Deep learning can correct model errors from the subgrid-scale for sea-ice dynamics

Poster contribution tothe "11th Sea Ice data assimilation workshop in Oslo". This poster summarizes the work currently under discussion for" The Cryosphere ". This work is a contribution to the project SASIP (grant no. 353), funded by Schmidt Futures – a philanthropic initiative...

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Main Authors: Finn, Tobias, Durand, Charlotte, Farchi, Alban, Bocquet, Marc, Chen, Yumeng, Carrassi, Alberto, Dansereau, Véronique
Format: Conference Object
Language:English
Published: Zenodo 2023
Subjects:
machine learning
sea-ice modelling
Sea ice
Online Access:https://doi.org/10.5281/zenodo.7788085
id ftzenodo:oai:zenodo.org:7788085
record_format openpolar
spelling ftzenodo:oai:zenodo.org:7788085 2024-09-15T18:34:12+00:00 Deep learning can correct model errors from the subgrid-scale for sea-ice dynamics Finn, Tobias Durand, Charlotte Farchi, Alban Bocquet, Marc Chen, Yumeng Carrassi, Alberto Dansereau, Véronique 2023-03-31 https://doi.org/10.5281/zenodo.7788085 eng eng Zenodo https://doi.org/10.5281/zenodo.7788084 https://doi.org/10.5281/zenodo.7788085 oai:zenodo.org:7788085 info:eu-repo/semantics/openAccess Creative Commons Attribution 4.0 International https://creativecommons.org/licenses/by/4.0/legalcode machine learning sea-ice modelling info:eu-repo/semantics/conferencePoster 2023 ftzenodo https://doi.org/10.5281/zenodo.778808510.5281/zenodo.7788084 2024-07-25T11:21:07Z Poster contribution tothe "11th Sea Ice data assimilation workshop in Oslo". This poster summarizes the work currently under discussion for" The Cryosphere ". This work is a contribution to the project SASIP (grant no. 353), funded by Schmidt Futures – a philanthropic initiative that seeks to improve societal outcomes through the development of emerging science and technologies. Conference Object Sea ice Zenodo
institution Open Polar
collection Zenodo
op_collection_id ftzenodo
language English
topic machine learning
sea-ice modelling
spellingShingle machine learning
sea-ice modelling
Finn, Tobias
Durand, Charlotte
Farchi, Alban
Bocquet, Marc
Chen, Yumeng
Carrassi, Alberto
Dansereau, Véronique
Deep learning can correct model errors from the subgrid-scale for sea-ice dynamics
topic_facet machine learning
sea-ice modelling
description Poster contribution tothe "11th Sea Ice data assimilation workshop in Oslo". This poster summarizes the work currently under discussion for" The Cryosphere ". This work is a contribution to the project SASIP (grant no. 353), funded by Schmidt Futures – a philanthropic initiative that seeks to improve societal outcomes through the development of emerging science and technologies.
format Conference Object
author Finn, Tobias
Durand, Charlotte
Farchi, Alban
Bocquet, Marc
Chen, Yumeng
Carrassi, Alberto
Dansereau, Véronique
author_facet Finn, Tobias
Durand, Charlotte
Farchi, Alban
Bocquet, Marc
Chen, Yumeng
Carrassi, Alberto
Dansereau, Véronique
author_sort Finn, Tobias
title Deep learning can correct model errors from the subgrid-scale for sea-ice dynamics
title_short Deep learning can correct model errors from the subgrid-scale for sea-ice dynamics
title_full Deep learning can correct model errors from the subgrid-scale for sea-ice dynamics
title_fullStr Deep learning can correct model errors from the subgrid-scale for sea-ice dynamics
title_full_unstemmed Deep learning can correct model errors from the subgrid-scale for sea-ice dynamics
title_sort deep learning can correct model errors from the subgrid-scale for sea-ice dynamics
publisher Zenodo
publishDate 2023
url https://doi.org/10.5281/zenodo.7788085
genre Sea ice
genre_facet Sea ice
op_relation https://doi.org/10.5281/zenodo.7788084
https://doi.org/10.5281/zenodo.7788085
oai:zenodo.org:7788085
op_rights info:eu-repo/semantics/openAccess
Creative Commons Attribution 4.0 International
https://creativecommons.org/licenses/by/4.0/legalcode
op_doi https://doi.org/10.5281/zenodo.778808510.5281/zenodo.7788084
_version_ 1810475996085747712

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