @@
    "id": "3a180aea",
    "metadata": {},
    "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "INFO::2023-06-29 16:25::pcmdi_metrics:: Results saved to a json file: /Users/lee1043/Documents/Research/git/pcmdi_metrics_20230620_pcmdi/pcmdi_metrics/doc/jupyter/Demo/demo_output/mov_3/var_mode_NAM_EOF1_stat_cmip5_historical_mo_atm_ACCESS1-0_r1i1p1_1950-2005.json\n",
-      "2023-06-29 16:25:40,721 [INFO]: base.py(write:246) >> Results saved to a json file: /Users/lee1043/Documents/Research/git/pcmdi_metrics_20230620_pcmdi/pcmdi_metrics/doc/jupyter/Demo/demo_output/mov_3/var_mode_NAM_EOF1_stat_cmip5_historical_mo_atm_ACCESS1-0_r1i1p1_1950-2005.json\n"
-     ]
-    },
     {
      "name": "stdout",
      "output_type": "stream",
@@ -504,6 +501,14 @@
       " -----  ACCESS1-0  ---------------------\n",
       " ---  r1i1p1  ---\n"
      ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "INFO::2023-11-29 17:35::pcmdi_metrics:: Results saved to a json file: /Users/lee1043/Documents/Research/git/pcmdi_metrics_20230620_pcmdi/pcmdi_metrics/doc/jupyter/Demo/demo_output/mov_3/var_mode_NAM_EOF1_stat_cmip5_historical_mo_atm_ACCESS1-0_r1i1p1_1950-2005.json\n",
+      "2023-11-29 17:35:59,804 [INFO]: base.py(write:250) >> Results saved to a json file: /Users/lee1043/Documents/Research/git/pcmdi_metrics_20230620_pcmdi/pcmdi_metrics/doc/jupyter/Demo/demo_output/mov_3/var_mode_NAM_EOF1_stat_cmip5_historical_mo_atm_ACCESS1-0_r1i1p1_1950-2005.json\n"
+     ]
     }
    ],
    "source": [
@@ -585,14 +590,14 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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"
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"
      },
      "metadata": {},
      "output_type": "display_data"
     },
     {
      "data": {
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"
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"
      },
      "metadata": {},
      "output_type": "display_data"
@@ -671,7 +676,7 @@
       "\t\ttime:realtopology = \"linear\" ;\n",
       "\tdouble bounds_time(time, bound) ;\n",
       "\tdouble pc(time) ;\n",
-      "\t\tpc:long_name = \"variable_18972_pseudo_pcs\" ;\n",
+      "\t\tpc:long_name = \"variable_18940_pseudo_pcs\" ;\n",
       "\t\tpc:missing_value = 1.e+20 ;\n",
       "\t\tpc:_FillValue = 1.e+20 ;\n",
       "\tdouble lat(lat) ;\n",
@@ -852,7 +857,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 17,
    "id": "817fac35-e68b-4f6e-9af8-85e555f97ea7",
    "metadata": {},
    "outputs": [
@@ -918,7 +923,7 @@
               "tcor_cbf_vs_eof_pc": 0.9613378658270397
              },
              "eof2": {
-              "bias": 7.486977013706658e-05,
+              "bias": 0.00007486977013706658,
               "bias_glo": 0.11471972947548313,
               "cor": 0.0026427060721909926,
               "cor_glo": 0.023931446001973902,
@@ -968,14 +973,14 @@
        ],
        "json_version": 3,
        "provenance": {
-        "commandLine": "/Users/lee1043/mambaforge/envs/pcmdi_metrics_20230628/bin/variability_modes_driver.py -p basic_mov_param.py --case_id mov_1",
+        "commandLine": "/Users/lee1043/mambaforge/envs/pmp_devel_20231129/bin/variability_modes_driver.py -p basic_mov_param.py --case_id mov_1",
         "conda": {
          "Platform": "osx-64",
          "PythonVersion": "3.10.6.final.0",
-         "Version": "23.5.0",
+         "Version": "23.10.0",
          "buildVersion": "not installed"
         },
-        "date": "2023-06-29 16:24:27",
+        "date": "2023-11-29 17:34:24",
         "openGL": {
          "GLX": {
           "client": {
@@ -991,13 +996,13 @@
          "renderer": "Intel(R) Iris(TM) Plus Graphics 655",
          "shading language version": "1.20",
          "vendor": "Intel Inc.",
-         "version": "2.1 INTEL-20.6.1"
+         "version": "2.1 INTEL-20.6.4"
         },
         "osAccess": false,
         "packages": {
          "PMP": "v3.0.2-11-g06b151f",
          "PMPObs": "See 'References' key below, for detailed obs provenance information.",
-         "blas": "0.3.23",
+         "blas": "0.3.25",
          "cdat_info": "8.2.1",
          "cdms": "3.1.5",
          "cdp": "1.7.0",
@@ -1008,21 +1013,21 @@
          "esmpy": "8.4.2",
          "genutil": "8.2.1",
          "lapack": "3.9.0",
-         "matplotlib": null,
+         "matplotlib": "3.7.1",
          "mesalib": null,
-         "numpy": "1.22.4",
-         "python": "3.10.12",
-         "scipy": "1.11.0",
+         "numpy": "1.23.5",
+         "python": "3.10.10",
+         "scipy": "1.11.4",
          "uvcdat": null,
          "vcs": null,
          "vtk": null,
-         "xarray": "2023.6.0",
+         "xarray": "2023.11.0",
          "xcdat": "0.5.0"
         },
         "platform": {
          "Name": "ml-9713657",
          "OS": "Darwin",
-         "Version": "22.5.0"
+         "Version": "22.6.0"
         },
         "userId": "lee1043"
        }
@@ -1031,7 +1036,7 @@
        "<IPython.core.display.JSON object>"
       ]
      },
-     "execution_count": 18,
+     "execution_count": 17,
      "metadata": {
       "application/json": {
        "expanded": false,
@@ -1058,26 +1063,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 18,
    "id": "689bd896",
    "metadata": {},
    "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/Users/lee1043/mambaforge/envs/pcmdi_metrics_20230628/lib/python3.10/site-packages/numpy/ma/core.py:1013: RuntimeWarning: overflow encountered in multiply\n",
-      "  result = self.f(da, db, *args, **kwargs)\n",
-      "/Users/lee1043/mambaforge/envs/pcmdi_metrics_20230628/lib/python3.10/site-packages/numpy/ma/core.py:1013: RuntimeWarning: overflow encountered in multiply\n",
-      "  result = self.f(da, db, *args, **kwargs)\n",
-      "/Users/lee1043/mambaforge/envs/pcmdi_metrics_20230628/lib/python3.10/site-packages/numpy/ma/core.py:1013: RuntimeWarning: overflow encountered in multiply\n",
-      "  result = self.f(da, db, *args, **kwargs)\n",
-      "/Users/lee1043/mambaforge/envs/pcmdi_metrics_20230628/lib/python3.10/site-packages/numpy/ma/core.py:1013: RuntimeWarning: overflow encountered in multiply\n",
-      "  result = self.f(da, db, *args, **kwargs)\n",
-      "INFO::2023-06-29 16:32::pcmdi_metrics:: Results saved to a json file: /Users/lee1043/Documents/Research/git/pcmdi_metrics_20230620_pcmdi/pcmdi_metrics/doc/jupyter/Demo/demo_output/PDO/var_mode_PDO_EOF1_stat_cmip5_historical_mo_atm_ACCESS1-0_r1i1p1_1900-2005.json\n",
-      "2023-06-29 16:32:34,824 [INFO]: base.py(write:246) >> Results saved to a json file: /Users/lee1043/Documents/Research/git/pcmdi_metrics_20230620_pcmdi/pcmdi_metrics/doc/jupyter/Demo/demo_output/PDO/var_mode_PDO_EOF1_stat_cmip5_historical_mo_atm_ACCESS1-0_r1i1p1_1900-2005.json\n"
-     ]
-    },
     {
      "name": "stdout",
      "output_type": "stream",
@@ -1105,6 +1094,22 @@
       " -----  ACCESS1-0  ---------------------\n",
       " ---  r1i1p1  ---\n"
      ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/lee1043/mambaforge/envs/pmp_devel_20231129/lib/python3.10/site-packages/numpy/ma/core.py:1013: RuntimeWarning: overflow encountered in multiply\n",
+      "  result = self.f(da, db, *args, **kwargs)\n",
+      "/Users/lee1043/mambaforge/envs/pmp_devel_20231129/lib/python3.10/site-packages/numpy/ma/core.py:1013: RuntimeWarning: overflow encountered in multiply\n",
+      "  result = self.f(da, db, *args, **kwargs)\n",
+      "/Users/lee1043/mambaforge/envs/pmp_devel_20231129/lib/python3.10/site-packages/numpy/ma/core.py:1013: RuntimeWarning: overflow encountered in multiply\n",
+      "  result = self.f(da, db, *args, **kwargs)\n",
+      "/Users/lee1043/mambaforge/envs/pmp_devel_20231129/lib/python3.10/site-packages/numpy/ma/core.py:1013: RuntimeWarning: overflow encountered in multiply\n",
+      "  result = self.f(da, db, *args, **kwargs)\n",
+      "INFO::2023-11-29 17:38::pcmdi_metrics:: Results saved to a json file: /Users/lee1043/Documents/Research/git/pcmdi_metrics_20230620_pcmdi/pcmdi_metrics/doc/jupyter/Demo/demo_output/PDO/var_mode_PDO_EOF1_stat_cmip5_historical_mo_atm_ACCESS1-0_r1i1p1_1900-2005.json\n",
+      "2023-11-29 17:38:02,707 [INFO]: base.py(write:250) >> Results saved to a json file: /Users/lee1043/Documents/Research/git/pcmdi_metrics_20230620_pcmdi/pcmdi_metrics/doc/jupyter/Demo/demo_output/PDO/var_mode_PDO_EOF1_stat_cmip5_historical_mo_atm_ACCESS1-0_r1i1p1_1900-2005.json\n"
+     ]
     }
    ],
    "source": [
@@ -1122,7 +1127,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 19,
    "id": "53f7cee4-ee86-4c8d-ab01-65e3c0b18346",
    "metadata": {},
    "outputs": [],
@@ -1134,27 +1139,27 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 20,
    "id": "69abb970-23e9-41a6-85d9-6344275934ab",
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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"
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"
      },
      "metadata": {},
      "output_type": "display_data"
     },
     {
      "data": {
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"
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"
      },
      "metadata": {},
      "output_type": "display_data"
     },
     {
      "data": {
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"
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"
      },
      "metadata": {},
      "output_type": "display_data"
@@ -1186,7 +1191,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 21,
    "id": "b49584fb-e7e3-4bcf-9b28-c1baa78f76e7",
    "metadata": {},
    "outputs": [],
@@ -1196,7 +1201,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 22,
    "id": "09d4ff3c-d5fe-470c-a437-4377d31fa77e",
    "metadata": {},
    "outputs": [],
@@ -1206,7 +1211,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 23,
    "id": "1cffdb86-c204-4154-8d87-e96b12d5c9a2",
    "metadata": {},
    "outputs": [],
@@ -1217,7 +1222,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 24,
    "id": "97f38aab-f300-436f-b670-70ce829661f9",
    "metadata": {},
    "outputs": [
@@ -1590,9 +1595,9 @@
        "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
        "Dimensions:      (time: 1272, bound: 2, lat: 145, lon: 192)\n",
        "Coordinates:\n",
-       "  * time         (time) object 1900-01-16 12:00:00 ... 2005-12-16 12:00:00\n",
        "  * lat          (lat) float64 -90.0 -88.75 -87.5 -86.25 ... 87.5 88.75 90.0\n",
        "  * lon          (lon) float64 0.0 1.875 3.75 5.625 ... 352.5 354.4 356.2 358.1\n",
+       "  * time         (time) object 1900-01-16 12:00:00 ... 2005-12-16 12:00:00\n",
        "Dimensions without coordinates: bound\n",
        "Data variables:\n",
        "    bounds_time  (time, bound) object ...\n",
@@ -1604,14 +1609,7 @@
        "    intercept    (lat, lon) float64 ...\n",
        "    frac         float64 ...\n",
        "Attributes:\n",
-       "    Conventions:  CF-1.0</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-88976391-ec6e-4610-964e-b035b20ac1a9' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-88976391-ec6e-4610-964e-b035b20ac1a9' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 1272</li><li><span>bound</span>: 2</li><li><span class='xr-has-index'>lat</span>: 145</li><li><span class='xr-has-index'>lon</span>: 192</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-165f8883-23cc-4c1b-b5cb-655373dfc47b' class='xr-section-summary-in' type='checkbox'  checked><label for='section-165f8883-23cc-4c1b-b5cb-655373dfc47b' class='xr-section-summary' >Coordinates: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>1900-01-16 12:00:00 ... 2005-12-...</div><input id='attrs-e8915736-c612-4e59-a44c-8091d5c0e90f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e8915736-c612-4e59-a44c-8091d5c0e90f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f6dbda87-54ed-461f-8c54-e1ba5ffd7e55' class='xr-var-data-in' type='checkbox'><label for='data-f6dbda87-54ed-461f-8c54-e1ba5ffd7e55' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>bounds :</span></dt><dd>bounds_time</dd><dt><span>axis :</span></dt><dd>T</dd><dt><span>realtopology :</span></dt><dd>linear</dd></dl></div><div class='xr-var-data'><pre>array([cftime.DatetimeProlepticGregorian(1900, 1, 16, 12, 0, 0, 0, has_year_zero=True),\n",
-       "       cftime.DatetimeProlepticGregorian(1900, 2, 15, 0, 0, 0, 0, has_year_zero=True),\n",
-       "       cftime.DatetimeProlepticGregorian(1900, 3, 16, 12, 0, 0, 0, has_year_zero=True),\n",
-       "       ...,\n",
-       "       cftime.DatetimeProlepticGregorian(2005, 10, 16, 12, 0, 0, 0, has_year_zero=True),\n",
-       "       cftime.DatetimeProlepticGregorian(2005, 11, 16, 0, 0, 0, 0, has_year_zero=True),\n",
-       "       cftime.DatetimeProlepticGregorian(2005, 12, 16, 12, 0, 0, 0, has_year_zero=True)],\n",
-       "      dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lat</span></div><div class='xr-var-dims'>(lat)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-90.0 -88.75 -87.5 ... 88.75 90.0</div><input id='attrs-8dabb35a-98b6-483a-80c6-c28f602964b8' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-8dabb35a-98b6-483a-80c6-c28f602964b8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-043afdbb-d159-4737-9388-5ad1e482ce80' class='xr-var-data-in' type='checkbox'><label for='data-043afdbb-d159-4737-9388-5ad1e482ce80' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>bounds :</span></dt><dd>lat_bnds</dd><dt><span>axis :</span></dt><dd>Y</dd><dt><span>units :</span></dt><dd>degrees_north</dd><dt><span>long_name :</span></dt><dd>latitude</dd><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>realtopology :</span></dt><dd>linear</dd></dl></div><div class='xr-var-data'><pre>array([-90.  , -88.75, -87.5 , -86.25, -85.  , -83.75, -82.5 , -81.25, -80.  ,\n",
+       "    Conventions:  CF-1.0</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-011f9537-6815-46da-8bbd-1b4688744ab8' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-011f9537-6815-46da-8bbd-1b4688744ab8' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 1272</li><li><span>bound</span>: 2</li><li><span class='xr-has-index'>lat</span>: 145</li><li><span class='xr-has-index'>lon</span>: 192</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-9afadf5a-d97b-4b3e-bd2e-92c21096024f' class='xr-section-summary-in' type='checkbox'  checked><label for='section-9afadf5a-d97b-4b3e-bd2e-92c21096024f' class='xr-section-summary' >Coordinates: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lat</span></div><div class='xr-var-dims'>(lat)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-90.0 -88.75 -87.5 ... 88.75 90.0</div><input id='attrs-38b74cc1-d38a-4df2-8458-87f34dff91de' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-38b74cc1-d38a-4df2-8458-87f34dff91de' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c1662b98-a053-4998-aed8-214d4ac59f90' class='xr-var-data-in' type='checkbox'><label for='data-c1662b98-a053-4998-aed8-214d4ac59f90' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>bounds :</span></dt><dd>lat_bnds</dd><dt><span>axis :</span></dt><dd>Y</dd><dt><span>units :</span></dt><dd>degrees_north</dd><dt><span>long_name :</span></dt><dd>latitude</dd><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>realtopology :</span></dt><dd>linear</dd></dl></div><div class='xr-var-data'><pre>array([-90.  , -88.75, -87.5 , -86.25, -85.  , -83.75, -82.5 , -81.25, -80.  ,\n",
        "       -78.75, -77.5 , -76.25, -75.  , -73.75, -72.5 , -71.25, -70.  , -68.75,\n",
        "       -67.5 , -66.25, -65.  , -63.75, -62.5 , -61.25, -60.  , -58.75, -57.5 ,\n",
        "       -56.25, -55.  , -53.75, -52.5 , -51.25, -50.  , -48.75, -47.5 , -46.25,\n",
@@ -1627,7 +1625,7 @@
        "        56.25,  57.5 ,  58.75,  60.  ,  61.25,  62.5 ,  63.75,  65.  ,  66.25,\n",
        "        67.5 ,  68.75,  70.  ,  71.25,  72.5 ,  73.75,  75.  ,  76.25,  77.5 ,\n",
        "        78.75,  80.  ,  81.25,  82.5 ,  83.75,  85.  ,  86.25,  87.5 ,  88.75,\n",
-       "        90.  ])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lon</span></div><div class='xr-var-dims'>(lon)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0 1.875 3.75 ... 356.2 358.1</div><input id='attrs-a99eb0c8-cc98-45bc-8d76-252a12ae61f1' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a99eb0c8-cc98-45bc-8d76-252a12ae61f1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2dda2bea-6086-4f87-a1a9-6a2a15383dc8' class='xr-var-data-in' type='checkbox'><label for='data-2dda2bea-6086-4f87-a1a9-6a2a15383dc8' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>bounds :</span></dt><dd>lon_bnds</dd><dt><span>axis :</span></dt><dd>X</dd><dt><span>modulo :</span></dt><dd>360.0</dd><dt><span>topology :</span></dt><dd>circular</dd><dt><span>units :</span></dt><dd>degrees_east</dd><dt><span>long_name :</span></dt><dd>longitude</dd><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>realtopology :</span></dt><dd>circular</dd></dl></div><div class='xr-var-data'><pre>array([  0.   ,   1.875,   3.75 ,   5.625,   7.5  ,   9.375,  11.25 ,  13.125,\n",
+       "        90.  ])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lon</span></div><div class='xr-var-dims'>(lon)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0 1.875 3.75 ... 356.2 358.1</div><input id='attrs-be7f7aac-f6ad-470d-b963-a5fdb339ffb1' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-be7f7aac-f6ad-470d-b963-a5fdb339ffb1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5684a2f1-4b17-423c-8b0c-e6bd832f1714' class='xr-var-data-in' type='checkbox'><label for='data-5684a2f1-4b17-423c-8b0c-e6bd832f1714' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>bounds :</span></dt><dd>lon_bnds</dd><dt><span>axis :</span></dt><dd>X</dd><dt><span>modulo :</span></dt><dd>360.0</dd><dt><span>topology :</span></dt><dd>circular</dd><dt><span>units :</span></dt><dd>degrees_east</dd><dt><span>long_name :</span></dt><dd>longitude</dd><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>realtopology :</span></dt><dd>circular</dd></dl></div><div class='xr-var-data'><pre>array([  0.   ,   1.875,   3.75 ,   5.625,   7.5  ,   9.375,  11.25 ,  13.125,\n",
        "        15.   ,  16.875,  18.75 ,  20.625,  22.5  ,  24.375,  26.25 ,  28.125,\n",
        "        30.   ,  31.875,  33.75 ,  35.625,  37.5  ,  39.375,  41.25 ,  43.125,\n",
        "        45.   ,  46.875,  48.75 ,  50.625,  52.5  ,  54.375,  56.25 ,  58.125,\n",
@@ -1650,7 +1648,24 @@
        "       300.   , 301.875, 303.75 , 305.625, 307.5  , 309.375, 311.25 , 313.125,\n",
        "       315.   , 316.875, 318.75 , 320.625, 322.5  , 324.375, 326.25 , 328.125,\n",
        "       330.   , 331.875, 333.75 , 335.625, 337.5  , 339.375, 341.25 , 343.125,\n",
-       "       345.   , 346.875, 348.75 , 350.625, 352.5  , 354.375, 356.25 , 358.125])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-83eef571-dfb1-41d8-9240-8fdad71b9832' class='xr-section-summary-in' type='checkbox'  checked><label for='section-83eef571-dfb1-41d8-9240-8fdad71b9832' class='xr-section-summary' >Data variables: <span>(8)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>bounds_time</span></div><div class='xr-var-dims'>(time, bound)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-68189ccc-6f49-40b5-918e-a229c1342db5' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-68189ccc-6f49-40b5-918e-a229c1342db5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-92fee6a9-291e-4b23-ac8e-063c009bff54' class='xr-var-data-in' type='checkbox'><label for='data-92fee6a9-291e-4b23-ac8e-063c009bff54' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>[2544 values with dtype=object]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>pc</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-4e5919e6-fc02-4573-85c5-75cd73362372' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4e5919e6-fc02-4573-85c5-75cd73362372' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e45b7c0c-17c0-435d-8440-d1978f2417cf' class='xr-var-data-in' type='checkbox'><label for='data-e45b7c0c-17c0-435d-8440-d1978f2417cf' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>variable_16778_pseudo_pcs</dd></dl></div><div class='xr-var-data'><pre>[1272 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lat_bnds</span></div><div class='xr-var-dims'>(lat, bound)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-c2ecf7b7-42ec-4ada-9258-b0576695c21d' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-c2ecf7b7-42ec-4ada-9258-b0576695c21d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b5de455f-41ed-409e-9a09-688adf237d93' class='xr-var-data-in' type='checkbox'><label for='data-b5de455f-41ed-409e-9a09-688adf237d93' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>[290 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lon_bnds</span></div><div class='xr-var-dims'>(lon, bound)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-b63c352a-a2c7-4b93-848c-6a455e52a0ce' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-b63c352a-a2c7-4b93-848c-6a455e52a0ce' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b4c620c0-2606-4a1a-a099-88f11374c9e6' class='xr-var-data-in' type='checkbox'><label for='data-b4c620c0-2606-4a1a-a099-88f11374c9e6' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>[384 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>eof</span></div><div class='xr-var-dims'>(lat, lon)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-4dc13d94-fbad-449d-af5a-5af1ead42025' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-4dc13d94-fbad-449d-af5a-5af1ead42025' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d60449f6-7d7d-49e2-a062-89bb9eefbad4' class='xr-var-data-in' type='checkbox'><label for='data-d60449f6-7d7d-49e2-a062-89bb9eefbad4' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>[27840 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>slope</span></div><div class='xr-var-dims'>(lat, lon)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-68b6c599-d539-49d7-99ae-5e1bcbbff286' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-68b6c599-d539-49d7-99ae-5e1bcbbff286' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4cd7e586-829f-4b1c-8001-798237e3c0bf' class='xr-var-data-in' type='checkbox'><label for='data-4cd7e586-829f-4b1c-8001-798237e3c0bf' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>[27840 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>intercept</span></div><div class='xr-var-dims'>(lat, lon)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-dc123141-486e-411c-a90e-011029eb9a50' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-dc123141-486e-411c-a90e-011029eb9a50' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a4f01468-8ca4-4e6d-aa39-bdbd6f95ea7d' class='xr-var-data-in' type='checkbox'><label for='data-a4f01468-8ca4-4e6d-aa39-bdbd6f95ea7d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>[27840 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>frac</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-8009a597-7326-4ed7-8301-e03a989b5505' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-8009a597-7326-4ed7-8301-e03a989b5505' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-cc9eab72-741c-4890-9c97-bf24ae8e1036' class='xr-var-data-in' type='checkbox'><label for='data-cc9eab72-741c-4890-9c97-bf24ae8e1036' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>[1 values with dtype=float64]</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-2a5ee0b4-cf4a-481f-8054-e7a05c811897' class='xr-section-summary-in' type='checkbox'  ><label for='section-2a5ee0b4-cf4a-481f-8054-e7a05c811897' class='xr-section-summary' >Indexes: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-a83ddf82-bb76-4e8d-ad93-7c53893ec8b1' class='xr-index-data-in' type='checkbox'/><label for='index-a83ddf82-bb76-4e8d-ad93-7c53893ec8b1' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(CFTimeIndex([1900-01-16 12:00:00, 1900-02-15 00:00:00, 1900-03-16 12:00:00,\n",
+       "       345.   , 346.875, 348.75 , 350.625, 352.5  , 354.375, 356.25 , 358.125])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>1900-01-16 12:00:00 ... 2005-12-...</div><input id='attrs-336d5d03-3183-41c7-ba32-2a7b4800f2ac' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-336d5d03-3183-41c7-ba32-2a7b4800f2ac' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3d24247f-29db-4c57-b603-4a2d8cfb6824' class='xr-var-data-in' type='checkbox'><label for='data-3d24247f-29db-4c57-b603-4a2d8cfb6824' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>bounds :</span></dt><dd>bounds_time</dd><dt><span>axis :</span></dt><dd>T</dd><dt><span>realtopology :</span></dt><dd>linear</dd></dl></div><div class='xr-var-data'><pre>array([cftime.DatetimeProlepticGregorian(1900, 1, 16, 12, 0, 0, 0, has_year_zero=True),\n",
+       "       cftime.DatetimeProlepticGregorian(1900, 2, 15, 0, 0, 0, 0, has_year_zero=True),\n",
+       "       cftime.DatetimeProlepticGregorian(1900, 3, 16, 12, 0, 0, 0, has_year_zero=True),\n",
