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CEMS-Flood data comes primarily in GRIB2 format.

To read GRIB files we encourage using Python and the xarray's CFGRIB engine.

Follow the instructions below to install the required libraries, assuming you are working on a Linux OS.


First of all install Conda, a Python packages and environments manager.

Then open a terminal and type:

Set up the python local environment and install required packages 
# create a local 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 local environment. 
conda activate myenv

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

# make sure you have installed eccodes version >= 2.23.0 
python -c "import eccodes; print(eccodes.__version__)"

Start a python console (it is important that you have activated the local environment) and type:

Open a GRIB file 
# 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....

How to correctly read historical datasets

import xarray as xr

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




How to correctly read GRIB with heterogeneous types

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") 



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