Articles | Volume 12, issue 10
https://doi.org/10.5194/gmd-12-4185-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-12-4185-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Model description paper
| 
07 Oct 2019
Model description paper |
| 07 Oct 2019
| 07 Oct 2019
Pysteps: an open-source Python library for probabilistic precipitation nowcasting (v1.0)
Colorado State University, Fort Collins, CO, USA
Finnish Meteorological Institute, Helsinki, Finland
Daniele Nerini
Federal Office of Meteorology and Climatology MeteoSwiss, Locarno-Monti, Switzerland
Institute for Atmospheric and Climate Science, ETH Zurich, Zurich, Switzerland
Andrés A. Pérez Hortal
Department of Atmospheric and Oceanic Sciences, McGill University, Montreal, Canada
Carlos Velasco-Forero
Bureau of Meteorology, Melbourne, Australia
Alan Seed
Bureau of Meteorology, Melbourne, Australia
Urs Germann
Federal Office of Meteorology and Climatology MeteoSwiss, Locarno-Monti, Switzerland
Loris Foresti
Federal Office of Meteorology and Climatology MeteoSwiss, Locarno-Monti, Switzerland
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- Improving Nowcasting of Intense Convective Precipitation by Incorporating Dual-Polarization Radar Variables into Generative Adversarial Networks P. Cai et al. 10.3390/s24154895
- Advances in Deep-Learning-based Precipitation Nowcasting Techniques Q. ZHENG et al. 10.3724/j.1006-8775.2024.028
- Reliable precipitation nowcasting using probabilistic diffusion models C. Nai et al. 10.1088/1748-9326/ad2891
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- Real-Time Control Enhanced Blue-Green Infrastructure Towards Torrential Events: A Smart Predictive Solution H. Zhou et al. 10.2139/ssrn.4127960
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Latest update: 20 Nov 2024
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 precipitation forecasting. It aims to be a well-documented platform for development of new methods as well as an easy-to-use tool for practitioners. The potential of the library is demonstrated by case studies and scientific experiments using radar data from Finland, Switzerland, the United States and Australia.
Reliable precipitation forecasts are vital for the society, as water-related hazards can cause...
