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.
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.
the Creative Commons Attribution 4.0 License.
Research article
| 
30 Apr 2024
Research article |
| 30 Apr 2024
| 30 Apr 2024
Improving short-term sea ice concentration forecasts using deep learning
Development Centre for Weather Forecasting, Norwegian Meteorological Institute, Oslo, Norway
Thomas Lavergne
Research and Development Department, Norwegian Meteorological Institute, Oslo, Norway
Jozef Rusin
Research and Development Department, Norwegian Meteorological Institute, Oslo, Norway
Arne Melsom
Research and Development Department, Norwegian Meteorological Institute, Oslo, Norway
Julien Brajard
Nansen Environmental and Remote Sensing Center, Bergen, Norway
Are Frode Kvanum
Development Centre for Weather Forecasting, Norwegian Meteorological Institute, Oslo, Norway
Section for Meteorology and Oceanography, Department of Geosciences, University of Oslo, Oslo, Norway
Atle Macdonald Sørensen
Research and Development Department, Norwegian Meteorological Institute, Oslo, Norway
Laurent Bertino
Nansen Environmental and Remote Sensing Center, Bergen, Norway
Malte Müller
Development Centre for Weather Forecasting, Norwegian Meteorological Institute, Oslo, Norway
Section for Meteorology and Oceanography, Department of Geosciences, University of Oslo, Oslo, Norway
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Cited
12 citations as recorded by crossref.
- SICNetseason V1.0: a transformer-based deep learning model for seasonal Arctic sea ice prediction by incorporating sea ice thickness data Y. Ren et al. https://doi.org/10.5194/gmd-18-2665-2025
- Research Progress of Deep Learning in Sea Ice Prediction J. Ran et al. https://doi.org/10.3390/rs18030419
- A time series-based hybrid model for daily Arctic sea ice forecasting years in advance C. Hu et al. https://doi.org/10.1016/j.envsoft.2025.106768
- Deep Learning for Seasonal Navigability Prediction Along the Northern Sea Route: When Does It Add Value? S. Lee et al. https://doi.org/10.3390/su18104873
- Inversion of Shear and Longitudinal Acoustic Wave Propagation Parameters in Sea Ice Using SE-ResNet J. Bai et al. https://doi.org/10.3390/s25185663
- DB-SICNet: A dual-branch model for predicting Arctic sea ice concentration L. Tan et al. https://doi.org/10.1016/j.ocemod.2025.102658
- Arctic Sea Ice Concentration Prediction Using Spatial Attention Deep Learning H. Gu et al. https://doi.org/10.1109/JSTARS.2024.3486187
- Correcting errors in seasonal Arctic sea ice prediction of Earth system models with machine learning Z. He et al. https://doi.org/10.5194/tc-19-3279-2025
- Four-dimensional variational data assimilation with a sea-ice thickness emulator C. Durand et al. https://doi.org/10.5194/tc-19-5613-2025
- Developing a deep learning forecasting system for short-term and high-resolution prediction of sea ice concentration A. Kvanum et al. https://doi.org/10.5194/tc-19-4149-2025
- MET-AICE v1.0: an operational data-driven sea ice prediction system for the European Arctic C. Palerme et al. https://doi.org/10.5194/gmd-18-9751-2025
- Prediction of monthly Arctic sea ice concentration using physics-constrained U2-Net M. Liu et al. https://doi.org/10.1016/j.engappai.2026.115334
12 citations as recorded by crossref.
- SICNetseason V1.0: a transformer-based deep learning model for seasonal Arctic sea ice prediction by incorporating sea ice thickness data Y. Ren et al. https://doi.org/10.5194/gmd-18-2665-2025
- Research Progress of Deep Learning in Sea Ice Prediction J. Ran et al. https://doi.org/10.3390/rs18030419
- A time series-based hybrid model for daily Arctic sea ice forecasting years in advance C. Hu et al. https://doi.org/10.1016/j.envsoft.2025.106768
- Deep Learning for Seasonal Navigability Prediction Along the Northern Sea Route: When Does It Add Value? S. Lee et al. https://doi.org/10.3390/su18104873
- Inversion of Shear and Longitudinal Acoustic Wave Propagation Parameters in Sea Ice Using SE-ResNet J. Bai et al. https://doi.org/10.3390/s25185663
- DB-SICNet: A dual-branch model for predicting Arctic sea ice concentration L. Tan et al. https://doi.org/10.1016/j.ocemod.2025.102658
- Arctic Sea Ice Concentration Prediction Using Spatial Attention Deep Learning H. Gu et al. https://doi.org/10.1109/JSTARS.2024.3486187
- Correcting errors in seasonal Arctic sea ice prediction of Earth system models with machine learning Z. He et al. https://doi.org/10.5194/tc-19-3279-2025
- Four-dimensional variational data assimilation with a sea-ice thickness emulator C. Durand et al. https://doi.org/10.5194/tc-19-5613-2025
- Developing a deep learning forecasting system for short-term and high-resolution prediction of sea ice concentration A. Kvanum et al. https://doi.org/10.5194/tc-19-4149-2025
- MET-AICE v1.0: an operational data-driven sea ice prediction system for the European Arctic C. Palerme et al. https://doi.org/10.5194/gmd-18-9751-2025
- Prediction of monthly Arctic sea ice concentration using physics-constrained U2-Net M. Liu et al. https://doi.org/10.1016/j.engappai.2026.115334
Saved (final revised paper)
Latest update: 19 Jul 2026
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.
Sea ice forecasts are operationally produced using physically based models, but these forecasts...
