Articles | Volume 13, issue 6
https://doi.org/10.5194/gmd-13-2631-2020
© Author(s) 2020. 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-13-2631-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Model description paper
| Highlight paper
| 
11 Jun 2020
Model description paper | Highlight paper |
| 11 Jun 2020
| 11 Jun 2020
RainNet v1.0: a convolutional neural network for radar-based precipitation nowcasting
Institute for Environmental Sciences and Geography, University of Potsdam, Potsdam, Germany
Tobias Scheffer
Department of Computer Science, University of Potsdam, Potsdam, Germany
Maik Heistermann
Institute for Environmental Sciences and Geography, University of Potsdam, Potsdam, Germany
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- Multi-Level Circulation Pattern Classification Based on the Transfer Learning CNN Network Y. Liu et al. 10.3390/atmos13111861
- Skilful nowcasting of extreme precipitation with NowcastNet Y. Zhang et al. 10.1038/s41586-023-06184-4
- Improving radar-based rainfall nowcasting by a nearest-neighbour approach – Part 1: Storm characteristics B. Shehu & U. Haberlandt 10.5194/hess-26-1631-2022
- Deep Learning-Based Radar Composite Reflectivity Factor Estimations from Fengyun-4A Geostationary Satellite Observations F. Sun et al. 10.3390/rs13112229
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- TempNet – temporal super-resolution of radar rainfall products with residual CNNs M. Sit et al. 10.2166/hydro.2023.196
- EfficientRainNet: Leveraging EfficientNetV2 for memory-efficient rainfall nowcasting M. Sit et al. 10.1016/j.envsoft.2024.106001
- Skilful precipitation nowcasting using deep generative models of radar S. Ravuri et al. 10.1038/s41586-021-03854-z
- Probabilistic Attenuation Nowcasting for the 5G Telecommunication Networks J. Pudashine et al. 10.1109/LAWP.2021.3068393
- DeePS at: A deep learning model for prediction of satellite images for nowcasting purposes V. Ionescu et al. 10.1016/j.procs.2021.08.064
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Latest update: 20 Nov 2024
Short summary
In this study, we present RainNet, a deep convolutional neural network for radar-based precipitation nowcasting, which was trained to predict continuous precipitation intensities at a lead time of 5 min. RainNet significantly outperformed the benchmark models at all lead times up to 60 min. Yet, an undesirable property of RainNet predictions is the level of spatial smoothing. Obviously, RainNet learned an optimal level of smoothing to produce a nowcast at 5 min lead time.
In this study, we present RainNet, a deep convolutional neural network for radar-based...