+       "       ...,\n",
+       "       cftime.DatetimeProlepticGregorian(2005, 10, 16, 12, 0, 0, 0, has_year_zero=True),\n",
+       "       cftime.DatetimeProlepticGregorian(2005, 11, 16, 0, 0, 0, 0, has_year_zero=True),\n",
+       "       cftime.DatetimeProlepticGregorian(2005, 12, 16, 12, 0, 0, 0, has_year_zero=True)],\n",
+       "      dtype=object)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-3f938c4e-3168-43d2-a982-a39cefcda2db' class='xr-section-summary-in' type='checkbox'  checked><label for='section-3f938c4e-3168-43d2-a982-a39cefcda2db' class='xr-section-summary' >Data variables: <span>(8)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>bounds_time</span></div><div class='xr-var-dims'>(time, bound)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-5be8b2ce-7ad0-42d3-8b44-5bbac53478f3' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-5be8b2ce-7ad0-42d3-8b44-5bbac53478f3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1dcf8f30-2ed7-419b-ab2b-8b759de84c3b' class='xr-var-data-in' type='checkbox'><label for='data-1dcf8f30-2ed7-419b-ab2b-8b759de84c3b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>[2544 values with dtype=object]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>pc</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-01ca87f4-4eee-4695-a31e-483686cb97f2' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-01ca87f4-4eee-4695-a31e-483686cb97f2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c162d42b-9725-4166-9e12-78bcdfe35fa5' class='xr-var-data-in' type='checkbox'><label for='data-c162d42b-9725-4166-9e12-78bcdfe35fa5' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>variable_16738_pseudo_pcs</dd></dl></div><div class='xr-var-data'><pre>[1272 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lat_bnds</span></div><div class='xr-var-dims'>(lat, bound)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-dab0ea6b-8ce7-4639-b084-71772e09e6c4' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-dab0ea6b-8ce7-4639-b084-71772e09e6c4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ca21dfd3-5a96-4465-8037-38ad08da4af0' class='xr-var-data-in' type='checkbox'><label for='data-ca21dfd3-5a96-4465-8037-38ad08da4af0' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>[290 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lon_bnds</span></div><div class='xr-var-dims'>(lon, bound)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-308a24c3-25fd-400f-a102-6509b547afd0' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-308a24c3-25fd-400f-a102-6509b547afd0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-bc634be3-e36e-45a0-ac3e-0cf6cbd625ca' class='xr-var-data-in' type='checkbox'><label for='data-bc634be3-e36e-45a0-ac3e-0cf6cbd625ca' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>[384 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>eof</span></div><div class='xr-var-dims'>(lat, lon)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-240fb915-c800-49ba-b48f-492f31472656' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-240fb915-c800-49ba-b48f-492f31472656' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-96d4f3be-b963-4b5f-8ecf-eb25233718d4' class='xr-var-data-in' type='checkbox'><label for='data-96d4f3be-b963-4b5f-8ecf-eb25233718d4' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>[27840 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>slope</span></div><div class='xr-var-dims'>(lat, lon)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-c2368b85-73b7-4220-9590-7cfb6d6c6fc3' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-c2368b85-73b7-4220-9590-7cfb6d6c6fc3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4f132385-f3d4-4a93-b05c-f2c6f617b11b' class='xr-var-data-in' type='checkbox'><label for='data-4f132385-f3d4-4a93-b05c-f2c6f617b11b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>[27840 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>intercept</span></div><div class='xr-var-dims'>(lat, lon)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-3802cbf7-ec3e-42ef-b3d6-f7aa1b5f0c05' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-3802cbf7-ec3e-42ef-b3d6-f7aa1b5f0c05' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3f736930-92d6-4991-9b35-4ce0a6d0fba8' class='xr-var-data-in' type='checkbox'><label for='data-3f736930-92d6-4991-9b35-4ce0a6d0fba8' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>[27840 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>frac</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-ed0918ff-2a6e-4343-8b3a-64e3d3d17432' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-ed0918ff-2a6e-4343-8b3a-64e3d3d17432' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2d5f5f83-2e68-4e95-9cdf-6af06b0f08d1' class='xr-var-data-in' type='checkbox'><label for='data-2d5f5f83-2e68-4e95-9cdf-6af06b0f08d1' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>[1 values with dtype=float64]</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-661e170b-8fde-43a4-9c1b-607a5abcb277' class='xr-section-summary-in' type='checkbox'  ><label for='section-661e170b-8fde-43a4-9c1b-607a5abcb277' class='xr-section-summary' >Indexes: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>lat</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-3692b428-d4e4-48d3-860a-e1b2b1ccac5c' class='xr-index-data-in' type='checkbox'/><label for='index-3692b428-d4e4-48d3-860a-e1b2b1ccac5c' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([ -90.0, -88.75,  -87.5, -86.25,  -85.0, -83.75,  -82.5, -81.25,  -80.0,\n",
+       "       -78.75,\n",
+       "       ...\n",
+       "        78.75,   80.0,  81.25,   82.5,  83.75,   85.0,  86.25,   87.5,  88.75,\n",
+       "         90.0],\n",
+       "      dtype=&#x27;float64&#x27;, name=&#x27;lat&#x27;, length=145))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>lon</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-f8dc51a7-3eef-47a9-bcee-fe732edb42a5' class='xr-index-data-in' type='checkbox'/><label for='index-f8dc51a7-3eef-47a9-bcee-fe732edb42a5' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([    0.0,   1.875,    3.75,   5.625,     7.5,   9.375,   11.25,  13.125,\n",
+       "          15.0,  16.875,\n",
+       "       ...\n",
+       "        341.25, 343.125,   345.0, 346.875,  348.75, 350.625,   352.5, 354.375,\n",
+       "        356.25, 358.125],\n",
+       "      dtype=&#x27;float64&#x27;, name=&#x27;lon&#x27;, length=192))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-c2dc3064-b337-4e77-b164-21e060ce224c' class='xr-index-data-in' type='checkbox'/><label for='index-c2dc3064-b337-4e77-b164-21e060ce224c' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(CFTimeIndex([1900-01-16 12:00:00, 1900-02-15 00:00:00, 1900-03-16 12:00:00,\n",
        "             1900-04-16 00:00:00, 1900-05-16 12:00:00, 1900-06-16 00:00:00,\n",
        "             1900-07-16 12:00:00, 1900-08-16 12:00:00, 1900-09-16 00:00:00,\n",
        "             1900-10-16 12:00:00,\n",
@@ -1662,25 +1677,15 @@
        "            dtype=&#x27;object&#x27;,\n",
        "            length=1272,\n",
        "            calendar=&#x27;proleptic_gregorian&#x27;,\n",
-       "            freq=&#x27;None&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>lat</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-9bd38363-d9f1-4543-88d9-716cfcd1abb8' class='xr-index-data-in' type='checkbox'/><label for='index-9bd38363-d9f1-4543-88d9-716cfcd1abb8' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([ -90.0, -88.75,  -87.5, -86.25,  -85.0, -83.75,  -82.5, -81.25,  -80.0,\n",
-       "       -78.75,\n",
-       "       ...\n",
-       "        78.75,   80.0,  81.25,   82.5,  83.75,   85.0,  86.25,   87.5,  88.75,\n",
-       "         90.0],\n",
-       "      dtype=&#x27;float64&#x27;, name=&#x27;lat&#x27;, length=145))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>lon</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-0afb2dfe-6687-47de-8c1b-e573b810e737' class='xr-index-data-in' type='checkbox'/><label for='index-0afb2dfe-6687-47de-8c1b-e573b810e737' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([    0.0,   1.875,    3.75,   5.625,     7.5,   9.375,   11.25,  13.125,\n",
-       "          15.0,  16.875,\n",
-       "       ...\n",
-       "        341.25, 343.125,   345.0, 346.875,  348.75, 350.625,   352.5, 354.375,\n",
-       "        356.25, 358.125],\n",
-       "      dtype=&#x27;float64&#x27;, name=&#x27;lon&#x27;, length=192))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-0c287a79-bb73-4eef-9c0a-b465b473ae2b' class='xr-section-summary-in' type='checkbox'  checked><label for='section-0c287a79-bb73-4eef-9c0a-b465b473ae2b' class='xr-section-summary' >Attributes: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>Conventions :</span></dt><dd>CF-1.0</dd></dl></div></li></ul></div></div>"
+       "            freq=&#x27;None&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-46fd84f9-03aa-4ad3-803a-f9592444dd94' class='xr-section-summary-in' type='checkbox'  checked><label for='section-46fd84f9-03aa-4ad3-803a-f9592444dd94' class='xr-section-summary' >Attributes: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>Conventions :</span></dt><dd>CF-1.0</dd></dl></div></li></ul></div></div>"
       ],
       "text/plain": [
        "<xarray.Dataset>\n",
        "Dimensions:      (time: 1272, bound: 2, lat: 145, lon: 192)\n",
        "Coordinates:\n",
-       "  * time         (time) object 1900-01-16 12:00:00 ... 2005-12-16 12:00:00\n",
        "  * lat          (lat) float64 -90.0 -88.75 -87.5 -86.25 ... 87.5 88.75 90.0\n",
        "  * lon          (lon) float64 0.0 1.875 3.75 5.625 ... 352.5 354.4 356.2 358.1\n",
+       "  * time         (time) object 1900-01-16 12:00:00 ... 2005-12-16 12:00:00\n",
        "Dimensions without coordinates: bound\n",
        "Data variables:\n",
        "    bounds_time  (time, bound) object ...\n",
@@ -1695,7 +1700,7 @@
        "    Conventions:  CF-1.0"
       ]
      },
-     "execution_count": 25,
+     "execution_count": 24,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1706,7 +1711,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": 25,
    "id": "f737821e-b469-4c84-8197-8da4b1891210",
    "metadata": {},
    "outputs": [],
@@ -1716,17 +1721,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": 26,
    "id": "77d4a028-f110-4f73-9fef-5efb1b32dcf1",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "<matplotlib.legend.Legend at 0x15de7d090>"
+       "<matplotlib.legend.Legend at 0x164c23be0>"
       ]
      },
-     "execution_count": 27,
+     "execution_count": 26,
      "metadata": {},
      "output_type": "execute_result"
     },
@@ -1759,7 +1764,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": 27,
    "id": "79d7ee24-33a3-42c8-b11a-6289b71663fa",
    "metadata": {},
    "outputs": [
@@ -1769,7 +1774,7 @@
        "0.8768126176637406"
       ]
      },
-     "execution_count": 28,
+     "execution_count": 27,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1796,7 +1801,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
+   "execution_count": 28,
    "id": "24bac96b-8bef-44c2-9fdd-860bdb82b17f",
    "metadata": {},
    "outputs": [],
@@ -1807,7 +1812,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 30,
+   "execution_count": 29,
    "id": "f7a12959-e61e-48fe-82b1-26af598eed9c",
    "metadata": {},
    "outputs": [
@@ -1842,26 +1847,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 32,
+   "execution_count": 30,
    "id": "8c20a0d2-0961-4583-8d70-7e421f25c8c4",
    "metadata": {},
    "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/Users/lee1043/mambaforge/envs/pcmdi_metrics_20230628/lib/python3.10/site-packages/numpy/ma/core.py:1013: RuntimeWarning: overflow encountered in multiply\n",
-      "  result = self.f(da, db, *args, **kwargs)\n",
-      "/Users/lee1043/mambaforge/envs/pcmdi_metrics_20230628/lib/python3.10/site-packages/numpy/ma/core.py:1013: RuntimeWarning: overflow encountered in multiply\n",
-      "  result = self.f(da, db, *args, **kwargs)\n",
-      "/Users/lee1043/mambaforge/envs/pcmdi_metrics_20230628/lib/python3.10/site-packages/numpy/ma/core.py:1013: RuntimeWarning: overflow encountered in multiply\n",
-      "  result = self.f(da, db, *args, **kwargs)\n",
-      "/Users/lee1043/mambaforge/envs/pcmdi_metrics_20230628/lib/python3.10/site-packages/numpy/ma/core.py:1013: RuntimeWarning: overflow encountered in multiply\n",
-      "  result = self.f(da, db, *args, **kwargs)\n",
-      "INFO::2023-06-29 16:50::pcmdi_metrics:: Results saved to a json file: /Users/lee1043/Documents/Research/git/pcmdi_metrics_20230620_pcmdi/pcmdi_metrics/doc/jupyter/Demo/demo_output/AMO/var_mode_AMO_EOF1_stat_cmip5_historical_mo_atm_ACCESS1-0_r1i1p1_1900-2005.json\n",
-      "2023-06-29 16:50:22,139 [INFO]: base.py(write:246) >> Results saved to a json file: /Users/lee1043/Documents/Research/git/pcmdi_metrics_20230620_pcmdi/pcmdi_metrics/doc/jupyter/Demo/demo_output/AMO/var_mode_AMO_EOF1_stat_cmip5_historical_mo_atm_ACCESS1-0_r1i1p1_1900-2005.json\n"
-     ]
-    },
     {
      "name": "stdout",
      "output_type": "stream",
@@ -1889,6 +1878,22 @@
       " -----  ACCESS1-0  ---------------------\n",
       " ---  r1i1p1  ---\n"
      ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/lee1043/mambaforge/envs/pmp_devel_20231129/lib/python3.10/site-packages/numpy/ma/core.py:1013: RuntimeWarning: overflow encountered in multiply\n",
+      "  result = self.f(da, db, *args, **kwargs)\n",
+      "/Users/lee1043/mambaforge/envs/pmp_devel_20231129/lib/python3.10/site-packages/numpy/ma/core.py:1013: RuntimeWarning: overflow encountered in multiply\n",
+      "  result = self.f(da, db, *args, **kwargs)\n",
+      "/Users/lee1043/mambaforge/envs/pmp_devel_20231129/lib/python3.10/site-packages/numpy/ma/core.py:1013: RuntimeWarning: overflow encountered in multiply\n",
+      "  result = self.f(da, db, *args, **kwargs)\n",
+      "/Users/lee1043/mambaforge/envs/pmp_devel_20231129/lib/python3.10/site-packages/numpy/ma/core.py:1013: RuntimeWarning: overflow encountered in multiply\n",
+      "  result = self.f(da, db, *args, **kwargs)\n",
+      "INFO::2023-11-29 17:40::pcmdi_metrics:: Results saved to a json file: /Users/lee1043/Documents/Research/git/pcmdi_metrics_20230620_pcmdi/pcmdi_metrics/doc/jupyter/Demo/demo_output/AMO/var_mode_AMO_EOF1_stat_cmip5_historical_mo_atm_ACCESS1-0_r1i1p1_1900-2005.json\n",
+      "2023-11-29 17:40:10,187 [INFO]: base.py(write:250) >> Results saved to a json file: /Users/lee1043/Documents/Research/git/pcmdi_metrics_20230620_pcmdi/pcmdi_metrics/doc/jupyter/Demo/demo_output/AMO/var_mode_AMO_EOF1_stat_cmip5_historical_mo_atm_ACCESS1-0_r1i1p1_1900-2005.json\n"
+     ]
     }
    ],
    "source": [
@@ -1906,7 +1911,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 33,
+   "execution_count": 31,
    "id": "477d3330-fdc3-4be4-9766-689dba5c1e82",
    "metadata": {},
    "outputs": [],
@@ -1920,20 +1925,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 34,
+   "execution_count": 32,
    "id": "fb739d36-9677-4084-a6d1-377189e088dd",
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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"
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"
      },
      "metadata": {},
      "output_type": "display_data"
     },
     {
      "data": {
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"
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"
      },
      "metadata": {},
      "output_type": "display_data"
@@ -1957,26 +1962,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 35,
+   "execution_count": 33,
    "id": "cb6307bb-19c8-4727-b046-a1ea4beaa214",
    "metadata": {},
    "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/Users/lee1043/mambaforge/envs/pcmdi_metrics_20230628/lib/python3.10/site-packages/numpy/ma/core.py:1013: RuntimeWarning: overflow encountered in multiply\n",
-      "  result = self.f(da, db, *args, **kwargs)\n",
-      "/Users/lee1043/mambaforge/envs/pcmdi_metrics_20230628/lib/python3.10/site-packages/numpy/ma/core.py:1013: RuntimeWarning: overflow encountered in multiply\n",
-      "  result = self.f(da, db, *args, **kwargs)\n",
-      "/Users/lee1043/mambaforge/envs/pcmdi_metrics_20230628/lib/python3.10/site-packages/numpy/ma/core.py:1013: RuntimeWarning: overflow encountered in multiply\n",
-      "  result = self.f(da, db, *args, **kwargs)\n",
-      "/Users/lee1043/mambaforge/envs/pcmdi_metrics_20230628/lib/python3.10/site-packages/numpy/ma/core.py:1013: RuntimeWarning: overflow encountered in multiply\n",
-      "  result = self.f(da, db, *args, **kwargs)\n",
-      "INFO::2023-06-29 16:51::pcmdi_metrics:: Results saved to a json file: /Users/lee1043/Documents/Research/git/pcmdi_metrics_20230620_pcmdi/pcmdi_metrics/doc/jupyter/Demo/demo_output/AMO_yearly/var_mode_AMO_EOF1_stat_cmip5_historical_mo_atm_ACCESS1-0_r1i1p1_1900-2005.json\n",
-      "2023-06-29 16:51:11,471 [INFO]: base.py(write:246) >> Results saved to a json file: /Users/lee1043/Documents/Research/git/pcmdi_metrics_20230620_pcmdi/pcmdi_metrics/doc/jupyter/Demo/demo_output/AMO_yearly/var_mode_AMO_EOF1_stat_cmip5_historical_mo_atm_ACCESS1-0_r1i1p1_1900-2005.json\n"
-     ]
-    },
     {
      "name": "stdout",
      "output_type": "stream",
@@ -2004,6 +1993,22 @@
       " -----  ACCESS1-0  ---------------------\n",
       " ---  r1i1p1  ---\n"
      ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/lee1043/mambaforge/envs/pmp_devel_20231129/lib/python3.10/site-packages/numpy/ma/core.py:1013: RuntimeWarning: overflow encountered in multiply\n",
+      "  result = self.f(da, db, *args, **kwargs)\n",
+      "/Users/lee1043/mambaforge/envs/pmp_devel_20231129/lib/python3.10/site-packages/numpy/ma/core.py:1013: RuntimeWarning: overflow encountered in multiply\n",
+      "  result = self.f(da, db, *args, **kwargs)\n",
+      "/Users/lee1043/mambaforge/envs/pmp_devel_20231129/lib/python3.10/site-packages/numpy/ma/core.py:1013: RuntimeWarning: overflow encountered in multiply\n",
+      "  result = self.f(da, db, *args, **kwargs)\n",
+      "/Users/lee1043/mambaforge/envs/pmp_devel_20231129/lib/python3.10/site-packages/numpy/ma/core.py:1013: RuntimeWarning: overflow encountered in multiply\n",
+      "  result = self.f(da, db, *args, **kwargs)\n",
+      "INFO::2023-11-29 17:41::pcmdi_metrics:: Results saved to a json file: /Users/lee1043/Documents/Research/git/pcmdi_metrics_20230620_pcmdi/pcmdi_metrics/doc/jupyter/Demo/demo_output/AMO_yearly/var_mode_AMO_EOF1_stat_cmip5_historical_mo_atm_ACCESS1-0_r1i1p1_1900-2005.json\n",
+      "2023-11-29 17:41:07,466 [INFO]: base.py(write:250) >> Results saved to a json file: /Users/lee1043/Documents/Research/git/pcmdi_metrics_20230620_pcmdi/pcmdi_metrics/doc/jupyter/Demo/demo_output/AMO_yearly/var_mode_AMO_EOF1_stat_cmip5_historical_mo_atm_ACCESS1-0_r1i1p1_1900-2005.json\n"
+     ]
     }
    ],
    "source": [
@@ -2013,7 +2018,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 36,
+   "execution_count": 34,
    "id": "29d9c958-6607-41ee-93ed-3ee9043aed3a",
    "metadata": {},
    "outputs": [],
@@ -2027,20 +2032,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 37,
+   "execution_count": 35,
    "id": "69f15354-b573-45b0-8a8b-5a66105c145e",
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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e3ly8eBEfHx+MjIxYtWoVBgYG2nrnz5+nffv2mJmZUalSJfLmzYufnx+enp68fv0aHR0dFi5cGO/qY+nh8uXLDBkyRPvfMZ/Tli1boqenB0D//v3p379/sttcunSp9n2Lmaf19OlTqlevri2zYMECKlasmOb4hRCfN0lAhBCZKmb4VdeuXZMs261bNxYsWMDatWuZMmVKus/BqFSpEnfv3mX+/Pls376dU6dOERwcTJ48eejWrRuDBw+mTp066XrNtBowYADTp0/H09OTvXv3xlrCGEi0Z8Tc3DzHJSAACxcupHjx4ixbtowjR45gbm5Oo0aN+N///sfy5cvjrVOjRg1Onz7NL7/8goeHBw8ePKBKlSosXLiQ+/fvJzsBKV26NEeOHGH//v2cPHmSmzdv4uXlhYGBAQ4ODvTs2ZPhw4fHWqDA3Nyc06dPs3//fo4dO8ajR484deoUOjo62Nvb07JlS4YOHRrv3ibpJSAggHPnzsU5HpNYAynufXnx4kWcNkNDQ2MdCwgISGGkQogvkUpJ7kL4Qggh0sTV1ZWVK1fy77//Ztqytxnh2LFj1K9fn969eye6rK4QQggRH+kBEUKITObm5saKFSuoWLEiw4YNy+pwkm3u3LlcvnyZN2/eZHUoQgghcjBJQIQQIpMdOHAAiJ6snJMSkKNHj7Jjx46sDkMIIUQOJ0OwhBBCCCGEEJlGluEVQgghhBBCZBpJQIQQQgghhBCZRhIQIYQQQgghRKaRBEQIkSkuXbqEm5sb7du3J3/+/KhUKgwNDZOs9+LFCwYNGoS9vT0GBgbky5cPV1dXnjx5kmi906dP06JFC3Lnzk2uXLmoWrUqK1euTPJaffv2JV++fBgaGuLs7Mz48eMJDQ1Nya0CcOfOHaZNm0bDhg21sefNm5f27dtz8uTJdI8jNDSUCRMm4OzsjKGhIfny5aNv3768ePEi3vITJ05EpVIl+BozZkyK7zknWrFiBSqViokTJ2bYNTL7sxCfJ0+eJPrzTmhDxE2bNlGiRAkMDAwoWbIkW7ZsSfAaAwcOxMLCgrdv36YoNiHEF0gRQohM0KZNGwWI9TIwMEi0zvXr1xUbGxsFUAoXLqy0b99eKVu2rAIo5ubmyrVr1+Ktt23bNkVHR0dRqVSKi4uL0qFDB8XCwkIBlO+//z7eOg8ePNBeq3Tp0krnzp0VR0dHBVBq1KihhIaGpuh+8+fPrwCKmZmZ0rhxY6Vz585K6dKlFUBRqVTKH3/8kW5xhISEKDVr1lQAxc7OTuncubNStWpVBVBsbGyUBw8exKkzYcIEBVBq1aql9O7dO85r06ZNKbrfnGr58uUKoEyYMCHDrpGZn4WEPH78WAGUPHnyxPvzHjZsWJw6Hh4eikqlUmxsbJS2bdsq1tbWikqlUk6fPh2n7MWLFxW1Wq24u7snOyYhxJdLEhAhRKZwc3NTxo8fr+zatUt58+ZNkgmIRqPRJht9+/ZVIiIitOdmz56tAEqpUqWUqKioWPV8fHwUc3NzBVC2bt2qPf7mzRulSJEiCqAcPXo0zvXq1q2rALEexCIiIpR27dopgDJ+/PgU3W/jxo2VdevWKWFhYbGOL1q0SAEUHR0d5ebNm+kSx7hx47QPpYGBgdrjs2bNUgClbt26cerEJCDLly9P0X19bjIjAcnMz0JCYhIQFxeXZNdp2bKlYmBgoDx+/FhRFEV5+PChoq+vr7Rs2TJWOY1Go1SvXl0pU6aMEhkZmez2hRBfLklAhBBZIqkE5OTJkwqgWFpaKgEBAXHOV6lSRQGUHTt2xDo+ffp0BVDatGkTp862bdsUQGnVqlWs4+fPn1cAxdbWNs63ym/evFH09PQUS0tLJTw8PAV3mLAmTZoogDJx4sQ0xxEeHq7t3bl8+XKca8UkcRcvXox1XBKQaJmRgCQmPT8LiUlNApIvXz6ladOmceLNnz9/rGN///23AijHjx9PdttCiC+bzAERQmRLly5dAqBy5cqYmprGOe/i4gIQZ2O83bt3A9CxY8c4dVq2bImhoSGHDx+ONYY+ps5XX32FgYFBrDp58uShTp06+Pr6curUqTTc0X/KlSsHwKtXr+KNPSVxeHh44Ofnh5OTExUqVIhzrZj3YdeuXekS+6dev36Nnp4e9vb2aDSaeMusXLkSlUpF//79Yx1XFIWVK1dSt25dLCwsMDIyomzZssycOZOIiIg47Xh6ejJ69GgqVaqEjY0NBgYGODo6MmTIkDjvJfw376FevXoEBAQwcuRIChcujJ6eHiNGjEjwnlq2bIlKpeLQoUPxng8KCsLMzAxzc3OCgoISeXeSlp6fhfTm6+uLpaVlrGOWlpb4+Pho/9vf358xY8bQvXt36tatm2GxCCE+L5KACCGypZgHu08fgGLkzp0bgKtXr8Y6fu3aNQAqVqwYp46+vj6lS5cmNDSUu3fvao/HtBFfnY+Pf3qt1Hr06BFAnIm/qYkjrbEfPXqUESNGMGjQICZPnqxN/JLLzs6O1q1b8/z5c/bv3x9vmaVLlwLwzTffaI9pNBq6dOmCq6srV69epXLlyjRt2pR3794xatQo2rZtGyehcXNzY/bs2URFRVGrVi1atGiBoigsXLiQypUrx5uEAISEhODi4sLy5cspX748rVu3TvBzBTBo0CAAlixZEu/5DRs2EBgYSPfu3TExMUn4zUmG9PwsJMfbt2+ZMGECAwYMYNSoUWzZsoXw8PB4y9rb23P//v1Yx+7du0ehQoW0/z1u3DhCQkKYMWNGiuIQQnzhsroLRgjxZSKJIViLFy9WAKVatWrxnh84cKACKFZWVtpj/v7+2gnu/v7+8dZr27atAig7d+7UHqtQoUK8w7lizJkzRwGUH374ITm3lqgHDx4oBgYG8Q6LSk0c33//faKT6z09PRVAqVixYqzjMUOw4nt16NAh1lySpBw8eFABlLZt28Y5d/v2be0k6o9NmzZNAZTGjRsrXl5e2uMfPnxQvvrqKwVQ5s+fH6vOkSNHlFevXsU6FhUVpfz2228KoPTp0yfWuZhhR/z//BhfX9848cU3BCsyMlIpWLCgoq+vHyu2GNWqVVMA5dKlSwm+J8mR3p+FxHz8Xnz6sre3V86ePRunTsxna/HixYq/v7+ycOFCBVBGjhypKIqiXLt2TdHR0VFmzJiRwjsXQnzppAdECJEtxQznuHDhArdu3Yp17sOHD9rlQAMDA2Mdj2FsbBxvuzHfWH9cNubfKamTGpGRkbi6uhIWFkaXLl2oVKlSrPOpiSO1sRcpUoSZM2dy8+ZNPnz4wPPnz1m7di358+dn69at9OzZM9n31ahRI5ycnNi9ezdv3ryJdS6m92PAgAHaY5GRkcyYMQNTU1PWrVuHjY1NrHiXLFmCgYEBf/31V6y2GjRogJ2dXaxjarWa8ePHkz9//jjD8T42d+5cLCwsknU/Ojo69O/fn/DwcFatWhXr3I0bNzh37hwVKlRIsHciOTLis5AYAwMDBg8ezLFjx3j79i3+/v6cOXOGFi1a8OzZM5o1axZnaesxY8aQP39+BgwYgLm5OYMHD8be3l67RPN3332Hs7Mzw4cP19aJiIiId/icEEJ8TBIQIUS2VKxYMTp06IBGo6FNmzb8+++/fPjwAU9PT1q2bIm/vz8Q/QAaQ1GUJNuNr0zMMZVKlew6qTF06FA8PDxwdHRkwYIF6RJHamPv0aMHI0eOpGTJkpiYmFCgQAG6d+/OhQsXsLKy4p9//uH06dPJui+VSsWAAQOIjIxkxYoV2uMxD/CGhob06NFDe/zKlSt4e3tTu3ZtrK2t47SXJ08eihYtyo0bNwgJCYl17v379yxfvpyRI0fSr18/XF1dcXV1JSIiAh8fn1jzE2LY2dlRuXLlZN1LjP79+6Orq6tNoGLEDMv6OKFKjYz4LCTGzs6OBQsW4OLigq2tLWZmZlSvXp09e/bQvXt3/Pz8mDJlSqw6tra2eHp6MmXKFL755hvc3Ny4fPky1tbWrF27lhMnTjBv3jz09PR4/PgxTZo0wdDQEENDQ5o0acLTp09TFKMQ4suhm9UBCCFEQpYuXcr79+85duwYDRo00B43NjZmypQpjB49OtZY/o8nqwcHB2NmZhanzeDgYABy5coVp15CE4rjq+Pq6hqnXNu2bWnbtm28bUyaNIlFixaRJ08eDhw4oJ3D8rHUxJGaOomxs7OjT58+zJw5kwMHDlCzZs1k1evTpw/jxo1j6dKl/PTTT6hUKv755x/evXtHjx49Yv2cYr5p37dvX4IP2DF8fHzInz8/AOvXr2fAgAGJfusfGBgY5721t7dP1j18LF++fLRq1Yp//vmHkydPUqdOHcLCwlizZg3GxsZ07949xW3GyKjPQmr98ssvrFu3jgMHDsQ5Z21tzc8//xzr2IcPHxg9ejSdOnWiYcOGaDQa2rdvz8uXL1m2bBkqlYpRo0bRoUMHLly4kOTPWAjx5ZEERAiRbVlYWHD06FEOHDjA0aNH8ff3x8HBge7du2uHZZUqVUpbPmZlIn9/f168eEHJkiXjtBmzM/jHD6X29vZcuXIlwV3D46sT367qDg4O8SYgf/75JxMmTMDc3Jz9+/dTpEiReK+Tmjhi/p2SOkkpWrQoEL3CVXLZ2NjQvn17NmzYwLFjx6hfv368k88BoqKitNdJKsGJWQHq6dOnuLq6oigKc+bMoWXLluTPnx8jIyMAatasyZkzZ+LtGTA0NEz2fXxs0KBB/PPPPyxdupQ6deqwdetWfHx86NOnT7zJbXJk5GchtVL68/7tt9/w8/Nj1qxZABw+fBhPT09WrFhB7969gegemj59+nD06FEaNmyY5hiFEJ8XSUCEENmaSqWiWbNmNGvWLNbxuXPnAlCvXr1Yx8uVK8eJEye4fPlynAQkIiKCGzduYGBgQLFixWLV2bFjB5cvX443hpjjZcuW1R5L7hCYtWvXMnToUIyNjdmzZw/ly5dPsGxq4ohZxjUldZLi6+sLpPzb9UGDBrFhwwaWLFlC4cKFOXz4MM7OznGWZy1QoAAApUuXjjVkKzF79+4lPDyckSNHxppzECNmNan01KRJExwdHdm8eTPu7u7a4VefJlTJldGfhdRKyc/7zp07uLu789tvv1GwYEHtMYAqVapoy1WtWhWAW7duSQIihIhD5oAIIXIcHx8fVq5cib6+vvYb1xgtW7YE0E5S/9ju3bsJDQ2lYcOGsb4Vj6mza9cuwsLCYtV5+/YtJ0+exNzcnNq1a6cozr179+Lq6oqenh7bt2+nVq1aiZZPTRy1atXC3Nychw8fcuXKlThtxrwPrVq1SlbMiqKwfft2gDgTo5Pi4uJC8eLF2bZtG9OnT0dRlHgf1qtUqYK5uTn//vsvAQEByWo75iE55qH3YydOnODt27cpijU5VCoV33zzDSEhIfz2228cP36cUqVKUaNGjRS3lRmfhdTaunUrkLyf99ChQ3FwcGDkyJFxzsUMC4P/ho7J8CshRLwyf+EtIYRIehleRVGUu3fvxllO9+3bt4qLi0u8u0criqK8f/9eMTMzUwBl69atseoVKVJEAZTDhw/HqVerVi0FUIYPH649FhERobRv314BlF9//TVF9+fh4aEYGRkpurq6yvbt25NdLzVxjB07VgGUmjVrKh8+fNAenzVrlgIotWvXjlX+3bt3ysqVK+PssB0YGKhd3jhv3rxKUFBQsuOO8ccff2iXd9XT04t3GVtFUZTff/9dAZRGjRopT548iXP+6tWryoYNG7T/vXnzZgVQKleuHOseX7x4oZQoUUJ7zcePH2vPJWf376R2Qn/79q2ir6+vbX/OnDmJvwHxyMzPQs+ePZVixYop27Zti3V85cqVyvPnz+OU37p1q2JqaqoAcep8KuZnsHfv3ljHDx8+rADKiBEjtMeGDx+uAMrRo0eTc6tCiC+MJCBCiEyxe/dupVq1atoXoKhUqljHdu/eHavOhAkTFCMjI6VOnTpK165dlSZNmihGRkYKoLi6uipRUVHxXmvLli2KWq1WVCqVUq9ePaVjx46KhYWFAijDhg2Lt869e/cUKysrBVDKlCmjdOnSRXF0dNTuRRISEpKi+425XuHChZXevXvH+1qyZEm6xBESEqJ9T+3s7JTOnTtr/9vKykq5f/9+rPIxD+ZmZmZKtWrVlE6dOimNGzfWXtfCwkLx8PBI0f3G8PHxUQwNDRVA6dSpU4LloqKilG7dumkT0Ro1aihdunRRGjZsqBQuXFgBlDZt2mjLh4WFKaVKldImRx06dFBatmypGBsbKzVr1lRq1qyZIQmIoihK586dtXG+f/8+Be9GtMz8LMQk58uXL49zXK1WKyVLllRatGihtG/fXilevLg2sRo1alSi9xAUFKTY29vH+pnE0Gg0SpUqVRRAqVevnlK/fn0FUKpXr56i90kI8eWQBEQIkSliHvQSe3360HT8+HGlTZs2SoECBRR9fX0ld+7cSpMmTZL8plZRor91btasmWJhYaEYGxsrlSpVUv7+++9E6zx79kxxdXVV8ubNq+jr6ytOTk7Kr7/+qgQHB6f4fpO6V0Dp3bt3usURHBysjBs3TnFyclL09fWVPHnyKL1791aePXsWp2xAQIDy008/KS4uLkr+/PkVAwMDxdjYWClVqpQycuRI5cWLFym+34/VqFFDAZSDBw8mWXbLli1Ks2bNFGtra0VPT0+xs7NTqlevrkycOFG5c+dOrLI+Pj7K4MGDFQcHB8XAwEBxdHRUfvrpJyUoKEj74J0RCchff/2lAEr37t2TvJ/4ZOZnIaEEZM2aNUrHjh2VIkWKKGZmZoqenp6SL18+pX379sqhQ4eSvIexY8cqhoaGyqNHj+I9//LlS6VDhw5Krly5lFy5cikdOnSIs2mkEELEUClKOi1wL4QQ4ov34sULChUqhL29PY8ePfos5gA0adKEQ4cO8e+//8ZZ9EAIIUTKySR0IYQQ6Wbq1KloNBq+/fbbzyL5OH/+PIcPH6ZUqVKSfAghRDqRHhAhhBBpcvfuXWbMmMGjR4/4999/KVCgAHfu3MHExCSrQ0u1MWPG8OzZM/bs2UNAQAA7duygdevWWR2WEEJ8FmQfECGEEGny+vVrli1bhpGRES4uLsybNy9HJx8AGzZs4Pnz5zg4ODB9+nRJPoQQIh1JD4gQQgghhBAi08gcECGEEEIIIUSmkQRECCGEEEIIkWkkARFCCCGEEEJkGklAhBBCCCGEEJlGEhAhhBBCCCFEppEERAghhBBCCJFpJAERQgghhBBCZBpJQIQQQgghhBCZRhIQIYQQQgghRKaRBEQIIYQQQgiRaSQBEUIIIYQQQmQaSUCEEEIIIYQQmUYSECGEEEIIIUSmkQRECCGEEEIIkWkkARFCCCGEEEJkGklAhBDiE4qiZHUIQsShKAonTpzgq6++oly5coSEhGR1SEJkW69evSIiIiKrwxAJkARECCE+EhgYiEPhwtSuU5fdu3ej0WiyOiTxhYuKimLr1q1Urlqd5q1ao5+3MBqNhpUrV2Z1aEJkS4GBgeTPn5+jR49mdSgiAZKACCHER/766y+MzHPjWLkOrn3741y8OPPnzycwMDCrQxNfmODgYBYtWoRT0WJ88+0wjEvV5esF++j9/S807jGQWbNmERUVldVhCpHtXL16FYArV65kcSQiISpFxhoIIQQA4eHhOBQujOuYyVRv2JyI8HBOHdjJwt9+okKF8lSvVo1vv/0WJyenrA5VfMYePHjA/D//5O+/l5PLxo5SLXtSpGZTdHT1AGhSwpbIiAhGfFWLP/74gw4dOmRxxEJkL/PmzWPYsGE4YsxDJSirwxHxkARECCH+3/Lly/ntf1OZ888x1Or/OohHdm5GkI8XW7duZf369bx//56vv/6apk2boqOjk4URi89FVFQUe/fuxX3ufI4fP0bRGg0p3rgzdsUroFKpYpVtUsIWgB0r/+LGsb2cPXs2ThkhvmTFVbl4TwQRaPBTZB5IdiQJiBBCABqNhuIlS9Ksx0Aate+mPR4c9IEJPb/iwf17XL58GR0dHTQaDadPn2bfvn1UqFCBvn37Ym9vn4XRi5zq+fPnrFy5koWLlxAcGk6xhu0p0bADJpbWCdaJSUCCgz4wuEkVduzYQd26dTMrZCGyPWuVPiUwxQMfAgICMDU1zeqQxCd0szoAIYTIDnbt2oWfXwAurdprj927foWxvdoSHh5Ow4YNtb0iarWa2rVrU7t2bXx8fJg6dSo+Pj507tyZr776Cn19/ay6DZEDhIaG8s8//7Ds7785fuwY5WvUpecP46havylHH/gmux1jk1w06tST6dOnSwIixP8LDw/HlwgKYogxOly7do1atWpldVjiE5KACCG+eIqiMMXNjZY9v0FP30B73NTcAkVRKFSoEJMnT453mEvu3Ln59ttvURSFK1eu0KNHD2xtbenSpQu1atWKNZRLfLkUReH8+fOsXLmS9evXY2llTe2vOvPXz9OwymOnLdekhC0Hb3sl2E5M70eMVl/3Z1Czaty8eZNSpUplWPxC5BS3bt1CFxWm6GKFHleuXJEEJBuSIVhCiC/eyZMnadGqFUsOXcQ4139d9ZXsTNm76x8G9+1NpUqVWLRoEXp6ekm2FxYWxpEjR/Dw8KBUqVJ0796dMmXKZOQtiGxIURSuXr3K+vXr2bhpE76+vrT8qi2du/egcrXqXH7zIdH6nyYinyYfMRZM+BG7XHosX7483WIXIqdavnw5Y/sOpjV5OYcvoWi4rcgqhtmNJCBCiC9esxYtMC1QhJ4jfol1vJJddDKyZOF8Jv36M82bN2fq1Kkp6tX48OEDe/fu5ebNm5QsWZL27dtTvnx5mTT8mVIUhVu3brFx40Y2bNzIq5cvadK8JV+164BLg0YYGBjEKn/pddofjF48fsAPHRrx8OFD8ufPn+b2hMjJyqjMAKhFbh4ShCcBvFPCsjgq8SlJQIQQX7Tr169TpWpVFh84j4W1DRA9IX3Lkrkofl6YGRvwVafubN+8gaWLFjBq1Ch69eqVqmsFBgZy4MABtmzZQsOGDenQoQO1atWSlbRyuIiICDw8PNixYwc7d+3izevXNGzSjFZt29OwcVOMjI0TrJseCQjAtOF9qVWhNNOnT0+X9oTIqexUhhQnF8XIhT8RbOIVoeHhyeq9FplHEhAhxBetR4+eqPQMcJvtrn0YnPb9N5w+uFtbxtraGs97T6hXvSJeb95w6NAhjBN5qExK1apVMcttTVhIMGqViqZNm9CieXOaNGmCrW38w2xE9vL+/XsOHz7Mjh072bdvL/r6BjRq1pwmzVtSq249jIyMktVOeiUgt69c4H+Dv+b58+eYm5unS5tC5DQajQZDHV3akBcr9FFQWM5zLnheoVy5clkdnviIzI4UQnyxnj59ypYtmxk0dLj22I5VizlzaA89evTg8uXLTJo0CW9vb44fOcyseQsJDAxk/fr1qbqet7c3mzZtIjIykjwFC/H3v578+McSonLZMHn6TOzs7KhQsRJjx47Fw8ODiAhZvz67CAsL4+jRo4wZM4ZKlSpja2vLb79PxtouH6s2befirftMnzOfRk2bJzv5gP+G+aVViQpVKFu2LIsXL06X9oTIiR49ekQUChZE93aoUGGFPp6enlkbmIhDekCEEF+sYcOG8eTFSxYuW8Wl14Hc8bzI2N7tqFixIn/99Re6urqEhYVRo0YNqtWoxYZ/dtOgRmVev3rJoUOHMDExSfIavr6+7N69mwMHDnDt2jUATM0tGTTejVpNv4pV1u+9N56nj+F56hhXTh0jLDSEatVr0LB+PVxcXKhatWqcOQQiY4SGhnLp0iU8PDw4fOQoHh4nyWVqTt369XGp35A6LvWxzZMnXa6VXr0g547uZ8XUsTx+/FiWghZfpM2bNzOkcw868N/KcqfwAeC6EpBVYYl4SAIihPgivX//noL29mzbc5Ay5cpz6XUgY3q24dndm+zfvx8LCwsgeo5I9+7d+WHML/ww+heuXLxA66YNGDlyJL17907yOhMmTGDbtm1YW1vz1Vdf0XfIcAo7FUnyoVOj0WDq/4Kzpz04f+Y0Z0954O/vR7Vq1alTpzbVqlWjatWq5Emnh+Av3fv37zl9+jQnT57k2EkPPC9dwtTcnBIVqlC8YjXK13ShoJMzKpUq3XotPpYeSYhGo2FMxwaMGTMGV1fXtAclRA5TQWVOKBpcsNIeu8MH7vGBV0poFkYmPiX7gAghvkjz5s2jRPnKhNs6aR/+Xjy8R7Vq1bTJB8CmTZvQ19fnuxE/AlChchVMTEx4/vx5sq7TvXt3duzYgZOTE7/Pmpfs+NRqNcVLlqJ4yVK49h+Ioig8enCfs6dPceHcGTZv2cr9e3fJX6AAVatUpVq1qlSpUoXy5ctjaWmZ/DfiC+Tt7c2lS5e4dOkSFy9d4vyFi7x8/ow8hZywLVoOm0rN6fz1z3SqXzne1couvQ7MkCQkrdRqNY2/HsCMGTPo1auX7EEjvjjvCcee2PPzrNHnDOFoNBr5nchGJAERQnxxgoKCcJ87j+9nLNQeCw76QFBggHa/jvDwcNavX8+OHTto2KRZrCEtKpWKqKioZF2rWLFifPfdd7i7u7Ny2WJ69xsARI/9T+xb708fcFUqFU5FnXEq6szXvfsAEBgQwLWrV/C8fIkTp84wb647L16+Il8+O8qULkOZsmUpU6YMpUuXpkSJEimam/A5CAwM5M6dO9y6dYsbN25w7do1Ll26xPv377F3KEzhEmVwKFGWAS268EzHDkPT2JO3E1sqObsmIfW+6sCWBTPYt28fLVu2zOpwhMhU74mgIrFXu7JEj0gUnjx5gqOjYxZFJj4lCYgQ4ouzbNkybPIVoFz1Otpj79+8QlEU5s6dy6FDh/Dx8eHNmzdUq1aNpatjTzqPiopCVzf5fz779OnDv//+G72XSKs2Sc4dSO6DramZGbXquFCrjgsAthHv8PXz5+adu9y8c48bt+8yedI23r33oXz58lhbW6NSqTA3N6do0aIUKVIEJycnChUqhLm5eY7bm0RRFHx8fHj8+LH29fTpUwICAggPD8fY2JgSJUpQuHBhduzYwbNnz/hz6Qpq1K6DublFrATQK5HdxxMSXwKZ2qQkqYQ0ufT0DWjctS/Tp0+XBER8Ud68eUMwUVgRe/6TDios/39HdElAsg+ZAyKE+KJERETg6OREtxG/UrtZ61jnnj24y9Vda9i/fz9qtZp58+ZRqU7DWGUiIyNxzJubH3/8kZ49eyb7us+ePaNdu3Y4OTlx69YtAHbeehNv2dQ8xNpGvEvw3OSZ7vw+0508efLw9u1bAGxsbMiTJw9PnjyhXr16GBoaotFo0NXVxcTEBBsbG/Lly4eVlZX2lTt3bqysrDI0WYmMjCQgIAA/Pz/8/Pzw9fXlzZs32tf79+8JCwvT9kBZWlpSuHBhChcujJ2dHWZmZrGGWWg0GkaNGsXhw4fZtWsX5Wq4aM99+sCf3J3Hk5LaJCS9JqN/CPBnYOPKHD58mGrVqqVLm0Jkd/v27aNbi9Z0Je5mnMfwxhgdLiv+WRCZiI/0gAghvigbN25EUetSo3Hcb4ftixTjuyVLtP/9aYJw6XUgFt730Gg02Nvbp+i69vb2/PTTT/z+++8MHz4cd3d3WpfMG+c6GTGs59cfh6OTy5KVK1fSs2dPhg8fTsWKFTlx4gQuLi40a9aMunXrxqoTERFBYGAg/v7+vH37lps3b+Ll5YW/vz+BgYFERUWh0WjQaDSoVCrUajW6urranqGY77ZUKpX2PEQnGJGRkbHqxlAUBV1dXczMzDAzM8PW1lab/BQsWBBTU9MUbSamKArTp0/n4MGDjB49mhYtWvDSNyjZ9Q/e9kpVEpLVw7NymZnTqMPXzJgxgy1btmRZHEJkpoktumNN/Ku/WaPPc2QSenYiPSBCiC+GoiiULlsWlw49adY5/t3M40sKYtiHv6FWrVqo1Wp2796NlZVVnDJJXX/06NHs37+fatWqcezYMQwNDWNdL717P2Lo2TrEOdajRw/Wrl3LiRMnPsuJ68uWLWPOnDl0796dtWvXAsRKQOLrcUivXhBI3c8yvXpB3r1+yZAWNblx4wZFixZNlzaFyM6cVCZYo08F4m7E+ZpQDuNNkBKZBZGJ+MhyAEKIL8a+fft489aLBm06J1hm56038SYfJq9vUr16dYyNjVmzZk2Kkw+I7g2YPn06I0eO5MKFC9jZ2aV5g6zkJB/xCQ8PZ+vWrTRo0OCzTD527NjBnDlzcHFx0SYfH0uvB/3EZMY1EmJjl5/azVoze/bsLItBiMzkTXiCPSBW6BNMFF5eKZ/rJTKGJCBCiC/GlKlutPy6H/oGhimqF3L9OM2bNydPnjysW7cOJyenVMegUqlwdXXl77//BqBVq1apbiu5NLdOEXbsv4fwyMhIOnToQGhoKAMGDMjw62e2kydPMn78eIoXL87Ro0dTVDctPR7ZTZs+Q1ixYoV23o8QnzOVuQnFjAxxMtGP8yphYoghaklAshFJQIQQX4QzZ87g6elJsy5Jbx74sadHNvP111/j5OTE6tWrsbOzS7pSMlSqVInu3bvz5s0bwsPDo49l8LyByMhIRo8ejbm5Obt376Zdu3aUKlUqQ6+Z2a5fv86IESOwtbVl91EPXvuH8NI3SPtKjvRMQlLaC5KenwEH5xKUrlKT+fPnp1ubQmRnOWsdvy+bJCBCiC/ClKluNOnUg1xmcccHJ2THqsWMGDGCihUrsmLFCnLnzp2uMVWtWpWoqCg2b96cru3G59LdR1haWjJjxgzKlCnD6tWrmTRpUoZfN7M8efKECRMm0LNnT/QMDDnkcV47vyY+ST3oNylhm26JSFYOxWrTZwh//vknHz58yLIYhBDiU5KACCE+e7dv3+bgwQN81fObZNd5eu8Oq2dPplKlSixatAgTE5N0j6tMmTLo6+uzYcOGFNeNmfvhpWeTrPL93dejVqtZvnw5S5cupXz58im+ZnZ08+ZNvv/+e1q3bs2uXbto2rQpJ89fwTKdk8W0yqokpEzVmtjkt9cO+RNCiOxAEhAhxGdv2vTpuLRsj6VNHmb/9C2bFv1BZGTCq6FERkbya98OmJiYMH369Fi7oKcnfX19KlSowIULF2hdMi/5LZOX5KR04vmVcFPu3LlDnz59qFy5cmpCzTYURcHf35/Tp0/Tv39/unbtioeHBz169MDLy4vFazYludFjjMxeKjcrkhCVSkWbPkOYPXt2op95IYTITJKACCE+ay9evGD9+vW07j2Q0d1acnz3NtbOm073qkWYNOhrnj+8F6fOylm/E+Drw7Rp07C2ts7Q+KpVq4a3tzehoalfoz6pXpBvf/4NIyMjunTpkuprZLWoqCh++uknKleuTO3atRk4cCA3btzg+++/x9/fn1WrVmFhYZHidivZmWZqIpKcJCS946nRqAURiipThvoJIURyyEaEQojP2uw//qBy3YYsdRvH/RuejB49mjJlyrBx40b279/P0Db1sLC2pWjp8lRr0IzazVrz5vlTDA0NqVmzZobHV6VKFaKioujbty+LFy8mqWmUKe39uPvgIZ6envTv3x9T06zbHC+t3N3d2bt3Lw0aNKBcuXJUrFiR7t27x9r1HKLfn+QOS/tYJTvTTOuhyIiNCj9t7+N70dHVpVXPAUyfPp2uXbtm2C72QgiRXLIRoRDis+Xr60tBe3scS5bj+vlTfPvttwwaNCjW+Z07d3LkyBFu3LhBRERE9AOtSoVprlx4eHik6rqbNm3i+fPnGBsbY2JiQtWqVSlevHi8ZSMiIhg8eDDnzp1DV1eXsmXLMmrcb9RxqR9v+U8TkJiH7YQSk/rte3DhwgUOHz6cY/f72LVrF7/88gtt2rThn3/+SbRshNeTVCUgMRJLQmIe8tMrUUksCUnuNZJKZGLaCQsJZnDTqmzYsIFGjRolP0ghcggLCwv6hZuTV20Q7/nxQQ+5cP0qpUuXzuTIRHwkARFCfLb+97//sXDp37x88og+ffrw/fffJ/jtb0REBLdu3cLT05PLly9ja2vL2LFjU3zN58+f07JlS/T09LTtOjo6Jvng/ODBA7Zv386mTZvQ0dHl7vO4myFCynpAnr98iXMVF77++mtGjRqV7HrZyYcPH6hfvz758+fn3r17cXo8Phbh9UT77/RMQuJ7yP+4zM1L59i1egkvHz3Ax/sNIR8+ULtFW35wS97yt0m1n5J68Ylpa/2fM3l//xoHDx5MVj0hchJJQHIWGYIlhPgshYSEMMd9LkXKVeblk0e0b98+0aEnenp6lCtXjnLlytG7d8r2CvnYxo0b0dHR4eXLl1hbWzNkyBAWLlyIt7d3ovNJihQpwoABA9iwYQOVq6bPRPEhP45FpVKl6X6y2t69ewkNDWXu4uVEeT8j6qNzXno2qd4JPjHJebCPGbL19uVzJvTrhKIo2NvbU6t6dZ49e8b5I/uTfb1Ph2Sl9zyRmFhbdHNlYJOqeHp6fjaroAkhciaZhC6E+CytWLECcysbvp04Ax0dHfbt25fh1wwJCWHLli2UK1dOm2zEDPk6f/58kvW3bNlCZGQkv0+fGe/5lD5s+/r5Y2BgkCFLCGeWTZs2kTdvXspVqBjv+fh6OtLS+5ESlexMmdCvM7q6uuzbt48dO3Ywc+ZMmjVrRnhYKBqNJtltXXodqH1lVKzmua1p0K4rM2bMyJBrCCFEckkCIoT47ERGRjJt+gyGf/8D9UsWwiZfAdatW8fChQvx9PSMsxzpnTt3WLlyZZqve+HCBYKCgvj555+1x8qWLYuxsTFnz55NMua1a9dSyKEwRZ3jny+SUrMmjyc4ODhV+4xkBzdv3uTu3bt88030/i0JJRaZlXDEyG9pQn5LExbMnMLr50/4+eefyZs3r/a8g4MDUVFRPLpzI1PjSo7WvQayZcsWnj59mtWhCCG+YJKACCE+O1u3bkWjiaJ1uw4ArF+1AhMTE/766y969uxJzZo1GTp0KOvXr+fp06fMnTuXmTNncuXKlTRd18nJCYBTp07FOl6sWDE8PDxIbMrd6tWreffuHaN+GZemGD5WrVIF7AvkZ8uWLenWZmbaunUr+vr6lG3bN865T5OO9EhCHj98wP17d2Idi0k2rp45TiEbc0o7FqBVq1asXLmSGTNmULduXdq2bRurjoODAwDPT2VMr1tqV9CqZGdK3oKFqN6wGX/88Uc6RyWEEMknCYgQ4rOiKApubtMY8O0wdHWjp7nVq1ePFy9e8OHDB5YsWUKNGjW4cuUKU6dOpVWrVpw8eRKAOXPmJJokJCV//vw4OjqyY8eOWMfbtGnDu3fvEvzWef369cyePZsKlarQun3HeMukdq6Dt48PJUuWTFXdrBQcHMyuXbuoXr06+oaG2uNeejaJ9oSkJhG5dOEcjWpXw6VaRRrWrMrkX37UJh4A4eHhdOvWjTx58lC+fHkOHjyIq6sr+vr6TJw4Mc7cokKFClG1alXmzp3LsaXTUxxPRmvtOpilS5fi4+OT1aEIIb5QkoAIIT4rhw8f5vnz53T9ulecc4aGhvTv359VW3Zy8/FLLty4x4hRYyhf04XWvQZw+fJlzpw5k6brN2zYkGfPnsXaWHDgwIGoVKp4h2Ft3ryZKVOmULpsOXYcOBJvm6lNPvYfOUZQUDDNmzdPVf2sdODAAUJDQ5kyZUqGXUOj0dDMpRZtmzXixbOnDBo0iBYtWrBo0SJKliyJn58fAJ07dyYwMBA3NzfmzZuHh4cH7u7uLFu2DBubuAmPjo4OCxcupFmz6J6Gv38ZFKdMVqlkZ0rR0uUpWqYCCxcuzOpwhBBfKElAhBCfFTe3abh+MxAjY+N4z7/0DdL+O6+dHfVdh/Pbkg30GTUBMzMz3N3d09QL4uLiQmRkJH/99dd/18mbF0tLS7Zs2UJU1H/rOG3fvp1JkyZRtmxZ9h49Ge8Ss2lZ5WnWn4sxNDSkdu3aqW4jK7x48YL58+dja2vLe0unDLvOqZPHuXH9Kq6urhw5coTBgwczZcoUJkyYwP379ylYsCBubm7s2rWL7t27U6FCBQCMjY1p0KABpUqVSrBtfX19pk6dSr9+/dixYwcjOjSKM/coK7V2HcTcuXNjJcpCCJFZJAERQnw2Ll26xLlzZ3HtPyDW8Zik4+PkA2Ivd6pWqxk/fjy3bt3i6NGjqY6hTJky2NvbM2HChFgPnFOnTuXu3bvaCeE7d+5kwoQJlCpVit1HPdI9+dBoNJy/fIVGjRph+NEQpuzu6dOn9OzZE/+AAH6c8zeQ+jkPSTl68AAAvXv3xvj/E1aVSkXHjh1Zt24dpqam/Pzzz+TJk4dhw4aluH21Ws2IESMYPnw4j+/c5NCWtekaf1pUrN0AEwsrVq1aldWhCCG+QJKACCHilRP3KHVzm0bXHr2xzG0V59zHyUdCy52OHDkSKysr5syZE6unIiXUajUTJ07E39+f/v37a48PGDCAUqVKMWfOHFauXMmvv/6Ks7Mze/49le7JR4ywsHDs7OzS3E5mefToEb169eLDhw/sOHCU7s3qZej1Ll04j5WVNVZWcT8vJUqUYMuWLfTo0YNZs2ZpE5TU0NfXR6VSU6Nxy7SEm65UKhVt+gxm5syZqf6sZzZvb2/WrFlDeHh4VocihEgjSUCEEPFSqVQ8e/aMefPm4ebmxp07d5KulIXu37/Pzp07GPDt0ATLJGefhRkzZvDkyRP27NmT6liqVKnCV199xdq1a7l9+7b2+J49e4iKimLmzJk4OTlx4OQ57UT55EjJBGu1Wk2Rwg7s2LEjRzxg3r9/n169ehEaGsruw8cpXaYskHG9HwBPHj+kVKmEJ+ibmZnx008/UaZMmTRd5+jRo5jnzo2FVcIbUWammPe0TvO2+H8IirNoQna1efNmxowZk6YeSiFE9iAJiBAijqioKJYsWULFihU5dOgQ+/fvp1q1ajRr1owXL15oy2WnXpIZM2bwVbsO5C9QME3t9OnTh3z58jFv3jwiIiJS3c7IkSMxMDCgXbt22mOFChXCzc0NFxcXbt68mWjykR7LzP4w5Bu8vLw4d+5ciutmlpCQEE6dOkXv3r2JjIzk8uXLFC+Z8NyK9BT04QMnTpygWrVqtGjRgj59+vDTTz+l67LF/v7+XLlyhTLVst88HF09PVr1+Ibp06dnq9/lZ8+eMXDgQObNm0dwcLD2ePXq1SlZsiTHjx/PwuiEEOlBEhAhRByHDh1i5cqVTJs2jZ07d3Ls2DFu3LhBly5dYi05qlKpePcu7UOF0urNmzesXr2awUNHpKmdnbfeALBgwQLevHnD9u3bU92WlZUVvXr14sGDB7Em+v7www8cO3aMd0H/JTe2Ee+0r8SkdFiWa/fOGBkZsXXr1pQFnwmePHnCqFGjqF69una3+OvXr1OsWLF0u8bH72t8r79WrGHQoEHUq1cPGxsb3r59y7///svkyZNjPfimxalTp9BoNLTuNSDpwsmUnrulN+7Ug5u3b+Ph4ZFubabGx710586dY8mSJYwaNSrW3JvixYvj6OjI+fPnc0SvnhAiYZKACCHiGD9+PLa2tnh6evLNN9+wfft2ChYsSPfu3bG0tATg5cuXTJkyhdatW2NjY0Pv3r159OhRlsQ7e/Zs6rjU135z7vPemyuXLqWqrW3XXjB27FiANI37B6hRowZRUVFs3LiRl75BcSbDJyfpgNRvsqdWq2nSpAlHjhzB19c3VW2kt1evXjFhwgTatGnDkSNHaN26NWvWrMHLy4vChQvHWSggNZL7vnZvUJlfp8xkz549XLp0iWfPnjFixAiioqLw9PRMcxwA//77L0bGJjiXqZAu7aU3Y5NcNO3cixkzZmT6tX19fRk4cCA1a9bE3d2doKDon33Tpk1xcHCgQ4cOrF+/nlGjRhEQEICRkRHlypXDx8cnzmafQoicRRIQIUQsgYGBXLx4kTNnzmBqaoqRkRG///47S5cuxcDAQPtQPnToUJYuXUrbtm3ZuHEjjx494o8//kCj0WTqcI6AgAAWLFjA4OHf89efcylTxJ7yxRz5qrELg/v1TnF7h7eu4+bNmwwfPpxWrVolWO7GjRvaB6aE5M+fH4j+tj/Gx8lHUlK7sd7Hpk2bhkajYffu3WlqJ608PT0ZOXIkzZs3Z+fOnbRu3RovLy+2b9/O119/nS4rdSU38fi0DqBNsqdOnYqdnV26TN7ftWsX+/fvp0zVWmlu62PpPS+mVY/+HDx0iFu3bqVru0k5efIk79+/Z+TIkWzbto1NmzYRGhqKmZkZxYoVw8zMjGnTpnH06FGGDBlCaGgo9evXx8TEhEOHDmVqrEKI9JX82Y9CiC/CsWPHMDIyYt68eXTsGL0r97hx45gyZQqtWrUib968HD9+nJ07d7Jv3z4aN24MRG++1qBBA4YMGUKJEiUACAsLY8uWLSiKQufOndHX10/3eBcuXIhjkSJUrlqd7u1bU6BAAb7p35+nT5+yceNGFEXDor9XJ7u9uq3as3TqrwnuWh5j5MiRFClShPnz58fZCTtGzGo9ae1JSQtHSwPy5cvHzp076dmzZ6Zf39vbm+HDh3Pt2jUMDQxo36oZs/83kby2NngpegSloccjPVYKCwgIpFLlgjx9/oK8efMyadIkWrVqhZ6eXpraPXz4MGPHjsXZ2Zmf5y1Pc5zp6dMhXLlt8uDSsj0zZ87k77//TtdrRUVFsWXLFu7fv0+nTp1wdnbW/r4sX76cevXq0aFDB65cuYKvr682EW3fvj1z5szhm2++oVq1avTt25cuXbqwdu1aSpQoEe+mnkKInEN6QIQQsRw4cICaNWtSp04d7bGYbx3v3r0LRD/016pVi8aNG2t7O4oVK4aFhUWsSerdunXjwIEDzJw5k+HDh6dpUnd8wsLCmDVrNt+P/oUdWzcTGhrKyJEj6dmzJ2PHjqVnz57s/mc7A3p/new2jU1yUa6mC7t27cLLyyvBcmXLluXEiROJ9izEJCBhStr/1KblYdvS0jLd3/vkWrRoETdv3uTn77/D59EN1i35k7y2aevVgfRJPgA+BAfj/d4HgKCgIG7cuMHVq1fRaDSpbtPDw4Mff/wRe3t7rl+/Hu8yy9lNG9dBrF23jlevXqVbm2fPnqVcuXKMHTuWgwcPUqdOHaZNm6b9vWjdujUeHh7ky5ePxYsX4+zsrK3btm1b/P39OXv2LFWqVGHLli2cP3+eUaNGkTt3bsLCwjhz5ky6xSqEyFzZ/6+iECJT5c2bl+fPn5MnTx7tsdevX+Pl5YWjoyMfPnzg8OHD2m/TYx7ULly4QNGiRfHz8wOih1e8efOGVatWsWvXLq5evRrrgb5Xr17MmTMnTbtDr1mzhtxWVjRq2pwli/7EzMyMatWqAdET5EeNGkXv3r3Zu2sHfbq0S/JalexMqWRnyqBxbgCsXZvwxnE1atQA4H//+x/e3t7xlol56Dc2NolzLq1Dq1LC39+f3LlzZ9r1Yjx79ozNmzdTr2Fjvh3nFmvVr7Tcf3olHwD58ubh/cPrrF/yJyVLluSff/6hT58+NGzYkNmzZxMSEpKi9i5cuMCwYcOwsbHh1q1b6d7rl9bhVwlNYC/gWJSKterh7u6eqnYDAwN59OiR9gsJRVFYsGAB+fPn5969exw6dIjx48czd+5c1q1bB0T/DZg4cSJz587l7t272iGPGo0GGxsbSpUqxenTp3n16hXFihVj1apVPHz4kL///pt3795l+cR5IUTqSQIihIilSpUqREVFceBA9C7Rhw8fZvXq1VSoUIGCBQty//59fH19adiwIRA99AqiVzDSaDTa4VclSpTA3Nycfv36sXLlSooUKaKdM3Hu3DnWrFnDDz/8wL1791IVZ1RUFNOmTWfgd8PRaDTcuXmD5s2bxxo6o1KpGDlyJIMGDeLQoUMMaFCe0BsnmPvr9/RtUJGxru21ScfHD3bNKhanQoUKrEvkG+GYBCQoKIjffvst3nkv2iFYJnETkJQ8RKf1gTs4ODhLEpB58+aho6PDnAWLgfRJutIz+YihVqvp0LoFJ3duwO/xTeZN+52I8DCWL1/Os2fPkt3OtWvXGDJkCGZmZty+fVs79C4j9zJJT237DGHRokUEBAQkWVZRFCIjI1m9ejWVKlWiQIECLF68mPfv3wPw/PlzLly4QNu2bVGr1RgYGPDdd99Rt25dtmzZQmBgIDo6OpQqVYqOHTtiaWmp/TIj5nepTZs2eHp6audQNW7cmD///JNChQpx9+5d/v3334x5I4QQGU4SECFELA0bNqRjx460a9eOypUr8/3332NoaIibW3SvwM2bNylQoAAGBgbaOu/evePKlSsULFiQ0qVLA2Btbc3vv/+Oj48P58+f54cffqBIkSJAdM9CpUqVsLa2jrWm/8OHDxk0aFCylr/dsWMHQcFBtO3YmVV/LyE8PJwWLVrEKadSqfj2229ZvHgxiqLQpUsXjv6zEUNdNTcunOHIwf3xtr9p0yY0Gg0//PBDvMOX7OzsKFCgAGq1mmPHjrFv3744Zf7rAcm6OSAQPVTNwsIiU6959+5d9u/fz1ftOmD5SfKT2kQkI5KPT+nq6tKpTStCQsNwcXFJ9rLAd+/eZcCAARgYGHDr1i3t+x2z6EB2SEKSWr63RMWqFCxSjMWLFyfZlkql4vDhw8ydO5e2bdty7tw5vv32W+0cDnt7e549e6btSY1ZNrdRo0a8fv2akydPao/HJBwxQ9Vi/rdt27Z8+PCBa9euaa9btGhRNm3axNatW9m0aVNKbl8IkY1IAiKEiEVXV5epU6fy8uVLfvjhB9zd3dm4cSPly5cHoh8swsPDY42/3r9/P/fv39cmADHfZFauXJnt27ezc+dOypcvr32wOHr0KK1atcLKyirW8rDXr1/n5MmTSe4toigKbm7T+GbwUPT19dmwajm2trbaGONTo0YNduzYwbx587ST6K2trRn/00jyW8btoShcuDALFizg1q1bzJkzJ942a9eujb6+Pvb29vz+++9xhmLFfJNsamqW6P3EiO/BPK0P3ZpbpwgPD9Mun5xZrl69CsD0OfNjHc/OyUeMrwcOJTIykh9//DFZ5W/cuEHfvn1RqVRcu3YNW1tbIryexFlSOK1JSGYkMa1dBzNnzhxt711CwsLCGDRoEP369WPcuHEUL14cc3NzcuXKpS1TrFgx7XK5MUlG5cqVMTMz48KFC0B0svHpIg4qlQpFUciXLx9mZmZcvHiRwMD/kidHR0fatWsX61pCiJxFEhAhRLwsLS3p3r07DRo0iNXbUbduXerVq8eqVas4fvw4CxYsYNCgQdSvX5+2bdsCaB8oFEXRJiMx34BevHgRPz8/SpQoQY8ePWL1HFy+fBkzMzNatmyZaGzHjx/n/oP7dO/lSnBwMPfu3aNly5ZJTva1sLCgXr16WFpaoq+vz+DBg3n69Cl79+6NU/albxDN2nelSfOWrFq1iqNHj8YpU6NGDUJDQ5k4cSKhoaFMnjw51lCsdevWYWRkRMUqVRONKyHp9dAdGRmFqWnmfgMfFRWFSqVKl+V1M9txjzNoNBo6depEw4YN6dChA9988w2jR4/mf//7H/PmzWPZsmWsW7eOVatW4erqCsCFw7uxM9QQ4fUkwbZTm0RkVg9K1fpNUesbsn79+kTLPX78GAsLC4oVK8bkyZNxcnKiS5cuTJw4UfsFQqNGjdixYwfwX69GyZIl0Wg0BAUFaT8j8Yn5u7Fjxw4WL16c6Z9fIUTGkgRECJFiEyZMQFdXl3bt2rFq1SqGDx/O7NmzsbKyAv5LQFQqVZykYNu2beTLl4/atWtTqFAh7RyLp0+fcvPmTRwdHbX7ZyTEzc2N3n2/wSRXLtYtXUBkZGSSSUt82rVrh5mZGePHj0+wzJJV68iTNy+//PILL1++jHWuWrVq6OrqsmvXLoYNG8aRI0eYP38+iqLg6enJmTNn6N+za6zJ15C53+YD6Ovp8fbt20y9ZmIPlymV2e/Xsnkz6dO9Mw3r1sLRPnqY3fPnzzl37hy7du1i5cqVzJ8/Hzc3N2bMmIGVlRUPHz7EqXChZMUd37yjhCS3XHpRq9W07j2IGTNmJLqfj66uLrq6umzYsIGLFy8yY8YMGjRowLJlyxg5ciQAXbt25fHjxxw+fFj7d0BfXx8fHx8MDAzQ0dFJcLWxmLllhQsXzhGriAkhUkb2ARFCpFjx4sXZunUrAG/fvo21YlZCYh4oDh48SM2aNTE3N6dYsWJERkby4MEDXr58ybNnz/jmm28Sbefq1aucOHGCaXMXArBy5UoKFSoUawnP5NLT0+Orr77SboAW37f1arWabfsOUb9aJUaOHMny5csxMjICwMTEhOrVq3Ps2DG2bdvGnTt3WLx4MVFRUdy4cQNjIyOmTfgF3Yh32qFHCT2Ufjo0KT0fus1NDHnw4EG6tZccnyYgl14HpupBOrOTD4BuHdrSrUPbBM/r2Tpo//3x5ybC60OKr5Ud5oZ8ql7rjqz/cwb79u2Ld14VRCcGly9f5ubNm+zbt4969eoBUKhQIUaMGMHp06epWbMmDRo0YOrUqejo6FC/fn1OnDhBcHAwZcuWBZDkQogvlPzmCyHSJDnJR8w3qbdv3+bNmzdUrVoVExMTypQpg6+vL+/fv+fMmehhLzGrayVkqts0OnfvgbWNLUZKGM+ePaNVq1ap/ra9devWRERE8OecWQmWKVSoMG6z53L79m26desWa2fzxo0b4+vry82bN9mzZw+tW7dm2bJlnDt3jgG9u8fp/cgKhfJYc//+/Uy9ZkhISJp7QLIi+UiOj4dY5cQhZknRNzCkRfe+TJ8+PcEyOjo69O7dGxMTExwcHLTHK1asiKOjI8eOHQNg+vTpGBgY0Lt3b1q1akXbtm2pU6cOHTp0yOC7EEJkZ5KACCEyXMz8j3Xr1mFnZ6ddKevDhw/Ur1+fRYsWce/ePRwcHHByckqwncePH7N921YGfDsMgKVLlxIVFZVk0pKYEiVK4OjoyMa1qxIt17l7D7Zs2cLLly/p1KkTBw8eBKBevXqoVCrcfhtPhNcTtixxp0u71hQqWIAp48bEaiMrej8ASjva8+7dO4KDg9O13cScO3cOc4voie8xqy8ltQrTx7Jr8pFSOfU+mnfpzYWLlzh//nyCZYYOHYqenp52ojlEL/n8+PFjSpUqBUCFChXYtm0bU6dOpWLFiuzevZsVK1ZIz4cQXzj5CyCEyHAxvQAvXrygdOnS2NvbA9FL9VpbW3Po0CFevXpFzZo1E21nxsyZNG/VmkIOhclvacLBgwcxNjZONGlJikqlol27drx+9QqvJOZJtGvXjocPH2JjY8PIkSOZPn06pqamlCtXlqMn/3sIW73InfsXT8bq/ciKB1HNreiY2tSpBKBd+jSj+fn5cfnyZerWq58p18sKiU00/xzkMregccfuzJgxI8Ey5cqVo0qVKowfP57jx4/z6tUrtm/fjrOzM7Vq1QKiJ5MbGhry9ddfM2nSpCR/x4UQXwZJQIQQmWb58uUsXLgw1qZ4pUqV4tWrVzx8+FC7E3J83r17x4rlyxk87HvtsevXr1OuXLk0f5tasGBBFEUhMMA/ybL58uXjyZMndOjQgTVr1uDq6kpQULB2jktKZXTvB0DTauWxMDVhw4YN6d52fE6cOIFGo2HwsB/i9Hokpxckp/Qa5KQkJCW9TwBKRCitWrVix44dCc4f0tHRYdmyZZQoUYKRI0fi7OzM5s2bGTt2LNbW1oDM8RBCxE/+MgghMtWnY+bbtm1LvXr1KFu2bKKbvrm7u1O1ek1KliqNqU4UGo0GHx+fRPf+SK7Q0FAASjoWjHdPkE+p1Wrcl6xk2h/zuHPnDvfv38fW2irF102PncGTq0uDGly8eJFHjx5l+LUOHjyIubk5IVaFki78iZySfHwswutJoslITrsnJSQA3jzEJm8+ajdtxezZsxMsa2Njwz///MOyZcu4efMmN27coFGjRpkYrRAiJ5IERAiRpRwdHTl69CibN29OsMyHDx9YsGABQ0b8QEhwEFevXmXt2rVERkZSrly5NMcQsxRotWrV2LlzZ7Lrde/lyqV/91KsiCN9e3RJcxzpKWb4VYzf+3dGV1c30fc5PWzZsoXjx4/TsWPHDL1OamlundK+0kNO6gVJiKIoKEH+KG8foURGgEEusHNGlTs/bfp8y/IVK/Dy8kqwvq6uLuXKlaNQoZQnnEJkZwsWLKBw4cIYGhpSqVKlZA9jPXXqFLq6uunyBdnnShIQIUS2kNhQjaVLl1KosCM1a9fF2d6O6tWr4+HhAaCd0J4WzZo1Y/To0QQEBNCmTRsqlSzCxrWr45R76RukfcUoVsSJ66eO8E2vr1N0zYzY9TwxZrmMqVCkENu3b9f2+KS3M2fO8Pvvv1O6dGkmTPsj3iVmE1t2NiPvP76kIz0TkaRk114QJfA9vLoL/m/AyAzUalRqNSpdPQAKFy9F6crV+fPPP7M4UiEy18aNGxkxYgRjx47lypUr1KlTh+bNm/Ps2bNE6/n7+9OrV680LY7yJZAERAiRrUVERDBr9mwGD/teu6zrrFmzWLZsGeXLl8fc3DzN19DT06Nnz54cPHiQSZMmoa+nx8ihgxnS3zXNbWeFhB6qJ33TmaCgIPbv35/u13z06BEjRozAysqKCxcupHjsf0YnH2k5/7lRwkNQgv9/vpNKDbnzRfd4mFmjUsedy9TGdQhz580nKCgozjkhPlezZ8+mX79+9O/fnxIlSjBnzhwKFizIwoULE603cOBAunfvTo0aNTIp0pxJEhAhRLa2fv169PUNaNbyKzQaDZ06deKXX36hWrVqLFiwIF2vpaenR7t27bTtWltn3hyNtEjuN/n1K5bC2sKM9evXp+v1fXx8GDRoEACXL19OcG+MhHo/UpJ8fDyEKqHXp2WT225Gy8peEEVRUIKjh1nx9hGEhwCgymWJysgs0T1bylSrhU2+Avz999+ZFa4QWSo8PJxLly7RpEmTWMebNGnC6dOnE6y3fPlyHj58yIQJEzI6xBxPEhAhRLal0WiYMtWNgd8NJywsjGZ1q7NlyxZ69OjBn3/+ialpxuwivW/fPnR0dBgx6qcMaT89J5+n9IHbtXldbt26xa1bt9Ll+uHh4QwdOpR3795x8OBBChQoEGuIWnpJaTKRmoTic+wJCQsNjd6HJyoS/N6CkSnkK4bKIm+y21CpVLTpM4QZs2YRGRmZgdEKkbHCw8MJCAiI9QoLC4tTztvbm6ioqDgb7ebJk4c3b97E2/b9+/cZM2YMa9euzRYb0GZ3koAIIbKtPXv24OPjQ6WqVahaphh3797lt99+Y/To0Rn2B15RFHbs2IGDfQGKmykZco34ZNa347+6tkdPTy/dJqN7enpy7do1Jk2apN374WOV7Ey1r/gk574zMzHI6Gtlxs85KioKv/fveHLvFo9uXyck6EP0nA67oqjMbFDppPx3p2bjlkRGKWzZsiUDIhYifZQvZkXN8nnifenqqFi6dCnm5uaxXlOnTk2wvU97BhVFibe3MCoqiu7du/Pbb7/h7Oyc7vf1OZIUTQiRbU2Z6kbDxk1p2cAFfX19li5dSuXKlTP0mtevX+fly5dMHR+9i7ltxLt07bHIqN6P5DLU16d4QTvOnj2bLjHky5cP+G+3+5T0fiT1MJ5VPRKaW6dQl4ybTKWX9P5MQfSDUWRkBHp6+rx98ZTQ4CAsrGwo4OgcnawHBSY6zCopOrq6fNVrIDNmzKBLly5pakuIrNK/f3/c3NxiHTMwMIhTztraGh0dnTi9HV5eXnF6RQACAwO5ePEiV65c4bvvvgOie/AVRUFXV5eDBw/SoEGDdLyTnE96QIQQ2dKpU6e4ceMGnpcvoaenx8aNGzM8+QDYvXs3enp6DP2mj/ZYdlzBKC0P56UcC/Dy5ct4hx6kVL58+TA0NEx0XHRqZPVwqMxcISstIsLD8X7zioe3rvHi4X0A8hZ0wLFEGXLb5k3XnsKG7bry8PETjh49mm5tCpGZ9PX1MTMzi/WKLwHR19enUqVKHDp0KNbxQ4cOUbNmzTjlzczMuH79Op6entrXoEGDKFasGJ6enlSrVi3D7imnkgRECJEtTZnqRtMuvbDIX4jQ0FDy5k3+mPW0OHHiBMWKOKKvr5/iul56Nol+s53QucxOcKqXKoqiKDx58iTNbanVapydnbl9+3aK6iV2z9npwT+99w2JkZafeUR4OBqNhvCwMB7c9CQoMAAbuwIUci4BJH/38dYlU/Y7ZWhsTPOursyYMSPFMQuR0/zwww8sXbqUv//+m9u3b/P999/z7Nkz7YIbP//8M7169QKif+dKly4d62Vra4uhoSGlS5fGxCTpDW6/NJKACCGynZs3b3LkyGFa9ehP+Rp1iYyM5OHDhxl+XT8/P16+fEmDhg3xCfigPZ6c4TIfl8noHc7T+jDctFr05o3p9Z4WK1aMd++S/0CdU5KPT6V3MpKSJCQyIhyfd295cu8W9296EhochL6BAUXLVKBQ0eKY57ZK8dLHEDcJuXv1Egc2r0mwfIvufTh+/ARXr15N8bWEyEm6dOnCnDlzmDRpEuXLl+fEiRPs3btXu+Hm69evk9wTRCRMEhAhRLYzbfp06n3VkcZlizCoR2eAdFu1KTHXrl0DoEHt6tpjN7xC8X33lsiIiATrxZdwfHoso5OSlChsZ4u+nh4PHjxIl/YcHBwIDg7W7iifWtk5+fhUZsQaGRFBaEgwAK+fPSHA9z1mllY4ly6Pca7oSf26/79hYFq0LpmX8PBwpgztw5gerfnr9zEJljXPbU2Dtp2lF0R8EYYMGcKTJ08ICwvj0qVL1K1bV3tuxYoVHDt2LMG6EydOxNPTM+ODzKEkARFCZCvPnz9n48aNtHGN7ua2trHF2Ng4UxKQq1evoqenRzOXWuQ2ywWArr4+/j7vuX/jCk/u3SYiPDx6TwVF+e8b7Fe3qVLamcZ1qsdqL6khWZC6oTjpMUHa1Ngw3RKQhw8fYmRklKxv4OO735wy3+JT6RHzp+9HRHg43m9f8eTebe7fuIKvtxcABRyL4uBcktw2edDVS/nwwPhWIfvw4QNbt26lb9++uNYuyfl/D2Bvbw8qVaLDs1r3GsimzZt5+vRpiuMQQgiQBEQIkc3MnDWLKi6Nye/gpD2WN29erl+/nuHXvnr1KlaWFtoHaS89G3Lb5MGhWEmKlCqPuWVudHR1CQsJ5sHNqzx86cW1O/cpVbMhr1+94umTx/G2m516P2IUsLHk+vXrRCTSs5Ncp0+fpkiRIkmWSyj5yMnSGn+URoPu+8e8ffkMRVGICA8j+EMgZpa5KVKqPHYFHYC4y4GmBysrKzp27Mjy5cspUKAAa9asoV27dsRcKaEkxK5QYarVb8qcOXPSPSYhxJdBEhAhRLbh4+PD0qVLadf321jf2JYoUSLD54BERUVx9epVSpcsHu95PX19LG3yoFar0Tc0omQ+C569eMFXXXsD0KBBA6IiIzELeoVtxLtsuXLWx37o2gpvb282bNiQpnZevnzJmzdvaNGiRYrr5vTkI7UURUGjKNx6/JJzNx/y6NU7NBoNGo0G41ym2DsVI7dNHvRSsRBCckVGRhIeHs6AAQPw8PBgy5YtlC1bFo1GEyvZSSgJadtnCIuXLMHX1zfDYhRCfL4kARFCZBvz58/HuUwFipYpH+t4REREqlalSolHjx4RGhpK86/aomfrkGivhVqtxsRAj669+hESEsLKVauoVasW4eHhXLj1kEt3n/DgxVuM/J9l20SkS8OalHQowLx58/D29k51O+fPnwdgwIABiZbLru9DekgqkQqPiOSdXyAPXrzl0t0nvPMLRK1SYWVhSvmi9lQq5kC5vCbo6OhkaJwfJ/WR4eEA2NvbY25urj2uKLE339RoNNzds5phLaqzddl87fGiZcpTtHR5Fi5cmKExCyE+T5KACCGyheDgYNznzqPtJ70fEN0zYmZmlqHXj5mA3r179yTL2ka8o2nHHvj5+zN79mycnJzInTs3iqJgbaxLYTtr1Go1EZHRk7J9Hlwl4PENVF4Pifj/B7/sYPPk74mIiOCPP/5IdRsXLlwgV65cFC5cOEWbEH7OvR9hEZG88wvA2z8QgCdvvHn5zge1Wk1hO2tym0UvyZnH0gxjQ4Ms2dQvIjx6D5hPk56YHhCNRkP//v0xNTVl9OjRPH/+nIPrlsUq29p1MHPc5xIaGpppcQshPg+yE7oQIltYvnw5VnntKF/TJc65gICAWN/SZoSXL1+iVqv5999/6datW4Ll8luaMHnsRE6du8B3331HlSpVgOjx9AAPHj+hhHMR7SR2AFtLM/yDQnjp7Yffy/c4Fi+NvoEhAX4+mOqHY6ivlyUPoU7589CtWzdWr17NqVOnsLKywsbGBisrK6ysrKhQoQL169dPsL6iKJw5c4ZixYolep3PufdDUSBcUWEE+AYG8ejVO0LDwjExMiSPZXTSXLRAnmT9fGPep8yYMxT+/wnIpxsVxuze7OzszMOHD6lXrx49e/bk2rVrzJs3j+APARjnir6vSnUakMsiN6tXr+abb77J8JiFEJ8PSUCEEFkuMjKSadNnMGbcRCrni9vTERgYiKOjY4bG0LVrV06ePEmPHj3w9PRk2JjxccrktzTh4sWL/DZ9DtWrV4/10JU7d24AHj56EqdebrNc2oTktdoStVqHyIhwQt4+5UpIGGq1CjNjI0o45CMySkOURoOBnm6mJCULezXExMSEmzdv8u7dOx49esSNGzcIDQ1l+fLlLF++PMEd6J8+fYq3tzdDhw79Ino/YkYnRQKvwvX4oFHzIUqNGoWqioKRgT6F7awxMzFC96OehZT+HG0j3mV4EhIZFn8PiKIoRERE8PTpU6ZOnUqrVq0AMDc3x93dnX0bVtKh/1Ag+r5a9x7EtBkz6NevX6r2IRFCfJnkr4UQIstt3rwZtVpFyzbtYh2PmcwdGhqa4UOwbG1tWbVqFfXq1WPGjBm8fPE81vn8liaEhobSuHFjzM3NmTZtWqwHLltbWwBu37uf6HV0dKITCz19A8oVsad66SKUdiyAbW4zVCoVfh+CuXTnMedvP+Lm45e88fEHICpKE2d8fnrQ1dVl4cKFnDhxgtu3b/Pq1Sv8/PwICAjAwsKCX375heDg4Hjrnj9/HpVKRf/+/RNsP6f3fvhGqnkWpsutEH0uBhsSqqhQA5EKWOlGUsoolEomoSi3T2Oor0dus1yxko/Uyqj3LWZ4Y6QmCog758PKyorcuXOzfPlybfIB4OzsjJWVFR77d8UqX6dFWwICP7Bz584MiVcI8XmSBEQIkaUURcHNbRoDvh0WZzhIjNDQ0AwfggVgZGTEt99+i6IonD9+OM75+vXrExAQwOzZs7U9Hh/XzZMnDzfu3E3RNdUqFbmMDLE2j34wtLEwpXrpIpQolA9LU2Ptt+ePX7/j7M2HXL3/jHvP3+AXGf3nO0L575v51Ao7tjbOSzm9mfVL5vP27dsEl1s9d+4cZmZmKEbx/2wya9ld7+Mn0tyGf5Sal+G63A/V51qwAe8jo5OINxG6hClqLHU0FDcMx0CloKMCJ8MI8upFkUtHQZ1BHVUZmbzZFXRAV1c3zl4wnTt35siRI5QvXz7WcZVKRb169Xj5OHZ5PX19WvUcwBQ3twxJkIUQnydJQIQQWerAgQO8evWKzt17xnt+3+F/CQkJ0c61yGiFCxdGV1dXu8OtRqNh88ol2NnZcfbsWYYNG0bFihXjrVu0aFGePn+Z5hh01GrMTIzIZ22pnUfgmM+GskUKks/GEkP9/3a/vh1iwLkgI64GG3AvVJ+gqOin4cAoNaEaFZr/fyb0Pn4i1is56ltD61oVWb9+vXa1qxgajYazZ89SpkyZeOtmds9HYvemUaITNYh+X56H63IvVJ+rwQY8C4tOev0idfigUWOo1pBPPxJTdXQPQQmjcIoahmOnH4mpjibDko3MplarsbKy4sqVK7GOq1SqBL8IqFSpEmGhIbz3ehPreJOOX3P71m1OncqZQ+uEEJlPEhAhRJZyc5tGnwGDMDIyinXcNuIdmlunmDplKiYmJri4xJ2cnhH09PRwdHTk0qVLjBr+HSUK2fH9999jZGTEpEmT6NOnT4J1nZycCAj8kCFxqdVqTAwNsLEwxT6PFRa60StslTEKo5xxKAX0IzFWRz8gKwo8CNXnSrAh54KMOPMsmHBdAzQqNV5W9via5+HJhWsERamS7D1ZM/47LExNGDt2bKyhWPfv3ycgIIB27eIfNhefjOj9UBSI0NEn1MCED8YWBOSKXgzAP1LNjWADLgVFvwf3Qg0ACNWoCNWoMVZrKKAfSR696ESjkEEExQzDKagfibVuFPrZ5P8dMzKRK1euHJ6enkRFRSWrfMx8ER2d2AmKcS5TmnbpxbRp09M9RiHE50kmoQshssz58+e5ePEC8/9eFe/5yMhILt97Qpu2bTEwMMi0uEqVKsX27dt58mQFtWvXpmfPntSoUSPJycROTk6Ehobi4+tHbkuLTIlVpQIjlYKROvZDZAWTUBQF3pw+S4SeIbpR4WjUOkTq6hFiaEqEngHvQg2obBxKQJSa2yEG6KkU9FQKRmoNRQwjCIpS4a8xZPnkMXQa+Ru9evXCxMSEwMBAvL29UavVdGleD6uwt7w3yJPih2VFgSiieyg0qND7/+FN/pHq/z+uQgPk1o2+tydhekQoKiL//1XOOJSX5y7z2KkKOlGR6ERFYBAWhPfxE5jUcSGPXiQGagUDlYK+KjrTstGLwkYveQ/cn7sWLVpw8OBBHjx4kORKZhD9+wigb2gY51zLr/sxqFl1bt++TYkSJdI9ViHE50USECFElnFzm8bXvftiaZk7/vNrdhIeEUHr1q0zNa7OnTtjYWFB27ZtU7T6VkzZw8dP0rntV0mW99KzydBvuN+fOIEeoBcZvfeIOiqSfG//21HeulBdAEzUGkobhWof7mNEoiIwSo190eL8OLg/W/cfwcfHB1tbW5ydnSlVqhT3XnqTJySCogXUPHj5lne+Adr6BWxzU9DWiltPXuLn9RaI7uVyNAjHVi8Kz2BDQv7/eiqghGEYFroanoTroQA6gFoFZjoa9P8/iTBRRydJuioFFWAY9oES906jInZXjpE6blKWU2XUqlhWlRuhUqm4evVqshKQmJ4SfYO4CYiVbV5cWrZnxsyZ/L1sWZzzQgjxMUlAhBBZ4u7du+zdu4eTF6/FOWcb8Y5Nf85i6podVKhQgXLlymVqbKVLl6Z06dIprheTgHicvZCsBAQyPglJjPfxE1i71EVHBSY6CnzyEG+uo8HcKDp5KdW+NhPb18ag3tfa8xFeT9Ao/82Ct7e1Ir+1pfZ8zGpQTvltifzwVHtc7/97I0obh6Iieizwx3MryhmHxRuvvUFknGPR1eKOI4u5N5EwM0srcuXKhaenJ507d06yfEREBBB375AYbVwH8UOnJvxv8mTs7OzSNVYhxOclm4xyFUJ8aaZPn0GbDp3Ilz9/rOO2Ee/Yc/AIrlMWUbRoUf78888s2aQvNUxNTbGysuLazVspqpcZG89lFLVKpV2OWF9PFyMDfe1LTzc6AdG7f/7/eySiX7r//+PUU4Gu6r/k49OJ8imZMP+5y6gk1SpfQU6cOMHr16+TLBsVFYVKpaJZkdzs27CSeeN+4EOAv/Z8QSdnKtZ0Ye7cuRkSqxDi8yEJiBAi071+/Zq169YyeOiIOOeOnDhF5z6DsLe3Z8mSJZiammZ+gGlQtGhRHj99keD5hB4kvfRstK/kUJeslej55D64p/QBP2aZ3givJ8kqn9yJ54nFkVAiklTskrwkbdC4qYSGhtKjRw+ePn2aaNnIyEgURcHExIRFv4/h8Lb1fPdVHYKD/lt4oU2fISxYsJCAgIBEWhJCfOkkARFCZLrZf/xBnXoNKFqsuPZYeHg4v37bh9bd+5DXzo5ly5ZhYWGRdUGmUpEiRfALCIiVUOTkHo6EJCexSE6ZlPRypKZn5HNKQjKiF6REhaqcPHmSgIAAevbsGWdfkI8VL16c8uXL06dPH9asWcOCBQsI9PPl21Z1CP3/FdJKVKxKAceiLFmyJN1jFUJ8PiQBEUJkKj8/PxYtXMR3w38AoveTmD19CqUK52fF+s3Ur1ePFStWYG1tncWRpo6DgwOhoaGEhobGOv45JyPxSW7yITJfzG7oMV6bFOTixYuEhobyyy+/JLihYJUqVVi9ejXDhg2jXLly1KlThzlz5uD//h3fta5LeGgoKpWK1n0GM/uPPwgPD8+M2xFC5ECSgAghMtXChQspVaYMFatU5fDOLZR3dmC22xRKlCjBunXrmDV7Nra2tlkdZqoVKlQIRVE4dybnbMqW2kQgviRDc+tUhuz3kRaS6CTtocqKMWPGcPv2bTw9PZNdz8XFhVmzZvH+7WuGtq1HeHg4Ves3Ra1nwIYNGzIuYCFEjiYJiBAi04SGhuLm5sbVK5dxsLXA1dUVc3Nz/vzzT1asWJHgrto5iYODAwAXz53N2kAySUyykdLEQ5KC1MnIFdNKt+2LgaEhq1evZvXq1YwcORI/P78k6zVs2JBp06bx9sUzhrerj6IotO49CLdp0xPsTRFCfNlkGV4hRKZZtWoV9evXx9TUlLx581KoUCHq1Kmj3WH5c2Bra4u+vj43rl1NdRtZsTRvWpatTWmPR1YkH5mxLK/m1qkkFwfIznR1dalavxmH9+/g7t27PHv2jGvXrrF8+XIKFCiQaN1mzZoRERHB2LFjGdetKRNW7WLDnzPYv38/zZs3z6Q7EELkFJKACCEyRVRUFHv37mXy5MlZHUqGUqvV2Nvb8+TRw6QLZzMZ/ZCe1b0esjfIfyrZmXLpdWCc4/1//p3TB3fx7NkzChQowKtXrzhy5Ai9e/dOss2vvvqKyMhIxo8fT7965YjUaBj76zhJQIQQccgQLCFEpti+fTtt2rTJ6jAyhZOTE++8vDL8OhnxbXtGJQlZnXzEyC5xZFcWVtY4lSwLgF2REujo6Gg3IEyOdu3aMWPGDOrUqUPd2rXR09XhwoULGRWuECKHkgRECJHhFEVh6dKlVKlSJatDyRSFChUiODgoq8NItfTcADA7biaY3eJJqYwentdvzCQAnEqURaVSpXg1q2bNmjF9+nRmzZrF2LFj+d///pcRYQohcjBJQIQQGe7o0aPkzZs3q8PINA4ODoSHh+P19m1Wh5ImqU0ecsIu5tk5tszy8XK8rUvmpXXJ6N/R4uUrs/jgeboM+QEdXT0OHTqEn58fiqLg6+vL3bt3uXHjRrImmDs6OmJpacnDhzlvSKIQIuNIAiKEyHATJkygQoUKWR1GpgkJCQHg5YuEd0TPSZKTUOSEpONTOSnWjJDf0oTWJfNSTMef6tWrY2hoyPQBHfF+85o8+QuiVqsZNsWdJ0+e0KRJEypWrEjdunXp2LEj3bp149dffyUsLCzJ6wwZMoRZs2Zlwh0JIXIKmYQuhMhQly9fxsvLi2rVqmV1KJniw4cPzJs3jwIFC1KhUqWsDifdfW4P7V/qxPT8liYADBs2jAULFqCrq0v9+vU5cuQI55pWpVrD5gybPIeajVtiMH8lW5bMxTy3NXkL2JPfsQi3L19g945N3L9/n7lz5ybaw2lkZERwcDDv3r3DxubL2IhTCJE4lSKLdAshMlDr1q158+YNf//9d1aHkilmzZrFqlWr2LH/CBUqp37OS3LH+Se0BO7nlihktPRMQjJrKV4vvdQ/zMckIMbGxpQvXx43Nzdy587Nmzdv+PPPP9mxYwd6+vq0cR1Mj2E/xdvG8d1bmT/uB4yMjJgzZw6VK1dO8Hrv37/nyJEjTJo0KdUxC5EYCwsL5hV2xNHEJN7zzc6e4YynJ6VLl87kyER8ZAiWECLDPHz4kBMnTlCjRo2sDiVTPHnyhNWrV1O3ZrU0JR8i832JCdu+ffsICQmhW7du5M6dG4C8efPy+++/s3nzZsqWKcOWxe6893oTb32XVh2YveUQarWawYMHa4cexsfKyoo7d+4QHBycIfcihMhZJAERQmSY6dOnExoaSq1aOXdztpS4cuUKUVFRdOrWQ/sNs8g5vpQkJOazOW/ePPT19alevXqcMsWKFaNnz54oikJQgH+CbRV0cmb9+vWEhoZy7ty5RK/bq1cvli9fnrbghRCfBUlAhBAZwsvLi+XLl2NgYEDx4sWzOpxM0apVK4oVK8aPP/6In5+fJCEJeHLoVqKvrJQeE+lTujN8aqV1Od5Tp05Rs2ZNjIyM4j2vqxs9TTQ8NDTRdsIKlsPQ0JDjx48nWs7BwYHDhw8TGRmZuoCFEJ8NSUCEEBli7ty56OnpUadOHdTqL+NPjZ6eHm5ubkRERNC4ceNUt5PR+zxkhrQkGNklEfmcHT16lICAAJo2bZpgmZgEpGq+pBPpokWLcvTo0SSX5m3fvj3btm1LWbBCiM/Ol/FUIITIVIGBgcyfP5/g4OAvZvhVjCJFijBs2DAuXrzIggULvphekIzoxcjqRCQnJCGpTVYnTZqEoaEhDRs2TLCMnp4eQLxL7YaHhrJvw0rG9GhN1ypFuH79OsHBwUnO8ahQoQIbNmxI1h4iQojPlyQgQoh0t3jxYkz+fyWSL2UC+sd69epF2bJl+eGHH/Dx8UlR3ZQ8UGbWUJ/EZEaSkF2GZ6VEdvjZJCQyMpKzZ8/SvHnzBIdfAdrf4UOHDsU6/vLpI7pVK8qi38fw4sEdGtSvh5ubG4cOHdLWSUydOnX4999/03QPQoicTRIQIUS6Cg8P548//iAyMhJnZ2esra2zOqRMp6Ojw+TJkwkPD2fYsGGZfv3M+OY+qxKCzJ4v8jn2gixatIiwsDBat26daLkSJUpQtmxZ5s+fTwvn/36PTXKZERUVRZ8+fTh58iRubm60bNkSCwuLZF2/YcOGLFmyJEUxCyE+L5KACCHS1dq1a9FoNLx79y7R8eWfu8KFC1O/fn22bduGRqNJVp2cMvcjO/VE5LSekexg3rx55MmTh4oVKyZaTqVS8cMPPxAUFMQPP/ygPW5hZY2JqSnPnj1DR0cnxddXq9U4Ojpy7dq1FNcVQnweJAERQqQbjUbD1KlT8fLyokqVKvTp0yerQ8pSvXr1IiQkhJUL3bM6lHSTXR/2s2NcmTkMK7nJq6+PDw8fPqRdu3bJWhyiUqVK1KlTh8WLF9PAPpf2eKGiJTl79myyk+tPdezYkfnz56eqrhAJyVfDCYfGJeN9qXVTniyLjCMJiBAi3ezatYuXL1+SL18+Zs+erZ3E+qWqWLEixYoVw93dnfyWJolOSM8pvR/ZWUYlITlhGFZyHdy3h6ioqBQtjT1ixIg4wwlrNG5BUFAQt26l7j3X09NDV1eXZ8+epaq+ECJnkwRECJEuFEXh119/RaVSsWDBAszNzbM6pCynUqno06cP3t7eFCtWjNu3b8ebhKQm+ciKSc7ZsZfhU9ktxuw2Gf27AX0xNzdnxowZie5c/jFnZ2fMzMx4+vSp9ljDdl3Q09fnu++Gcvv27VTF0qtXL+bNm5equkKInE0SECFEuvDw8CA4OJg//vgDBweHrA4n22jRogW//vorL1++pEyZMnTu3Jnw8HDteen5SH/ZLQnJLMn5LOnr67Nx40Zev37N3Llzk912REQEBgYGtC6ZFwDjXGa4rd1FSGgoPXr04OjRoymON1euXLx+/RpfX98U1xVC5GySgAgh0sWECRPo1q3bF7nsbmJUKhVdunRh7969tG3bli1btlDaqSDrVq3IkOQjo4YL5bSH+vSONy3va3brBWnatClfffUVa9as4eLFi8mqExkZiaGhYaxjRUqW5a8DZzG1tGLEiBEcOXIkxbH06dOHRYsWpbieECJnkwRECJFm169fJ2/evHTt2jWrQ8m2cufOzcSJE1m7di32BQsy5odheJy9kNVhfdayUxKSWZKb1G7atAlzc3N++eWXJDcPhOgekJgEJKYXBMDM0oqCTs4oioKZmVmK482TJw+XL18mNDQ0xXWFEDmXJCBCiDRzd3fnxx9/zOowcoQyZcqwfPlycufOTee+g1K1ilBmf6Oe03o/PpZdlunNLr0gL32DgOihWJs3b+bt27e4uye+SltkZCSKosTpAQHYtXoJnqePM3DgQKpUqZKqmLp168aaNWtSVVcIkTNJAiKESJOnT59iYGCAvr5+VoeSY+TKlYuJEyfi/d6H0RP/l65tW7vUTdf2PhfplYjkhF6Q5GrcuDHNmjVj3bp1+Pn5JVguIiICQLtr+s5bbwB4eu8OK2b9TuXKlRk8eHCq43B2dmbPnj2pXtJXCJHzSAIihEgTd3d3evXqldVh5DguLi7Y2dlx6lzyxuCL9JEdekOyWkwvCEC5cuUAiIqKSrB8zCpXVlZWsY6fPryHyIgIateuHWdDwnfv3nHq1KlkTTBXFIUSJUqwa9euZN+DECJn083qAIQQOdf79+/x9vbGxCTh/S1EwoyMjAgLC8vqMEQm0dw6hbpkrQy/jm3EO7z0bJJVNigoOhmJb3gVQEBAAKNHj8bMzIwxY8bEOtdl0PecO7Ifd3d3FEUhLCyMmzdvcuPGDW3iMWbMGL7++usErx8eHs64cePYu3cvZcqUoU2bNsmKWwiRs0kPiBAi1ebPn0+/fv2yOowcy9DQkPAoBT1bh6wO5YuS1l6Qz2kYVkwCYmBgEOecoij89ttveHt7s2fPHoyNjWOdV6vVzNy4D9v8BXF3d2fp0qVcu3lLOxyzevXqdO7cOcFrBwYGMmjQIPbt28eAAQO4//Axp05lj7kyQoiMJQmIECJVgoKCuHnzZpxhGSL5DA0NU9wDIhPQc7bsNhk9KCgItVqNrm7cARHbtm3j4MGDDBs2jNq1a8fbjq6uLov2nWHRvtNsvvKUqWt24uPjQ7FixZgzZw56enrx1vPy8qJXr15cvnyZWq6j0GvyLaWadGKK27T0u0khRLYlCYgQIlWWLVtG7969szqMHM3Q0FA7wVd6QTKXJFbRSUhwcHC8C0g8fPiQKVOmULx4cWbPnp1oO2q1Gjv7wqjVaq7tXE1ERATz58+PNTQzPDycw4cP8/r1ax4+fEi3bt148vQpTUbOpFzL6CFaZVp059DBg9y5cyd9b1QIke3IHBAhRIpFRETw77//0qBBg6wOJUczMDDAx8eH+/fvU7Ro0awOR6SA9/ETn8WKY77+gXGGX3348IGRI0eio6PDyZMntcdjVr9KzLFjxyhUqBB58+aNdXzPnj2MHz8eiE5Y9A0MmLp6B09082vLmOS2pVid5kyfMZO/ly1Ny20JIbI56QERQqTYxo0b6dSpU1aHkeO1b98elUpFiRIl6NSpU7otQ5qZD8anPd/GeuUkWdULkl2GYQGEhYUSFhbGuHHj6NWrFy4uLtSoUYPHjx+zevVqTntFsvPWm2QlHxC9LHf16tXjHL906RLGxsb8+OOPlK/pwvxdJ3EuU4EmJWxjlSvTqhdr1qzh9evX6XJ/QojsSRIQIUSKKIrCli1bKFu2bFaHkuPVq1ePffv20bp1a7Zs2cLf2w9kdUgpEl/CkVOTkdTIzpPRk7sjumORokRGRnL4yBF8fHwoWbIk3Xq6snrjNqo3aJaiaz66c4OQkBAqV64c59yFCxdwcnJixowZTPhrHTZ2//V8fJyE5C7oRKHyNZgzJ/HNEYUQOZskIEKIFNm3bx9NmjTJ6jA+G1ZWVkycOBELCwuWLVuWbu1mh+FBX1IyklPNnLuAJ15+3Hn6mlNXbrJh535muM/HpWGjFLd1ZPtGAG0CotFoeP78Ofv37+fVq1fUr18/3nqBfrH3CinTqhfu7u4EBgamOAYhRM4gc0CEECmyfPly7VhukT7UajWNGjVi165daDQa1Or4vxvKTkN3UppUxJSvWT5Pso6LnOfamZMUKFAAa2trVq1axdy5c7WrvOnq6jJgwIA4dY7u2IT7L8MxNDLGLL8jTtUbo2toiJ6eLosXL2bkyJGZfRtCiEwgPSBCiGQ7e/YsFSpUQKVSZXUon50iRYoQHh6ebvNAIHv0gnwqoTkjWdVbIvNA0o/Xq2dUq1aN4OBg5s+fj7W1Nb///jsXLlwgLCyMUqVKxalTvWEzDAwNCQ0JJvTdc86s+YOTS6diZ2eHh4eHdpU4IcTnRXpAhBDJ9ueff/Ljjz9mdRifpRcvXmBoaBDvfgzZTUYnCac930qPSDZRyc6US6+THgr1/OE9Qv9//seBAwcICQlhw4YNcfYP+XQyu3EuM/qN+Z0FE0fxTf/+1KhRg/Pnz1OnTh0+fPjAhg0b6NmzZ7rekxAi60kPiBAiWW7fvk3evHnR0dHJ6lA+S0+fPsXUJFdWh5FtyLyRnOXMoT0ArF+/nkWL/sI8t1WCmxd+6s8JP1KhQgXmzp2LkZERPXv2xMHBgdKlS7N161YURcnI0IUQWUASECFEsri7u9O1a9esDuOz9fjxY/LlTd9v/bPzKk3JkRlJiEPjkhl+jS9Bk049qVKvMc9fvcHrnRdtXAcluXRv65J5aV0yer+Q/fv3o1arGTduXKxhiI0bN+bAgZy1OpwQImnZv69fCJHlXrx4gaIocTYsE+kjMjKSV69eUaNS+awOJQ6HxiWzdNfw7D4c63PZkDCtLKys+fXPVXGOx5eExCQdH7O1tWXy5MmMGjWKLVu20LlzZwDq1q3L77//TrNmKVsSWAiRvUkPiBAiSXPnzsXV1TWrw/hsvX79Go1GQ7ky6fdtfE7v/fgSfI4T0VPLz8+PokWLkitXLmbMmMHbt9G9XyqVilKlSnHx4sUsjlAIkZ4kARFCJMrX15eXL19iamqa1aF8tp49ewZA9UoVsziS7Enmg3x+Nm7cSNmyZbGxscHQ0BBLS0vatm3Lhw8f0NHR4f3799qybdq04c8//8zCaIUQ6U2GYAkhErVw4UL69euX1WF8tiIjIzl06BAqlYrqlSukS5ufY+9Hdh+K9TlL7kpYyTVy5EjmzJmDjY0NZcuWxcHBAQcHB+zt7SlUqBBWVlaxlvrW1dXF1NSUR48e4ejomG5xCCGyjiQgQogEhYSEcOXKFVq3bp3VoXyWnj17xujRo7l16xYtmzREX18/q0MSIsNoNBp+G9gdz9PHadasGb///juGhobJqtujRw/mzp3LnDlzMjZIIUSmkCFYQogErVixgh49emR1GJ8djUbDxo0bad++PQ8ePGDx4sVsW7Uk8ToyXyDdh2LJCliZJ/hDAINb1MTz9HEGDx7M9OnTk518ABgbG+Pr64u3t3cGRimEyCySgAgh4hUZGcmBAwdwcnLK6lA+K0+ePMHV1ZXJkydTuHBhBg8ezMOHD/nw4UO6tP85Dr/6nH0pieWGBbN58/wpY8aMYciQIbGGWCVX3759ZS6IEJ8JGYIlhIjX1q1bad++fVaHkWZv3rxh1KhR+Pr64uzsTJEiRXBycqJKlSrkzp07Q6+tKAqvX7/m9u3b3Lp1i1u3bnH27Fl0dHRo1KgRZ86c0Q4pmTlzJheP7KFkcecMjSknkzkgWSet80Da9B7InrXLePz4carbsLKy4ubNmwQHB2NsbJzqdoQQWU8SECFEHIqisGHDBn7//fesDiVNbt++zeDBgwkMDMTZ2ZlLly5x7NgxIiIiUKlUlChRgnr16lG3bl1KlCiBWp0+ncI7d+5k165d3Lx5k8DA6Ic2PT09LC0tqVy5Mjdu3ODw4cNUrFiRYcOGsWjRIi5fukTePLbpcn2RNBl+lXJpSUKs8thRtnpdtmzZQt++fcmXL1+q2unZsycrVqxgyJAhqaovhMgeZAiWECKOw4cPU69evawOI01OnDhBz549CQ8P58KFC1y/fp33798THh7O1atX6devHwEBAfz111907dqVevXqMWvWLLy8vFJ9zYiICCZNmsTYsWO5f/8+lSpVYty4cVy6dInQ0FCuXr3K9evXMTIyYsmSJaxYsYKgoCDOnj1Lv57dyG1pkX5vwGdGej9yvuFT3FGpVPz111+pbqNw4cIcOnSIqKiodIxMCJHZpAdECBHH0qVLGTt2bFaHkSqKorB582YmT55Mnjx5uHr1Kra2sXsWypYty5Il0ZO+g4ODWbx4MatXr2b16tWsWbOG1q1b4+rqSuHChZN9XW9vb0aMGMG1a9fo1asXK1eujHU+PDycChUqEBUVxaJFi3B2dsbf359x48ZhY23FH/+bkPab/0yld/IhvR+pl5ZeEAsrayrWacA///zD69ev0dPTQ1dXN9bL2dmZTp06JTrEqm3btmzbto1OnTql9jaEEFlMEhAhRCyXLl1K1+FImcHb25uzZ89y9uxZTp06hbe3N+XKleP8+fOJLm07YsQIlixZgoWFBZs2bcLe3p5Bgwaxa9cutm/fTsOGDenfvz+lSpVK9PrXr19n6NChBAQEsGjRIgYMGBCnTPXq1Xn79i0LFizA2Tl6nseUKVPw9/fHY++2HPV+52TZMfnQ3DqFumStrA4j2dKShAybPIdx/Tpz9+FjNJooFI0GjUZBTw1RUVHs2rWLJUuW0L9/fzp37hxvIhLTs9ixY8dUTWYXQmQ9SUCEELHMmzePESNGZHUYSVIUhTVr1rBlyxYePXoEgImJCcWLF2f06NGMGjUqyTbWrFlDSEgIRkZGuLi40KdPH3bt2kVAQADDhg1j8+bNHD58mKpVq9KyZUvMzMwwMTHBxMSEXLlyYWJiwunTp5k0aRLGRkZ47N1GhbKlifB6gp6tg/Y6EydO5MqVK/zyyy/Url0bgCNHjrB3714GDhxIpfJlM+Q9+hykZ+9Hdkw+vjSmFpbM2XoowfNWvg/p378/s2fPZunSpQwdOpTOnTvHSTTq1KnD8ePHc/xQUSG+VJKACCG07t+/j4WFBbq62ftPQ3h4OOPHj2fPnj04ODjw3XffMXDgQEqXLp2idtzd3enRowedO3fmxYsXLF26lL1793L48GFWrVrF0qVLGTNmDEuWLOH8+fMJtuNcxJHT+/7BzMxUeyzC6wkAAQGBTHNzo2rVqnTt2hUAf39/Jk6cSN68eVmwYAFR3s+SjFVdstYXs2RrTmLtUjerQ8gS6b07eoz3lk7cvn0bDw8P+vbty+TJkzlx4gSTJ0/G0tJSW65Ro0a4ublJAiJEDpW9nzKEEJnK3d2d3r17Z3UYifL19WXYsGFcu3aNoUOHMnfu3FjnHz9+zP/+9z92795NSEgI1tbWFCtWjKpVq9KoUSOqV6+uTbBMKzSkUNHiLF++nD179lC/fn0mTJhAuXLlOHr0KLVr12b27NnMnj2b4OBgXr9+zevXr3nz5g1eXl54e3ujMjBmdO/2CQ6h6vrNt4RHRPDLL79ov8X9448/CAwM5MiRI8lKPrKbmuXzpGlTwJhejcTakHkf2V9GJSE7b72hde3a3Lt3j/HjxzN16lTatWvH9OnTqVq1KgBqtRoHBweuX79OmTJl0j0GIUTGkkHHQggger+MmOFI2dWjR4/o0qULN27cYOHChdrk4+HDh/Tt25c8efLg5OTE33//Td68ealfvz5GRkb8+++//Pbbb9SpUwdDQ0Pc3d21bY6dt4LIyEj+97//Ub9+fXbs2IG1tTXNmjWLteuysbExTk5O1K5dm44dOzJkyBD6DR1J3wGDE0w+Lly5ytETp/j666+1GzpevHiRrVu30rlzZypWrJiB71b2UrN8Hu0rvmOfvtLTl5p82Ea8y+oQUm3nrTfs/D/27jq+qvqP4/jr3rvuAgYMGN3dISXdnRKjEUQBBYUfiqC0dDcI0i1dCiJII9KdGwPGunfv74+5ydgGi7t77t0+z8fjPpRzT3zuGNt5n29d92HChAmcPn0arVZLv379mDdvHtHR0QB06NCBefPmKVypyMwWLlxI/vz5sbKyomLFipw8eTLZfbdv307Dhg3Jli0bDg4OVK9enYMHDxqwWtMiAUQIAcDcuXPx8vJSuoxknTlzhm7duhEQEMDx48epV68evXv3Jnv27BQuXJjVq1eTO3duRo8ezZEjR1i3bh0//PADGzdu5OzZsxw6dIhFixZRrFgxxowZQ2RkJAA58uSjefd+HD9+nEGDBmFhYcG8efOIioqiYqXKPHmd9BPeZ29CPlhzt/5DcXBwYPDgwQBERETw3Xexs11ptVq0Wq2evjr/MbYuQRkRKFIjq4YPQ6mY0/7DO6XD7us+VKpUCR8fHz7++GOWLVtG7969ef78ORYWFqjVap48eZKhNYisadOmTXzxxReMHTuWS5cu8dFHH9G0aVMeP0661frEiRM0bNiQffv2ceHCBerVq0fLli25dOmSgSs3DRJAhBAEBgby4MEDnJyclC4lSVu3bmXgwIFYW1uzc+dOunTpQtGiRVm7di158uThm2++4ejRo6xdu5bu3bsnmnZXpVKRM2dOatWqxdixYwkNDU2wkFmfUd/Re+Q4zl+4QIcOsVN7Tpw4kcePHjKw9yc8exOS6PU2X/NsiWpesHw1j548ZeTIkdjbx96kLV++PP6X16ZNmyhUqBDBwcEp/joY00xJ7wsVGdWSkVqmFD5kfE/ydl/3wcLCgsOHD7NgwQJu3LhBu3bt+Pvvv+nVq5e0gogMMXPmTPr27Uu/fv0oXrw4s2fPJk+ePCxatCjJ/WfPns2oUaOoXLkyhQsXZtKkSRQuXJg9e/YYuHLTIAFECMGSJUuMsvUjJiaGn376ie+//54SJUrw5Zdf0rx5c0JCQhgzZgzHjh1jzZo1dO3alWzZEoeApJQuXZpGjRqxdu1agvzfxG9v4zWYiSu34h8QQLdu3YiKiqJp06Yc2LsnvstHSoWHhzP2x2mUKlWKVq1aAfDs2TOWL1+OlZUV48aNY9q0aTx58oQiVepkSEuIoRlL6DAkY2ttUkpGt4LAf12yBg8ezM2bN9FqtSxevBh7e3uePXuGv79/htcgso7IyEguXLhAo0aNEmxv1KgRf/75Z4rOodVqCQoKwsXFJSNKNHkSQITI4iIiIjhz5gy5cuVSupQE/P39GTZsGKtXr6Z169a4ubnx9ddfU65cOXbs2EGXLl1wc3NL07kHDBhAVFQUW6ePSbC9ZMWqLDt8Fke37Pzvf/9j//798QukpUafz0YSFhbO2LFj48eHWFpa0rlzZ3bu3EmnTp2oUaMGLi4uREREpOkzGANjDh2m1PqRkQw1DsQQIQRig0j+/Plp27Ytf/zxBz4+PvTp04fFixcb5PrCtEVGRhIYGJjgldTP4FevXhETE0OOHAl/tuXIkQMfH58UXeunn34iJCSETp066aX2zEZmwRIii1u3bh3dunVTuowETp8+zTfffENAQABeXl7s3r0bPz8/Pv30UwYMGIBGo0nzuW/evMmIESNQq9VUqlQp0fsOzq4sPfgX5347xKVTv+Oex5ML3kEfvMHyNc9G9qiX3Lp7jx17D9KmTZsE0wLHBag4X3/9Na/9/Di87RdZhFDPJHxkbruv+/Bx3y/ZuHEjO3bsYPDgwZw7d46IiAgsLS2VLk8oyLlyRdzy50nyPdX0dSxfvjxRF6rvvvuO8ePHJ33MO+vP6HS6FC1+uWHDBsaPH8+uXbsSdQkWseS3nhBZWNzKw0WLFlW6FCD26dT06dPjVxLv168f69atQ6vVsnz5cgYPHpzm8KHT6di4cSNdu3bFz8+PvXv3Mnbs2CT3VavVVK3fhEHjJtOm90CAFE832rHvUCwtLfn888+T3Uer1fLo0SNUwLWbtwB47vOC3p9+waMnT997fiXGgZjSDb2hapXuV4kZqhUEwM09J+55Pdm6dSsxMTF069aNdevWGez6wjT169ePgICABK9vvvkm0X5ubm5oNJpErR2+vr6JWkXetWnTJvr27cvmzZtp0KCBXuvPTCSACJGF7dq1i5YtWypdBgB3796lc+fO/PzzzzRv3pySJUuyePFiKlSowPbt2+Pn/0+L4OBgRowYwY8//kixYsV4+vQpTZo00WP1saGiVa/Y/unDhg3D1dU12X3VajXr1q2jTJkyDPv6Wxq260bRKnX4ZdsuStdswJ6DR9JVS1a9OTZE+HCrUzvLfn2NTVuvT/H19eX06dMULVqUX3/9NVOMpxIZx8LCAgcHhwSvpFrNLCwsqFixIocPH06w/fDhw9SoUSPZ82/YsIHevXvzyy+/0Lx5c73Xn5lIABEii9LpdKxbty5dN/b6snHjRjp16sS9e/fo1KkTp0+f5uTJkwwZMoQlS5akeaxHnF9++YVjx47x5ZdfcvXq1fjZvnZfT1lf3jjJtYKEhoZSr1olDhw4wMCBA1PUpc3FxYXly5fTpk0bfj91miJFirB06VJy5spFx94DGfvDtFTVlpV5NixhsPAh3s+QrSAN2nXFytqarVu3AtCsWTN+/fVXg11fZG4jRoxg+fLlrFy5khs3bjB8+HAeP37MoEGDAPjmm2/o2bNn/P4bNmygZ8+e/PTTT1SrVg0fHx98fHwICAhQ6iMYNRkDIkQW9fvvv1OjRo0U9WfNSC9fvmTSpEnodDogtvna2dmZFStWULlyZb1c4/Hjx9jZ2TF9+vT4bc/ehMTfLKVmNed3x4M8e/qEpvVqEeDvzw8//EDr1q1TfC5zc3O+//57unTpQpEiRTAzM2Pjxo2MHTuW6fMWERQczNwpE1J8vqzClLqEGYPsUS+TnCra1KnVasrVrMfxYwd49eoV1apV4/vvv4+feU6I9OjcuTOvX79mwoQJeHt7U6pUKfbt20e+fPkA8Pb2TrAmyJIlS4iOjmbIkCEMGTIkfnuvXr1YvXq1ocs3ehJAhMiilixZkmTfV0PLli0bx44dIzg4mJiYGKKjo/Hw8MDW1lZv13j+/DmOjo7xf07JIoIpodVqqV+9MqBj6dKlaWpNUqlUlCjx3w21jY0NI0aM4MiRI+maIcutTm1e/X4izccbGwkdIim9R/6Ps8cOsGvXLvr27UuFChX4888/39tNRoiU+vTTTxOsGfW2d0PFb7/9lvEFZSLSBUuILOjKlSsUKFDAaGZfcnNzw9PTk4IFC1K0aFG9hg+IXYMjbuCgPsJHXIuJWq3G3NyMnDlzUrFixXSfN86OHTvQaDT8+L/RejunocV1i0pvcDBU96oPke5XKWfIblg58+YnWy4PtmzZgk6no3nz5skuFCeEMB7SAiJEFjRv3rwETcSZmU6nw9fXl2rVqumt5QP+64o18pv/MW70l+zdu1cvXT+ioqLYtm0bxYsUws3VOBawenj4eor2Sy4ovC9AvHtuYwgbwrS06tGfZZPHce7cOapUqUL27Nm5deuW0czuJ4RIzDgefwohDObBgwdYW1tjbm6udCkG4efnR3R0NEWKFEl2n7Q+sZ26YBk/fjcOtVqtt/UHTpw4gZ+fHxGRkXiWrca6zdv0ct60Smn4SKu3W0okfGQehmwFadatD9ZvDUbv2rUrc+fONdj1hRCpJwFEiCxmzpw59OjRQ+kyDCZuHveSJUvq9byLJ37DvHHD8fTMx+bNm2ncuLFeznvs2DEAHjx6wnOfF+zceyjN50pPt6GHh69nePgwFdL9yrip1Wrq16/P4cOH8ff3x8rKioiIiBSvWC2EMDwJIEJkIS9fvsTf3x8bGxulSzGYuJuQ8uXLExERwbOnT/Ry3r//+oO8efOyYcMGvXb1GDBgAEuWLOH06dOYmZlhbp544cWMXIxQgocylFhgMjOZPn06MTExHDoUG9h79+7N/PnzFa5KCJEcCSBCZCHz58+nT58+SpdhUIGBgfH/X6qAB1XLFOfx44fpPq+NrR1v3vhz4MABvL29032+OPny5aNGjRpYWVmh1WrRaBIP1dNeP6W3671NgofQJ0N2wypevDhqtZqwsDAAnJycuHv3LkFBKZ9iWwhhOBJAhMgigoODuXnzJi4uxjGw2VAiIiJQqVRUqlSJ6OhoAC789Ve6z/tR87ZERUXxzTff0KhRI+rXr8/o0aPZvHkzd+7c0cuKzHFroxiChI+kSfcr0xAdHU1MTAx2dnbx23r16sWKFSsUrEoIkRyZBUuILGL58uX07t1b6TIMLiIiArVaTXh4OHPnzmXYsGFcv/YPbTt2Ttd5W/ccQOueA3j24C7Hdm3m8p8n+P3ESfbv349Op8Pa2prs2bOTLVs2smXLhpubGx07diR//vxJnk+r1SaaFtnFxYWnemxdSY6ED2Hq4rpavj2Fd548eVi2bBlDhgzJMpNuCGEqpAVEiCwgKiqKEydOkCdPHqVLMbjIyMj41d6tra2xsLDg1InfUtxC8eTebb7uUJ+SlklP4Zs7fyF6fDGGnzYf4Je/bnPp5j3GTZxE1Rq1sLV34Lm3D6fPnGHjxo106NCB9evXJ7r2zz//TM2aNTl37lyC7R4eHjx7LgNpRfoouQq6obphPXr0CCBBCwhAx44d2bx5s0FqEEKknAQQIbKADRs20Llz+p74m6rw8HA0Gg0qlYrdu3fTvn17/r58iQrFC3Lh3Pu7YgUHBjDmk5bcuHGDa9eupeh6btmyM3DIMNZt2cGhE6c5e/Um/9x7wtOnTylatChTpkyhX79++Pj4oNPpWLRoEdOmTSMsLIyRI0cm6HaVN29e/AMC33O19JPWj+RJ9yvDaVXCPU3HXTx1nJGdmlC3bl1UKlX8gqNxSpcuHb9IoRDCeEgAESKT02q1bN++Xe/T0JqKHDlyEBERQZkyZdizZw/R0dHMmTMHnVZLmyYNKJInBx9VLseMLwfj/fhB/HHR0dF807kRgYGBqFQqGjRokK46osxt+fvvv2nQoAHnzp1jxYoVzJw5k4ULF1K7bn08PDxwdXWNb60ByJ07N2HhYem67vtI+DAOWXUGrFYl3ONfb//5Q0JDglk2eRyf1CjO9wO64fPoHl27dmXnzp0ULlw40f4NGjSInx1LCGEcZAyIEJnc3r17adKkidJlKKZDhw6cOXOGo0ePUqtWLbZs2RLfGnLw4EGuXbvGkSNHeHBvJwFvXjFxxRYAZn7ahcePH5MrVy4CAwOxsbGhVQkbdl9Pe5coLy8vjh49SoUKFfDz8+PQoUM0bdGKWQuXUjJ/bgYOHJhg/5s3b0rf9UwuK4SPijntueD932xUHwoZrUq4J/nvLDgwgJmjh3Dlz9+Jjo6mbNmydO48ikaNGr13IdDatWvz448/6m2tHiFE+kkAESKTW716NePHj1e6DMWo1WomT55M3759OXPmDE2aNGHz5s2oVCpGjx5NdHQ027dvJ3fu3BzfuwsXFxf69evHqVOnGDFiBIcOHcLCwiLddXRo0YQzf/5Bo0aNUKlUHDx4kE5dP2Hd7IlMnh27hkH9+vXj93/y5Am///473dq3Sfe1k6Lv1o/Mtoq5IbpfZYXw8a6UdrV6O4SEh4ayZuYPHN62npjoaNq3b0+3bt0oVKhQis6lVqspXrw4Fy9epEKFCmmuXQihPxJAhMjETp06RaVKlRJ06zF1L168IDw8nBw5cmBlZZWiYywtLZk/fz5du3blt99+o23btmzatIlNmzYBUKFCBU6fPo2FhQWzZ89m5cqVtG/fnt69e7Nw4UJq1KiR7rofP3oIwKFDh1CpVPQb9CkLv/8KgA3bduHu7k6RIkXi99+4cSMajYbpE/6X7muL1JHwoX8Vc9qT29n2g/tdv36db7/9lmvXrvHy5UtCQkKIiIhAp9NRr149hg8fnuxMcu/Tpk0b5s6dy8qVK9NSvhBCzySACJGJLVq0iK+++krpMvSqW7du+Pr6AmBvb0+OHDnInz8/FStWpFKlShQuXDjRdLYQuzDZ0qVL6dq1K3v37sXZ2RmNRkP27NmpXr06CxcuRK1W89VXX1G1alXGjh2LSqWiZMmSnDp1ilevXvGnb3Sa6z579SZXr1xm67pVFC1dni+6Ngdix5rce/CIjh07xgfF0NBQtm7dSoUypXBzzVrrtihNwofhabVa5s+fz8yZM3n8+DEajYb8+fNToUIFPDw8yJMnDyVKlEjXODYzMzNsbW158OBBmgKMEEK/JIAIkUldu3aN3Llzo9FolC5Fr0JDQylcuDB169blwYMHPHv2jPPnz3PkyBF0Oh329vZUqVKFKlWqUK1aNQoUKBB/bNy6AGvWrMHHx4cXL15w//59rl//rzuSp6cnM2fOjB978d1339GuXTuq12vIT5v2p6v20mXLUbrsHLJHvYzfFh0djZmZGVu3bqVw4cK0b9+eXbt2ERYWxoxkWj/UJWpm2GroWZXMeGU44eHhXLhwgXPnznH8+HEOHz5MWFgYefLk4csvv6R169Y4Ojrq/bqffPIJc+fOZdasWXo/txAidSSACJFJzZ07N9GgZlP24sUL9u/fT1hYGIUKFWLp0qUJ3vfz82P16tVs27aNY8eOcfToUQB27NiRoK948eLFmTJlSoJjQ0JCePnyJa9evaJo0aLY2/+3dkH+/Plp3bo127Zt48LJY1T8qD7p8Xb4ALCysuLOuRM0aNuF77//njlz5hASEkKunDmoVrliuq4l3k9Ch+Hs3r6VcaNHJuhSBbHjMxo0aEDnzp2pXLlyhnYXtbW1xc/Pj1evXuHm5pZh1xFCfJgEECEyoSdPnqBWq/UyeFpJQUFBHDlyhD179nD+/HlUKhW5cuVi1KhRifZ9+vQp+/bt4/z58+h0OkqXLk337t0pWLDgB69ja2uLra0tnp6eCbbfv3+fn376iRMnTmBjZ0+2nLn19dESyJ7Njb//OMKkmfM4euIUKpWKb7/6IkOuJZQNHlmx+1VkZCRfDhuCq6sLrVq1wsPDg1y5cpE7d25y5syZ4rFc+uDl5cXChQv59ttvDXZNIURiEkCEyITmzJlDr169lC4jxXQ6HYGBgdy9e5ebN29y/fp1rl27xoMHD9BqtWgsbTDPVQ7rwh8Tau1E3bp1gdi+44sXL2bq1Kk8efIEc3NzWrZsSefOnSlevHia6/Hz82PRokVs3rwZMzMzWnzSj76jv09ybElqvNv68a4xIz5jzIjP0nWNlMjKM2BJq4fhjfxsMKGhISxbtpQyZcooWoubmxtXr14lLCwMa2trRWsRIiuTACJEJuPn58eLFy+ws7NTupRkHT58mMOHD+Pj44OPjw+vX78mMjISAJVKhZ2dHblz56ZLly78+tARM8eELQ+uzX4kwucfdLd2ExoaioeHB1999RWtWrVKU99xnU7HixcvuHXrFleuXGHdunVERkZSvlY9RkxdgJ2D/vujC8OT8GF4jx8/ZM+ObbRq1Urx8BGnR48erF69msGDBytdihBZlgQQITKZBQsW0KdPH6XLSNaqVauYOXMmdnZ2uLq64uHhQdWqVfH09KRmzZo0a9YMGxsbXJv9yIE3YJbMvX/YrUNks7dn1qxZVKtWLcWtE6Ghody7d4/bt29z+/Ztbty4wa1btwgNDQVAo9HgUbAIo2cuJXf+lK0zoARTGIj+vhv+V7+fMGAlxhE+smL3qwE9umNmZsYXX3yhdCnxChQowKpVqxgwYECmm6RDCFMhAUSITCQ0NJS///6btm3bKl1KIlqtllmzZrF69Wpq1arF77//nmxocG324wfPpYoMpGnTTz64RkdMTAznzp3jwIED/PXXXzx79ix+AKy1tTVubm7UrFmTatWq0bx5c7xt86S7q5X48A1/3PuGCCLGED6UlD3qJb7m2Qx+3SMH93Ptn78ZNmwY2bIZ/vrv07p1a3bs2EGHDh2ULkWILEkCiBCZyKpVq+jZs6fSZSQSFRXFd999x549e+jYsSObN28GoOK4Awn2uzCxyQfDB0CU701ioqOpXr16ku/rdDquXr3Kvn372LdvH2/evMHcwoJiRYvy8ccfU79+fZo1a5ZoJpzd131IT/SomNM+2fc+NP4jLdLaCuLZsITexoEkNf4jNTf8bnVqZ2gIyerhQylarZaRQweTM2dOo/yZVKlSJcaNG0f79u0z1UKtQpgKCSBCZBLR0dEcPnyYH374QelSEoiOjmbUqFEcPXqUL774glmzZiUKHnFSEj4AIp9dwszMjAoVKiR67+HDh0yaNInTp09jbm6OY+4C1OsyjGL12sS3bJwFzv7+nPnt/wsgQ7f9TaPi2VP/AY1cRt/gJ3W9tB6j7zolfCjn153bef36Ff369TPa2fhq1qzJiRMnqFOnjtKlCJHlSAARIpPYsmWLUXYn2L17N0eOHGHcuHFMmDAh2fDx8MKFFJ9TG/CEihUqJJi+MzQ0lOXLl7Nq1So0Gg2jRo0ioHxHzMyTv/kZuu3vlH+QD3hf64cx0kcriL5nv9JXEJHgobwGTZqRJ29eli9fjqWlJQMHDjS6loaGDRsydepUCSBCKEA6OguRCeh0OjZt2kS5cuWULiWR7du34+bmlmz4eHjhQqrCB4AuKgw/Pz/OnTuHTqfjyJEjtGzZkhUrVlCjRg2eP39OSJXu7w0f+qRk+EjPwGbPhiX0GiL0dePvVqd2gldqjxHKs7Gx4dTFf6hZuw4LFixg5syZSpeUiEajIV++fPzzzz9KlyJEliMtIEJkAgcPHqRBgwZKl5HIkydPuHLlCp9++mmKu1elhIVnLR4+OUufPn3IkSMHL168IEeOHBw+fJj69evrtWUjOakJHRkx/kOf0tIaYsi1PyRUmCa1Ws2mnXvp0KwBmzZtYtiwYZibmytdVgIdO3Zk3rx5LFmyROlShMhSpAVEiExgxYoVRtmNYP/+/Wg0Gn65pd91NGyKNqTUyK241+7Om3DIUasLufuvpn79+sl28dInU+tulRJxrSEpaRVJ78BzkbUMHz6csLAwzp07p3QpiVhYWKBSqXj69KnSpQiRpUgAEcLEnT17ltKlSxtd/2qAwMBALC0tUVvY6v3carWaXLU/odRnq8ldN3bVd0OEj6wiuTBiSquei/8o2QrXoUMHLC0tOXbsmGI1vE+vXr2YN2+e0mUIkaVIABHCxM2fP59WrVopXUaSLCwsiIyKJuLZFfz/mIdWq1W6JL244B3EBe8gpcswmA+1jEjrh3gf74AwihcvzpEjR+LX4DEm9vb2PH36lICAAKVLESLLkAAihAm7desWbm5umJkZ53AuCwsL0OkIvbEXbdALIp+e18t5PStW1Mt53nXohm+q9jeGIGLsq6ELAdC7d29ev37N+fP6+Rmgb15eXjIORAgDMs67FiFEisyZM4c+ffpk6DUCAgJYvHgxUVFRhIWF8fTpU6Kjoylfvjzly5enZMmS5MiRI8kuYHZ2dsTERKMjBoCIx2exylslQ+sVhiWtHyIlhgwZwjfffMPq1aupXLmy0uUk4u7uzl9//UVERASWlpZKlyNEpicBRAgT5e3tTVRUVIK1MPQpICCADRs2EBoayrp16xK9//fff7NmzRoArKyssLCwiO9eUahQIT755BNq1KhBrVq1OHnyJAAae/cMqVVpF7yDjHpgekoWI0wqSKTlGGGcfM2zKXr9F0ERdO3alZUrV3LmzBmqVaumaD1J6dq1K7/88gteXl5KlyJEpicBRAgTNXfu3Az7RXnixAmGDBkCQOfOnfn555/x9fXl7t273Llzh1u3bvH06VN0Oh3FixcnX758REVFxbeCnD9/npEjRyY4p8bMApvSbTKkXmPna57NKKfi/VCAkIAh9GnBggXs3r2b4cOHs3nzZvLkyaN0SQkUK1aM//3vf/Tq1Qu1WnqoC5GRJIAIYYL8/f159OgRDg4OGXL+qVOnxv//pk2b2LlzJ56entjY2GBra0uxYsUoWbIkx44dw9vbm+vXE64hYVG+Dxr/h+jC/UGlAbUG+yJ1UKsz748cY28FAcMGCnWJmjI+RSRgZWXF2bNnKVGiBKNHj+aXX35RuqREmjVrxt69e2nZsqXSpQiRqUnEF8IELV68OEPHfvTv3x+AYcOGYV9tADH2ebjz9DVv3rzh3r17XLx4kdOnT2NhYYGjY8I1PizKx9aldvJE414OTY7SaLKVIOTNS4K872VYzfqS2oHob3vfgHSlu8AYKnyoS9SMX509Pau0i8zn2ZsQ8ufPz/Dhw7l69WqiBxfGoHr16vFdS4UQGUcCiBAmJjw8nHPnzuHunjHjKcLCwli4cCHOzs789NNPmDvnxaFyb5xqf0Fg8X5ElBvCq1ev8Pf3JygoiIcPH8YfGxc+3ifI+166g8jDCxfe+/7py8/TdX5j4WueTfHgklJvBw9hXIzte+jbb7/FwsKCrVu3Kl1KIiqVigoVKnDmzBmlSxFpoCpQPv5n0bsvNBqlyxNvkQAihIlZu3Ytn3zySbrO4ePjw8uXSY9JWLBgAS9evGDTpk3kaDU1yX1cm/2IRfk+8YHj7f9PCfucBVNftIlIbytIXOh4e19D3UAm+oWdiuOESCkrKyvKli3L0aNHlS4lSc2bN2fRokVKlyFEpiYBRAgTEhMTw759+yhcuHCKj4mKimL58uW8fv06flvDhg2pX79+on2vXLnC2rVradKkCQ0bNkzR+VMTPPQpI1tB0tMN60PeDhhJvZSSVIhISRBJ7/siYxlb68ezNyEAlCpVCj8/PyIiIhSuKDGNRoOrqyu3b99WuhQhMq3MOyJUiExox44dtG7dOsX763Q6unTpwu3btylbtiyurq4J3n/9+jWurq7odDp27drFDz/8gJ2dHdu2bXvveU1hLIeSTGFA+tskRGQ8Q30NjS1wJKdMmTIAPH/+nPz58ytcTWLdunVjxowZLF26VOlShMiUpAVECBOh0+lYv359qhbxWr9+ffxTvAoVKgAQHBwc//6ECRMICgpi1KhRjBs3jrx583L79m2srKxwbfajfj9ABsgqY0Eg424sDXFjnNUDTFb//O/afd2HSpUqAbEBxBhZWVnx6tUrXrx4oXQpQmRKEkCEMBHHjx+ndu2Uz2QUGhrKjBkzAGjfvj2afwfgPX78GIDcuXNz7NgxGjRowOHDh/nss8+4ffv2Bwe3Z5XWj0M3fJPtihX33vu6ar1vLIixkBvjjGfIr7GptH4AVKpUCZVKlWASC2PTtWtXZs+erXQZQmRKEkCEMBFLlizh448/TvH+t2/fJiYmBiDBeA5LS0sgdiA6gJmZGceOHWPu3LlA7ABzU2j9iJPRrSBvB42kQsf7goi+Q4g+bzAlfGQ8CR9Jq5jTHisrKxwcHLh8+bLS5SSrYMGC/PrrrwlajYUQ+iEBRAgTcPHiRYoWLfrB1Xl1Oh1ly5ZlyJAh8V2vLCwsqFKlSvw+BQoUIFu2bGg0Gpo1a8aLFy/iW1YMETyUmAHr9OXnegkiaXk/o1tCUnuT+76B5Rk5ID4rBR6ZkvjDdl/3oWjRopw9exadTqd0OYlERUUxcuRIHjx4wIoVK5QuR4hMRwKIECZg3rx5tG3b9oP7qVQqtFotJ06cYNasWQCUL18ec3Pz+H1evHjBy5cv6dSpE3v37o0f72FKrR5ppY8gorT0BIP33RQb4gl6Zr4pT8v0xfpiSq0fb7OzsyM0NFTpMhKJjo7m66+/5vfff6d58+bM+OknoqKilC5LiExFAogQRu7evXvY29snCBHvs3//fuC/weaXLl1K8Et+w4YNqNVqfvjhB8AwrR5ZnTG0gqQlfGTEjW1mDCGZ8TNltIo57Tl//jy1atVCpVIpXU4Ce/bs4dChQ3z11Vf88ssvaFUatmzZonRZQmQqEkCEMHJz5syhR48eKd7fw8ODzz77DICSjToRGRnJ8ePHiYyMZPz48axcuZLq1avTbvkNKo47kFFlJ8lYFiDMqFYQJQelJ3cT/KGn8h8KGRJC3i8zfRZDunfnNoGBgamaWMNQ7ty5g7W1NVOnTkWj0dCy50AmT5lqlF3FhDBVEkCEMGK+vr4EBQVhbW2dquMsLS1RqVSUa/kJZuYW7Ny5k549e7Jjxw569+5NWL3/xe/rWbFiqs6tRIgI8r6XKWbfuuAdlOClb++uZP6hm+OUhgsJIYnJOI/08fd/A4C9vfGtl/P48eMEdX3cphNPnz3jyJEjClYlROYiAUQIIzZv3jz69EndSuM6nY5NmzZh7pCN3bO/JzoqkjNnznD79m08mn/B3x6dEx3jWbFi/MsUfWgmrKQYw1iQtIYRfQSC1J4jo0KI3MRnTRUrV8XS0oozZ84oXUoiDx48IGfOnPF/trS2oVnXPkyeMlXBqoTIXCSACGGkgoKCuHPnDs7Ozqk67tKlSzx58gS1hQ1B92JvzK2trfHsOhm3sg0/cHTqW0RSKq0tJ3EtH8bSfSujZFSriD5l5GKIphRGTKVOY5fP05NTp04pXUYCMTExPH/+nEKFCgGxs3UBNO3ai9OnT3Pp0iUlyxMi05AAIoSRWrp0KV5eXqk+btu2bQCEv3wIgJm1Hfm9FmCft6Q+yzMqaQ1NGdEK8qHpej8kpUEkrWEgvVPsZvSMS6npQqYEY6zJVNVr0IinT5/i7e2tdCnxXrx4QXR0NOXKlUuw3cHZlQZtuzB12jRlChMikzFTugAhRGKRkZGcOnWKxo0bp+q4wMBADhz4b2C5uZ0zxQctxczKLlXn8axY8b3dmuxzFkzVmIz0tn5kRXEhpGJO/fWRT0l4uOAd9MFr+ppnI3vUS32V9V5J3fBrryf91Dw94SC5c+rr/CKxHn36sWzRfKZNm8aMGTPQaDSK1aLT6Th//jyLFy8GoG7duvGtH3Fa9RrIkJYf8fDhQzw9PRWoUojMQwKIEEZo/fr1dO3aNdXHHT16lMjISLJXa09U0GvytR6JWp0x/8xTGkIye9cpJaUmCHwofLzd6pLSEPI2QwUSyJggEHfOpIKIBI+M4Zm/AJ9+PoL5s2bw/fffU69ePSIjI6lTpw5WVlYGqSEmJoajR4+yYsUKrl+/jq2tLa17DcLPpVCifXN45KV6g6bMmjWLOXPmGKQ+ITIrCSBCGBmtVsvOnTv58cfUr89RtmxZnJyc8L9ykFIj0zdv/YdaQeC/cJFRLRWpbWnJjD4UBlISQlITPtIq7hqGDCIZQcKGYX09bjwvfX3ZtH4tO3bsAGDQoEEMGTIkQ68bEBDA3r17WbNmDc+fP8fFxYXuw0bTof8w1Orke6e37j2YMT1a8+233+Lq6pqhNQqRmckYECGMzJ49e2jRokWaji1QoAANGzZEp43Rc1XvZ5+zYILX29v1de6MUL1crgw5r759KCAkNa4jbpuhV8k21VW5hWG9PQNctzFTuX79OlevXqVMmTL8/PPPBAXpf0KGuG5W33zzDfXq1WPKlClEamHYj7NZdfIanQZ+8d7wERkZybJJ4zAzM2PevHl6r0+IrEQCiBBGRKfTsXbtWqpVq5bmc1y9ehWVjZNe6knr4O6MDA3vMsapg9M7ED2t0hI6kgs36WkVkRAikpPcJAt3dM6UKlWKVatWERoayqZNm/R6Xa1WS79+/fDy8uLw4cOUqlKTWVuPsOLoBT5uk3hq8qSM7taCW1fO079/f/755x/CwsL0WqMQWYkEECGMyB9//EHVqlVRqVRpOj4gIIDbt2/ToWk9vdVkjDf4cYy5Nn2HkIyYpjcjp/2VECLelZLvtwoVKlCiRIn4IKIvBw8e5OzZs7Ts0Z9fzt5l/NIN5C9WIsXH37pygQc3/2HIkCH07duXHj16sGbNGr3VJ0RWIwFECCOyaNGiVM989bYTJ06g1Wq5YF1dj1UZ542+MdZkCMa+VogQSUnJ923crFOTJk0iMDAw2TU3bt68yb59+1iyZAnjxo2jb9++7NmzJ9nzRkZGMmvWLJyz5aDf1xMwM0v98Nd540ZgZ2fHJ598AkDBggU5cOAAMTGG7e4qRGYhg9CFMBJXr14lX7586ZqK8tixY9jb22ObM/EMLumVkkHphpJVw0eclMxSlZJzvI8+pv815HS9wnilNjS3aNECMzMz/v77b2rWTDgpwJ49exgzZgwAZmZm2NjYxI/tcHZ2platWonOt2XLFry9vdm1a1ea6r924S+e3r/DF198ga2tbfz21q1bs3PnTtq3b5+m8wqRlUkLiBBGYs6cOXTs2DHNx0dERHDy5EmqV9dv68fbPCtWzDQ3/6YyAD05bw/iTcuxQhij3dd9UKvVODs7c+XKlQTvRUdHM3/+fNzd3Xn06BFRUVEEBATw6tUrsmXLxogRI7h9+3aic+7du5fcuXPTqlWrNNW05sfRODg40KVLlwTbK1euzPr169HpdGk6rxBZmQQQIYzAo0ePsLCwwMLCIs3n+Ouvv4iIiOCuU9oHsKdUXBBRIozo45qmHj7e9XYYSS6UpCewpIeMBRGptfu6DyVLluTy5cucPn2apk2bUrNmTWrXrs3z58+ZPXs2efPmjd/fwsKCyRsPoEPF4MGD+eeff+LfCw8P5/r169SoUQOAViXcU1RDqxLuFNS9ZuGI3ty+fZv+/ftjY2OTaL/q1atz8uTJdH5iIbIe6YIlhBGYM2cOvXr1Stc5jh07hpWVFQ6FquqpqpRJLhDEdddKTWBIrouXPoNOZgsfyZFWjqzB2ANeWr8PPcpW47fffmPAgAG4urpStWpVYmJiKFKkCJ07x85a9fZK5a7Z3Zm4aivj+3akW7dutGvXjo8++ohLly4RExPz3m5SL5484pcFM/B+fJ9w/9f4+/sTFhZGZGQk5ubmtG3bNv6a72rUqBFTp06ldu3aafqcQmRVEkCEUNjr16959epVgr7FqaHT6dizZw979uzBLFv+985jb0hpCQ0Z2aKSVYKHyBqMPXikV91WHdm8eBZFy1bi/MljCVqH3w4ebytSujyvXr2iZ8+ebN++nW3btsXPKPj111+za9cuZsyYQVznj9DgQGaOHsrFk8fQ6XRky5aN3LlzU7ZsWXLlyoWHhwd169bF0dEx2To1Gg158+bl2rVrlCxZUn9fACEyOZVOOi8Koajvv/+eunXrpmlV3YCAACZOnMjBgwdxdM9D24mr+ftRZAZUabqUDB6NimdX7NrppY9B6G+Twej6Y0rhQ4mWuFYl3Ll8+TLt27fnyZMnREVFkS9fPh4/foxaraZo0aJkK1CMM4f3EhUVRceOHRk0aBBubm5pul5ERARLly5lyZIlev4kIjWcnJw4vmsTpYoXTfL9bIXL8MepPylVqpSBKxNJkRYQIRQUEhLCtWvX0jSLyrlz5xg9ejR+fn6Uad6dj7xGxb7x6LmeqzR+xti6YcrhQxgPUwobxuSXX37h/v37dO7cmU6dOlGkSBF8fHzYtm0bmzdv5vr169StW5fhw4dToECBdF3L0tISgGfPnpE7d259lC9EpicBRAgFrVixItmxH6GhoWzZsoWff/6ZsLAwLC0tsbS0xMrKCgsLC27cuIGlrT1tJq7GvUgZAE5fzjrhwxhDRxwJH0IfMkP4qJjT3uCtILuv+2Bubg7A4MGD41uX3d3dGTJkCAMGDODVq1fkzJlTb9fs1asX8+bNY8qUKXo7pxCZmQQQIRQSFRXF8ePHqV27NpcuXSJbtmx4eHjEB49ly5YRGBhIoUKFKFOmDGFhYYSFhREREcGTV4Hkr1Kfhl9Mwcw87TNnmRJjDhxvk/Ah9CEzhA8lxc1YFRmZuEuqubm5XsMHgIODA0+ePCEgIOC9Y0aEELEkgAihkE2bNmFvb0+9evXw9/cHYp/QhYWFERgYSJEiRdi9e3eC2VXiBl8euuGb6HxKtn6YSjjIaBI+RHpJ8NAPOzs7IOkAklF69+7N0qVL+eqrrwx2TSFMlQQQIRSg0+mYNm0aN27coHTp0gwaNIiHDx+yd+9eAObNm5doWsfkZn4xJAkayZPwIUTSlOiGZW1tDcS2NBtKzpw5Wbx4MZGRkela00mIrEACiBAK2L9/P3fv3uXly5eceB4OwIAB7kyaNCnRvhI8jJ+Ejw/zNc8mM2F9gLR+6EerEu5M2e0H/BdEDKVLly788ssv9O7d26DXFcLUGMeCAUJkMd999x3t+n8eHz4gNmi8HTbe/bMSqpfLJeFDCJFu+p7W+UNevHgBxI7NMKTixYuzc+dOtFqtQa8rhKmRACJMXkBAAH5+fkqXkWJnzpzhxs1bNOvaO8n30xo89BUU4kKHBI+UyaytH7KSumFlhdYPQ4aQly9folKp0rzAa3o0adKE/fv3G/y6aRUaGoosCScMTQKIMHnbtm2jcePGlC5dmjFjxvDw4UOlS3qvSZOn0KjjJ9g56H+mlPSEBgkdIqNlhZts8X6GCiGvX7/GxsYGtdrwtzk1a9Zk9erVBr9uWuzevZuqVaty5MgRAAkiwmAkgAiTptPpKF++PLNmzWL48OGcO3eOTp06cfXqVaVLS9KNGzc4ePAALXv0z7BrpCZESGtH+mTW1o+MJCEksaz2NcnIENKqhDsA/v7+2NsbtttXHJVKRdmyZfnrr78UuX5yHjx4wOXLlxNsy549O66urvzxxx+ABJB3LVy4kPz582NlZUXFihU5efLke/f//fffqVixIlZWVhQoUIDFixcbqFLTIwFEmDSVSkX58uWpVasWffr0Yc+ePdjZ2TFhwoT4faKjozly5AhnzpxRsNJYU6dNo07zdrjm0O8c9O96O1i87yWEErLaDff7ZNWvRUa3hAQEBBh8/MfbWrRowcKFCxW7/rsOHTpEwYIFqVu3LsuWLYvfXrp0aQoXLszp06cBFGkxMlabNm3iiy++YOzYsVy6dImPPvqIpk2b8vjx4yT3f/DgAc2aNeOjjz7i0qVLjBkzhmHDhrFt2zYDV24aZBYsYfIiIiKwtLQEwMrKCgcHByIiIgA4e/YsEyZM4N69e7x+/RpLS0vmzZtHmzZtDF7n06dP2bBhA7O2Hjb4tTOD1LQ2JLVOir5J64dIr6waPuLEhZCMGG8UHBys98UGU8PMzAwXFxfu3LlD4cKFDXrtixcvcujQIapUqUL9+vUBqFixIu7u7uTIkYOhQ4fi7OxMmzZtsLW1pXTp0ly4cIGzZ89SpUoVdDodKpXKoDUbo5kzZ9K3b1/69esHwOzZszl48CCLFi1i8uTJifZfvHgxefPmZfbs2UDshATnz59nxowZtG/f3pClmwSJusLknTx5EltbW9q1a8fw4cPZt28fPXv2BGDgwIFEREQwb948nj59SocOHZg9ezY+PoafXWrmrFlU+qg+HgUM+8vI1DUqnj3VN/txxyT30kdNIn2y+s13Vv/8b6uY016vLSKPHj3Cx8eHvHnz6u2cadGtWzfmzZtn0GvevXuX/v37c+7cOb799lt27dpFZGQkrq6ulCxZkho1atCtWzcmTJjAzJkzAahWrRrW1tYcOnTIoLUas8jISC5cuECjRo0SbG/UqBF//vlnksecPn060f6NGzfm/PnzBl2PxlRIABEmr3bt2mzatImwsDBOnz7N1atX6dChA5s3b+bGjRvMmjWLBg0aYGFhwZgxYzhx4kSCgerXrl3jyy+/pGnTppw4cSJDanzz5g1Lly6lTZ8hejmfqd4ApzYAZNTnTE8wMdWvvTHyNc+W6JUVZJXPmVrpDSJx4z+aNWuGRqNh0KBB+iotTaytrQkODo6fEljfrly5Et/aH2fZsmW0aNGCbdu20aJFC86dOxe/GnzLli3566+/6Nu3LyNHjmTGjBlMnz6dKlWqULBgwfhuWJm59SMyMpLAwMAEr3e/hgCvXr0iJiaGHDlyJNieI0eOZB9g+vj4JLl/dHQ0r1690t+HyCQkgAiTZ2FhQYsWLVi3bh2vX79m3759AKxevZpGjRpRqlQpYmJiAAgKCsLT05MHDx4AsdMPduvWjezZs1OqVClmzZpFUNB/3QHCwsL0UuPChQspWKIMRctW1Mv5wLRvhN9Xuz5bKlLrfddWqqasxpRDSVK1m/LnUUp6gsjSpUu5fv06X3zxRaKbQSV4eXmxYMECvZ3v5cuXDB48GFtbW1q2bEmLFi349ddf4993cnLi1q1bzJw5kw0bNmBra4udnR0A7dq1w9vbm5s3b9KrVy+mTp3Kt99+y+rVqylUqBBv3rzh0qVLgOkORvczc072350ONcuXL8fR0THBK6nuVHHeDWMf6p6W1P5JbRcyBkSYqKioKHbt2kWHDh3it7m6ulKhQgVu3LjB48ePOX36dKIZKK5cuUKOHDnQaDQA3Lx5Ew8PD0aNGkVgYCCNGjXi2bNnFCtWjJiYGIoVK0aOHDnYt28fbm5uaao1LCyM2XPm8unEmWn/wMmIuyF+e8xDSm6SDTFGIjXXNcYbe2OsKat696b9Qyuqp+QmX1+rskugyDhJhZD3jRcJDg5m+PDhFC9enC5dumRkaSnm7OzMrVu3CA4Ojg8CqfHuDe+mTZv4888/OXz4MNbW1kyfPp1BgwaxatUqGjZsyNChQ/n111+ZP38+PXv25PPPP48/j4eHB4UKFeLPP/+kTZs2eHl58eLFCxYuXIhGo8Ha2pqTJ09Svnz5TDsOpF+/fkyZMiXBtrgxpG9zc3NDo9Ekau3w9fVNNti6u7snub+ZmRmurq7prDzzkQAiTNKdO3eYMWMGjx8/pkOHDlhbW7Nr1y6uXr1Kx44diYqKIjAwkMqVKwPEB45Lly6h1WqpUKECACVLlsTOzg47Oztq1qxJjRo14n8YHT9+nGfPnvHkyRPOnz9PkyZNgNgf5G/evMHFxSVFta5ZswZH12xUqFVf31+GeGkZI/EupUKJME6GXrk6NfRx0y/BwTQlN3C9VQl3ypQpQ0REBN9//338z3xj0KtXL1auXMmwYcNStL+fnx8///wzBw4coHDhwjRs2JCWLVvi5+fH1q1bqVSpEjVq1ADgl19+oV69eqxatYqPPvoIe3t7unbtSteuXROcU6vVotFoaNGiBevXr+f+/fu4ubkxcuRISpQoQbdu3QgNDUWtVjNs2LBMOxuWhYVFimZHs7CwoGLFihw+fJi2bdvGbz98+DCtW7dO8pjq1auzZ8+eBNsOHTpEpUqVMDc3T1/hmVDm/A4TmV7+/Pnp378/P//8M2XKlKF58+bMnDmTihUr8uWXX/LgwQM8PDwIDw+PP+bJkydcvHiRokWLUqhQISD2ycemTZvYsGEDLVq0YOzYseTPnx+An3/+mSZNmpArVy7Onz8ff55r167RunVrvv/++w/WGR0dzZRp02jlNdjonyZlxGBtIYTICO920xo0aBBXr15lzJgxFC9eXMHKEsubNy+//fYb0dHR790vOjqaGTNmUK5cOX755RcqV66Mv78/rVu35sCBA7i4uPDPP/9Qr149IDZUALRt25bbt2/HD47WarXodLr49+G/6XXbtm3LmzdvuHnzJgDm5ua0atWKtWvXUrt2bdq3bx/fZTmrGzFiBMuXL2flypXcuHGD4cOH8/jx4/ixRd988038hDcQ+z346NEjRowYwY0bN1i5ciUrVqzgyy+/VOojGDVpAREmydramr59+9K3b1/8/f35888/KVSoEAULFkSj0VCwYEEADhw4QIkSJQBYtWoVjx49on//2EUAtVotKpUKlUpFy5YtgYT9Xs+cOUOvXr2IiorixYsX8dP9Xrt2jfDw8PjzarXaZJ8Wbdu2jYjIaD5qkvQTE2P3dgjJyBaSQzd8JfAIIVKtYk57dm/fyrJly2jdujUdO3ZUuqQktW/fni1btiRqmXibmZkZzs7OLFmyhAYNGsQ/Nb948SKHDx+mSZMm5MmThytXrvDJJ5/E/+6pUaMGW7du5cSJE9SvXz/+99rbD73i/r9QoULY2tqyf/9+WrVqhZOTExA7PqRdu3YZ9wUwQZ07d+b169dMmDABb29vSpUqxb59+8iXLx8A3t7eCdYEyZ8/P/v27WP48OEsWLCAXLlyMXfuXJmCNxkSQITJc3JyolmzZgm25c+fn4EDB7JgwQLu3LlDQEAAmzdvZsGCBTRt2hQgwQ/ouOAR1+/1t99+IzQ0lPLly5M3b16+++67+OkUL168iLm5OR9//DGQ/MJNOp2OSZOn0LLXQDRmpv9PzVBhRAghUurRowcMHzKQggUL8r///c9oW5rLli3LuHHj6NKly3tr7NKlC7a2tvF/jomJwdXVlSpVqgBQt25d9u/fz/Tp0+N/9xQtWhQnJyd8fX3f+0AsOjoaMzMzvv76a5ycnBRdqNFUfPrpp3z66adJvrd69epE2+rUqcPFixczuKrMQbpgiUxrzJgxzJgxA19f3/g5zgcOHIiFhQVAoqdDKpUqPohs27aN/PnzU758eRwdHdFqtfj5+XHv3j2uX79OiRIlcHFxee9MIUeOHOHR48c0aJv8Ey9TJV20MjdjHv8hxNs+ad8GMzMz5s6di5WVldLlvFfdunU5evToe/eJCx8vXrygX79+5MqVi2fPnvHq1StCQ0Np27Yt169f59KlS6jVanQ6Hfb29oSEhKDT6d47dsPs3wdhPXv2pFWrVpl2nIcwDfLdJzK1tm3bsm3bNlasWBG/Iuz7xA1cPH78ONWrV8fe3p7KlSsTGhrKo0ePePjwIc+ePYs/1/sCyKTJU2jerS9WNjb6+TBGSJ9BRFpVhBCpER4ezpPHj/jkk0/w8PBQupwPqlevHsuWLUvRvi9fviQgIIAJEyYwatQoJk+ezNChQ8mXLx+1atVi+vTpeHt7o1KpeP36NU+fPk3xxChCGAPT7xcihJ7ENV2fP3+e4OBgKlWqhK2tLebm5tjY2HDnzh2uXr2KmZlZ/CDA5J4gXbhwgb/++oulPxh2FVylJDUdsDBN0vohTMW2jb8QHR1NrVq1lC4lRdRqNUWLFuXy5cuUK1fuvfuWKlWKLVu2xP/Zw8OD0aNHc/fuXSZNmkS/fv1o06YNn3/+OVu2bMHV1TXFs2wJYQykBUSId6xYsQJHR8f4QeY6nY4GDRowb948nj9/TqFChciZM+d7zzF5yhQatu+Kg1PWeiIlXbOEEIayfesmbG1tKVWqlNKlpFjbtm2ZP39+ivePm5HK0tKSBw8eYGtrS82aNdmwYQPlypVjypQpWFtbM3/+fKNYeFGIlJIWECH+FdeaUb16dQoWLEjevHmB2B/8Tk5O8WuI9O7dG0h+9qs7d+6wZ88eFv76h8FqzwwkuChPWj+EKbl+9W9q1qwZP7bBFJibm2NlZcWjR4/iZ1NKjk6nQ6PR8OjRIzZs2ECDBg0oXrw4KpWK8uXLM2/evPgxjUKYGmkBEeIdPXv25Msvv8Te/r+bserVqxMaGsr9+/fjFyRMrvvV1GnTqFm7Hm5O9u8dI5KZSZgwPRI+hCl5+OA+QUFBJtP96m3du3dnzpw5791n/fr1jB49moYNG1KhQgUePHjAd999h6OjY/w+Ej6EKTOdxwZCKKhly5asWLGCV69ekSdPnmT38/HxYd26dUxbsQHe+IC5FTqzxLNuiYQksAghUuOl7wsAXF1dFa4k9ezt7Xn16hV+fn7JDhyvVq0a+/fvp3bt2ixcuJDChQsbuEohMpYEECFSwNzcHC8vrw/uN3v2bMpWq4VnuWrxa4roQgLA3xudnQvYuaDSyD87IYRIj/IVK6NWq3n48CG1a9dWupxU8/LyYtGiRYwdOzbJ9wsWLMi6desMXJUQhiNdsITQk8DAQBYtWkybPkOAt1o8bBzAJRdEhMDzW+iCXitYpfGR1g/lSfcrYWrMzMywtrbm0aNHSpeSJtmyZePy5cuEh4crXYoQipAAIoSeLF68mDyFilCiQlUq5rSPv6lTqVSorB1QZc8P7gXByhadTofu9TN0oQFZdpwISPgQQqRNbmdbnJycePz4sdKlpFn37t1Zu3at0mUIoQgJIELoQUREBDNnzaK115D3jvVQmVuhMv93tV5zS/D3iW0VCXiZpYOIEEKkloWFBREREUqXkWaFChVi37598VPtCpGVSAARQg/WrVuHtb0jles2jN92wTso2f1VKhUqBzfIWQScc0FMJAC6yHB0kWEZXq8xkNYPIUR6qNVqk795b9WqFbt27VK6DCEMTgKIEOkUExPD5KlTadVrcPzUvO8LH29TqVSobBxQueSObTmJCIUX99G9uG/S3bM+tCK6hA8hRFrldrYFMkcAqVy5MuvWrTPZn/VCpJUEECHSadeuXQQFh1C7edt0n0tl7wK5ioK1few0vqEBAOi0pv1L9m0SPoQQ+qDRaEw+gKhUKqpVq8Yff8jCtSJrkQAiRDrodDomTZ5Ci0/6Y66nRaFUGjNUDtkgVxGwcUQXHQXPbqF7/RRdRKhJPymT8GGcUtpiJ4QxyQwBBKBx48YsWbJE6TKEMCgJIEKkw++//87tO7dp2PETvZ9bpVLFvszMIUd+UKnh5UN4GTvtpLEGkQ91vxLGSUKIMDVWVla8evWKqKgopUtJF41GQ+7cubl+/brSpQhhMBJAhEiHSVOm0LRzb2xs7TL0OioLa1QuuSBXMXByj934xhvdy0foQgONNowIIURGGTduHG/evGHz5s1Kl5JunTp1Yu7cuUqXIYTBSAARIo2uXLnCyd9P0PyTfga7pkqtRmXx7zS+Dm5gYQ3+3vD8JrqIEIPVkRbS/cr4SSuIMAXP3sT+rGvdujVFihRh4cKFBAcHK1xV+lhaWqLVann+/LnSpQhhEBJAhEijKVOn8nHbzji5uilyfZWZBSrH7LFT+brmATNLdDHRsTNoBfspMnBdul8JIQxp7dq1BAcHs2bNGqVLSbfevXszb948pcsQwiAkgAiRBg8ePGD79u206jVI6VJix4lY2aHSmIFKBTZOEPwGnt1E90aeponUkVYQYUqqVq1KpUqVWLVqFa9evVK6nHRxcHDg0aNHBAYGKl2KEBlOAogQaTB9xgyqf9wU9zz5lC4lAZVag8reBZV7QchRECxj58vXBb1GF+AbO6OWAqT7lRBCn+K6YQFs3LiR6OhoFi9erGBF+tG7d2+WLl2qdBlCZDgJIEKk0suXL1m1ahVt+nyqdCnvpbKwQmXjGPsHMwuIDAPvW+hePDDoausSPkyPtIIIU5I/f34aN27Mli1bePTokdLlpEuuXLk4ffo0kZGRSpciRIaSACJEKs2dO5eSFatSoHhppUtJMZW1Paps+SBnUbBxALUGnVYbO4tWsB+6mGi9XKdR8eyJXsI0SQgRpsLb25sxY8bg4ODAqlWrlC4n3Tp37syGDRuULkOIDCUBRIhUCA4OZu68+bTxGqJ0KWmiMjNHZe+KyswC0MV20YobL/LykUznK4QwetHR0bx55cvNh88ACA0Nxd3dndDQUKytrRWuLv1KlCjBjh075OexyNQkgAiRCsuXLycyMoIpn/fhy85NOX1kv9IlpZlKrUHl4BY7XiRXEbB1QqVSxY4X8X2ILviN3lpGhGm64B0kLSHCaIQGB/H47i3uXL2I/6uX6LRaAAoWLMiWLVuIjIykRYsWClepH40bN2b/ftP9/SLEh0gAESKFoqKimDhxIh65c1P7o1q8evaIKZ/3YUCjqlw4eZyxvduxbblpTqGoMrP4b7yItT1Y2ULw69j1RYL9AONdeV1kPAkhQgkxMdH4v37F80f30el0qFQqbOztKViiLPmLlcTB2SV+3xUrVuDh4UGJEiUUrFh/atWqlSm6kwmRHDOlCxDCVGzYsIGo6GiyZ89Ox44dGT9+PL/++isLFi5kwqBuAPxz7jTVGjYnd74CClebdiozC3DIBg7Z0EVFgEoVGz68b6MztwIbR7C2R6XWKF2qMKC4EFIxp32ibXHefk+ItNJqtTx7cJfgwAAsraxwcHZFp9NhbWuHta1dgn2fvQnBWhfBgwcPGDBgACqVSqGq9UulUlG2bFnOnj1LlSpVlC5HCL2TFhAhUkCr1TJp8hTUZub8dfYsffr04aOPPuLw4cN07tQpwb4/fNpToSr1T2VuGds6olJBNs/YldcDX8WOGdHGxL6iZbaWrCSuW1ZSrSLSZUukRWREBH4vX/D47i0C/f1Qq9XYOTpRoHhpChQvjZt7LtTq5G9XJk+eTExMDE2bNjVg1RmvRYsWLFiwQOkyhMgQ0gIiRArs3buXV6/9WH7kPGqNGX8e3MPve7dz4+9LnD17FgD3PPnwefKI5w/v8esvK2nRrY/CVeuXytwSHLODY3Z00ZGo1Bp04cHg+xCduSVY2YONAypLG6VLFQq74B0krSEiWTqdluioKMwtLPF5+og3L19gY2ePnYMTVtaxaxc5u6V8Br0NGzZQrFgx8ufPn1ElK8LMzAxnZ2fu3r1LoUKFlC5HCL2SACJECkyaPIXmn/TDwtIKgNrN21K7eVsAQkOCef7gHnf+ucziiV8DsPaniTRo0wUrm8x5Mx47ixaorOzQeRSH8GAIC4LQALC0QRcSADptbFctjfyYESKri4mOJijgDcEB/gQHBWBja0/eQkVxze5OtpweaDRp69K5d8t6nj9/zldffaXnio1D9+7dmTt3LnPnzlW6FCH0SrpgCfEBp06d4p9//qFJ56S7VtnY2lGoVFkunz6BWq2mbt26REZE8PzxfQNXqgyVWoPKxhGVqwcq55yxG3VaCPaL7arlcw9deOyqxTKQPeuQrlhZm06nIywkmJfez9DpdERGhPPmpS+W1jbkK1ycPAWLAGBuYZnm8HH75nWGDh1K6dKl6dKliz7LNxrW1tYEBQXh6+urdClC6JU8mhTiAyZNnkLjTj2wtXdIdp/QkGAu/H4ENzc3fv/9dyrUqkeBYqUMWKVxUdk5g51z7DS+YUGgMft3IPsddJY2/860ZScD2YXIZHQ6Hd6PHxAc4I9Wp8XOwZGYmGisbe3IX6ykXq/VuXVzbGxsmDlzJubm5no9tzHx8vJi4cKFjB8/XulShNAbCSBCvMe1a9c4evQIiw+cee9+6+dOJSoqEl9fX+wdnRgzf42BKjRuKo0Z2DkD/7Z+uOSG8CAI8IXXT9HlLh7bWhIdARbWqFTSKCuEqdBqtYSHhhAaHEhwYCBOrm44uWbDysYWRxc3bOzsMuzf9NoVy3j58iWzZ88mR44cGXINY+Hi4sKNGzcICQnB1tZW6XKE0Av5bS/Ee0ydNo26LTvgku39v+Bcsv/3/rhF6zAzk2z/LpVKhcrKFpWTO6qchSFXUVRqNUSFw8vH8PQGOt8H8euOCCGMi1arJTQ4iOAAfwBePH3Mk/t3CA8NxcHZBVv72LWEXLLlwNbeIUMfKMyeMRVPT0/q1auXYdcwJj179mTlypVKlyGE3shdkhDJePLkCZs2bWL29qMf3Let16f8eehXqn3cjKJlKxqgOtMXNzhdZWWHLnex2FaQf8eKAOj8fSAyDCxt/31JC4kpkVmwTF/c4n9hIcH4Pn9CaEgwGrUGR1c37BydyJ47D+558hl87Y0jB/fj+8KHiRMnvnd63swkX758LF++nMGDB8sDLpEpyHexEMn4aeZMKtdpSG7PggC8eenLyE6NiYmJZuS0RZSpVit+X7VazU+bDihVqslTqVRgbhX7imPrDBoLiAiJXZXdITvYu6ILfgNmFhJIjJiED9Ok0+kIDQ4iNDiQkKAgwsNCKFK6AhozcxycXHD38MTCyio+cKR18Hh6fT/2a1xdXWnevLki11dKu3bt2Lp1a6YdcC+yFgkgQiTBz8+PZcuWMWHFVgAiw8P5on0DwkOCcHR05PtB3dhy8WGWefqmBJW5JZhbgr1L/OxZOp0uNpAEvABtDDoLa3DNAxoz0Oliu3QJRUn4MB1abQxhISGEBgXi4OKGuYUF3o8f/DuGw5Wcdp6oVCosLC2x+EA3VEPI7WzL+fPnefjgPiNGjMjUA8+TUr58ecaNG0fnzp0zzYrv+nb9VSghyczAFyOzMBoVCSBCJGH+/PkUKV2ewqXLodVqGdmpCYFvXrNw4ULu37/PjBkzJHwYUIJftq4esUEkOjI2jGg0EBUBPnfRmVuBpTVY2MTOxCUMSsKH8dLptESEhaFSq7G0subZw3sEvHmNmZk5tnb26HRa1Go1hUqWVbrURHI72+Lr60urVl05cOAAdnZ2dOjQQemyFFGnTh2OHTvGxx9/rHQpQqSLBBAh3hEaGsqcufP4bFLswk/zvx3J43u3GD9+PDVr1uTSpUuYmWWtJ2/GJrbL1r8tJBC7EnuuIrFjRiLDIDIUcEYX4g9Br8HCOvZlaRPbsiL0TsKH8dDpdMRER2Nmbk6A32v8XvoQHhaKSqUim3tuLK2scc3uTvZcHpiZWxj30/RQf5p0bc/Ro0fRarW0adOGfv36YWdnp3Rliqhfvz6TJ0+WACJMngQQId6xatUqcuTMRfmadYH/+jmXL18egICAADTm8k/HmKhUqthxIWYWYOP43xuW/05ZGRkGIW9i/+uSC13Q69gWlLhgYmbkN2FGRIKGcYkbKB4ZHs6bV76EhYYQHhqCpbU1+YuWxNzCApdsObCyscPC0jL++9zKxvinc/V+9oyaFUuj1Wpp164dffv2JVeuXEqXpSi1Wk3BggW5cuUKZcsaX2uVECklfUiEeEt0dDTTZ8xg6OfDqZTLgYo57fH66lvMzM1Zt24dEBtApAXENKjMzFHZOqFyzokqRwFULv/evJhZxK4/EvQKfO7ErtoO6IL90IUGoIuKkFXbkyDhQ3larZZAfz98nz/h0d2b3PnnMlqtFp1Oh1Ybg5OrG55FS+BZpAQANnb2OLq4YfnW4HFTMaD3JwDs3LmTcePGZfnwEad9+/bMmzdP6TKESBd5jCvEW7Zs2QJA89Zt47fZ2DlQsGQZDhw4wLfffktAQADmllbJnUKYAJW1fexq7MT2jScua0SGx44riY6MfS9HgdiZuQJf/tvlyyrLtpZI+DC8IP83hIeFEhEeRmR4GLk8C2JhacUr72dY2dhi7+hM9pweqFQqLK2tyZk3v9Il682ff5zg8sXzDBo0iHz58ildjlExNzfH0tKSR48eyddGmCwJIEL8S6fTMWXKVAYMGZZgnvWKOe2xsLDCzMyMyMhIrl27RjYPT+UKFXqlUqnh3zwR10ISP8jdzDy2pSQqAkIDY9cqAXQexSEmGkL8wezfsSjmFpl2WmAJHxkjrvtUcGAAYcFBRISHEREehmuOXDi5uhHw5jUqlQora2scnF0wN7dArVZToHhppUvPcMM/HYirqyteXl5Kl2KUevTowbx585gxY4bSpQiRJhJAhPjXoUOHeP78OZ269Uj0nioyFBsbG/bv309AQACfTx2lQIXCUOIHuUNsOHHLA/wbTGKiUKnU/4WUsKDYgGJmDjkLowsPjl1QMW6QvMY8ftFFUyThI31iu0Zp0Wg0hIUEExTwJjZohIVh5+CEe558hIcGExkZgZWtLY6ubljbxA6w9shfSOHqlREdHY2P93OGDBmCtbW10uUYJTs7O168eMGbN29wdpYZ/4TpMd3fikLo2eTJU/AaMCjJX3ihoSGYm5uzZs0anFzdqPhRfQUqFEqLH+zOv+uUuHoA/wYTbUzcXqCNhuCQ2GBibR87dXCAL0SExgYVMwuwskNlYY1OGwMqdZbs1pUZ6HQ6oqOiiIqMICY6GnsnZ8JCgnnp/YyoyAiiIiOwtLYhf9GSREVGEh0VhY2tPc6u2bG0sQHAzT23wp/CuJw98ycxMTEUL15c6VKMmpeXF4sWLWLMmDFKlyJEqkkAEQI4e/Ys58+fY/7KtUm+b21tw9XbtwHoPmy0IUsTJkClUsUuhgiorGzB6r8ZhuIHs1vZgVoDMVGxY0005rEzcL18BJFh6DTmseHEMTsqS1t0oYGx+5uZx7aiGDigSMvHf+JWCI+KjIwPFe558hEVGcn9G/+g02kxMzfH0soaeydnNGZm2Dk6YWFhibmFBeYWsa1pDs4uODi7KPxpjN/5P34DoGjRosoWYuSyZ8/OpUuXCA8Px8pKxiUK0yIBRAhgypSpdOvphXMyNwe7Dh7jwK+7OXXlOh36DzNwdcKUxQUHlaUNWNok3iG7J0RHxQaT6EhQ//tjOdgvtgUlJgoAXc5CoLGAV49i91FrYkOPgxvodP+GGk38e2kNLFkleERGhBMVGUlMdDTR0VHY2jtgaWWN95OHRIaHExMdRXR0NHkKFMbKxhbvx/cxM48NE+YWluh0YG5hSYHipTG3sEiwMKmFpRUu2eSGMK3++usvHBwccHNzU7oUo9e9e3d+/vln+vfvr3QpQqSKBBCR5d26dYt9+/Zy8tyVZPexsLCgVbsOtGoHF7yDDFidyOxUKnWCRRXjt2f3BP4bd4LGLHa2Lhun2C5eMdH/ztalgphI8HsKMTGxg+Y1GshdHF14CAS8iD1WrQFzK1T2ruiiwmMDi1oNKjVozKmU142Y6GiioiJRqzWo1cbZLUyr1RITE41Oq0Wr1cbOAGVlTUR4GOGhoeh0WmJiYrCwsMTeyRk/Xx8C/f3ig4ZLNney5czNi2ePCQ8NxczMDI1ZbOuFpZU1VtY2WNvYotGYoTEzw8LKGpVKRaGS5ZKsx1KePOvd7du3KV68uFF+/xmbQoUKsWbNGvr27ZsgBAth7CSAiCxvxowZdO3alVweHinaP+4JsQQRYQhvjztBBdglMeDU3ApyxXZX0Wm1oPt3PIqZBdi5xIYVbXRsOIHY8BHsh4ulGq1Wi62DI+DGS59n+Pn6xJ/WNUdOcuTOy/PHDwgNCowNJWo1rtlz4uDsgu/zp0RFhMfv75LdHWtbO7wfP0QbEx2/PVsuD8wtLHn+8F6Cst3zeKLT6Xjx9BG6f2vX6XTkLVSUsJBgnj28F7vGhTa2i1PBEmUI9Pfj+cN7qFQqVGo1tvaO5ClQmLCQYN688o0NTxo1agcnACytbXAyM4sPGnHdofIUKJLk19vZLXvK/mJEhsjtbMubN29o2LCh0qWYjBYtWrB7927atGmjdClCpJgEEJGleXt7s27dOkqWLMmOLZto3b5jip8iVcxpLyFEGB2VWg2o3+pKlVS3QnsgT6Kt7h75yJE7D9p/WxfU/04r7JrNHUdnF7Qxsdst/52owcLSMsG/F/W/42AsrazQarXv1BQbBhIWq0L11na1Wo1ao/n33Fa45/GM36ZWx253dHbF0dkl0ZTHTq7ZcHLNlugz2do7JPH5hbF6+vQp4eHhMv4jFapUqcJ3331H69atpdVImAwJICJLmz17Nk2bNuXgwYN8NrAvIz8bTIcu3Zk2O2WrzKa3NSSl/e0l6IiU0Mf4DZVKjUaj5t8cAICltTWWJJ4dLqkbfohtCUmKm3vSK1kntV1jZoadg2MS9amIX7hFZCq5nW0pV68marWasmXLKl2OyVCpVFStWpVTp05Rq1YtpcsRIkWkw6DIsvz9/Xny5AkTJkzgjz/+YNmyZdSoUYON69Zw9crlVJ2rYk77VN38ZfT+IuuI+96Q7w9h6jp27MiVK1cYM2aMrPCdSk2aNGHx4sVKlyFEikkLiMiyFi9eTJ8+fQAwNzenWrVqlChRgqZNm/JZ/978dvZyqs+Z1E3g260X6b1J1Mf4k7TUIC0wxklCh8gsls+dwdatW+nRowedO3dWuhyTo9FoyJUrFzdu3JD1U4RJkBYQkSWFh4dz9uxZ3N0TdhVxcHBg6NCh3L17lwsnj5Lb2TaZM6RcRjyhfvucKTlvemuQJ+zGRf4+RGYSHh7OxIkTqV27NiNHjlS6HJPVuXNn5s1LWfdhIZQmAURkSWvXruXZs2esXbuWqKioBO916NCBHDlyMGTIEIWqS713A0lqA0pqryOUIV9/kRmdOH6UmJgYevfujebtwUciVSwtLYmKisLb21vpUoT4IAkgIsuJiYnhxx9/5Pbt20yfPp2zZ88meN/c3JwhQ4bw9OlTNmzYoJdWkMxGboQNS77eIjO7dOYPAIoUSXpqZJFyvXv3Zv78+UqXIcQHSQARWc6OHTvQ6XSsWbMGAKskFhJr2bIlHh4eDB8+3NDlmZQPtbRkVEtMViBfN5FVnD9/HldXVxwdE896JlLH0dGR+/fvExgYqHQpQryXBBCRpeh0OqZMmcrIkSPx9fUFYsd9vMvMzIzPPvuMFy9esHz5ckOXabJSE0hEQhLWhLHLHvUy/qUvuZ1tuX//PsWKFdPbObO63r17s2zZMqXLEOK9JICILOX48ePcf3Cffv364eMTu+KzvX3SN3tNmjShQIECjB49WrphZYCsfqMtgUMYu7cDx7uh433vpZa5uTnBwcHpOof4T+7cufnzzz+JjIxUuhQhkiUBRGQpkydPplffAfhHEj8D1v3795PcV61W8/nnn+Pn58eUKVMMWWaWkhVuwDNyYgAh9C0toSItx8Q92KlWrRr//PMPYWFhqTpeJK9Tp05s3LhR6TKESJYEEJFlXLx4kT///JM+AwYB0KdPH2xtbfn555+TPaZevXqUKVOGH374gejoaEOVmiVlhpvyjJ6FTIiMlt7WjLQEkW7duhETE8Pff/+drmuL/5QsWZLt27ej0+mULkWIJEkAEVnG1KnT6Ny9By6ubkBsC0eXLl34448/mDVrFqGhoYmOUalUjBw5kpCQEH6aOM7QJWc5xnzD/qGpjo21biFSSp9jO1ISRJ69CQFiJ/0AePjwod6uL6BRo0YcOHBA6TKESJIEEJEl3Lt3j507dzBgyLD4bc/ehDD2x+nUrVuX1atX06xZM/bu3ZvoiVGFChWoXbs2ixYtonenNrx+cs/Q5Wc5xnJDL+FCZBX6DB+p9ejRIwBcXFwUqyEz+uijj1i5cqXSZQiRJAkgIkuYMWMGzVu1IU/efAm2W1hYsG77r2zdcwArKyu+/vrrJGe9+vbbb2natCknTpygYsWKnDq8VwamG4ASAUBCh8hK9D2rVVr8888/AGTLlk3ROjIblUpF6dKlOXfunNKlCJGIBBCR6fn6+rJmzRoGfz4i2X2qVK/BX1dvUa5iJRYuXBj/RC5Ojhw5mDRpEocPH6ZQoUJ069aNpUuXSggxoIzs9iShQ2Q1xhA84ty6dQsAV1dXhSvJfFq1asWCBQuULkOIRMyULkCIjDZnzhyq1/qIEiVLfXDfIZ+PYECv7kyePJnFixcnet/FxYVVq1YxZMgQBg8ejK2tLd27d4/vyywMT0KDEAkZS7CA2Fp8zZNv2Th79SY//vgj9vb25MiRw4CVZQ1mZmY4OTlx7949ChYsqHQ5QsSTFhCRqQUFBbFw4UIGD0t6RXOtVsvu7Vtp37wxRTyy079nNzQaDblz5072nHZ2dtSoUQOtVktIiAQPIYTyMmKRQENoXKcGMTExLFu2DAsLC6XLyZS6d+/O3LlzlS5DiASkBURkakuWLKFAocJUq1Eryfcn/O9rli9eCMR2s/r222+pW7cutrbJd63S6XSsX78etVrN8ePH6dKlC6DJiPKFEOKDTC10xNm68Rf837xhzpw5lCxZUulyMi1ra2sCAgJ4+fKljLMRRkNaQESmFRkZyazZsxk8bDgqlSrJfT79fCR1P26IjY0NL1684IcffmDmzJlcvHgRrVab5DEqlYpVq1bRqFEjNm3ahJubG8P69SQ8PDwjP44QQiRgii0eb5s3czrZs2enTp06SpeS6Xl5eclYEGFUJICITGv9+vXY2NjSuFmLZPfJniMH67bs4PZTX5auWUfhosXZuXMnvXr1olGjRsyZM4cHDx4kOq5gwYJMnz6dXbt20bRpU3bs2EH7Fo0z8uMIIUQ8Uw4eAM+ePuHB/Xt06tQJjUZakDOaq6sr169fT3K9KyGUIAFEZEparZap06YxcOjnqNUp+zZv1rINew4f5/ZTX8ZPmoqzszOrVq2idevWTJw4ET8/v0TH5M+fnx9//JGRI0dy5eIFfvjuf/r+KEIIEc/UWz3iTPx2DADt2rVTuJKso2fPnqxatUrpMoQAJICITGrPnj0EBATQrlOX9+6X29k20VS6ZmZm9Bs0hBs3buDn50fr1q3Ztm0bTZs2Ze3atURERCQ6T8+ePWnQoAHLFs7j+JFDev0sQgiRWYJHnJPHj1GnTh0Zk2BAnp6eHDlyhOjoaKVLEUICiMh8dDodU6ZMpd+goVhaWia5T1LB413P3oTg4ODAjh07uHr1Kp6enkyfPp1atWoxevRojh49Gj/uQ6VS8cMPP5A7d24G9OqO74sXev9cQojE3p796X0vU2Xq9SdFq9USHBxMmTJllC4ly2nXrh3bt29XugwhJICIzOePP/7g+o3rdO/lFb/Nx9ub8yeOkMPeMlWLBz57ExIbRNzzcu3aNQ4cOECNGjX47bff+OKLL6hZsybdu3fnxx9/5ODBg9SqVYuwsDB+XpV4NXUhRPqkJ1iYUigxhRrTI+jFE2JiYsiXL5/SpWQ55cuXZ+PGjeh0OqVLyVTevHlDjx49cHR0xNHRkR49euDv75/s/lFRUYwePZrSpUtja2tLrly56NmzJ8+fPzdc0QqTaXhFpjNlyhR69umPvYMDADu3bWHEkIFERkZiZmaGu7s7U6dOpVu3bvHH5Ha2/eBigs/ehFCqSi3Wbo2d0vf3o0dYtXwJN67/w65du9i4cSMAZcpXYPiobzLo0wmRtWTkTXhS537fonmpPVdKz5tZg0ZyTpw4AUDevHkVriRr+uijjzh+/Dj169dXupRMo1u3bjx9+pQDBw4AMGDAAHr06MGePXuS3D80NJSLFy8ybtw4ypYty5s3b/jiiy9o1aoV58+fN2TpilHpJAaLTOTq1atUqVKF05evkS177Kq6ZQp7EhoSzPTp07l69SoHDx7k8ePHVKxYkV9//TV+9d30rmb+6qUvd+/cTnbNESFEyhjzDbmveTajrs8YvR2+cjvb0q9fP1asWMHZs2extrZWsLKsSavVMnnyZDZs2KB0KXrl5OTExDU7yVe4WJLvd6tWlDN/nqJUqVJ6ve6NGzcoUaIEZ86coWrVqgCcOXOG6tWrc/PmTYoWLZqi85w7d44qVarw6NGjLBHOpQuWyFSmTp1Ghy7d4sMHwJDPhxMeHs7Lly/57LPP2LFjB8OGDePKlSvkzZuXMWPG6OXabtmypyp8pKYrmBCZnal0OzL2+kyBv78/Go2GyMhIpUvJktRqNQUKFODvv/9WupRM4fTp0zg6OsaHD4Bq1arh6OjIn3/+meLzBAQEoFKpcHJyyoAqjY8EEJFpPHr0iC1bNjNmUI8ENwkDh35O/oKFmDFjBr6+vpibm9OvXz927dpF5cqVmTx5Mnny5MHRXP+NgVevXKZ6uRIUcHehc+vmPHr0IMEA+JQMhhciszKV0CHSJ+7vN+5n3fjx49HpdPHdVoXhdejQgfnz5ytdhsFFRkYSGBiY4JXUzJap4ePjQ/bs2RNtz549Oz4+Pik6R3h4OF9//TXdunXD4d/u45mdBBCRafz000+0bNKQQvk9gYQ3Nz9v3k5kZCRjx44lLCwMgDx58rBo0SKmT5+Oj48PJUqW1Ov0hOf+Ok3zj2vz+tUrmjRpwtkzf1K7UjmqVKmSaHHDuCAiYURkdhI6RKlSpXBzc0vV02GhX+bm5piZmfH48WOlS9GrP++/5tAN3yRfUTE6li9fHj9QPO41efLkJM81fvx4VCrVe19x4zVUKlWi43U6XZLb3xUVFUWXLl3QarUsXLgwfV8AEyIBRGQKr1+/ZsWKFXw1dGCS71fxsGf81yM4e/YsPXv2xNfXF4j9odGkSROmTJnC0ydPaN3kY73VVKhwURydnAgNDcXNzY19+/bh5eXF1atXKVKkCAMGDECr1SY6TsKIyAwy23S4In3e/nmm1Wrx9/eXaXgV1rNnT+bNm6d0GQbVr18/AgICEry++SbpSWOGDh3KjRs33vsqVaoU7u7uvEhi6v2XL1/GjzFNTlRUFJ06deLBgwccPnw4y7R+gMyCJTKJefPmUb1yRSqULZ3sPqM+G0ze3LnpO+xLOnfuTP369Xn16hWenp60b9+eUaNGMXXqVFYtW4xX/0HprsnZxQXfFy9o1aoVq1at4syZMyxfvpxu3boxffp0li1bxpYtW1i1ahVBQUGcPXuWYcOGUbhw4fhzxP3STu8A+XelZ8YeYZrk5l8Yi8OHDxMZGUmVKlWULiVLs7Ozw8fHhzdv3uDs7Kx0OQZhYWGR4pt8Nzc33NzcPrhf9erVCQgI4OzZs/Hf03/99RcBAQHUqFEj2ePiwsedO3c4fvw4rq6uKfsQmYTMgiVMXkhICPny5WXdojl8XOfDg8Av/f0PLbr2JiwsHEtLC/wDAomJiaFixYpcuHCBlm3bs2jFmhRde/+vu8meIwcVK1dN9N7bT/yaNGnCwYMHOXz4MO7u7kDsLBkTJ07kyZMn8XOyq9VqcuXKxWeffcaIESMwM/vvGUF6Q0hab0AljBgnCRTCVJhn90zw5w4dOrBjxw5Onz6NjY2NMkUJAF68eMHZs2eTbQUwJU5OTjT9bgWueQsn+f6ynrW48Nefep8FC6Bp06Y8f/6cJUuWALHT8ObLly/BNLzFihVj8uTJtG3blujoaNq3b8/FixcTzMYJ4OLigoWFhd5rNDbSAiJM3sqVK8nrkZv6tWumaP/yZUrx7Np/82z7vfHnf5OmsWXnXgDC/RN2FUnuBvzIwf0M7P0JAJWrVmfZ2vW4uCb9tOT06dNUq1YtPnxA7CwZO3bsYP369ajVaurUqcPevXvZuXMno0eP5rvvvmPbtm00a9YsRZ/rfdJzs6rPtRJE2kngEJnBH3/8wc6dO2nQoIGEDyOQI0cOLl68SHh4OFZWVkqXY7LWr1/PsGHDaNSoEQCtWrVKNMj/1q1bBAQEAPD06VN2794NQLly5RLsd/z4cerWrZvhNStNWkCESYuKiqJQwYJMHjeKjq1bpPt8f5w5R7HCBXFzdUl2H1/zbERHR1PCMxdubm40bNiQNWvWoFar8eo/iLHf/4BarU7QAmJmZka3bt0YNWrUB2vQarVcuHAhvnVk48aNtG/fPk0tIErftEpQSTul/+6E0Cfz7J6Ehobi7u6Ora0tW7duxdHRUemyBHD79m38/Pzo16+f0qWki5ItICL1ZBC6MGmbNm3C3ExDuxZN9XK+WtUqvzd8AIkG0/bv359du3ZRtWpVliyYS9kinpw6vDfBMWXLlmX79u2EhHw4RKjVaipXrsyaNWvInz8/Xbp0YcWKFakelG4MN7AyADnl5GslMrvDhw8TFBTEN998I+HDiBQpUoS9e/cmOSmKEBlFAogwWTqdjmlTpzJ8cD80Go1Br21mZsbmlQt59uwZ48ePJ0+ePCxcuJAFCxZga2ND586dyZs3L/v27QNg/vz5hIaGsn379hRfw9nZmVWrVlGkSBH69euHlZUVm39Zl6JjjfUGVm6uE5LAIbKS6tWrAxAYGKhwJeJdzZo1SzBeQYiMJmNAhMnav38/L1740KNTe0Wu36heHYb068XcJSt5+fIl5ubmxMTEULduXdzc3Fi/fj3NmzenYMGCWFtbA7BlyxZ69OiR4ms4OjqyZs0aTp06xddff83BPTvo1O2T9x5jCjezKRljkxmZwt+NEBkle/bsWFpacv/+faVLEe+oVq0a48ePp3Xr1kqXIrIICSDCZE2dMoXP+nthba3cwLkZE8bx7Lk3f567iFodu+DQxYsXsbCwoEePHlhbW/PLL78QFRVFixYtqF+/fqqvYWVlxccff4yHhwc3b95MdrCgqd7cZtYwYqp/H0JkhCjfh5hn98TR0ZE7d+4oXY54h0qlonLlypw6dYqaNVM2oYsQ6SEBRJikM2fOcOnyJbaumP/hnTPYhuUJVy69efsuPQd/wbJly7C0tKR06dKUK1eOjz/+mJIlS6b5OqVLl2bHjh0UzZMDDw8PBg79gp59+2eqG93kPktGBpPM9PUTwphF+T4kb253CSBGqmnTpsyYMUMCiDAIGQMiTNLUKVPo36MrTo7Gt2posSKFOHv0V47v3kydOnV4+vQpq1evpmvXrsydO5eoqKg0nXf8+PFs2LCBfv368ezZM2ZPn0xuZ9tEc+xnRsmtqq2PlxDCcEoXL8aLFy/YuHEjkZGRSpcj3qLRaHB3d+fmzZtKlyKyAAkgwuTcuHGD/QcO8NmAPkqX8l41qlTiwIEDPH/+nMDAQD7++GOWL1/OJ598wsOHD5M8JiIigujo6CTfU6vVlCpVivr16xMdHU3BggXj980KIUQIYfomjvkKz7x5+PHHH2ncuLEEESPTuXNn5s6dq3QZIguQACJMzrRp0+javjW5c7p/eGeFRfk+BGLHcRw+fJhVq1Zx//59OnbsGD8TTGBgIJ999hmVKlWiUqVK9O7d+73ndHZ2pkSJEpw+fRp7e3sWLoztAiYhRAhh7LJnc+P2uRPs/mUVdjbW/Pjjj3Tp0oWgoCClSxPE/q6KjIzE29tb6VJEJicBRJiUp0+fsmHDBkZ8OkDpUtKkV69etGjRgqioKKytrXny5AndunXj5MmT1KtXj6JFi3L16lWuXbtGaGgoO3bsYO7cufj6+safI2fOnGzatIl169ZRqFAhvvjiCx49eqTgpxJCiNRp8nFdbp87wdpFc7h//z6fffYZERERSpclAC8vr0SreAuhbzIIXZiUWbNm0fjjuhQrXFDpUlIsrhUkroXCx8cHc3Nzjh8/zvfff09kZCR79+6lcePG7N+/n+bNm9OlS5f449VqNatXr6Zp06ZUqlSJihUrkjdvXsqWLcusWbNo1aoVDRo04M6dO5hn94y/nhBCGLsu7VrhHxDAF2PG87///Y/p06crXVKW5+joyL179wgKCsLe3l7pckQmJS0gwmS8efOGpUuX8tWQgUqXkiZRvg+J8n3I/B/HERMTw8iRI9FoNPz99980btwYiJ2F5OnTpyxZsoRevXqxZcsWHjx4QK1atTh27BjffvstzZs3Z/r06eh0Otzd3fnqq6+4e/cuP/74Y4prke5aQghjMcirBy0aNeDAgQMyHsRI9O7dm+XLlytdhsjEJIAIk7Fw4UIqlClF1UrllS4lXUoUK8Kq+T9Rv3ZN7p0/QeHChRO8nytXLgYMGMDq1avp0KEDefPm5dixYwQFBfHo0SNatGjB2rVrmTBhAocOHWLJkiWoVCoCAgKA94cL8+yeEj6EEEanTMliAPj7+ytbiADAw8ODkydPpnnWRiE+RAKIMAlhYWHMnTuHL4eaZuvHuzq1acmBLeuws7OLbxlJSdepvHnzsmfPHvr27cvWrVsZOXIkELsq/LRp05I9LqngIUFECGEsPHLlBGJbuoVx6NSpExs3blS6DJFJSQARJmHNmjXkyOZG4/p1lC4lQ6U0iCxfvpypU6cyefJkfHx84rtwveunlRvJWaISxYsX54cffiA4OFjPFQshRPpVqVAOlUrF6dOnlS5F/KtUqVJs374dnU6ndCkiE5IAIoxedHQ006dNY+SnA1CpVEqXYxApaRUZNWoUX3/9dZLHAZw5d4FvvvmGHDlyEBAQwLhx43BycsLT05OxY8cSGRkprSBCCKNQqkQxihUuxJIlS+KnKBfKa9CgAQcPHlS6DJEJSQARRm/btm3EREfRqU0LpUtRRErCSFLv7zl4FID58+dz8OBBtm7dysCBA7GwsGDSpEk4OTnx2WefyaBPIYRRWLNoNqGhoaxatUrpUsS/ateuzcqVK5UuQ2RCEkCEUdPpdEydMoUvBvXFzExmjX43jLwvmJy/fAWNRsPt27cJDQ2laNGiDB48mO3bt7NhwwYqVqzI/PnzyV+hFlqt1nAfQgghklCuVAmqVKnChg0bZE0QI6FSqShZsiTnz59XuhSRycgdnTBqR44c4fHjx/Tu2knpUozOh8aKlC1ZnJOnzzJo0CDUajXFihWjbt261K9fn5IlS7Jo0SL279/PqFGjmL9sNcMG9jFM4UJkQtrrp1K8r7pEzQysxDTFff3Gtq1NyzNnOHr0KM2aNVO4KgHQunVr5syZIy1TQq+kBUQYtalTpvBp357Y2tooXYrJmfb9/wh+cotd61fSpW1LgoKCWLJkCR06dKBhw4ZMmjSJJUuWAHD3wUNlixXCBGmvn4p/pfY4Eevdr1/DKmVwdnZmy5YtClYl3mZmZoaDgwP3799XuhSRiUgAEUbrwoULnPnrLz7t01PpUkyWWq2maYN6rF44m8ePH+Pv78/EMV/i6uTI9m3b8Hv9miUzpzB3ygSlSxXCZKQldGTEOUzZ+z5/586dOX/+PI8fPzZwVSI53bt3Z+7cuUqXITIRCSDCaE2dMoU+3Trh6uKsdCmZQpTvQyxDXzH68yFcPnGQoCe3eHHrEl7dOytdmhAmISNCQ1YLIin5vD/++CMajYYdO3YYqCrxITY2Nvj7+/Pq1SulSxGZhAQQYZTu3LnD7j17+HxQX6VLEUJkcVktJGSUlH4Nbf/eT4kSJWQNCiPj5eXFggULlC5DZBISQIRRmj59Oh1aNSevR26lSxFCZFGGDB5KBBxDfb60XKdp06b4+fnx+vXrDKpKpJarqyvXrl0jNDRU6VJEJiABRBgdHx8ffv75Z0YOGaB0KUKILEqpQJCR5373ZYjrplXlypUBePr0qcKViLf16NGD1atXK12GyAQkgAijM3v2bOp/VINSxYsqXYoQIgtS8oZcqTEmGfWZ03re2rVrA/DkyRN9liPSKX/+/Bw+fJiYmBilSxEmTgKIMCqBgYEsXryYL4cOVLoUIUQWktYpdTOqFiXOo8/Pnt6vpeP1w1hYWEgAMUJt27Zl+/btSpchTJwsRCiMyuLFiylepBA1q1ZWuhQhhIlL7gb47YUAjSFwJCWurrQuWpjWz6W9firdCyXq62tqY2MjU/EaoQoVKvDtt9/SoUMHVCqV0uUIEyUBRBiNiIgIZs+exfwpE+SHmhAiTZTsbpQR3q41pcFAH2uUGDr4JMXOzg5vb2+9nU/oT61atfj999+pW7eu0qUIEyUBRBiNdevW4ezgQPNGHytdihDCxJhSqEgrY/+M+q7v1atXfPTRR3o9p9CPBg0aMGXKFAkgIs1kDIgwCjExMUyfNo0Rn/ZHrZZvSyFEyhjLuI3MRsnxIwCn/7lNeHg4VapU0et5hX6o1Wo8PT25evWq0qUIEyV3esIo7Nq1i5DgYLq0a6V0KUIIEyDBw3hkxN/DuoMnAahUqZLezy30o0OHDsybN0/pMoSJkgAiFKfT6Zg6ZQrDBnphYWGhdDlCCCNlTDNViYyVO5sLAA8ePFC4EpEcCwsL1Gq1zFQm0kTGgAjF/f7779y+fZu+n6xUuhQhhAFIgDANKRmMnlF/l6O6tWTaL7+yevVqypcvnyHXEOnXq1cv5s2bx7Rp05QuRZgYCSBCcVOmTGGQ1yfY29kpXYoQIoNI6Mh8MvLv1MzMjDYfVWTT0eM8ffoUDw+PDLuWSDt7e3uePXuGv78/Tk5OSpcjTIgEEKGoK1eucOLECabOXaR0KUJkiPTcpKVnPYa0TN+qbxI6TJ8+1gVJq6Y9BrLxyJ/4+flJADFiffr0YfHixXz99ddKlyJMiAQQoaipU6fRqdsnuGXLji+QPeql0iUJkYhSN9KGWBFb3zeXEjoyHyX+Ti3rdue3Pn1QqVQUKlTI4NcXKZcjRw7Onz9PREQElpaWSpcjTIQEEKGYhw8fsnnzJk6cuxy/zdc8m4QQkWHk5jixd78mhlrsTogPuXz5Mrly5cLGxkbpUsQHdO3alXXr1tG3b19F67hy8yXWfrZJvhcTozVwNeJ9JIAIxUyePBmdTseiubOZMGV6/AxYvubZAGkNEfojN8spJ18roTTLut0BuHPnDjVrKtP9S6RO0aJFGTduHF5eXrKWl0gR+S4Rinj58iUBAYHUrVuXdatXUDJ/blYvX5JgH1/zbPFhRF/8/QPTfQ7z7J4J/hwdHc3Dx0/TfV6RMeSGWgjTs2vXLoKDg2nSpInSpYgUatasGb/++qvSZQgTIQFEKGLWrFn06tWTOXPmsG7dOgoVKsS3X3/F9EkTE+2b1hCi1Wo58vtJ+n72JcWq1ME+bzGyFy1LzuIV+Hz8NALUdmw+fIqmnXpw596H55o3z+6ZIHzcvH2XVt28cC5QiiKVP6Jk9fpc+vufNNUqMoaEDyFMS1zrx48//oi9vT116tRRuCKRUtWqVWPt2rVKlyFMhEqn0+mULkJkLcHBweTLl48yZcowZ84c1Go1kZGRjBo1imPHjuHVfxATpkxP8tiUdssKDw+nQIWavHrth0qlokjhwlSuUgUPDw8OHTrExYsX0Wg0AMTExKDRaKhRowYrZ00iX57/ZluJCxy+vr4cPnyYy5cvc/PmTf7++2+ePHmCRqOhSZMmFCpUiBUrVhAaGsrpQ7spV6pE+r5IIt0kfAhheizrdic8PBwHBwc6duzIN998o3RJIhV2795N3bp1qVGjhsGv7eTkRK7OU7B+p5dCnCvTO3D5/BlKlSpl2MJEkmQMiDC45cuXY2try2+//caiRYsYMmQIFhYWzJgxgzFjxrB6+RKGf/YppUqV4tmbkATHpnSQepOOn+D3xp/JkydTp04d7O3t49/r3r07T548Yffu3Tx//pxBgwaxefNm1q9fT7Gqdalbty558uTh6tWrPH78mMDAQCIiIuKPt7S0JF++fIwcOZJWrVrh7OwMQNu2bWnWrBl9PxvJheP79fTVEmkh4UMI0zV58mSioqJo3bq10qWIVGrevDkzZsxQJIAI0yItIMKgoqKiKFiwIDNnzmTx4sUcPXqUffv2kSdPHgACAgJo2LAhJUqU4Pz58/HHvRtEIPnWkBnzFjPmh6kMGzaM/v37p7i2ly9fsnz5cjZv3kxMTAzu7u4UKVKEAgUKULBgQQoUKECePHlwdHREpVIlOl6n0zF8+HCOHTvGs2vncXN1SfG1hf5I+BDCdI1ZsoE5Ww5QrFgxNmzYkOTPWmHcVq9eTf/+/SlatKhBrystTqmWegAAOepJREFUIKZFWkCEQW3YsAErKyvatm1LoUKFKF++PHfu3IkPII6OjvTq1YulS5dy+vRpqlevDkBu59hp9d4OIu+2hphn9+Sff/7hu6kzqVq1aqqnA8yWLRvffPMNQ4cOxdzcHCsrqxQfGxUVxejRozl69CjVK1fAxdkpVdcWQoisrsnISfx28Tp16tRh8uTJEj5MVJcuXZg7dy4LFixQuhRhxGQQujAYrVbL1GnT+Oqrr9BoNJQpUwa1Ws3Ro0eJiYmJ369Xr164uLjQunVrtNqE83bndraNf8F/A8Pjxmo0atQIW1tbpkyZkuapAO3t7T8YPrZt20bDhg359NNPmT17NtOmTePw4cOMGDKA33/dluja0dHR/LRgCeXrNGbLbpklRAgh3uX9yh+A06dPM2nSJM6dO6dsQSJNrKysiIiIwMfHR+lShBGTACIMZu/evbx69ZoePXoAsQPFc+bMye7du/nuu+/i97Ozs+P777/n5cuXtG/fPlEIifP45t90796dcuXK0aVLFz799FO8vb0ZPXo0bm5uGfpZYmJi8PHx4cqVK/z8889s3LiRgp75mPJt4gGTI/43Aaf8JflmwhSu3bzNT/OWJJpRS+iHdL8SwnRdWTONY3O/5aPqVTh69Ch9+vRhy5YtSpcl0qB3797Mnz9f6TKEEZMAIgxmypSp9B30Ka/DYls7qlSpwrNnz8iTJw+VK1dOsG/t2rVp164dO3fuxMXFhZ9++onAwECmTZtGpUqVsLW1pUaNGmzcuJGXL1+yZcsWFi1ahLu7u0Hmja9fvz4qlYp27doRERHBkydPuPT7gST33bxjN3ny5OHnn3+mXr16PH7+31MhCSFCCPGfWp292L/5Z4KDgylTpgwTJ05kz549SpclUsnJyYm7d+8SFBSkdCnCSMkYEGEQp06d4uo/V1n+y+b4bQEBAVSqVImVK1cm2dd3/PjxfPzxx8yfP58vv/ySr776Cp1Oh4uLC40bN6ZmzZpUq1YNR0dHoqKiuH//Po6OjpiZZfy3tZubG+XKlWP//v0cPXoUtVpNzpw58XBxw87Ojijfh/H7xsRoKVSoEOXKlaNIkSL88ccfGV6fEEKYGnWJ/1Y9V6vVXLhwgZIlSzJ27FgsLCxo3LixgtWJ1OrduzcrVqzgiy++ULoUYYQkgAiDmDJlCp/07ouDgyMQO5hcpVKh0WiSHWioUqmoXbs2H330ESdOnODhw4dUq1aNIkWKJDrG3Nzc4DNuhIaG8urVKxo0aJBgu0qlwszMDI1Gg7m5OSEhIVhYWABQuHBhoqKiuHHjBsWLF4+tPbtngsAihBBZzdvhI46ZmRlXr16laNGijBo1ChsbGz766CMFqhNp4eHhwdKlSxkyZAjm5uZKlyOMjAQQkeGuXbvG4cOH+fPS7ETvRUdHf/B4lUpFnTp1jGpF3GvXrnHr1i2srKywsbFh1qxZBAcHExISEv/foKCg+D83b94cID4k1a5dmx49evDtt9/i5OSk4CcRQghlJRU+onwfYp7dEwsLC65du0aOHDlYu3atBBAT07FjRzZv3kz37t2VLkUYGQkgIsNNmzad9p26ksPdPcH2AoWKcOrk7zx9+hQPD49kjjY+3t7e/PTTT1hYWKDRaKhVqxZVq1ZN0bGenp5MnDiRDRs2MGvWLObOnUvBggVZtGgRH5UqkMGVCyGEcUkqfLzLxsYGe3t7eYpugkqXLs3//vc/unXrJtMqiwRkELrIUE+ePGHTpo0M+uzzRO/NWbwMlUrFrFmzFKgs9YKDg5kzZw7Nmzfn8uXLdOvWjZCQkFQ/kWvTpg2bNm1iz5499OvXj9evX9OiRQv8/QMzqHIhhDA+KQkfcaKiorC0tMzAakRGadCgAYcOHVK6DGFkJICIDDVz5kwaNG5KgUKFE73nli073bt359ChQzx8+NDwxaXC6dOnadKkCStXrqRatWo8fvwYBwcHgBS3frzL09OToUOHsmzZMqKiomjVa6A+SxZCCKOVmvABkDNnTs6ePUtISMiHdxZGpXbt2qxcuVLpMoSRkQAiMoyfnx/Lli1j8LDhye4zYux41Go1R44cMWBlqXf69GmCg4P5888/OXHiBO7u7tSsGfsL9Pz58+k6d+HChfn00085c+YMP/96TB/lCiGEUVKXqJni8PH25BwrVqwgODiYtWvXZlBlIqOo1WqKFy/OxYsXlS5FGBEJICLDzJ8/n3IVKlKuQsVk93FxdSNXrlxG3zzr4eFBTEwMpUuXjt/WqVMnsmXLxvLly9HpdOk6v5eXF8WLF2fo0KHSFUsIkSmlttXjbZUrV6Z8+fKsXLmSN2/e6LEqYQht2rSRhQlFAhJARIYIDQ1l3rx5DB424oP7NmjSjBs3buDt7W2AytImT548AJw5cybB9vz583Pnzp10L7ZkZmbGpEmTiIiIYNA336frXEIIYQziWjtS0+rxPuvXrycqKorly5froTphSGZmZtja2vLgwQOlSxFGQgKIyBCrVq3CPWcu6tT/+IP7tu/UFYDr169ndFlpljdvXiC2K1ac8ePHc/bsWfr37x8/HkQf14hbM0Sknj5ucoQQqfNu0NBX4HhX0aJFqVu3LuvXr+f8+fPpbnkWhvXJJ58wd+5cpcsQRkKm4RV6Fx0dzfQZM/hq7Lcpmnbv9KnYlcELFDDeaWjd3d3RaDTs2rWL7t27888///DDDz9Qp04dhg4dqpdrPHv2DK1Wy969e+mh0zFqUC9Klyie5L7m2T3j/18WMRRCGIISAT/u51vcz7zt27eTK1cuvLy8yJkzJ0OHDqVVq1YGr0uknq2tLX5+frx69Qo3NzelyxEKkxYQoXdbtmwBoEXrdina/88/TmBlZUW+fPkysqx00Wg0lC9fnnPnzpE/f35at25N3rx5mTJlCmq1fv4Z5cuXj4kTJ1KyZEm2bNlCxXrNyFG0HF37fcofZ86h1Woxz+6ZIHwAif4shBD6kNEtGqkR5fuQKN+HODg44OPjww8//EB4eDiTJ0/m8ePHitYmUs7Ly4uFCxcqXYYwAtICIvRKp9MxZcpUBgwZhplZyr69bt24RokSJfR2I59RVq5cyePHj7ly5QoPHz6kQ4cO2NnZ6e38arWaNm3a0KZNG/z9/Tl+/DgHDhxgx96DbNuzH3Nzc9zc3ChdujSNGjWia9eu5MqVS2/XF0IIYxfl+xBLYOzYsVSrVo3mzZvTpk0bmjVrxuvXr+MXtm3RogX16tXDxsZG6ZLFW9zc3Pjrr78ICwvD2tpa6XKEgiSACL06dOgQz58/p1O3Hina/+GD+7x+9YqGDRpkcGXpp1KpyJcvn0FaapycnGjbti1t27YlICCAixcvcvXqVa5cucKJEyc4dOgQX375JTY2NvTo0YPFixdLVywhhN4o3eLxIVG+D6lduiD3LvxBq25eHDlyBDsba7K5uXL177/5448/sLS0pGHDhtSpU4fixYuTJ08eo3/QldndunWLmzdvsnr1agYPHqx0OUJBEkCEXk2ePAWvAYNS9GSjVaN6XL54AZVKRb169QxQnWlydHSkXr168V+jmJgYHjx4wNWrVzl69ChLly6lZMmSDOrcUuFKhRDCsLJnc+PM4T2Jtv9+6gxTFizn6NGj/PrrrwAUK1aMmTNnxs9qKAzryJEjfP3116hUKmbMmMGAAQPQaDRKlyUUIo8ChN6cPXuW8+fP0bNv/w/uGxgYyMXz52jbti1HjhyhUqVKBqgwc9BoNBQqVIi2bdsye/ZsKlSowIgRI7j86GWaz5nU2BJTZexPboUwdpnh31CdmtU4cuQIoaGh3Lx5k3HjxvHgwQO6dOnC69evlS4vS9FqtSxatIjhw4eTPXt27t+/T3R0NDt27FC6NKEgCSBCb6ZMmUq3nl44O7t8cN9L584C0LRpU5kNIx3MzMz46aefcHJyokGDBoRZvf9r/+DRE67fvB3/58wUPIQQ4m1x3VKLFi3KhAkTuHTpEqGhoUybNk3ZwrKQ0NBQRo4cycKFC2nQoAEPHz5Ea+VAn4FDmDp1mkylnIVJABF6cevWLfbt20v/wSmbkvbvK5cByJ07dwZWlTW4uroyd+5cQkNDyZEjB72HjyXIzCHJYFGhXhPK1WmMQ77iFKv+MV27dmXz5s1ERkYCMqOWEFldZmj9SE7x4sXp3bs3+/btY9q0aVy/fp3o6Gily8pUdDod3t7enDx5ktWrV9O9e3eOHz/O119/zerNO/EOCAOga49e3L13lxMnTihcsVCKSifxU+hBv379CAwNZ9aCJSna/7MBfdm5bTMXLlzA3Nw8g6vLGq5fv86yZcs4evQoGo2GmjVrMn78eK5cucLvv//O5cuXefDgAV5eXkRGRnLx4kVu3bqFVqtFo9Hg6OhIiRIlaNKkCT1aNSRnjuzvvZ55dk+jHfiuvX5K6RKEMEmZNYDEPVzRarXUrFmT8+fPEx0djVqtJk+ePHTs2JEOHTpga2urbKEmJiYmht9++43ffvuN27dvc//+fcLDw4HYmR0dHByYNmc+zVq2SXTs1B/Gc/fGNfbt26eXWpycnMjVeQrWyTxIuzK9A5fPn6FUqVJ6uZ5IHwkgIt28vb0pUKAA+46dpEixpBfOe1erRvV5+vghR48ezeDqsp4nT57w888/s23btviWDTMzMwoVKkT16tX57LPP4kNfWFgY169f5/Lly1y+fJmLFy8SGBiISqXCzs6WIgUL0Lh+Hby6dSJfHg8gcSuJMYYQCSBCpE1mDSBx4n5+hYaGsnjxYs6dO8dff/3Fw4cPsbGx4ZNPPqF37956nWI9s9HpdLx8+ZLff/+dlStX8vTpU2xtbXF3d6dIkSJUqVKFBg0a4OvgSdU8zsmex/fFC2qUL8m5c+f0EgokgJgWCSAi3UaNGsWVf66zcv2mFO1/+MA+BvTqTo0aNZg/f34GV5d1+fv7c+HCBfLmzUv+/PlTtC6LTqfjwYMHbN26lT179uDv7x//np2dHeXLl6dTp078+uuv2NraYmdnh4OFmiH9elMwv/EsJGlMASSpGzpjqk+It2X2ABLn3Qcpp0+fZsiQIVy5cgVXV1fGjx9P7dq1lSnOyAQFBXH8+HFu3LgRP41uUFAQAB4eHkyYMAEvLy8Adl/3SXBsxZz27z336OGfodFpWbNmdbrrlABiWiSAiHQJCAggb968rN20nUpVq31w/317dvJp397ky5ePFStW4OrqaoAqRWpotVoqVKhATExM/DaNRoOZmRlWVlYEBARgaWmJTqeLb2FRqVQM6duLmT9+p1TZiSh9k/++GzmlaxMiOVklgMR5N4icOnWKtm3b8vLlS5o1a8bXX3+Ns3PyT/Ezs8ePH/PLL7+wbds2wsPDsbKywtXVlSJFilCtWjXatWsXP4Plu8Hjbe8LIffv3qHhR9W4e/cuHh4e6apXAohpkUHoIl0WLlxI8ZKlUhQ+fLy9GdynF56enqxevVrCh5FSq9XUqVMHCwsLnjx5QkxMDNHR0YSHh7NkSewYn+XLl3PhwgX+/vtvdu3ahU6nw/eVTG0JsTdwWe0mTghT5GuejWdvQnj2JiR+W82aNfHx8WHgwIEcOnSI1q1b4+fnp2CVhnfjxg2GDh1KixYt2LRpE5UqVeLChQuEhYXx9OlTjh07xqRJk6hUqRK7r/u8N3wAXPAOSva9AoUKU79hY2bPnq3nTyGMnQQQkWbh4eHMnjOHwcOGp2h/F1dXrK1tCAgIQKvVZnB1Ij3iBqovXLgQtVpNaGgoffr0oVevXlhaWlKkSBEgtuXj5s2bAHw2wEvJkhNRIgRI8BDC+PmaZ8PXPFuCbXFB5NmbENRqNYsXL+b06dMEBgayfPlyhSo1LB8fH8aOHUvnzp3566+/6NmzJy9evODkyZNUqFAhwb4pCR4pNWjo5yxZspSAgAC9nE+YBgkgIs3Wrl2Ls4sr9Rs2TtH+FhYWHDlymNevX/PVV19lcHUiPcqVK0fZsmWZPn06FStWpHr16qxatYro6GimTp2aYOay3377DVtbW2o1batgxcpLTfiQoCKEMt4NHkmJCyKVKlWiTp06bNiwAW9vbwNUp4yQkBDmzZtH8+bN2bdvH126dOHNmzesXr0aF5f/1paKCx1pCR5JtYJc8A6K3Z6nBJ7FSrB48eJ0fQ5hWj48KlWIJMTExDBt+nQ+GzEKtfrDOfbVS1/Gff0Vh/fvjW/90Ol0qFSqjC5VpNG0adNYvXo1+/fvx9/fH41GQ0xMDF988QUqlQoXFxdy5crF7du3qVq1qtLlKkbChBDGLyXB413P3oQwY8EyqpQuysKFC5k4cWIGVKacuNXI586dS2BgINWqVWPTpk2JxmLoq6XjfV2xWvf+lFn/b+/e46Iq8P+Pv2e4yXW4g4qmGCiYiEqKqHjBFTNELVFXpdTdVis1+3XZ6ttm7a5a7da2mVau2dpFTVIhFVERQZCLgnhBbgqICsKg3K/DMOf3B8p6QQWFOXN5Px8PHgMzw8yHpOG85tz+/mesWrUKJiYmXfJ8pNm4BoQeyZ49e9DY2IgZz4cAaH0hK2nnHaJLBfkImf4MfAa7Y1/4bvj7++PHH3/Ed999x/jQcL169cJ7772H2NhYfPfdd5g1axYsLS0hlUoxfPhweHh4oLa2FpaWlli9WnN2Pr9dd8cB44N0iS7+Pre3uVVnODo5YcozzyIiIgJRUVFdOJm4zpw5g+eeew5//etfYWNjg4SEBBw/frwtPh5nbcej8Bk/GSbmlti2bZtanq+rVVRUIDQ0FDKZDDKZDKGhoXccRfJhli5dColEolf7wjBAqNMEQcDHH3+Cl15ZgZaWFrzzxmsY1LcnfAa7YbCrC5YtCcWBfb9hVuBEjB85DGknUzB37lxERUXhs88+g7e3t9g/AnWCgYEBRo4cidWrV+PIkSMYOXIk0tPTERAQgPz8fJSVlWHSpElij3lfurhQRUT3dys6Hic8bvflt9/BpU9fvP3229i+fbvWnz09MjISixYtwvXr17F161bk5+dj9OjRALp2347OkEqlmLHoZXz86adauY/o/Pnzcfr0aURFRSEqKgqnT59GaGhoh743PDwcKSkp6NWrVzdPqVl4GF7qtJiYGMwOCUHKmSyM9fGCvLQU/v7+GDNmDGJjY5GSkgKVSgVTU1MsWLAACxcu5BGvdIhCocBbb72Fo0eP4rXXXsO//vUvAJp5QsK7deXhbx83bHgoXtI02h7rXRUc7VEoFJg63g+5OdlwdHREaGgonnvuOVhZWXXbc3Y1QRDwzTffYOPGjXB1dUV6enrb/GJEx92aFU1YFuiLLZs3Yfr06Z3+frEOw5uVlQVPT08kJye3bY6cnJyM0aNHIzs7GwMHDrzv9xYVFWHUqFE4ePAgnn32WaxatQqrVq3q0vk0FfcBoU5bt24dVixfjqqSy5CXluIvf/kL5syZA6D1XYCqqiqkp6dj+PDhWvXiTB1jbGyMzz77DO+//z6++OILVFVVYcuWLWKP1SG3FrAed+Ff2xfUiHRFd0bH7YyNjRGTlIrfdv+KT/7+ET7//HNs2LABwcHB8PDwgIuLC3r37g1nZ+c7DtKhKRQKBT744APs378fAQEBOHToEPZlywHUiz1aGyNjE0xb8Ad8/MmnjxQgYklKSoJMJrtjX0hfX1/IZDIkJibeN0BUKhVCQ0Px1ltvYfDgweoaV2MwQKhTTp06hcTERCQmJmLDhg0wMDDA1KlT77iPTCbDhAkTxBmQ1MLQ0BBr166FIAjYunWr1gTILbcHREdjhNFBukzbfr/VFR53C35uNoKfm42Mc2fxwZ/fQETEb9i5c2fb7VKpFA4ODlAqlaipqcGwYcMwc+ZMBAQEwNTUVJSZr127hlWrViE7OxsrVqzA5GXv3YwPzRM4JxS/bvo3kpOT4ev78POLdZZCoUB1dfUd15mYmDzWju8lJSVwdHS853pHR0eUlNx/zdInn3wCQ0NDrFy58pGfW5sxQKhT1qxZA4VCAW9vb+Tm5iIgIIBrOfSUVCrFsGHDcODAAQCtZxTWhs2w7ibWgpfUcww3wyLqJLHC425PDfHC7sjDAID6+nqkp57E6VOpyM48j0sFBTAyMoKttRWSk5Px7rvvokePHnjmmWcQHByM4cOHd+jokV0hMTERb775JpqamrBy7b8xcfpstTzvo7KwkmFKyEJ8/OmnCN+9u9PfX5SZCcMr7Z8UV9XSgs2bN+Prr7++4/rVq1fjww8/vOf+H374IT766KMHPt/JkycBoN2D6jzoSJ9paWn497//jVOnTuntAXkYINRheXl5SEpKglKpxCuvvAIfHx+9/R+HWrW0tPB3gEjLafraD02JjvsxMzPDGP/xGOM/vt3bExOOYePnnyIyMhJ79uyBTCaDm5sbBgwYAFdX17YPBweHLns9ValU2LRpEzZu3Ah7e3ukpaUhq9mySx67u00PfQkvT/NDbm5u20lvu8of//hHfPzxx3dcd7+1H8uXL8e8efMe+Hj9+vXD2bNnUVpaes9tZWVlcHJyavf74uPjIZfL0bdv37brWlpa8MYbb+CLL77ApUuXHvKTaD8GCHXY559/jo8++gimpqbw8vLigifh7mNYaOtaELFwLQiJTZPjQ9PDo6P8xvrDb6w/VCoVIrZvxc6dO5Gfn4/MzEw0NDS0vY6amZlh2LBh8PPzg6+vL9zc3B7p72xVVRXeffddxMfHY/z48YiOjkZk7vWu/rG6jb1zL4ybGox//POf+M+mTV362MbGxh3easPe3h729vYPvd/o0aNRVVWFEydOYOTIkQCAlJQUVFVVwc/Pr93vCQ0NxeTJk++4LjAwEKGhoVi8eHGH5tN2DBDqELlcjvr6+rZD9REBre+yCYKA999/H8HBwfDx8WGEEGkJTY4PXSSVSjFrwWLMWtC6gNnbxhyNjY2Ii4tDXFwcTp48ifT0dCQmJkIQBNjY2GDMmDEYPXo0fH19293P4BZBEJCamor9+/cjJiYG1dXV+Oijj/DBBx9oxBGuOit40ct4+/fT8Pe//e2+axE0hYeHB6ZOnYqXXnoJ3377LQDgT3/6E4KCgu7YAX3QoEFYt24dZs2aBTs7u3uODmpkZARnZ+cHHjVLlzBAqEPWr1+PJUuWiD0GaRh3d3dYWlpizZo1WLNmDQwNDREaGootW7YwQog0lDaEh66s/XiQooo6AK3vfAcGBrZdX15ejs2bN2PXrl2IjY3Fvn37AAAWFhaQSqWQSCSQSqV3fK5QKFBZWYkePXrA3d0dGzZswNixY0X5ubpCP3cPeI0cgy+//BJr1qwRe5yH+vnnn7Fy5UpMmTIFABAcHIyvvvrqjvvk5OSgqqpKjPE0Es8DQh0yZ84cLF26VOPfiSD1EwQBpaWlyMrKaj204759GDVqFI7u/gmGhnyPoyO4GRZ1N22IjtvpQ4AAQNq1mju+DvZ0vuc+WVlZ+Oabb5CTkwOVSoXSmgYIKuHmGmgVbHq0vs7Onj0bK1euvGMHd21c+yEIAs4kx2P7+k9ga2GG4wnxHfo+a2trCENegKFl+8spFYf/jrPpJ7v8PCD0aLh0QB3y1VdfYePGjcjJyUFoaOgdO06RfpNIJHB2doazszMmTJiAQYMG4bPPPsMAH38k7A9Dn969xR6RSGdpW1hQq7vD45bbg+FWjHh4eCBg6bsI6MDjquvoWt1B2dyMhKgI/Lb1G5SXlmD5q69g+fLlYo9F3YRrQKhT6uvrsXXrVsTExCA4OBjDhg0TeyTSQHFxcXjrrbcgCAJ+3rQe0wMnP/yb9BzXglBH6Etw6OoakPuFhz6rq6nGwbCfEPnzZliam+OtN9/ACy+8ADMzs049DteAaBftTWUShZmZGV5++WXs2LGjbdv/Q4cOQaVSiT0aaZDx48cjLCwMPXv2RMiipXh79d/FHolIq0k9x+hNfOiitGs1jI+7yIuvYMsnq/HHgBHITYrBpq83IjcnG8uWLet0fJD2YYDQIzEwMMDMmTOxfft2+Pn54YsvvsDWrVvR0NAg9mikIZ544gns2LEDkydPxhfffIfQZa+JPZJG48IltYfhof0YHne6kHEan7/1Ml55diyktTdwJPowEo8nYObMmTAwMBB7PFIT7gNCj83X1xe+vr7Iz8/HV199hYqKCrzwwgtwcNDNVejUcWZmZvjnP/+JNWvWICwsDDOnBeL54Glij6WxeF4Quh3DQ/sxPlqpVCqkHTuCvT98i9xz6ViyZAm2ZGXB1dVV7NFIJNwHhLpceXk5vvnmG5w6dQohISEYPHiw2CORyJqamjBv3jwUXb2KvFPHYW9nK/ZIGosBQvoeHrqw/wfDo1VdTTViwn9B1I7/QtFQj9dWrsCyZctga9v1fwO4D4h2YYBQt1EoFAgLC0NERASGDx+OqVOn8rCseiwvLw9z5syBa7++OBt/WOxxNBojRL/oe3DcjvGhG67k5SJy+/c4GhGGp556CqteW4nZs2fD2Ni4256TAaJdGCCkFqmpqdi8eTOkUinmz58Pa2trsUciEYSFheGvf/0r/t+rf8LHH7wr9jgajRHSeZ1ZkBfrvy9j48G0PUD0OT5aWlqQGncYkdu2IPPUCYSEzMGq11bCx8dHLc/PANEuDBBSK7lcjs2bNyMjIwMzZszg5ll6RhAEzJ8/H2XyUlw5d0LscTQeI+R/unvB/XH/WzMsHp82x4c+h0dNZQUO796OQ79sBYQWvPryy3jppZfg6Oio1jkYINqF28OQWjk6OuK9995Dc3MzwsPDsWbNGnh4eCAoKKhbV82SZpBIJKitrYWj071n+6V73Vqo1acQEWtBngEhLm2ND30Oj0s5mdi/bQuO7duN4T4++PJfn2HmzJnc1Jo6hL8lJAojIyOEhIQgJCQEZ86cwaZNm1BfX4+FCxfCyan9dy9I+6lUKhQVFWHIkCFo6GEL08ZysUfSCrcvHGtLjHCBnnSVPkdHs6IJyUeicHDHf3Hh/BksWLAAyclJGDp0qNijkZZhgJDohg4dig0bNqC8vBzfffcdkpKSEBAQgHHjxkEq5alqdIlKpYKdnR1OnToFGxsbDBo0CCcO7uHar07ojgX7h0UNY4K6kzas/dDn6ACA4sJ8HAr7GUcjfoGNjTVeWbYMS5bs75ajWZF+4D4gpHFUKhViY2Oxbds2GBsbY968eXyR0yEqlQo5OTmIi4vDxo0b4f2UJ1Ki94k9FhGJQJPjQ9+jo1nRhKToA4je9RMyT53EjJkz8fLSpZgwYYJGvjnIfUC0C9eAkMaRSqWYNGkSJk2ahLKyMmzduhVpaWmYOHEifH19NfKFjzpOKpXCw8MDHh4ekMlkWLt2LeYsWYadW74RezQiUiNNjQ99D4+iS3k4FPYTjkbshJ2tLZYt/RMW/baHJxemLsUAIY3m4OCAN998E4IgIDY2Fl999RUMDQ0xd+5c2NnZiT0ePabf//73KC4uxn//+1+897ePsfYv74g9EhGpgabFh75HR7OiCUmHIxG962dknU7FjBkzEb57F8aPHw+JRCL2eKSDGCCkFSQSCSZOnIiJEyeirKwMP/zwA5KTk+Hv7w9/f38YGBiIPSI9otdffx3FxcX4fON/0Nd9MFasWIFm+SWxxyKibsL40BxX8y/g8K8/4+hvYbC3s8OypX/Ci3u5toO6HwOEtI6DgwPeeOMNCIKAY8eOYf369RAEASEhIXBxcRF7POokqVSKdevWoaKiAq+//jqcnJwwZ86cdu/LMCHSXgwPzVBbVYn4qAgc++1XXMw6h1mznkPEnt3w9/fn2g5SG+6ETjqhvLwcv/zyC2JjYzFo0CAEBQXB1NRU7LGoE2pra7Fo0SLk5eXB29sby5cvR2ho6EP3+WGUEGk2TQsPQP/io0WpxOmkOMRGhCE5JgpeXkPxhyWLMXfuXNjY2Ig9XpfgTujahQFCOicrKws//vgjLl26hClTpmD48OHccV1LlJeXY+PGjThw4ACqq6thYmICLy8vLF++HAsXLoRUKoVKpUJWVhbc3NweePhehgmRuDQxPAD9io/LF3MQE74T8ft3wcBAihdfeAGLFy3CoEGDxB6tyzFAtAsDhHRWS0sLoqOjsWvXLkgkEjz33HPo3bu32GNRByiVSqSlpeHgwYM4ePBgW4wMHToUeXl5uHHjBgDA2NgYJiYmMDU1ha2tLYKDg7F69WqYmZk98PEZJ0RdT1OD43b6EB/VleWIj4xA3G87celCNmbMmIklixdh8uTJOr2/JANEuzBASC9UVlZi586dOHr0KFxdXREcHAxzc3Oxx6IOUCqVSE1NbYsRpVKJP//5z2hpacH169dRVlaGsrIyFBYW4tKlSzAyMoKXlxc++OADBAcH3/dxGSFEj0cbguMWXQ8PZXMz0o/H4mjETpyIPYRhw0fgj0sWY86cOZDJZGKPpxYMEO3CACG9k5ubix9++AHZ2dnw9/fHuHHjYGRkJPZY1AFKpRItLS0wMTFp9/acnByEh4cjIiICNTU1sLKywowZM7B27dp2D1DACCHqOG0Kjlt0OTxUKhWyTqUgPjIciYf2wdzcDIteeAEvvvgi3N3dxR5P7Rgg2oUBQnpLpVIhISEBv/76K8rKyhAYGMj9RXSEQqFAbGwsdu/ejcTEREgkEri7u+Mf//gHgoKC7rgvI4ToTtoYGu1RV3yM6GmptucVBAF5mWdbo+Pgb2huakJIyGwsmD8fY8eO1eu/XwwQ7cIAIQLQ3NyM6OhoREREQKFQICgoSC/fQdJFJSUl2LNnD7Zv346Kigo4ODhg5cqVeOedd2Bo2HokckYI6StdiY3biRUeD/K4M10tuIj4yHAcPxCOcnkpZsyciYUL5mPy5Mlcg38TA0S7MECI7tLQ0IB9+/bhwIEDMDIyQnBwMJ544gmxx6LHpFAoEBUVhR9++AE5OTkwNTVFUFAQvvzyS9hJG8Uej0gtdDE4btHE8LhbZ2Ysu1aE+AMRSIwKR+HFXEx9ZhpCF8zHs88+y8PMt4MBol0YIEQPUFVVhT179uDw4cOwtrbGjBkz0KtXL7HHoscgCALS09Px008/4ciRI5BIJFi8eDE2rvk/sUcj6haMjq7zOPFxy4NmriiTIyk6EokHI5CVnorxEyZi4YL5mDVrlt7sTP6oGCDahQFC1EFyuRxhYWE4duwYHB0dMX36dMaIlrt27Rq+/PJL7Nu3D+PHj8fBHVv0ehtq0g26HBy3aGN43O3WzyAvvoKkw5E4cSQSWafTMMp3NOb/fh5CQkLg6OjY5c+rqxgg2oUBQvQISkpKEB4ejoSEBMhkMgQFBaFPnz5ij0WPQBAE/Oc//8H69esxYMAAnDwUDgsLC7HHIuo0fQgPQDfiI//iBUTujcDuPXuQn30e48ZPwNyQ2ZgxYwacnZ27/Pn0AQNEuzBAiB7TjRs3sHfvXsTFxcHExATPPPMMBgwYIPZY1En79u3D+++/D5lMhpRDEXiiz72H7SXSNPoSHbdoa3wIgoCszPOI/C0cUfv3Iv/iBUwMmIx5c0IQHBwMW1vbLnkefcYA0S4MEKIuVF1djf379+Pw4cNQqVQIDAzE4MGDuVmPljh58iRWrFgBlUqFg2E/wvfpEWKPRNQufQsPQL3x0RXhIQgCTp9Kw4F9v+HA3giUllzDM888g9mzZ2PatGmwsrLqgknpFgaIdmGAEHWT+vp6HDx4EJGRkaitrcWkSZPg4+PDQyZquPz8fCxduhQ3btzA5n9/it8/P1PskYja6GN4ANoTHw319TgeH4fogwdw5FAU6mprERQ0HbNnP4/AwECYmZl14aR0OwaIdmGAEKlBU1MTjh07hsjISBQVFcHLywuTJk3iO2Aa6vr163j11VeRk5ODK+dOwN6Om0eQ+Bgf3e9R4uNacTGOHDqA6INROH4sFk5Ozpg+PQjTp0+Hv78/TExMumFSuhsDRLswQIjUTBAEZGVlYe/evcjIyICdnR1+97vf8VwjGuby5ct49tln8frrr+OTd1aKPQ7pMX0ND0Azz+2hUqlw9nR661qOgweQeT4Dvr6+CAoKQnBwMDw8PCCRSLpxWmoPA0S7GIo9AJG+kUgk8PT0hKenJ4DWd9ujoqKwZ88eKBQKjBs3jptqaYC+ffvCw8MDGzduxPHjx/FET0d8/dlaWFl2/RFxiO5Hn+NDXToSH/V1dYiPO4roqAM4cvggmhobEBgYiLffehNTp06Fvb29GiYl0h0MECKR2dvbY+HChVi4cCGam5uRkJCAbdu2obCwEAMHDkRAQAD/uInklVdewU8//YTm5mbs2hsJmZUlNv5zrdhjkZ5gfHSvB4WHIAjIzjqPYzExiDsajZTE43Dp0wfTg4KwY/s2jB07lm8SET0GboJFpMFycnJw6NAhnDx5EhKJBOPGjcPw4cNhbGws9mh65/XXX0dycjJuXDzLzSuo2zE+WnXHJlj3C4/rZXLEx8UiLiYa8bExqKmuxoQJEzB16lRMmTIFAwcO7PJZqOu0boIVCkPL9s+jUnH4bzibnspNsDQE14AQabCBAwe2/dFrampCUlISdu3ahcLCQjg5OWHSpEno27cvF4jVYM6cOYiOjsb34YewZFag2OOQjmJ4dI/2oqOpqQmpJ5Jx7GgMjsVE43zGOQwd6o0pU36HV7dvx+jRo7kDuRapalDBoDgXUqu6e24TWhQQlE08s7wG4RoQIi0ll8sRHR2NhIQEVFZW4qmnnoK/vz+sra3FHk0n3Tqvi62tLc6fP49m+SWxRyIdw/i41+OuAbk9PARBQN6FXMQdPYL4ozFIOh4PSysrTPndFEydGojJkyfDwYH/BtpKatMfElM7GDh53XObqrYELYVxEBT3xgmJgwFCpAMEQUBGRgYOHTqE9PR0SKVS+Pn5YcSIETA1NRV7PJ2xZs0ahIeHo6GhAQAYIdQlGB4P1tkIuRUdgiCgID8PifHHkHw8HknH41FVWYlx48YhMDCw7USxXIOsG9atW4f/+/Q/MOw34Z7bWsoyIdQUQVV1Rf2DUbu4CRaRDpBIJBgyZAiGDBkCAGhsbERKSgr279+PixcvokePHhg9ejS8vb25ScFj8PPzw44dOxATE4NJkyaJPQ5pIcZG5929+VR7QTKip2VbcPy89de24KgoL4evry8mTJiAVStexahRo9CjRw91jU5q5O3tDaHhRru3CQ3lkJjyfE6ahGtAiPRAQ0MDUlJSEBcXh4KCAhgbG8PX1xdeXl78Y9wJtbW1GDNmDObOnYtt27ZxDYieYTxoFkEQcKkgH0kJ8UhMiEdKYjxuXL/eFhwTJ06Er68vX+P0RElJCXr27AnDIQshMbjzCGXNORH49cdNeP7550Waju7GACHSQw0NDUhOTkZsbCzy8vLagmTYsGHcZOshFi5ciOvXr+Pq1asMEB3H4NAsLS0tyMnOQtqJZJxMTkJyYgKul5Vh1KhRdwQHX8P0l8TIDAb9JkJq8b+TEQqqFijP/YS8ixfg6uoq4nR0OwYIEd0RJPn5+QAALy8vjBw5EnZ2diJPp1m+/vprfPvtt6itrYVBdYnY41A3YHhohprqaqSnpSL1RDJSTyTjVOpJQBAwatQo+Pn5MTjoHlIrF0is+sDAwaPtOqH+BpQXD6CluRFSqVTE6eh2DBAiuodSqURGRgYSExNx5swZ1NfXo3fv3vD19UX//v1hYGAg9oiiOXPmDBYuXIhvv/0Wi2dOEXsc6iKMDnEJgoDLhZdwMiUZaSeSkXbyBLIzz6Nv3yfg5zcaY8aMgZ+fH4YMGaLXrz/0YAZOQyEoG2DYd2zbdaobF6AqvwBVLd8w0iQMECLqkOLiYiQlJSElJQXFxcUwNjaGj48PvL29YWVlJfZ4aqNUKjF27FiMHDkSB3dsEXscekwMD3HU1tTg3NnTOHPqFNJOpiDtRAoqKsoxfPhw+Pn5tQVHz549xR6VtEhYWBjmvPgyjAYGt13XcjUZgAQtZefFG4zuwaNgEVGH9OrVC88//3zbTnyNjY1ITU1FQkICzp8/j6amJvTq1QsjR46Em5ubzh5ty9DQEL6+vkhNTRV7FHoEDA71a6ivx/lzZ3Hm9CmcST+Fc6fTcfFCLnr17o0Rw4djwrgx+L933saIESO4wzg9lmHDhgGNFRAEFSSS1s2thIZyfL/hE5Eno7sxQIjokfTo0QNjx47F2LH/W9V97do1pKWlYe/evbh8+TKUSiX69u2LESNGoH///jA01I2XHD8/P8TExOBaoxQ9e6jEHocegMGhXo2Njcg6n4Gzp0/hbHo6zp5JR05WJuzs7fG0jw98fHywOHQBRowYwbUb1OVcXV0BiQHQWAmY2kIQBAgNN+Dt7S32aHQXboJFRN1GEARcvXoVqampSEtLw5UrV6BUKtG/f3+MGDEC/fr1g5GR0cMfSMMUFxdj2rRpkEqlGDJkCFa99CLmzAziDo4agtGhHjXV1cjKzEDW+QxknjuLs6fTkZV5HlZWVhgxYgR8fHzw9NNPw8fHBy4uLjzhH6mF1MIZUjs3SG3dIDRVQ5m9B4qmRq38W6PLGCBEpFaCIKCgoKAtSoqLi6FUKuHs7Axvb2+4u7vD0tLy4Q8kspycHISHh2Pv3r2oqqqCvb09is+fFHssvcTg6F4tLS0oLMhHZsY5ZJ3PQFbmeWRnZuByYSGce/aE15Ah8PLywtNPP42nn34a/fr1Y2yQaAwcPAFIYOAyCqrKArSUnoNQf13sseguDBAi0ghyuRznzp3D2bNncfHiRdTU1MDY2Bhubm4YPHgwXFxcNHITLqVSiXXr1mHPnj1QKBQ8N4gaMTy6XkVFObIzz7eu1cjIQHZmBnKyMqFSqeDp6Qkvr6EYOtQLXl6tHw4O/DcgzfL999/jDyvehaHbNLQUp0FQNkB1I1fssegumvfXnIj0kqOjIwICAhAQENB2nVKpRG5uLs6ePYuwsDBcvXoVCoUCtra2GDx4MNzc3ODo6CjqYTkNDQ1hbm7eFkdGjv0YId2I0fH4BEHAtaIiXLyQiwu52ci7kIu8C7m4kJsLeWkJeru4wGvIEAwdOhQznp0KLy8vuLu7a+QbAER3GzZsGISG8rb9P75c+47YI1E7+GpCRBrL0NAQnp6e8PT0xLx589quLy8vR3Z2NrKysnD48GGUlZWhubkZFhYWcHNzw8CBA9GzZ0+1HYmrurqaC2fdhMHx6BQKBQryLuLizcC4mJvTenkhFw0NDejXrz8GeQyCx6BB8H/xBXh4eGDQoEGwtbUVe3SiR+bp6QkILYCiFkJDeeuRsUjjcBMsItIZjY2NuHDhArKzs5GdnY0rV66grq4OUqkULi4u8PDwQN++fWFra9tlO4yfPn0ab775JgCgpKT1RFdcA9J5DI1H09DQgCuFl1B4qaDt40rhJeRfvIjCSwUwMTGBu/tAeHgMagsMDw8PPPnkkzzkLeksiZkdpLbuUBUlo7q6Wiv2K9Q3DBAi0nkqlQpXrlxBVlYWLl68iIKCApSXl6O5uRkGBgZwdnaGu7s7+vbtC3t7+w4dLaW+vh7r16/Hzz//DAsLC4SFhSEwMBAAA6SjGB0PJwgCblwvQ2FBAQoLL6GwIB+XCy/h8s3YKLl2DWZmZujfvz9cXV0xYMAADBgwAG5ubvDw8ICLiwuPzkZ6R2rnDjSUQ1A1Q2isEnscagcDhIj0miAIKC0tRV5eHvLz85GXl4fS0lLU1tZCpVLBwsIC/fr1g6urK5ycnGBtbY20tDS8//77kMvlmDBhAnbt2gVra+u2x2SA3B+j404N9fW4VlyE4qKrKC5qvbxWXNT6UVSEK5cLUVtbCydnZ/Tv3x9PDhgAV1dXPPnkk23B4eTkxKNOEd1m/fr1WLlyJSTW/aCqKBB7HGoHN1omIr0mkUjg7OwMZ2dnjBkz5p7b6+rqUFBQgIKCAmRmZuLy5cvIzc2Fg4MDevTogePHj8PGxga2trbo06cPejvZw6WnM1x69YRL755w6dkTTo4OcHKwh421TG8XFPUtPARBQGVlBa7L5ZDLS3GtuBjXiq6iuOgqSoqLUVx8FcVXi1BRUQ4TExP07u0CF5fe6NOnD/r06YMxo56Gi4sL+vfvj/79+8Pc3FzsH4lIa9za72Ptn5eKPAndD9eAEBE9BkEQUFZWhqtXr+LKlSttH1evXG69vFqEUrkc9fX1MDIygqODPRzs7eBkbw9HBzs4OtjDycEejvY3L29+bWdrI+rRvbqKLoVHS0sLKspvoKxMjuvyspuX8tbLWx9tX5dBqVTCzMwMjo5Od8TF7R8uLi5wcHDQ2zAl6g41NTWwsrJCVFRU26axpFkYIEREalBbW4vS0tI7PuRyOUpKSiAvLbl5nRylcjmqq6shlUphY20NG2sZrGUy2FhbtX4ts4K1zAo21jLYyGSwsbGGjUwGa2ur1q+tZbAwN9eIBVpNjA9BEFBXW4uqqkpUVVaisrKi9bKi9bKq6ublbbfd/rVKpYKVlRUcHZ3g6OQIZycnODk5wdnZGU5OTnB0dITTzeucnJxgYWEh9o9MpJeKi4vh4ODAM6BrKAYIEZGGaWhogFwuR3l5OSoqKtoub31eXl6OyopylFdUoLKiAhUVlSivqEBVVRUEQYBUKoW5uRnMzcxgYW4OMzNTWJibw9zU9H+fm5nCwtwMZmZmsLh5XzMzUxgbGcPY2AjGRkao62EPR2l929eGRq2XxsZGMDI0hLGxMYyNjGBkZAgDqQHKjOw7/bO2tLRAqWyGQtGM5mYFmpub0ay4eXnzc0WzAs2K5pv3U7TdXl9fh/q6etTV1aK+vh71d1021N+8ra4edXV1N+9fB5VKBYlEAplMBhsbW9jY2MDaxhq2Nzels7nt0uau6xwdHWFqatoN/+pERPqDAUJEpCNUKhWqqqpQVVWFuro61NXVoba2tt3Luro61NTUtH7edl0tmpoUaFYqoVAooFDcXOBvbr4ZATc/VyigUCjQ1X8+JBJJa9QYG8PIyAhGxsYwNmr9vPV6o7bbjI2NYW5uDnNzc1hYWMDc3ByWlpb3XHf3pbm5OWQyGWQyGY8ORUQkEgYIERE9kpaWlrYYUalUj/QYUqm0LTp0YZ8XIiJ6OAYIERERERGpDdc/ExERERGR2jBAiIiIiIhIbRggRERERESkNgwQIiIiIiJSGwYIERERERGpDQOEiIiIiIjUhgFCRERERERqwwAhIiIiIiK1YYAQEREREZHaMECIiIiIiEhtGCBERERERKQ2DBAiIiIiIlIbBggREREREakNA4SIiIiIiNSGAUJERERERGrDACEiIiIiIrVhgBARERERkdowQIiIiIiISG0YIEREREREpDYMECIiIiIiUhsGCBERERERqQ0DhIiIiIiI1IYBQkREREREasMAISIiIiIitWGAEBERERGR2jBAiIiIiIhIbRggRERERESkNgwQIiIiIiJSGwYIERERERGpDQOEiIiIiIjUhgFCRERERERqwwAhIiIiIiK1YYAQEREREZHaMECIiIiIiEhtGCBERERERKQ2DBAiIiIiIlIbBggREREREakNA4SIiIiIiNSGAUJERERERGrDACEiIiIiIrVhgBARERERkdowQIiIiIiISG0YIEREREREpDYMECIiIiIiUhsGCBERERERqQ0DhIiIiIiI1IYBQkREREREasMAISIiIiIitWGAEBERERGR2jBAiIiIiIhIbRggRERERESkNgwQIiIiIiJSGwYIERERERGpDQOEiIiIiIjUhgFCRERERERqwwAhIiIiIiK1YYAQEREREZHaMECIiIiIiEhtGCBERERERKQ2DBAiIiIiIlIbBggREREREanN/wfdR2PFNNATQwAAAABJRU5ErkJggg=="
+      "image/png": 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"
      },
      "metadata": {},
      "output_type": "display_data"
     },
     {
      "data": {
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"
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"
      },
      "metadata": {},
      "output_type": "display_data"
@@ -2062,7 +2067,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 38,
+   "execution_count": 36,
    "id": "0111a650-bdfe-4901-97b6-460c66fb338c",
    "metadata": {},
    "outputs": [],
@@ -2072,7 +2077,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 39,
+   "execution_count": 37,
    "id": "df23376b-bc4b-4cc4-ac51-e95afacf1a3f",
    "metadata": {},
    "outputs": [],
@@ -2082,7 +2087,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 40,
+   "execution_count": 38,
    "id": "f7720bd7-09cf-4aa0-a4d4-cc8b551e6791",
    "metadata": {},
    "outputs": [],
@@ -2093,7 +2098,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 41,
+   "execution_count": 39,
    "id": "643b5bf3-2b44-422f-ad80-e75ffb6b17cd",
    "metadata": {},
    "outputs": [],
@@ -2104,17 +2109,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 42,
+   "execution_count": 40,
    "id": "6b3573ab-bb91-4fc2-9b11-01d8850c93e4",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "<matplotlib.legend.Legend at 0x16086aa10>"
+       "<matplotlib.legend.Legend at 0x1655277f0>"
       ]
      },
-     "execution_count": 42,
+     "execution_count": 40,
      "metadata": {},
      "output_type": "execute_result"
     },
@@ -2149,7 +2154,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 43,
+   "execution_count": 41,
    "id": "153e397d-3456-4611-98d8-52f285293439",
    "metadata": {},
    "outputs": [],
@@ -2159,7 +2164,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 44,
+   "execution_count": 42,
    "id": "8434e28d-c4c1-4cc9-bd3a-1f0dce5c3931",
    "metadata": {},
    "outputs": [],
@@ -2169,7 +2174,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 45,
+   "execution_count": 43,
    "id": "47f4729a-2c7e-4be2-bd45-ebb308cb0f6c",
    "metadata": {},
    "outputs": [],
@@ -2180,7 +2185,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 46,
+   "execution_count": 44,
    "id": "1ad43899-2ed1-4de9-9338-c4fe495e36af",
    "metadata": {},
    "outputs": [],
@@ -2191,17 +2196,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 47,
+   "execution_count": 45,
    "id": "6be690c6-64d4-4e5b-a368-2ce7268161c6",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "<matplotlib.legend.Legend at 0x15dfcc040>"
+       "<matplotlib.legend.Legend at 0x1655c65f0>"
       ]
      },
-     "execution_count": 47,
+     "execution_count": 45,
      "metadata": {},
      "output_type": "execute_result"
     },
@@ -2249,7 +2254,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.7"
+   "version": "3.10.10"
   }
  },
  "nbformat": 4,

