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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Bibliographic Details
Main Authors: Finn, Tobias, Durand, Charlotte, Farchi, Alban, Bocquet, Marc, Chen, Yumeng, Carrassi, Alberto, Dansereau, Véronique, Òlason, Einar
Format: Conference Object
Language:English
Published: 2023
Subjects:
machine learning
sea-ice modelling
Sea ice
Online Access:https://zenodo.org/record/8154679
https://doi.org/10.5281/zenodo.8154679
Description
Summary: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.

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