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...
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GFZ German Research Centre for Geosciences
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ftdatacite:10.57757/iugg23-3328 2023-07-23T04:21:40+02:00 Hybrid modelling with deep learning for improved sea-ice forecasting ... Finn, Tobias Durand, Charlotte Farchi, Alban Bocquet, Marc Chen, Yumeng Carassi, Alberto Dansereau, Veronique Ólason, Einar 2023 https://dx.doi.org/10.57757/iugg23-3328 https://gfzpublic.gfz-potsdam.de/pubman/item/item_5019666 en eng GFZ German Research Centre for Geosciences Creative Commons Attribution 4.0 International https://creativecommons.org/licenses/by/4.0/legalcode cc-by-4.0 Article ConferencePaper Oral 2023 ftdatacite https://doi.org/10.57757/iugg23-3328 2023-07-03T21:27:30Z <!--!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) ... Conference Object Sea ice DataCite Metadata Store (German National Library of Science and Technology) |
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DataCite Metadata Store (German National Library of Science and Technology) |
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English |
description |
<!--!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) ... |
format |
Conference Object |
author |
Finn, Tobias Durand, Charlotte Farchi, Alban Bocquet, Marc Chen, Yumeng Carassi, Alberto Dansereau, Veronique Ólason, Einar |
spellingShingle |
Finn, Tobias Durand, Charlotte Farchi, Alban Bocquet, Marc Chen, Yumeng Carassi, Alberto Dansereau, Veronique Ólason, Einar Hybrid modelling with deep learning for improved sea-ice forecasting ... |
author_facet |
Finn, Tobias Durand, Charlotte Farchi, Alban Bocquet, Marc Chen, Yumeng Carassi, Alberto Dansereau, Veronique Ólason, Einar |
author_sort |
Finn, Tobias |
title |
Hybrid modelling with deep learning for improved sea-ice forecasting ... |
title_short |
Hybrid modelling with deep learning for improved sea-ice forecasting ... |
title_full |
Hybrid modelling with deep learning for improved sea-ice forecasting ... |
title_fullStr |
Hybrid modelling with deep learning for improved sea-ice forecasting ... |
title_full_unstemmed |
Hybrid modelling with deep learning for improved sea-ice forecasting ... |
title_sort |
hybrid modelling with deep learning for improved sea-ice forecasting ... |
publisher |
GFZ German Research Centre for Geosciences |
publishDate |
2023 |
url |
https://dx.doi.org/10.57757/iugg23-3328 https://gfzpublic.gfz-potsdam.de/pubman/item/item_5019666 |
genre |
Sea ice |
genre_facet |
Sea ice |
op_rights |
Creative Commons Attribution 4.0 International https://creativecommons.org/licenses/by/4.0/legalcode cc-by-4.0 |
op_doi |
https://doi.org/10.57757/iugg23-3328 |
_version_ |
1772187657415163904 |