The Cryosphere
The Cryosphere
TC
Articles | Volume 18, issue 4
Articles | Volume 18, issue 4
https://doi.org/10.5194/tc-18-2161-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/tc-18-2161-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 18, issue 4
Research article
 | 
30 Apr 2024
Research article |  | 30 Apr 2024

Improving short-term sea ice concentration forecasts using deep learning

Cyril Palerme, Thomas Lavergne, Jozef Rusin, Arne Melsom, Julien Brajard, Are Frode Kvanum, Atle Macdonald Sørensen, Laurent Bertino, and Malte Müller

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Peer review completion

AR: Author's response | RR: Referee report | ED: Editor decision | EF: Editorial file upload
ED: Publish subject to revisions (further review by editor and referees) (15 Feb 2024) by Yevgeny Aksenov
AR by Cyril Palerme on behalf of the Authors (16 Feb 2024)  Author's response   Author's tracked changes   Manuscript 
ED: Referee Nomination & Report Request started (20 Feb 2024) by Yevgeny Aksenov
RR by Valentin Ludwig (26 Feb 2024)
RR by Anonymous Referee #2 (05 Mar 2024)
ED: Publish as is (21 Mar 2024) by Yevgeny Aksenov
AR by Cyril Palerme on behalf of the Authors (21 Mar 2024)  Manuscript 
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Short summary

Sea ice forecasts are operationally produced using physically based models, but these forecasts are often not accurate enough for maritime operations. In this study, we developed a statistical correction technique using machine learning in order to improve the skill of short-term (up to 10 d) sea ice concentration forecasts produced by the TOPAZ4 model. This technique allows for the reduction of errors from the TOPAZ4 sea ice concentration forecasts by 41 % on average.       

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