Geoscientific Model Development
Geoscientific Model Development
GMD
Articles | Volume 18, issue 3
Articles | Volume 18, issue 3
https://doi.org/10.5194/gmd-18-885-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-18-885-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 18, issue 3
Methods for assessment of models
 | 
14 Feb 2025
Methods for assessment of models |  | 14 Feb 2025

Tuning parameters of a sea ice model using machine learning

Anton Korosov, Yue Ying, and Einar Ólason

Viewed

Total article views: 379 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
295 68 16 379 9 11
  • HTML: 295
  • PDF: 68
  • XML: 16
  • Total: 379
  • BibTeX: 9
  • EndNote: 11
Views and downloads (calculated since 27 Aug 2024)
Cumulative views and downloads (calculated since 27 Aug 2024)

Viewed (geographical distribution)

Total article views: 379 (including HTML, PDF, and XML) Thereof 375 with geography defined and 4 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 
Latest update: 21 Feb 2025
Download
Short summary
We have developed a new method to improve the accuracy of sea ice models, which predict how ice moves and deforms due to wind and ocean currents. Traditional models use parameters that are often poorly defined. The new approach uses machine learning to fine-tune these parameters by comparing simulated ice drift with satellite data. The method identifies optimal settings for the model by analysing patterns in ice deformation. This results in more accurate simulations of sea ice drift forecasting.
Read more
Share