Articles | Volume 16, issue 3
Clim. Past, 16, 1075–1095, 2020
https://doi.org/10.5194/cp-16-1075-2020
Clim. Past, 16, 1075–1095, 2020
https://doi.org/10.5194/cp-16-1075-2020

Research article 22 Jun 2020

Research article | 22 Jun 2020

Reconstruction of Holocene oceanographic conditions in eastern Baffin Bay

Katrine Elnegaard Hansen et al.

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ED: Publish subject to minor revisions (review by editor) (16 Apr 2020) by David Thornalley
AR by Anna Mirena Feist-Polner on behalf of the Authors (30 Apr 2020)  Author's response    Manuscript
ED: Publish as is (01 May 2020) by David Thornalley
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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.