Reconstruction of Holocene oceanographic conditions in eastern Baffin Bay

Hansen, Katrine Elnegaard; Giraudeau, Jacques; Wacker, Lukas; Pearce, Christof; Seidenkrantz, Marit-Solveig

doi:https://doi.org/10.5194/cp-16-1075-2020

Articles | Volume 16, issue 3

https://doi.org/10.5194/cp-16-1075-2020

© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

https://doi.org/10.5194/cp-16-1075-2020

© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

Articles | Volume 16, issue 3

Research article

|

22 Jun 2020

Research article |

| 22 Jun 2020

Reconstruction of Holocene oceanographic conditions in eastern Baffin Bay

Katrine Elnegaard Hansen, Jacques Giraudeau, Lukas Wacker, Christof Pearce, and Marit-Solveig Seidenkrantz

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Final revised paper (published on 22 Jun 2020)
Supplement to the final revised paper
Preprint (discussion started on 10 Jan 2020)
Supplement to the preprint

Interactive discussion

Status: closed

AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment

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- Supplement

RC1: 'Review', John Andrews, 06 Feb 2020
- AC1: 'A detailed reply to the reviewer's comments is given in the supplementary file', Katrine Elnegaard Hansen, 31 Mar 2020
RC2: 'Baffin Bay Holocene palaeoceanography', Anonymous Referee #2, 10 Mar 2020
- AC2: 'A detailed reply to the reviewer's comments is given in the supplementary file', Katrine Elnegaard Hansen, 31 Mar 2020

Peer-review completion

AR: Author's response | RR: Referee report | ED: Editor decision

ED: Publish subject to minor revisions (review by editor) (16 Apr 2020) by David Thornalley

AR by Katrine Elnegaard Hansen on behalf of the Authors (21 Apr 2020)

ED: Publish as is (01 May 2020) by David Thornalley

AR by Katrine Elnegaard Hansen on behalf of the Authors (07 May 2020)

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.