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Hybrid modelling with deep learning for improved sea-ice forecasting

Authors

Finn,  Tobias
IUGG 2023, General Assemblies, 1 General, International Union of Geodesy and Geophysics (IUGG), External Organizations;

Durand,  Charlotte
IUGG 2023, General Assemblies, 1 General, International Union of Geodesy and Geophysics (IUGG), External Organizations;

Farchi,  Alban
IUGG 2023, General Assemblies, 1 General, International Union of Geodesy and Geophysics (IUGG), External Organizations;

Bocquet,  Marc
IUGG 2023, General Assemblies, 1 General, International Union of Geodesy and Geophysics (IUGG), External Organizations;

Chen,  Yumeng
IUGG 2023, General Assemblies, 1 General, International Union of Geodesy and Geophysics (IUGG), External Organizations;

Carassi,  Alberto
IUGG 2023, General Assemblies, 1 General, International Union of Geodesy and Geophysics (IUGG), External Organizations;

Dansereau,  Veronique
IUGG 2023, General Assemblies, 1 General, International Union of Geodesy and Geophysics (IUGG), External Organizations;

Ólason,  Einar
IUGG 2023, General Assemblies, 1 General, International Union of Geodesy and Geophysics (IUGG), External Organizations;

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Citation

Finn, T., Durand, C., Farchi, A., Bocquet, M., Chen, Y., Carassi, A., Dansereau, V., Ólason, E. (2023): Hybrid modelling with deep learning for improved sea-ice forecasting, XXVIII General Assembly of the International Union of Geodesy and Geophysics (IUGG) (Berlin 2023).
https://doi.org/10.57757/IUGG23-3328


Cite as: https://gfzpublic.gfz-potsdam.de/pubman/item/item_5019666
Abstract
We present our vision on how to advance short-term sea-ice forecasting with deep learning, based on two specific examples. To incorporate multifractal, anisotropic, and stochastic-like processes in sea ice, we envision the combination of geophysical sea-ice models together with neural networks in a hybrid modelling setup. On the one hand, deep learning can surrogate computationally expensive sea-ice models, like neXtSIM. This not only allows us to speed-up simulations by orders of magnitude, but also to improve forecasts of sea-ice thickness by up to 35 % compared to persistence on a daily timescale. On the other hand, deep learning can parametrize subgrid-scale processes in sea-ice models and correct persisting model errors, improving the forecasts by up to 70 % across all model variables on an hourly timescale. Based on these results, we conclude that hybrid modelling with deep learning can lead to major advancements in sea-ice forecasting.