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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.
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:
In [1]: import xarray as xr
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 to be set in the backend_kwargs argument of the xarray.open_dataset function.
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 GRIB GloFAS historical datasets with multiple product types:
GloFAS historical has 2 product types, consolidated and intermediate, that you could download together in a GRIB file.
In order to open the file you need to specify the experimentVersionNumber in the backend_kwargs:
consolidated: '0001'
intermediate: '0005'
import xarray as xr
ds = xr.open_dataset('glofas.grib', engine='cfgrib',
... backend_kwargs={'read_keys': {'experimentVersionNumber':'0001'}})
Read GRIB file that has multiple product types:
There are 4 datasets that may have more that one product type in a GRIB file:
- FAS forecast: "control reforecast", "ensemble perturbed reforecast", "high resolution forecast"
- EFAS reforecast: "control reforecast", "ensemble perturbed reforecast"
- GloFAS historical: "consolidated", "intermediate"
- GloFAS forecast: "control reforecast", "ensemble perturbed reforecasts"
- GloFAS reforecast: "control reforecast", "ensemble perturbed reforecast"
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':''})