The Cryosphere
The Cryosphere
TC
Articles | Volume 18, issue 4
Articles | Volume 18, issue 4
https://doi.org/10.5194/tc-18-2161-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/tc-18-2161-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 18, issue 4
Research article
 | 
30 Apr 2024
Research article |  | 30 Apr 2024

Improving short-term sea ice concentration forecasts using deep learning

Cyril Palerme, Thomas Lavergne, Jozef Rusin, Arne Melsom, Julien Brajard, Are Frode Kvanum, Atle Macdonald Sørensen, Laurent Bertino, and Malte Müller

Viewed

Total article views: 1,436 (including HTML, PDF, and XML)
HTML PDF XML Total Supplement BibTeX EndNote
1,133 254 49 1,436 68 44 33
  • HTML: 1,133
  • PDF: 254
  • XML: 49
  • Total: 1,436
  • Supplement: 68
  • BibTeX: 44
  • EndNote: 33
Views and downloads (calculated since 13 Nov 2023)
Cumulative views and downloads (calculated since 13 Nov 2023)

Viewed (geographical distribution)

Total article views: 1,436 (including HTML, PDF, and XML) Thereof 1,376 with geography defined and 60 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 

Cited

Latest update: 13 Dec 2024
Download
Short summary

Sea ice forecasts are operationally produced using physically based models, but these forecasts are often not accurate enough for maritime operations. In this study, we developed a statistical correction technique using machine learning in order to improve the skill of short-term (up to 10 d) sea ice concentration forecasts produced by the TOPAZ4 model. This technique allows for the reduction of errors from the TOPAZ4 sea ice concentration forecasts by 41 % on average.       

Read more