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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- Real-time bias correction of rainfall nowcasts using biward tracking method W. Na & C. Yoo 10.1016/j.jhydrol.2023.129642
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- WITHDRAWN: Incorporating IMERG satellite precipitation uncertainty into seasonal and peak streamflow predictions using the Hillslope Link hydrological model S. Hartke et al. 10.1016/j.jhydrol.2022.129012
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- Multi-Channel Spectral Band Adjustment Factors for Thermal Infrared Measurements of Geostationary Passive Imagers D. Piontek et al. 10.3390/rs15051247
- Experimental Study on Generative Adversarial Network for Precipitation Nowcasting C. Luo et al. 10.1109/TGRS.2022.3177625
- Assessment of deterministic and probabilistic precipitation nowcasting techniques over New York metropolitan area A. Tounsi et al. 10.1016/j.envsoft.2023.105803
- Probabilistic Attenuation Nowcasting for the 5G Telecommunication Networks J. Pudashine et al. 10.1109/LAWP.2021.3068393
- Two-Stage UA-GAN for Precipitation Nowcasting L. Xu et al. 10.3390/rs14235948
- Skillful Radar-Based Heavy Rainfall Nowcasting Using Task-Segmented Generative Adversarial Network R. Wang et al. 10.1109/TGRS.2023.3295211
- Precipitation Nowcasting with Orographic Enhanced Stacked Generalization: Improving Deep Learning Predictions on Extreme Events G. Franch et al. 10.3390/atmos11030267
- Reliable precipitation nowcasting using probabilistic diffusion models C. Nai et al. 10.1088/1748-9326/ad2891
- Conditional simulation of spatial rainfall fields using random mixing: a study that implements full control over the stochastic process J. Yan et al. 10.5194/hess-25-3819-2021
- Weather Radar in Complex Orography U. Germann et al. 10.3390/rs14030503
- Short-term rainfall forecasting using cumulative precipitation fields from station data: a probabilistic machine learning approach D. Pirone et al. 10.1016/j.jhydrol.2022.128949
- Nowcasting the precipitation phase combining weather radar data, surface observations, and NWP model forecasts E. Casellas et al. 10.1002/qj.4121
- Lagrangian Integro-Difference Equation Model for Precipitation Nowcasting 10.1175/JTECH-D-21-0013.1
- The Role of Weather Radar in Rainfall Estimation and Its Application in Meteorological and Hydrological Modelling—A Review Z. Sokol et al. 10.3390/rs13030351
- Real-Time Control Enhanced Blue-Green Infrastructure Towards Torrential Events: A Smart Predictive Solution H. Zhou et al. 10.2139/ssrn.4127960
- Precipitation Nowcasting Based on Deep Learning over Guizhou, China D. Kong et al. 10.3390/atmos14050807
- Using Conditional Generative Adversarial 3-D Convolutional Neural Network for Precise Radar Extrapolation C. Wang et al. 10.1109/JSTARS.2021.3083647
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Latest update: 23 Apr 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...