From 11abb82c9bcdff03d3bb05b4be9e017716b66ebc Mon Sep 17 00:00:00 2001
From: Jiwoo Lee <lee1043@users.noreply.github.com>
Date: Thu, 30 Nov 2023 09:25:18 -0800
Subject: [PATCH 131/133] Update metrics_precip-distribution.rst

Add overview text and reference paper
---
 docs/metrics_precip-distribution.rst | 6 +++++-
 1 file changed, 5 insertions(+), 1 deletion(-)

diff --git a/docs/metrics_precip-distribution.rst b/docs/metrics_precip-distribution.rst
index 5a9e87e11..a83664ea1 100644
--- a/docs/metrics_precip-distribution.rst
+++ b/docs/metrics_precip-distribution.rst
@@ -6,7 +6,7 @@ Precipitation Distribution
 
 Overview
 ========
-
+With the global domain partitioned into 62 regions, including 46 land and 16 ocean regions, we apply 10 established precipitation distribution metrics. The collection includes metrics focused on the maximum peak, lower 10th percentile, and upper 90th percentile in precipitation amount and frequency distributions; the similarity between observed and modeled frequency distributions; an unevenness measure based on cumulative amount; average total intensity on all days with precipitation; and number of precipitating days each year. 
 
 Exampe parameter files
 ======================
@@ -63,3 +63,7 @@ Options available to set in the parameter file include:
 
