Articles | Volume 13, issue 6
Geosci. Model Dev., 13, 2631–2644, 2020
https://doi.org/10.5194/gmd-13-2631-2020
Geosci. Model Dev., 13, 2631–2644, 2020
https://doi.org/10.5194/gmd-13-2631-2020

Model description paper 11 Jun 2020

Model description paper | 11 Jun 2020

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

Georgy Ayzel et al.

Data sets

RYDL: the sample data of the RY product for deep learning applications G. Ayzel https://doi.org/10.5281/zenodo.3629951

RainNet: pretrained model and weights G. Ayzel https://doi.org/10.5281/zenodo.3630429

Model code and software

hydrogo/rainnet: RainNet v1.0-gmdd G. Ayzel https://doi.org/10.5281/zenodo.3631038

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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.