Readers and converters for data from the GLDAS Noah Land Surface Model. Written in Python.
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README.rst

gldas

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Readers and converters for data from the GLDAS Noah Land Surface Model. 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.596427 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

Setup of a complete environment with conda can be performed using the following commands:

conda create -q -n gldas-environment -c conda-forge numpy netCDF4 pyproj pygrib
source activate gldas-environment
pip install gldas

This will also try to install pygrib for reading the GLDAS grib files. If this does not work then please consult the pygrib manual.

Note

Reading grib files does not work on Windows as far as we know. It might be possible to compile the ECMWF C library but we have not done it yet.

Supported Products

At the moment this package supports GLDAS Noah data version 1 in grib format (reading, time series creation) and GLDAS Noah data version 2.0 and version 2.1 in netCDF format (download, reading, time series creation) with a spatial sampling of 0.25 degrees. It should be easy to extend the package to support other GLDAS based products. This will be done as need arises.

Downloading Products

In order to download GLDAS NOAH products you have to register an account with NASA's Earthdata portal. Instructions can be found here.

After that you can use the command line program gldas_download.

mkdir ~/workspace/gldas_data
gldas_download ~/workspace/gldas_data

would download GLDAS Noah version 2.0 in 0.25 degree sampling into the folder ~/workspace/gldas_data. For more options run gldas_download -h.

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 a conda environment. You can create one including test dependencies and debugger by running conda env create -f environment.yml. This will create a new gldas environment which you can activate by using source activate gldas.

Guidelines

If you want to contribute please follow these steps:

  • Fork the gldas repository to your account
  • Clone the repository, make sure you use git clone --recursive to also get the test data repository.
  • make a new feature branch from the gldas 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

Note

This project has been set up using PyScaffold 2.5.6. For details and usage information on PyScaffold see http://pyscaffold.readthedocs.org/.