import logging
# External libs
import xarray as xr
# Optional libs
try:
import salem
except ImportError:
pass
# Locals
from oggm import cfg
from oggm import utils
from oggm import entity_task
from oggm.exceptions import InvalidParamsError
# Module logger
log = logging.getLogger(__name__)
GSWP3_W5E5_SERVER = 'https://cluster.klima.uni-bremen.de/~oggm/climate/'
_base = 'gswp3-w5e5/flattened/2025.11.25/'
BASENAMES = {
'GSWP3_W5E5': {
'inv': f'{_base}monthly/gswp3-w5e5_glacier_invariant_flat_v2025.11.25.nc',
'tmp': f'{_base}monthly/gswp3-w5e5_obsclim_tas_global_monthly_1901_2019_flat_glaciers_v2025.11.25.nc',
'temp_std': f'{_base}monthly/gswp3-w5e5_obsclim_temp_std_global_monthly_1901_2019_flat_glaciers_v2025.11.25.nc',
'prcp': f'{_base}monthly/gswp3-w5e5_obsclim_pr_global_monthly_1901_2019_flat_glaciers_v2025.11.25.nc'
},
'GSWP3_W5E5_daily': {
'inv': f'{_base}daily/gswp3-w5e5_glacier_invariant_flat_v2025.11.25.nc',
'tmp': f'{_base}daily/gswp3-w5e5_obsclim_tas_global_daily_1901_2019_flat_glaciers_v2025.11.25.nc',
'prcp': f'{_base}daily/gswp3-w5e5_obsclim_pr_global_daily_1901_2019_flat_glaciers_v2025.11.25.nc',
}
}
def _get_w5e5_server():
return cfg.PARAMS.get('gswp3_w5e5_server', GSWP3_W5E5_SERVER)
def _get_w5e5_basenames():
return cfg.PARAMS.get('gswp3_w5e5_basenames', BASENAMES)
def get_gswp3_w5e5_file(dataset='GSWP3_W5E5', var=None):
"""Returns the path to the desired GSWP3-W5E5 baseline climate file.
It is the observed climate dataset used for ISIMIP3a.
For OGGM, it was preprocessed by selecting only those gridpoints
with glaciers nearby.
If the file is not present, downloads it.
var : str, default None
:inv: invariant
:tmp: temperature
:prcp: precipitation
:temp_std: mean of daily temperature standard deviation
dataset : str, default 'GSWP3_W5E5'
Dataset name.
"""
# check if input makes sense
basenames = _get_w5e5_basenames()
if var not in basenames[dataset].keys():
raise InvalidParamsError(f'{dataset} variable {var} not '
f'in {basenames[dataset].keys()}')
# File to look for
return utils.file_downloader(_get_w5e5_server() + basenames[dataset][var])
@entity_task(log, writes=['climate_historical'])
def process_gswp3_w5e5_data(gdir, settings_filesuffix='',
y0=None, y1=None, daily=False,
output_filesuffix=''):
"""Process and write GSWP3-W5E5+W5E5 baseline climate data for a glacier.
Extracts the nearest timeseries and writes everything to a NetCDF
file. Uses GSWP3 data until 1979, then W5E5 data from 1979 onwards.
Data source: https://www.isimip.org/gettingstarted/input-data-bias-adjustment/details/80/
Parameters
----------
gdir : :py:class:`oggm.GlacierDirectory`
the glacier directory to process
settings_filesuffix: str
You can use a different set of settings by providing a filesuffix. This
is useful for sensitivity experiments. Code-wise the settings_filesuffix
is set in the @entity-task decorater.
y0 : int
the starting year of the timeseries to write. The default is to take
the entire time period available in the file, but with this kwarg
you can shorten it (to save space or to crop bad data). If y0>=1979,
it only uses W5E5 data!
y1 : int
the end year of the timeseries to write. The default is to take
the entire time period available in the file, but with this kwarg
you can shorten it (to save space or to crop bad data)
daily : bool, default False
Provide data at a daily resolution if True, otherwise provide it
at monthly resolution. Adds an additional '_daily' to the filename
output_filesuffix : str, optional
'' by default
"""
if not daily:
dataset = 'GSWP3_W5E5' # 'W5E5_monthly'
else:
dataset = 'GSWP3_W5E5_daily'
tvar = 'tas'
pvar = 'pr'
# get the central longitude/latitudes of the glacier
lon = gdir.cenlon + 360 if gdir.cenlon < 0 else gdir.cenlon
lat = gdir.cenlat
path_tmp = get_gswp3_w5e5_file(dataset, 'tmp')
path_prcp = get_gswp3_w5e5_file(dataset, 'prcp')
path_inv = get_gswp3_w5e5_file(dataset, 'inv')
# Use xarray to read the data
# would go faster with only netCDF -.-, but easier with xarray
# first temperature dataset
with xr.open_dataset(path_tmp) as ds:
# sanity checks, safeguarding for unwanted future side effects
# we do it only for temp and assume no mistake on the data prep side
info = utils.climate_file_info(ds)
assert info['lon_bounds'][0] >= 0
assert info['is_flat']
yrs = ds['time.year'].data
y0 = yrs[0] if y0 is None else y0
y1 = yrs[-1] if y1 is None else y1
if not daily:
period = (f'{y0}-01-01', f'{y1}-12-01')
else:
period = (f'{y0}-01-01', f'{y1}-12-31')
if y1 > 2019 or y0 < 1901:
text = 'GSWP3 climate data are only available from 1901-2019.'
