Hybrid modelling with deep learning for improved sea-ice forecasting

Presentation for the "Data Driven Cryospheric Sciences: Machine Learning, Data Assimilation and Inverse Methods for the Cryosphere" session at the IUGG 2023 in Berlin. This research has received financial support from the project SASIP (grant no. 353) funded by Schmidt Futures – a philanth...

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Main Authors: Finn, Tobias, Durand, Charlotte, Farchi, Alban, Bocquet, Marc, Chen, Yumeng, Carrassi, Alberto, Dansereau, Véronique, Òlason, Einar
Format: Lecture
Language:English
Published: Zenodo 2023
Subjects:
machine learning
sea-ice modelling
Sea ice
Online Access:https://doi.org/10.5281/zenodo.8154679
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record_format openpolar
spelling ftzenodo:oai:zenodo.org:8154679 2024-09-15T18:34:35+00:00 Hybrid modelling with deep learning for improved sea-ice forecasting Finn, Tobias Durand, Charlotte Farchi, Alban Bocquet, Marc Chen, Yumeng Carrassi, Alberto Dansereau, Véronique Òlason, Einar 2023-07-17 https://doi.org/10.5281/zenodo.8154679 eng eng Zenodo https://doi.org/10.5281/zenodo.8154678 https://doi.org/10.5281/zenodo.8154679 oai:zenodo.org:8154679 info:eu-repo/semantics/openAccess Creative Commons Attribution 4.0 International https://creativecommons.org/licenses/by/4.0/legalcode IUGG 2023, IUGG General Assembly 2023, Berlin, 11-20 July 2023 machine learning sea-ice modelling info:eu-repo/semantics/lecture 2023 ftzenodo https://doi.org/10.5281/zenodo.815467910.5281/zenodo.8154678 2024-07-26T15:08:04Z Presentation for the "Data Driven Cryospheric Sciences: Machine Learning, Data Assimilation and Inverse Methods for the Cryosphere" session at the IUGG 2023 in Berlin. This research has received financial support from 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. Lecture 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
Òlason, Einar
Hybrid modelling with deep learning for improved sea-ice forecasting
topic_facet machine learning
sea-ice modelling
description Presentation for the "Data Driven Cryospheric Sciences: Machine Learning, Data Assimilation and Inverse Methods for the Cryosphere" session at the IUGG 2023 in Berlin. This research has received financial support from 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 Lecture
author Finn, Tobias
Durand, Charlotte
Farchi, Alban
Bocquet, Marc
Chen, Yumeng
Carrassi, Alberto
Dansereau, Véronique
Òlason, Einar
author_facet Finn, Tobias
Durand, Charlotte
Farchi, Alban
Bocquet, Marc
Chen, Yumeng
Carrassi, Alberto
Dansereau, Véronique
Òlason, 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 Zenodo
publishDate 2023
url https://doi.org/10.5281/zenodo.8154679
genre Sea ice
genre_facet Sea ice
op_source IUGG 2023, IUGG General Assembly 2023, Berlin, 11-20 July 2023
op_relation https://doi.org/10.5281/zenodo.8154678
https://doi.org/10.5281/zenodo.8154679
oai:zenodo.org:8154679
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.815467910.5281/zenodo.8154678
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