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https://github.com/pySTEPS/pysteps

Python framework for short-term ensemble prediction systems.
https://github.com/pySTEPS/pysteps

advection ensemble-prediction forecast-verification hydrology nowcasting optical-flow precipitation rainfall rainfall-prediction stochastic-processes weather weather-radar

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Python framework for short-term ensemble prediction systems.

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README 

        
pysteps - Python framework for short-term ensemble prediction systems

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    * - docs

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.. |docs| image:: https://readthedocs.org/projects/pysteps/badge/?version=latest

    :alt: Documentation Status

    :target: https://pysteps.readthedocs.io/



.. |test| image:: https://github.com/pySTEPS/pysteps/workflows/Test%20pysteps/badge.svg

    :alt: Test pysteps

    :target: https://github.com/pySTEPS/pysteps/actions?query=workflow%3A"Test+Pysteps"



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    :target: https://github.com/pySTEPS/pysteps/actions?query=workflow%3A"Check+Black"



.. |codecov| image:: https://codecov.io/gh/pySTEPS/pysteps/branch/master/graph/badge.svg

    :alt: Coverage

    :target: https://codecov.io/gh/pySTEPS/pysteps



.. |github| image:: https://img.shields.io/github/release/pySTEPS/pysteps.svg

    :target: https://github.com/pySTEPS/pysteps/releases/latest

    :alt: Latest github release



.. |conda| image:: https://anaconda.org/conda-forge/pysteps/badges/version.svg

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    :alt: Anaconda Cloud



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    :alt: Latest PyPI version



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    :target: https://opensource.org/licenses/BSD-3-Clause



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    :target: https://github.com/pySTEPS/pysteps/graphs/contributors



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    :alt: Conda downloads

    :target: https://anaconda.org/conda-forge/pysteps



.. |colab| image:: https://colab.research.google.com/assets/colab-badge.svg

    :alt: My first nowcast

    :target: https://colab.research.google.com/github/pySTEPS/pysteps/blob/master/examples/my_first_nowcast.ipynb



.. |gallery| image:: https://img.shields.io/badge/example-gallery-blue.svg

    :alt: pysteps example gallery

    :target: https://pysteps.readthedocs.io/en/stable/auto_examples/index.html

    

.. |stable| image:: https://img.shields.io/badge/docs-stable-blue.svg

    :alt: pysteps documentation

    :target: https://pysteps.readthedocs.io/en/stable/

    

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   :alt: Codacy Badge

   :target: https://app.codacy.com/gh/pySTEPS/pysteps?utm_source=github.com&utm_medium=referral&utm_content=pySTEPS/pysteps&utm_campaign=Badge_Grade



.. |zenodo| image:: https://zenodo.org/badge/140263418.svg

   :alt: DOI

   :target: https://zenodo.org/badge/latestdoi/140263418



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What is pysteps?

================



Pysteps is an open-source and community-driven Python library for probabilistic precipitation nowcasting, i.e. short-term ensemble prediction systems.



The aim of pysteps is to serve two different needs. The first is to provide a modular and well-documented framework for researchers interested in developing new methods for nowcasting and stochastic space-time simulation of precipitation. The second aim is to offer a highly configurable and easily accessible platform for practitioners ranging from weather forecasters to hydrologists.



The pysteps library supports standard input/output file formats and implements several optical flow methods as well as advanced stochastic generators to produce ensemble nowcasts. In addition, it includes tools for visualizing and post-processing the nowcasts and methods for deterministic, probabilistic, and neighbourhood forecast verification.



Quick start

-----------



Use pysteps to compute and plot a radar extrapolation nowcast in Google Colab with `this interactive notebook `_.



Installation

============



The recommended way to install pysteps is with `conda `_ from the conda-forge channel::



    $ conda install -c conda-forge pysteps



More details can be found in the `installation guide `_.



Usage

=====



Have a look at the `gallery of examples `__ to get a good overview of what pysteps can do.



For a more detailed description of all the available methods, check the  `API reference `_ page.



Example data

============



A set of example radar data is available in a separate repository: `pysteps-data `_.

More information on how to download and install them is available `here `_.



Contributions

=============



*We welcome contributions!*



For feedback, suggestions for developments, and bug reports please use the dedicated `issues page `_.



For more information, please read our `contributors guidelines `_.



Reference publications

======================



The overall library is described in



Pulkkinen, S., D. Nerini, A. Perez Hortal, C. Velasco-Forero, U. Germann,

A. Seed, and L. Foresti, 2019:  Pysteps:  an open-source Python library for

probabilistic precipitation nowcasting (v1.0). *Geosci. Model Dev.*, **12 (10)**,

4185–4219, doi:`10.5194/gmd-12-4185-2019 `_.



While the more recent blending module is described in



Imhoff, R.O., L. De Cruz, W. Dewettinck, C.C. Brauer, R. Uijlenhoet, K-J. van Heeringen, 

C. Velasco-Forero, D. Nerini, M. Van Ginderachter, and A.H. Weerts, 2023:

Scale-dependent blending of ensemble rainfall nowcasts and NWP in the open-source

pysteps library. *Q J R Meteorol Soc.*, 1-30,

doi: `10.1002/qj.4461 `_.



Contributors

============



.. image:: https://contrib.rocks/image?repo=pySTEPS/pysteps

   :target: https://github.com/pySTEPS/pysteps/graphs/contributors