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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 an extrapolation nowcast in Google Colab with this interactive notebook.
You can get in touch with the pysteps community on our pysteps slack. To get access to it, you need to ask for an invitation or you can use the automatic invitation page here. This invite page can sometimes take a while to load so please be patient.
To install pysteps please have a look at the pysteps user guide.
You can have a look at the gallery of examples to get a better idea of how the library can be used.
For a more detailed description of the implemented functions, check the pysteps 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 are available here.
We welcome contributions, feedback, suggestions for developments and bug reports.
Feedback, suggestions for developments and bug reports can use the dedicated Issues page.
More information dedicated to developers is available in the developer guide.
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. [source]
Pulkkinen, S., D. Nerini, A. Perez Hortal, C. Velasco-Forero, U. Germann, A. Seed, and L. Foresti, 2019: pysteps - a Community-Driven Open-Source Library for Precipitation Nowcasting. Poster presented at the 3rd European Nowcasting Conference, Madrid, ES, doi: 10.13140/RG.2.2.31368.67840. [source]