Articles | Volume 13, issue 6
https://doi.org/10.5194/gmd-13-2631-2020
https://doi.org/10.5194/gmd-13-2631-2020
Model description paper
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11 Jun 2020
Model description paper | Highlight paper |  | 11 Jun 2020

RainNet v1.0: a convolutional neural network for radar-based precipitation nowcasting

Georgy Ayzel, Tobias Scheffer, and Maik Heistermann

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Cited articles

Agrawal, S., Barrington, L., Bromberg, C., Burge, J., Gazen, C., and Hickey, J.: Machine Learning for Precipitation Nowcasting from Radar Images, available at: https://arxiv.org/abs/1912.12132 (last access: 28 January 2020), 2019. a, b
Austin, G. L. and Bellon, A.: The use of digital weather radar records for short-term precipitation forecasting, Q. J. Roy. Meteor. Soc., 100, 658–664, https://doi.org/10.1002/qj.49710042612, 1974. a
Ayzel, G.: hydrogo/rainnet: RainNet v1.0-gmdd, Zenodo, https://doi.org/10.5281/zenodo.3631038, 2020a. a, b, c
Ayzel, G.: RainNet: pretrained model and weights, Zenodo, https://doi.org/10.5281/zenodo.3630429, 2020b. a, b, c
Ayzel, G.: RYDL: the sample data of the RY product for deep learning applications, Zenodo, https://doi.org/10.5281/zenodo.3629951, 2020c. a, b
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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.