def get_cmip(model: str,
scenario: str,
member: str,
region: Dict = None,
account_key: str = None) -> xr.Dataset:
"""Load downscaled CMIP data, concatenating historical and future scenarios along their time dimension
Parameters
----------
model : str
CMIP model_id
scenario : str
CMIP scenario
member : str
CMIP member_id
region : dict
Dictionary of slices defining a single chunk, e.g. {'x': slice(1150, 1200), 'y': slice(700, 750), 'time': slice(None)}
account_key : str
Secret key giving Zarr access to Azure store
"""
# open the historical simulation dataset
hist_mapper = get_store(
f'cmip6/{downscale_method}/conus/4000m/monthly/{model}.historical.{member}.zarr',
account_key=account_key,
)
ds_hist = xr.open_zarr(hist_mapper, consolidated=True)[force_vars]
ds_hist = maybe_slice_region(ds_hist, region)
ds_hist = ds_hist.pipe(preprocess).pipe(load_coords)
# open the future simulation dataset
scen_mapper = get_store(
f'cmip6/{downscale_method}/conus/4000m/monthly/{model}.{scenario}.{member}.zarr'
)
ds_scen = xr.open_zarr(scen_mapper, consolidated=True)[force_vars]
ds_scen = maybe_slice_region(ds_scen, region)
ds_scen = ds_scen.pipe(preprocess).pipe(load_coords)
# combine the historical and future simulation datasets together
ds_in = xr.concat([ds_hist, ds_scen],
dim='time',
data_vars=force_vars,
coords='minimal',
compat='override')
for v in force_vars:
ds_in[v] = ds_in[v].astype(np.float32)
return ds_in
def load_cmip(model, scenario, member, bias_corrected=False):
if bias_corrected:
prefix = f'cmip6/bias-corrected/conus/4000m/monthly/{model}.{scenario}.{member}.zarr'
else:
prefix = f'cmip6/regridded/conus/monthly/4000m/{model}.{scenario}.{member}.zarr'
store = get_store(prefix)
ds = xr.open_zarr(store, consolidated=True)
return ds
def get_out_mapper(account_key: str) -> zarr.storage.ABSStore:
"""Get output dataset mapper to Azure Blob store
Parameters
----------
account_key : str
Secret key giving Zarr access to Azure store
Returns
-------
mapper : zarr.storage.ABSStore
"""
return get_store(target, account_key=account_key)
def get_obs(region: dict = None, account_key: str = None) -> xr.Dataset:
"""Load observed climate data and auxillary variables
Parameters
----------
region : dict
Dictionary of slices defining a single chunk, e.g. {'x': slice(1150, 1200), 'y': slice(700, 750), 'time': slice(None)}
account_key : str
Secret key giving Zarr access to Azure store
"""
obs_mapper = get_store('obs/conus/4000m/monthly/terraclimate_plus_v3.zarr',
account_key=account_key)
obs = xr.open_zarr(obs_mapper, consolidated=True).pipe(load_coords)
obs = maybe_slice_region(obs, region)
return obs
def main(model, scenario, member):
print('---------->', model, scenario, member)
# get the output store
key = f'{model}.{scenario}.{member}'
target_uri = target.format(key=key)
print(target_uri)
store = get_store(target_uri)
if skip_existing and '.zmetadata' in store:
print(f'{key} in store, skipping...')
return 'skipped'
y_hist = get_obs().pipe(load_coords)
if xy_region:
y_hist = y_hist.isel(**xy_region)
print('y_hist:\n', y_hist)
x_hist = open_single(model, 'historical', member).pipe(process_cmip)
if xy_region:
x_hist = x_hist.isel(**xy_region)
print('x_hist:\n', x_hist)
x_scen = open_single(model, scenario, member).pipe(process_cmip)
if xy_region:
x_scen = x_scen.isel(**xy_region)
if 'hist' in scenario:
x_scen = x_scen.sel(time=hist_time)
else:
x_scen = x_scen.sel(time=future_time)
print('x_scen:\n', x_scen)
print('fitting models')
models = {}
y_scen = xr.Dataset()
for v in bc_vars:
print(v)
models[v] = PointWiseDownscaler(
TrendAwareQuantileMappingRegressor(QuantileMappingReressor(extrapolate='1to1'))
)
# train models with historical data
models[v].fit(x_hist[v].sel(time=train_time), y_hist[v].sel(time=train_time))
# predict this ensemble member
y_scen[v] = models[v].predict(x_scen[v])
y_scen = y_scen.chunk(chunks)
print('y_scen:\n', y_scen)
if dry_run:
print('skipping write of ... dry_run=True')
return 'skipped'
else:
store.clear()
write = y_scen.to_zarr(store, compute=False, mode='w')
write.compute(retries=3)
zarr.consolidate_metadata(store)
return 'done'
def open_single(model, scenario, member):
uri = f'cmip6/regridded/conus/4000m/monthly/{model}.{scenario}.{member}.zarr'
store = get_store(uri)
return xr.open_zarr(store, consolidated=True)
) as f:
df = pd.read_csv(f)
# target grid
for grid in ['conus', 'ak']:
grid_ds = cat.grids.albers4k(region=grid).read()
for i, row in df.iterrows():
if skip_unmatched and not row.has_match:
continue
target_key = f'{row.model}.{row.scenario}.{row.member}'
target_path = target.format(region=grid, key=target_key)
target_store = get_store(target_path)
# skip if existing
if skip_existing and '.zmetadata' in target_store:
print(f'{target_key} in store, skipping...')
