Merge pull request #973 from PCMDI/972_ao_targetgrid

Add custom target grid to mean climate

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(

pcmdi_metrics/mean_climate/mean_climate_driver.py

@@ -102,24 +102,31 @@
 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()