Geoscientific Model Development, 2019-10, Vol.12 (10), p.4185-4219
2019
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Details
- Autor(en) / Beteiligte
- Titel
- Pysteps: an open-source Python library for probabilistic precipitation nowcasting (v1.0)
- Ist Teil von
- Geoscientific Model Development, 2019-10, Vol.12 (10), p.4185-4219
- Ort / Verlag
- Katlenburg-Lindau: Copernicus GmbH
- Erscheinungsjahr
- 2019
- Quelle
- Free E-Journal (出版社公開部分のみ）
- Beschreibungen/Notizen
- 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.
- Sprache
- Englisch
- Identifikatoren
- ISSN: 1991-9603, 1991-959X, 1991-962XeISSN: 1991-9603, 1991-962XDOI: 10.5194/gmd-12-4185-2019Titel-ID: cdi_doaj_primary_oai_doaj_org_article_6bbd7fb8364441aba2c260301a9419cb
- Format
- –
- Schlagworte
- 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, Stochasticity, Warning systems, Weather, Weather forecasting