continue
source_store = get_store(row.path)
source_ds = xr.open_zarr(source_store, consolidated=True)
# perform the regridding
print(f'regridding {target_path}')
ds = regrid_one_model(source_ds, grid_ds).chunk({'time': 198, 'x': 50, 'y': 50})
# write output dataset to store
ds.update(grid_ds[update_vars])
def main(model, scenario, member):
print('---------->', model, scenario, member)
# get the output store
key = f'{model}.{scenario}.{member}'
target_uri = target.format(key=key)
print(target_uri)
store = get_store(target_uri)
if skip_existing and '.zmetadata' in store:
print(f'{key} in store, skipping...')
return
absolute_model = MontlyBiasCorrection(correction='absolute')
relative_model = MontlyBiasCorrection(correction='relative')
y_hist = get_obs().pipe(load_coords)
if xy_region:
y_hist = y_hist.isel(**xy_region)
print('y_hist:\n', y_hist)
x_hist = open_single(model, 'historical', member).pipe(process_cmip)
if xy_region:
x_hist = x_hist.isel(**xy_region)
print('x_hist:\n', x_hist)
print('fitting models')
# train models with historical data
absolute_model.fit(x_hist[absolute_vars].sel(time=train_time),
y_hist[absolute_vars].sel(time=train_time))
relative_model.fit(x_hist[relative_vars].sel(time=train_time),
y_hist[relative_vars].sel(time=train_time))
print('absolute_model:\n', absolute_model.correction_)
absolute_model = absolute_model.compute()
relative_model = relative_model.compute()
x_scen = open_single(model, scenario, member).pipe(process_cmip)
if xy_region:
x_scen = x_scen.isel(**xy_region)
print('x_scen:\n', x_scen)
if 'hist' in scenario:
x_scen = x_scen.sel(time=hist_time)
else:
x_scen = x_scen.sel(time=future_time)
# predict this emsemble member
y_scen = absolute_model.predict(x_scen[absolute_vars])
y_scen.update(relative_model.predict(x_scen[relative_vars]))
y_scen = y_scen.chunk(chunks)
print('y_scen:\n', y_scen)
if dry_run:
print('skipping write of ... dry_run=True')
return 'skip'
else:
store.clear()
write = y_scen.to_zarr(store, compute=False, mode='w')
write.compute(retries=3)
zarr.consolidate_metadata(store)
return 'done'
def main():
model_dict, data = cmip()
written_keys = []
for full_key, ds in data.items():
valid_members = 0
for member_id in ds.member_id.values:
# only extract `max_members` members (at most)
if valid_members >= max_members:
break
# get the output zarr store
member_key = slim_cmip_key(full_key, member_id)
prefix = target.format(key=member_key)
store = get_store(prefix)
print(prefix)
# extract a single member and rechunk
member_ds = ds.sel(member_id=member_id).chunk({'lat': -1, 'lon': -1, 'time': 198})
# check that there is data for the full record
if (
member_ds.isel(lon=0, lat=0)
.isnull()
.any()
.to_array(name='variables')
.any()
.load()
.item()
):
print('--> skipping, missing some data')
store.clear()
continue
# clean encoding
for v in member_ds:
if 'chunks' in member_ds[v].encoding:
del member_ds[v].encoding['chunks']
# write store
if skip_existing and '.zmetadata' in store:
print('++++ skipping write', prefix)
else:
store.clear()
member_ds.to_zarr(store, consolidated=True, mode='w')
valid_members += 1
written_keys.append(prefix)
d = {}
for k in written_keys:
if 'historical' in k:
if k not in d:
d[k] = False
else:
pieces = k.split('.')
pieces[1] = 'historical'
k2 = '.'.join(pieces)
if k2 in written_keys:
d[k2] = True
d[k] = True
else:
d[k] = False
df = (
pd.DataFrame.from_dict(d, orient='index')
.reset_index()
.rename(columns={0: 'has_match', 'index': 'path'})
)
for i, row in df.iterrows():
model, scenario, member, _ = row.path.split('/')[-1].split('.')
df.loc[i, 'model'] = model
df.loc[i, 'scenario'] = scenario
df.loc[i, 'member'] = member
with fsspec.open(
'az://carbonplan-downscaling/cmip6/ssps_with_matching_historical_members.csv',
'w',
account_name='carbonplan',
account_key=os.environ['BLOB_ACCOUNT_KEY'],
) as f:
df.to_csv(f)
