pysteps - Python framework for short-term ensemble prediction systems

.. start-badges

.. list-table:: :stub-columns: 1 :widths: 10 90

* - docs
  - |stable| |colab| |gallery|
* - status
  - |test| |docs| |codecov| |codacy| |black|
* - package
  - |github| |conda| |pypi| |zenodo|
* - community
  - |contributors| |downloads| |license|

.. |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"

.. |black| image:: https://github.com/pySTEPS/pysteps/workflows/Check%20Black/badge.svg :alt: Check Black :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 :target: https://anaconda.org/conda-forge/pysteps :alt: Anaconda Cloud

.. |pypi| image:: https://badge.fury.io/py/pysteps.svg :target: https://pypi.org/project/pysteps/ :alt: Latest PyPI version

.. |license| image:: https://img.shields.io/badge/License-BSD%203--Clause-blue.svg :alt: License :target: https://opensource.org/licenses/BSD-3-Clause

.. |contributors| image:: https://img.shields.io/github/contributors/pySTEPS/pysteps :alt: GitHub contributors :target: https://github.com/pySTEPS/pysteps/graphs/contributors

.. |downloads| image:: https://img.shields.io/conda/dn/conda-forge/pysteps :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/

.. |codacy| image:: https://api.codacy.com/project/badge/Grade/6cff9e046c5341a4afebc0347362f8de :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

.. end-badges

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

Installation

The recommended way to install pysteps is with conda <https://docs.conda.io/>_ from the conda-forge channel::

$ conda install -c conda-forge pysteps

More details can be found in the installation guide <https://pysteps.readthedocs.io/en/stable/user_guide/install_pysteps.html>_.

Usage

Have a look at the gallery of examples <https://pysteps.readthedocs.io/en/stable/auto_examples/index.html>__ to get a good overview of what pysteps can do.

For a more detailed description of all the available methods, check the API reference <https://pysteps.readthedocs.io/en/stable/pysteps_reference/index.html>_ page.

Example data

A set of example radar data is available in a separate repository: pysteps-data <https://github.com/pySTEPS/pysteps-data>. More information on how to download and install them is available here <https://pysteps.readthedocs.io/en/stable/user_guide/example_data.html>.

Contributions

We welcome contributions!

For feedback, suggestions for developments, and bug reports please use the dedicated issues page <https://github.com/pySTEPS/pysteps/issues>_.

For more information, please read our contributors guidelines <https://pysteps.readthedocs.io/en/stable/developer_guide/contributors_guidelines.html>_.

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 <https://doi.org/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 <https://doi.org/10.1002/qj.4461>_.

Contributors

.. image:: https://contrib.rocks/image?repo=pySTEPS/pysteps :target: https://github.com/pySTEPS/pysteps/graphs/contributors