CEMS-Flood data comes primarily in GRIB2 format. 

To read GRIB files we encourage using Python xarray and cfgrib packages.

This guideline provides instructions about how to install required libraries (assuming you are working on a Linux OS) and document dataset's specific configurations that must be set when reading GRIBs.


Tools and libraries installation


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

Then open a terminal and type:

# 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:

# 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....


Dataset's specific cfgrib configurations


The different GRIB data structure of the EFAS and GloFAS datasets may require some additional configuration. It usually consists in additional parameters specified in the backend_kwargs argument.

Read GRIB historical datasets:

CEMS-Floods offers two historical datasets: GloFAS and EFAS historical.


import xarray as xr

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

Read a GRIB file that has multiple product types:


There are 4 datasets that may have more that one product type in a GRIB file:

In order to read them you need to specify which product type you are reading using the backend_kwargs:

import xarray as xr

# Reading the Control reforecast (cf) data

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


 # Reading the Ensemble perturbed reforecasts (pf) data

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