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Geoscientific Model Development An interactive open-access journal of the European Geosciences Union
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Discussion papers
https://doi.org/10.5194/gmd-2019-94
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-2019-94
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.

Model description paper 13 May 2019

Model description paper | 13 May 2019

Review status
This discussion paper is a preprint. It is a manuscript under review for the journal Geoscientific Model Development (GMD).

Pysteps: an open-source Python library for probabilistic precipitation nowcasting (v1.0)

Seppo Pulkkinen1,2, Daniele Nerini3,4, Andrés A. Pérez Hortal5, Carlos Velasco-Forero6, Alan Seed6, Urs Germann3, and Loris Foresti3 Seppo Pulkkinen et al.
  • 1Colorado State University, Fort Collins, United States
  • 2Finnish Meteorological Institute, Helsinki, Finland
  • 3Federal Office of Meteorology and Climatology MeteoSwiss, Locarno-Monti, Switzerland
  • 4Institute for Atmospheric and Climate Science, ETH Zurich, Switzerland
  • 5Department of Atmospheric and Oceanic Sciences, McGill University, Montreal, Canada
  • 6Bureau of Meteorology, Melbourne, Australia

Abstract. Pysteps is an open-source and community-driven Python library for probabilistic precipitation nowcasting – that is to say, very-short range forecasting (0–6 h). 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. In this sense, pysteps has the potential to become an important component for integrated early warning systems for severe weather.

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. The pysteps library is described and its potential is demonstrated using radar composite images from Finland, Switzerland, United States, and Australia. Finally, scientific experiments are carried out to help the reader to understand the pysteps framework and sensitivity to model parameters.

Seppo Pulkkinen et al.
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Status: open (until 08 Jul 2019)
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Seppo Pulkkinen et al.
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Short summary
Reliable precipitation forecasts are vital for the society, as water-related hazards can cause economic losses and loss of lives. Pysteps is an open-source Python library for radar-based forecasting. It aims to be a well-documented platform for development of new methods as well as a easy-to-use tool for a wide range of practitioners. The potential of the library is demonstrated via case studies and scientific experiments using radar data from Finland, Switzerland, United States and Australia.
Reliable precipitation forecasts are vital for the society, as water-related hazards can cause...
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