I am downloading climate data in netcdf format. For each variable (eg 'precipitation'), I need to merge 9 netcdfs, each belonging to an unique climate model. Each netcdf has the same size (time, lat, lon). How can I merge 9 3D netcdfs into one 4D netcdf? Ultimately, I want to calculate cumulative precipitation per month. Here's my current code:
variables = ['pr']
scenarios = ['historical', 'ssp245'] #options ['historical', 'ssp126', 'ssp245', 'ssp370', 'ssp585']
models = ['UKESM1-0-LL', 'MRI-ESM2-0', 'MIROC6', 'MIROC-ES2L', 'IPSL-CM6A-LR',
'GFDL-ESM4', 'FGOALS-g3', 'CNRM-ESM2-1', 'CanESM5']
save_folder = processing_fn / 'local_climate_assessment' / f'{variable}' / 'output'
if not os.path.exists(save_folder):
os.makedirs(save_folder)
netcdfs = []
# Create one netcdf per model by merging annual netcdfs
for variable in variables:
for scenario in scenarios:
for model in models:
source = processing_fn / 'local_climate_assessment' / f'{variable}' / f'{scenario}' / f'{model}'
netcdf_fn = save_folder / f'{variable}_{scenario}_{model}.nc'
if not os.path.exists(netcdf_fn):
gdf_model = xr.open_mfdataset(str(source / '*.nc'), combine = 'nested', concat_dim="time", use_cftime=True)
# rename_dict = {variable, f'{variable}_{scenario}_{model}'}
# gdf_model.rename(rename_dict, inplace = True)
gdf_model.to_netcdf(netcdf_fn)
print(gdf_model.attrs['cmip6_source_id'])
netcdfs.append(gdf_model)
else:
gdf_model = xr.open_mfdataset(netcdf_fn)
netcdfs.append(gdf_model)
# Create one netcdf per variable by merging models
ds = xr.combine_nested(netcdfs, concat_dim = "time")
print(ds)
Out[33]:
<xarray.Dataset>
Dimensions: (time: 246095, lat: 47, lon: 50)
Coordinates:
* time (time) object 1981-01-01 12:00:00 ... 2060-12-31 12:00:00
* lat (lat) float64 31.62 31.88 32.12 32.38 ... 42.38 42.62 42.88 43.12
* lon (lon) float64 234.6 234.9 235.1 235.4 ... 246.1 246.4 246.6 246.9
Data variables:
pr (time, lat, lon) float32 dask.array<chunksize=(360, 47, 50), meta=np.ndarray>
The above code works, but I'm creating one big 3D netcdf instead of a 4D still containing the climate model names. The code below results in the following error:
a = ds.resample(time = 'M').sum()
ValueError: index must be monotonic for resampling
How to create a 4D netcdf with model names included, and resample to create monthly sum values?
I'd definitely recommend reading the xarray docs on combining data .
The concat_dim
argument to combine_nested
can be a list of dimensions along which you want to concatenate your data. You seem to be concatenating over variable, scenario, and model, not time. So passing time here and providing a 1-D list of netCDFs is creating a repeating time series with no information about your concatenation dimensions.
Instead, explicitly nest the datasets:
netcdfs = []
for variable in variables:
netcdfs.append([])
for scenario in scenarios:
netcdfs[-1].append([])
for model in models:
... # prep & read in your data
netcdfs[-1][-1].append(gdf_model)
# use nested lists of datasets and an ordered list
# of coordinates matching the list of datasets
ds = xr.combine_nested(
netcdfs,
concat_dim=[
pd.Index(variables, name="variable"),
pd.Index(scenarios, name="sceanrio"),
pd.Index(models, name="model"),
],
)
Alternatively, expand the dimensionality of each dataset first, then concat using combine_by_coords
:
netcdfs = []
for variable in variables:
for scenario in scenarios:
for model in models:
... # prep & read in your data
# add coordinates
gdf_model = gdf_model.expand_dims(
variable=[variable],
scenario=[scenario],
model=[model],
)
netcdfs.append(gdf_model)
# auto-combine using your new coordinates
ds = xr.combine_by_coords(netcdfs)
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