raise InvalidParamsError(text)
ds = ds.sel(time=slice(*period))
ds = utils.get_closest_grid_point_of_dataset(
dataset=ds, latitude=lat, longitude=lon)
# Fetch lon and lat
ds['longitude'] = ds.longitude
ds['latitude'] = ds.latitude
# temperature should be in degree Celsius for the glacier climate files
temp = ds[tvar].data - 273.15
time = ds.time.data
ref_lon = float(ds['longitude'])
ref_lat = float(ds['latitude'])
ref_lon = ref_lon - 360 if ref_lon > 180 else ref_lon
# precipitation: similar as temperature
with xr.open_dataset(path_prcp) as ds:
# here we take the same y0 and y1 as given from the
# temperature dataset
ds = ds.sel(time=slice(*period))
ds = utils.get_closest_grid_point_of_dataset(
dataset=ds, latitude=lat, longitude=lon)
# convert kg m-2 s-1
if not daily:
# into kg m-2 month-1 (monthly mean into monthly sum)
prcp = ds[pvar].data * cfg.SEC_IN_DAY * ds['time.daysinmonth']
else:
# into kg m-2 day-1
prcp = ds[pvar].data * cfg.SEC_IN_DAY
# w5e5 invariant file
with xr.open_dataset(path_inv) as ds:
ds = ds.isel(time=0)
ds = utils.get_closest_grid_point_of_dataset(
dataset=ds, latitude=lat, longitude=lon)
# w5e5 inv ASurf/hgt is already in hgt coordinates
hgt = ds['ASurf'].data
# temp_std only available for monthly
if not daily:
path_temp_std = get_gswp3_w5e5_file(dataset, 'temp_std')
with xr.open_dataset(path_temp_std) as ds:
ds = ds.sel(time=slice(*period))
ds = utils.get_closest_grid_point_of_dataset(
dataset=ds, latitude=lat, longitude=lon)
temp_std = ds['temp_std'].data # tas_std for W5E5!!!
else:
temp_std = None
# OK, ready to write
if daily:
output_filesuffix = f"_daily{output_filesuffix}"
gdir.write_climate_file(time, prcp, temp, hgt, ref_lon, ref_lat,
filesuffix=output_filesuffix,
temp_std=temp_std,
source=dataset,
daily=daily)
[docs]
@entity_task(log, writes=["climate_historical"])
def process_w5e5_data(gdir, settings_filesuffix='', y0=None, y1=None,
daily=False, output_filesuffix=''):
"""Processes and writes the W5E5 baseline climate data for a glacier.
Internally, this calls ``process_gswp3_w5e5_data``, but only for the
W5E5 part. ``y0`` defaults to 1979 and cannot be set to a lower
value. Extracts nearest timeseries and writes everything to a NetCDF
file.
Data source: https://data.isimip.org/10.48364/ISIMIP.342217
Parameters
----------
gdir : :py:class:`oggm.GlacierDirectory`
the glacier directory to process
settings_filesuffix: str
You can use a different set of settings by providing a filesuffix. This
is useful for sensitivity experiments. Code-wise the settings_filesuffix
is set in the @entity-task decorater.
y0 : int, optional
The starting year of the desired timeseries. The default is to
take the entire time period available in the file, but with
this argument you can shorten it to save space or to crop bad
data. If y0>=1979, it only uses W5E5 data.
y1 : int, optional
The end year of the desired timeseries. The default is to take
the entire time period available in the file, but with this
argument you can shorten it to save space or to crop bad data.
daily : bool, default False
Provide data at a daily resolution if True, otherwise provide it
at monthly resolution.
output_filesuffix : str, default ''
Used to distinguish between different daily datasets.
"""
y0 = 1979 if y0 is None else y0
y1 = 2019 if y1 is None else y1
if y0 < 1979 or y1 > 2019:
text = ("W5E5 climate data are only available from 1979-2019."
"If you want older climate data, "
"use 'process_gswp3_w5e5_data()'")
raise InvalidParamsError(text)
process_gswp3_w5e5_data(
gdir, settings_filesuffix=settings_filesuffix, y0=y0, y1=y1,
daily=daily, output_filesuffix=output_filesuffix
)
