use gaussian grid as common grid to be more consistent with the CDMS/…

…CDAT version of the code

pcmdi_metrics/mjo/lib/lib_mjo.py

@@ -14,24 +14,43 @@
 
 import numpy as np
 import xarray as xr
+import xcdat as xc
 from scipy import signal
 
 from pcmdi_metrics.io import base, get_time_key, select_subset
-from pcmdi_metrics.utils import create_target_grid, regrid
+from pcmdi_metrics.utils import regrid
+
+# from pcmdi_metrics.utils import create_target_grid
 
 
 def interp2commonGrid(ds, data_var, dlat, dlon=None, debug=False):
  if dlon is None:
  dlon = dlat
- # nlat = int(180 / dlat)
- # nlon = int(360 / dlon)
- grid = create_target_grid(target_grid_resolution=f"{dlat}x{dlon}")
+
+ # grid = create_target_grid(target_grid_resolution=f"{dlat}x{dlon}")
+ nlat = int(180 / dlat)
+ grid = xc.create_gaussian_grid(nlat)
+
+ # If the longitude values include 0 and 360, then remove 360 to avoid having repeating grid
+ if 0 in grid.lon.values and 360 in grid.lon.values:
+ min_lon = grid.lon.values[0] # 0
+ # max_lon = grid.lon.values[-1] # 360
+ second_max_lon = grid.lon.values[-2] # 360-dlat
+ grid = grid.sel(lon=slice(min_lon, second_max_lon))
+
+ # Reverse latitude if needed
+ if grid.lat.values[0] > grid.lat.values[-1]:
+ grid = grid.isel(lat=slice(None, None, -1))
+
+ # Regrid
  ds_regrid = regrid(ds, data_var, grid)
  ds_regrid_subset = select_subset(ds_regrid, lat=(-10, 10))
+
  if debug:
  print(
  "debug: ds_regrid_subset[data_var] shape:", ds_regrid_subset[data_var].shape
  )
+
  return ds_regrid_subset