Hybrid modelling with deep learning for improved sea-ice forecasting ...
<!--!introduction!--> 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 togethe...
| Main Authors: | , , , , , , , |
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| Format: | Conference Object |
| Language: | English |
| Published: |
GFZ German Research Centre for Geosciences
2023
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| Subjects: | |
| Online Access: | https://dx.doi.org/10.57757/iugg23-3328 https://gfzpublic.gfz-potsdam.de/pubman/item/item_5019666 |
| Summary: | <!--!introduction!--> 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. ... : The 28th IUGG General Assembly (IUGG2023) (Berlin 2023) ... |
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