| Build Status |
|---|
 |
Download GloFAS data from the Climate Data Store.
import climetlab as cml
dataset = cml.load_dataset(
'glofas-seasonal',
model='lisflood',
system_version='operational',
temporal_filter= '2022 01 01',
leadtime_hour = '24-72',
variable="river_discharge_in_the_last_24_hours"
)
ds = dataset.to_xarray()
ds
(<xarray.Dataset>
Dimensions: (realization: 51, forecast_reference_time: 1,
leadtime: 3, lat: 1500, lon: 3600)
Coordinates:
* realization (realization) int64 0 1 2 3 4 5 ... 46 47 48 49 50
* forecast_reference_time (forecast_reference_time) datetime64[ns] 2022-01-01
* leadtime (leadtime) timedelta64[ns] 1 days 2 days 3 days
* lat (lat) float64 -59.95 -59.85 -59.75 ... 89.85 89.95
* lon (lon) float64 -179.9 -179.8 -179.8 ... 179.8 540.0
time (forecast_reference_time, leadtime) datetime64[ns] ...
Data variables:
dis24 (realization, forecast_reference_time, leadtime, lat, lon) float32 ...
Attributes:
GRIB_edition: 2
GRIB_centre: ecmf
GRIB_centreDescription: European Centre for Medium-Range Weather Forecasts
GRIB_subCentre: 0
Conventions: CF-1.7
institution: European Centre for Medium-Range Weather Forecasts
history: 2023-01-02T10:51 GRIB to CDM+CF via cfgrib-0.9.1...,)
More example requests and documentation