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.

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Status: closed
Status: closed
AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment
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AR: Author's response | RR: Referee report | ED: Editor decision
AR by Georgy Ayzel on behalf of the Authors (06 May 2020)  Author's response    Manuscript
ED: Publish subject to minor revisions (review by editor) (06 May 2020) by Simone Marras
AR by Georgy Ayzel on behalf of the Authors (07 May 2020)  Author's response    Manuscript
ED: Publish as is (13 May 2020) by Simone Marras
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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.