Downloading, reading and TS conversion of ECMWF reanalysis data
Readers and converters for data from the ECMWF reanalysis models. Written in Python.
Works great in combination with pytesmo.
If you use the software in a publication then please cite it using the Zenodo DOI. Be aware that this badge links to the latest package version.
Please select your specific version at https://doi.org/10.5281/zenodo.593533 to get the DOI of that version. You should normally always use the DOI for the specific version of your record in citations. This is to ensure that other researchers can access the exact research artefact you used for reproducibility.
You can find additional information regarding DOI versioning at http://help.zenodo.org/#versioning
Install required C-libraries via conda. For installation we recommend Miniconda. So please install it according to the official installation instructions. As soon as you have the conda command in your shell you can continue:
conda install -c conda-forge pandas pygrib netcdf4 scipy pyresample xarray
The following command will download and install all the needed pip packages as well as the ecmwf-model package itself.
pip install ecmwf_models
To create a full development environment with conda, the environment.yml file in this repository can be used.
git clone email@example.com:TUW-GEO/ecmwf_models.git ecmwf_models cd ecmwf_models conda create -n ecmwf-models python=3.6 # or any other supported version source activate ecmwf-models conda env update -f environment.yml python setup.py develop
This script should work on Linux or OSX and uses the environment.yml file included in this repository.
At the moment this package supports
- ERA Interim (deprecated)
reanalysis data in grib and netcdf format (download, reading, time series creation) with a default spatial sampling of 0.75 degrees (ERA Interim), 0.25 degrees (ERA5), resp. 0.1 degrees (ERA5-Land). It should be easy to extend the package to support other ECMWF reanalysis products. This will be done as need arises.
We are happy if you want to contribute. Please raise an issue explaining what is missing or if you find a bug. We will also gladly accept pull requests against our master branch for new features or bug fixes.
For Development we also recommend the conda environment from the installation part.
If you want to contribute please follow these steps:
- Fork the ecmwf_models repository to your account
- make a new feature branch from the ecmwf_models master branch
- Add your feature
- please include tests for your contributions in one of the test directories We use py.test so a simple function called test_my_feature is enough
- submit a pull request to our master branch
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