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

Pysteps is an open-source and community-driven Python library for probabilistic precipitation nowcasting, that is, 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 d...

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Vydáno v:Geoscientific Model Development Ročník 12; číslo 10; s. 4185 - 4219
Hlavní autoři: Pulkkinen, Seppo, Nerini, Daniele, Pérez Hortal, Andrés A., Velasco-Forero, Carlos, Seed, Alan, Germann, Urs, Foresti, Loris
Médium: Journal Article
Jazyk:angličtina
Vydáno: Katlenburg-Lindau Copernicus GmbH 07.10.2019
Copernicus Publications
Témata:
Computer simulation
Early warning systems
Emergency warning programs
Forecast verification
Forecasts and trends
Hydrologists
Hydrology
Libraries
Methods
Neighborhoods
Nowcasting
Numerical weather prediction
Optical flow (image analysis)
Parameter sensitivity
Post-production processing
Precipitation
Precipitation (Meteorology)
Probabilistic methods
Probability theory
Python
Python (Programming language)
Radar
Radar imaging
Severe weather
Short-range forecasting
Warning systems
Weather
Weather forecasting
Germany
ISSN:1991-9603, 1991-959X, 1991-962X, 1991-9603, 1991-962X
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Popis
Shrnutí:Pysteps is an open-source and community-driven Python library for probabilistic precipitation nowcasting, that is, 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 various 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 neighborhood forecast verification. The pysteps library is described and its potential is demonstrated using radar composite images from Finland, Switzerland, the United States and Australia. Finally, scientific experiments are carried out to help the reader to understand the pysteps framework and sensitivity to model parameters.
Bibliografie:ObjectType-Article-1
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ISSN:1991-9603
1991-959X
1991-962X
1991-9603
1991-962X
DOI:10.5194/gmd-12-4185-2019