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Cited by 304Precipitation Measurement and AnalysisMeteorological Phenomena and SimulationsHydrological Forecasting Using AI

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

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Pulkkinen, Seppo, Nerini, Daniele, Pérez Hortal, Andrés A., Velasco-Forero, Carlos, Seed, Alan, Germann, Urs, Foresti, Loris·2019·Geoscientific Model Development
DOI 10.5194/gmd-12-4185-2019

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Abstract

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.

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  • Alternative radar-data methods such as PySTEPS and DARTS are based on advection schemes rooted in the continuity equation.

    “As a result, alternative methods using radar data, such as PySTEPS [6] and DARTS [9], have been developed based on advection schemes, which are fundamentally rooted in the continuity equation.”

    From MiMo: Unlocking the Reasoning Potential of Language Model - From Pretraining to Posttraining (2025) · cites this paper as [6]

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Authors
Pulkkinen, S., Nerini, D., Pérez Hortal, A. A., Velasco-Forero, C., Seed, A., Germann, U., & Foresti, L.
Affiliations
  • Finnish Meteorological Institute (FI)
  • Federal Office of Meteorology and Climatology MeteoSwiss (CH)
  • Bureau of Meteorology (AU)
Corresponding author
Pulkkinen, Seppo
ORCID
Venue
Geoscientific Model Development
Publisher
Copernicus GmbH
Published
2019-10-07
Volume
12
Issue
10
Pages
4185-4219
ISSN
  • 1991-959X (ISSN-L)
  • 1991-9603
DOI
10.5194/gmd-12-4185-2019
OpenAlex
W2945086703
Cited by
304
References
1
Citations by year
2026: 49 · 2025: 49 · 2024: 60 · 2023: 56 · 2022: 44 · 2021: 33 · 2020: 11 · 2019: 2
FWCI
8.92
Type
journal article
Language
en
Open access
Licence
Locations
  • Geoscientific model development (publishedVersion, cc-by)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
  • Repository for Publications and Research Data (ETH Zurich) (submittedVersion, cc-by)
  • ETH Zürich Research Collection (cc-by)
Funders
  • National Science Foundation
  • Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung (161316)
  • Suomalainen Tiedeakatemia
Topics
  • Physical Sciences › Earth and Planetary Sciences › Atmospheric Science › Precipitation Measurement and Analysis
  • Physical Sciences › Earth and Planetary Sciences › Atmospheric Science › Meteorological Phenomena and Simulations
  • Physical Sciences › Environmental Science › Environmental Engineering › Hydrological Forecasting Using AI
Keywords
very short-range forecasting, pysteps, optical flow, weather radar, forecast verification, Python library

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