 
 .. _this link: https://github.com/PCMDI/pcmdi_metrics/tree/main/pcmdi_metrics/precip_distribution/param
+
+Reference
+=========
+Ahn, M.-S., P. A. Ullrich, P. J. Gleckler, J. Lee, A. C. Ordonez, and A. G. Pendergrass, 2023: Evaluating Precipitation Distributions at Regional Scales: A Benchmarking Framework and Application to CMIP5 and CMIP6. Geoscientific Model Development, 16, 3927–3951, https://doi.org/10.5194/gmd-16-3927-2023

From 38c89a37b70c03db24f818d40e2b78521223c0db Mon Sep 17 00:00:00 2001
From: Jiwoo Lee <lee1043@users.noreply.github.com>
Date: Thu, 30 Nov 2023 09:26:34 -0800
Subject: [PATCH 132/133] Update metrics_precip-distribution.rst

Add placeholder for demo notebook
---
 docs/metrics_precip-distribution.rst | 4 ++++
 1 file changed, 4 insertions(+)

diff --git a/docs/metrics_precip-distribution.rst b/docs/metrics_precip-distribution.rst
index a83664ea1..3ca99697d 100644
--- a/docs/metrics_precip-distribution.rst
+++ b/docs/metrics_precip-distribution.rst
@@ -8,6 +8,10 @@ Overview
 ========
 With the global domain partitioned into 62 regions, including 46 land and 16 ocean regions, we apply 10 established precipitation distribution metrics. The collection includes metrics focused on the maximum peak, lower 10th percentile, and upper 90th percentile in precipitation amount and frequency distributions; the similarity between observed and modeled frequency distributions; an unevenness measure based on cumulative amount; average total intensity on all days with precipitation; and number of precipitating days each year. 
 
+Demo
+====
+In preparation
+
 Exampe parameter files
 ======================
 A set of example parameter files for models and observations can be viewed at `this link`_.

From 1baba965c9fa770675858113a1c847303c368fd6 Mon Sep 17 00:00:00 2001
From: Jiwoo Lee <lee1043@llnl.gov>
Date: Thu, 30 Nov 2023 14:19:13 -0800
Subject: [PATCH 133/133] clean up

---
 pcmdi_metrics/variability_mode/lib/plot_map.py | 8 ++------
 1 file changed, 2 insertions(+), 6 deletions(-)

diff --git a/pcmdi_metrics/variability_mode/lib/plot_map.py b/pcmdi_metrics/variability_mode/lib/plot_map.py
index 3a70e6f05..3aff47b23 100644
--- a/pcmdi_metrics/variability_mode/lib/plot_map.py
+++ b/pcmdi_metrics/variability_mode/lib/plot_map.py
@@ -174,9 +174,6 @@ def plot_map_cartopy(
     lat_min = lat.min().item()
     lat_max = lat.max().item()
 
-    if debug:
-        print(lon_min, lon_max, lat_min, lat_max)
-
     debug_print("Central longitude setup starts", debug)
     debug_print("proj: " + proj, debug)
 
@@ -266,7 +263,6 @@ def plot_map_cartopy(
         # the bottom left and go round anticlockwise, creating a boundary point
         # every 1 degree so that the result is smooth:
         # https://stackoverflow.com/questions/43463643/cartopy-albersequalarea-limit-region-using-lon-and-lat
-
         vertices = [
             (lon - 180, lat_min) for lon in range(int(lon_min), int(lon_max + 1), 1)
         ] + [(lon - 180, lat_max) for lon in range(int(lon_max), int(lon_min - 1), -1)]
@@ -274,9 +270,7 @@ def plot_map_cartopy(
         ax.set_boundary(
             boundary, transform=ccrs.PlateCarree(central_longitude=180)
         )  # Here, 180 should be hardcoded, otherwise AMO map will be at out of figure box
-
         ax.set_extent([lon_min, lon_max, lat_min, lat_max], crs=ccrs.PlateCarree())
-
         if gridline:
             gl = ax.gridlines(
                 draw_labels=True,
@@ -295,6 +289,8 @@ def plot_map_cartopy(
                 right_label = ea.get_position()[0] > 0
                 if right_label:
                     ea.set_visible(False)
+    else:
+        sys.exit("Projection, " + proj + ", is not defined.")
 
     debug_print("projection completed", debug)