Daniele Nerini, Seppo Pulkkinen, Andre Perez Hortal, Carlos Velasco, Loris Foresti, Ruben Imhoff, Petteri Karsisto, Codacy Badger, Thinker Hong, Jussi Tiira

Research output: Non-textual formSoftware


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.
Original languageEnglish
Media of outputOnline
Publication statusPublished - 7 Apr 2019


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