ecmwf_models

Readers and converters for data from the ECMWF reanalysis models. Written in Python.

Works great in combination with pytesmo.

Citation

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

Installation

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.

The following script will download and install all the needed packages.

conda env create -f environment.yml
source activate ecmwf-models
pip install ecmwf_models

This script should work on Linux or OSX and uses the environment.yml file included in this repository. On Windows the reading of grib files is not available at the moment. On Windows a solution would be to download the ECMWF data in netCDF format instead.

Supported Products

This version supports the following products:

• ERA-Interim
• ERA5

Contribute

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.

Development setup

For Development we also recommend the conda environment from the installation part.

Guidelines

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