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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.
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>_.
The recommended way to install pysteps is with conda <https://docs.conda.io/>_ from the conda-forge channel::
$ conda install -c conda-forge pystepsMore details can be found in the installation guide <https://pysteps.readthedocs.io/en/stable/user_guide/install_pysteps.html>_.
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.
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>_.
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>_.
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>_.
.. image:: https://contrib.rocks/image?repo=pySTEPS/pysteps :target: https://github.com/pySTEPS/pysteps/graphs/contributors