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Add custom target grid to mean climate #973

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merged 5 commits into from
Aug 17, 2023
Merged

Add custom target grid to mean climate #973

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e2995f2
add custom grid
acordonez Aug 15, 2023
39beee7
Merge branch 'ao_custom_obs_squashed' into grid_fix
acordonez Aug 15, 2023
5ccf8dc
Merge branch 'main' into 972_ao_targetgrid
acordonez Aug 17, 2023
371c3f1
explicit float
acordonez Aug 17, 2023
ca6d38a
add default target grid
acordonez Aug 17, 2023
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add custom grid
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@acordonez
acordonez committed Aug 15, 2023
commit e2995f27dd9113c71aa19b98f31f887bb228811c
43 changes: 25 additions & 18 deletions pcmdi_metrics/mean_climate/mean_climate_driver.py
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Expand Up @@ -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()
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