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This is a Quantitative Study study.

October 3, 2025The cryosphereOpen Access

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

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Authors

AKAre Frode KvanumNorwegian Meteorological Institute CPCyril PalermeNorwegian Meteorological Institute MMMalte MüllerNorwegian Water Resources and Energy Directorate

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Implication

This analysis demonstrates improved accuracy of a deep learning forecasting system for sea ice concentration, suggesting it outperforms traditional models.

Key Points

The deep learning forecasting system reduces error in predicting sea ice concentration contours compared to baseline forecasts and dynamical systems.
High-resolution predictions are generated for regional ice charts at 1 km resolution, focusing on the East Greenland and Barents seas.
Utilizing operational atmospheric forecasts and microwave observations as inputs enhances the model’s predictive capability.
Validation of the system shows it performs better across various lead times and seasonal conditions.

Cite This Study

Kvanum et al. (2025) studied this question.

synapsesocial.com/papers/68e0450fa99c246f578b406f https://doi.org/10.5194/tc-19-4149-2025

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Implication

This analysis demonstrates improved accuracy of a deep learning forecasting system for sea ice concentration, suggesting it outperforms traditional models.

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

Discussion

Member takes

Implication

Key Points

Cite This Study

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

Discussion

Member takes

Implication

Key Points

Cite This Study

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