CEMS-Flood data comes primarily in GRIB2 format.

The easiest way to read GRIB file is through Python using the xarray's CFGRIB engine. The following instructions assume you are working on a Linux machine.


# create a virtual environment, you can call it as you wish, here 'myenv' is used.
conda create -n myenv python=3.8 

# add repository channel
conda config --add channels conda-forge

# activate the environment. 
conda activate myenv

# install the required packages
conda install -c conda-forge/label/main xarray cfgrib eccodes





# assumed you have download from the Climate Data Store a GloFAS GRIB file named 'download.grib'
 
In [1]: import xarray as xr

# reading GloFAS GRIB file
In [2]: ds = xr.open_dataset('download.grib',engine='cfgrib')


In [3]: ds
Out[4]:
<xarray.Dataset>
Dimensions:     (latitude: 1500, longitude: 3600, step: 3, time: 3)
Coordinates:
    number      int64 ...
  * time        (time) datetime64[ns] 2019-12-01 2019-12-02 2019-12-03
  * step        (step) timedelta64[ns] 1 days 2 days 3 days
    surface     int64 ...
  * latitude    (latitude) float64 89.95 89.85 89.75 ... -59.75 -59.85 -59.95
  * longitude   (longitude) float64 -179.9 -179.8 -179.8 ... 179.7 179.8 179.9
    valid_time  (time, step) datetime64[ns] ...
Data variables:
    dis24       (time, step, latitude, longitude) 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:                 2021-02-11T11:00:21 GRIB to CDM+CF via cfgrib-0....



import xarray as xr

ds = xr.open_dataset("glofas_historical_201901.grib",engine="cfgrib",backend_kwargs={'time_dims':['time']})





If you download both 'control reforecast' and ‘ensemble perturbed reforecasts' products in a single GRIB file, in order to read it in Python you will need to pass a backward_kwargs dictionary in the open_dataset function, as in the examples below:

import xarray as xr

# Filtering and saving the Control reforecast (cf) data

glofas_cf = xr.open_dataset("Glofas_forecast.grib",  engine='cfgrib', backend_kwargs={'filter_by_keys': {'dataType': 'cf'}, 'indexpath':''}) 
glofas_cf.to_netcdf("Glofas_forecast_cf.nc") 



 # Filtering and saving the Ensemble perturbed reforecasts (pf) data

glofas_pf = xr.open_dataset("Glofas_forecast.grib ",  engine='cfgrib', backend_kwargs={'filter_by_keys': {'dataType': 'pf'}, 'indexpath':''}) 
glofas_pf.to_netcdf("Glofas_forecast_pf.nc")