English
 
Privacy Policy Disclaimer
  Advanced SearchBrowse

Item

ITEM ACTIONSEXPORT
Add to Basket
 Local TagsRelease HistoryDetailsSummary
  Hybrid modelling with deep learning for improved sea-ice forecasting

Finn, T., Durand, C., Farchi, A., Bocquet, M., Chen, Y., Carassi, A., Dansereau, V., Ólason, E. (2023): Hybrid modelling with deep learning for improved sea-ice forecasting, XXVIII General Assembly of the International Union of Geodesy and Geophysics (IUGG) (Berlin 2023).
https://doi.org/10.57757/IUGG23-3328

Item is

 

show all hide all

Basic

show hide
Item Permalink: https://gfzpublic.gfz-potsdam.de/pubman/item/item_5019666 Version Permalink: https://gfzpublic.gfz-potsdam.de/pubman/item/item_5019666_1
Genre: Conference Paper

Files

show Files

Locators

show

Creators

show
hide
 Creators:
Finn, Tobias1, Author
Durand, Charlotte1, Author
Farchi, Alban1, Author
Bocquet, Marc1, Author
Chen, Yumeng1, Author
Carassi, Alberto1, Author
Dansereau, Veronique1, Author
Ólason, Einar1, Author
Affiliations:
1IUGG 2023, General Assemblies, 1 General, International Union of Geodesy and Geophysics (IUGG), External Organizations, ou_5011304              

Content

show
hide
Free keywords: -
 Abstract: 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.

Details

show
hide
Language(s): eng - English
 Dates: 20232023
 Publication Status: Finally published
 Pages: -
 Publishing info: -
 Table of Contents: -
 Rev. Type: -
 Identifiers: DOI: 10.57757/IUGG23-3328
 Degree: -

Event

show
hide
Title: XXVIII General Assembly of the International Union of Geodesy and Geophysics (IUGG)
Place of Event: Berlin
Start-/End Date: 2023-07-11 - 2023-07-20

Legal Case

show

Project information

show

Source 1

show
hide
Title: XXVIII General Assembly of the International Union of Geodesy and Geophysics (IUGG)
Source Genre: Proceedings
 Creator(s):
Affiliations:
Publ. Info: Potsdam : GFZ German Research Centre for Geosciences
Pages: - Volume / Issue: - Sequence Number: - Start / End Page: - Identifier: -