February 16, 2024Open 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

Machine learning study demonstrates accurate high-resolution sea ice concentration predictions in the Arctic Ocean, highlighting improved operational safety for maritime navigation.

Key Points

A deep learning system accurately predicts Arctic sea ice concentration at one kilometer resolution across lead times of one to three days.
Model validation shows lower contour error across multiple thresholds compared to dynamical forecasting systems including neXtSIM and Barents-2.5.
Predictive framework using atmospheric forecasts and passive microwave observations supports navigational safety amid expanding Arctic operations.

Cite This Study

Kvanum et al. (2024) studied this question.

synapsesocial.com/papers/68e78d00b6db6435876ff58d https://doi.org/10.5194/egusphere-2023-3107

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Implication

Machine learning study demonstrates accurate high-resolution sea ice concentration predictions in the Arctic Ocean, highlighting improved operational safety for maritime navigation.

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

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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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