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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Latest update: 26 Jul 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...
