Articles | Volume 19, issue 10
https://doi.org/10.5194/tc-19-4149-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.Developing a deep learning forecasting system for short-term and high-resolution prediction of sea ice concentration
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- Final revised paper (published on 02 Oct 2025)
- Supplement to the final revised paper
- Preprint (discussion started on 16 Feb 2024)
Interactive discussion
Status: closed
Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor
| : Report abuse
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RC1: 'Comment on egusphere-2023-3107', Anonymous Referee #1, 03 Apr 2024
- AC1: 'Reply on RC1', Are Frode Kvanum, 03 Jun 2024
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RC2: 'Comment on egusphere-2023-3107', Anonymous Referee #2, 25 Apr 2024
- AC2: 'Reply on RC2', Are Frode Kvanum, 03 Jun 2024
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) (22 Jul 2024) by Xichen Li

AR by Are Frode Kvanum on behalf of the Authors (24 Jul 2024)
Author's response
Author's tracked changes
Manuscript
ED: Publish subject to revisions (further review by editor and referees) (07 Aug 2024) by Xichen Li

ED: Publish subject to revisions (further review by editor and referees) (10 Oct 2024) by Xichen Li

ED: Publish subject to revisions (further review by editor and referees) (01 Dec 2024) by Xichen Li

ED: Referee Nomination & Report Request started (04 Dec 2024) by Xichen Li
RR by Anonymous Referee #1 (14 Dec 2024)

RR by Anonymous Referee #2 (25 Jun 2025)

ED: Publish subject to technical corrections (02 Jul 2025) by Christian Haas

AR by Are Frode Kvanum on behalf of the Authors (15 Jul 2025)
Author's response
Manuscript
The manuscript addresses the critical need for accurate sea ice forecasting in the Arctic, driven by the increasing maritime activity due to sea ice retreat. A deep learning approach is developed that leverages operational atmospheric forecasts, ice charts, and satellite data to enhance short-term sea ice concentration forecasts within a 1 to 3 days timeframe, aiming for a detailed 1km resolution. The model's performance, validated against various thresholds of sea ice concentration contours, outperforms both baseline forecasts and two state-of-the-art dynamical sea ice forecasting systems across all considered lead times and seasons.
Nonetheless, the paper could stand to delve deeper into the model's limitations. Addressing potential biases from the training data and the effects of missing or inaccurate data could enrich the study. Suggestions for improvement are listed as below.