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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.

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.

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.

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.

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.

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.