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Set up the environment
Using conda ...
Cartopy, xarray,cfgrib,netcdf4,pyproj, matplotlib, cdsapi
EFAS
Retrieve data
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model outputs
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# 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
conda install cartopy
# install the cdsapi
pip install cdsapi
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EFAS
Retrieve data
These exercises require EFAS model output parameters and auxiliary data. You can retrieve them using the CDS API, as described in the code block below:
Model outputs
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import cdsapi c = cdsapi.Client() # Climatology c.retrieve('efas-historical', { "format": "netcdf", "hday": ["15","16","17","18"], "hmonth": "november", "hyear": "2020", "model_levels": "surface_level", "system_version": "version_4_0", "time": ["00:00","06:00","18:00"], "variable": "import cdsapi c = cdsapi.Client() # Climatology c.retrieve('efas-historical', { "format": "netcdf", "hday": ["15","16","17","18"], "hmonth": "november", "hyear": "2020", "model_levels": "surface_level", "system_version": "version_4_0", "time": ["00:00","06:00","18:00"], "variable": "river_discharge_in_the_last_6_hours" }, 'clim_2020111500.nc') # Forecast c.retrieve( 'efas-forecast', { 'format': 'netcdf', 'originating_centre': 'ecmwf', 'product_type': 'ensemble_perturbed_forecasts', 'variable': 'river_discharge_in_the_last_6_hours'" }, 'model_levels': 'surface_level','clim_2020111500.nc') # Forecast c.retrieve( 'efas-forecast', { 'yearformat': '2020netcdf', 'monthoriginating_centre': '11ecmwf', 'dayproduct_type': '15ensemble_perturbed_forecasts', 'timevariable': '00:00river_discharge_in_the_last_6_hours', 'leadtimemodel_hourlevels': [ 'surface_level', '6year': '2020', '12', 'month': '1811', 'day': '15', '24time',: '3000:00', '36', 'leadtime_hour': [ '426', '4812', '5418', '6024', '6630', '7236', ], }'42', '48', ''54', '60', '66', '72', ], }, 'eue_2020111500.nc') # Forecast c.retrieve( 'efas-forecast', { 'format': 'netcdf', 'originating_centre': 'ecmwf', 'product_type': 'high_resolution_forecast', 'variable': [ 'soil_depth', 'volumetric_soil_moisture', ], 'model_levels': 'soil_levels', 'year': '2019', 'month': '01', 'day': '30', 'time': '00:00', 'leadtime_hour': [ '6', '12', '18', : [ '6', '12', '18', '24', '30', '36', '42', '48', '54', '60', '66', '72', ], 'soil_level': [ '1', '2', '3', ], }, 'eud_2019013000.nc') |
Auxiliary data
From the CDS Static files link, download the following NetCDFs:
thmin1.nc, thmin2.nc, thmin3.nc, thmax1.nc, thmax2.nc, thmax3.nc
When the marsurl will be in production one can access the auxiliary data simply requesting then through a CDS API request:
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import cdsapi c = cdsapi.Client() c.retrieve( 'efas-forecast, { '24format',: '30netcdf', '36', '42variable', '48', '54',: [ '60field_capacity', '66', '72wilting_point', ], 'soil_level': [ '1', '2', '3', ], }, 'auxiliary.zip') |
And then unzip the auxiliary.zip
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$ unzip auxiliary.zip Archive: auxiliary.zip extracting: thmax_1.nc extracting: thmin_1.nc extracting: thmax_2.nc },extracting: thmin_2.nc extracting: thmax_3.nc extracting: 'eudthmin_2019013000.nc') |
auxiliary data
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This is available only when marsurl adaptor will be in production. For the moment I am using the datasets from the test stack. |
Before the marsurl
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dsd3.nc |
Plot map discharge
In order to plot a map, we are going to use
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