Articles | Volume 19, issue 10
https://doi.org/10.5194/tc-19-4149-2025
https://doi.org/10.5194/tc-19-4149-2025
Research article
 | 
02 Oct 2025
Research article |  | 02 Oct 2025

Developing a deep learning forecasting system for short-term and high-resolution prediction of sea ice concentration

Are Frode Kvanum, Cyril Palerme, Malte Müller, Jean Rabault, and Nick Hughes

Download

Interactive discussion

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on egusphere-2023-3107', Anonymous Referee #1, 03 Apr 2024
    • AC1: 'Reply on RC1', Are Frode Kvanum, 03 Jun 2024
  • 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 
Download
Short summary
Recent studies have shown that machine learning models are effective at predicting sea ice concentration, yet few have explored the development of such models in an operational context. In this study, we present the development of a machine learning forecasting system which can predict sea ice concentration at 1 km resolution up to 3 d ahead using real-time operational data. The developed forecasts predict the sea ice edge position with better accuracy than physical and baseline forecasts.
Share