Nonlinear Processes in Geophysics
Nonlinear Processes in Geophysics
NPG
Articles | Volume 31, issue 1
Articles | Volume 31, issue 1
https://doi.org/10.5194/npg-31-75-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/npg-31-75-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 31, issue 1
Research article
 | 
13 Feb 2024
Research article |  | 13 Feb 2024

A two-fold deep-learning strategy to correct and downscale winds over mountains

Louis Le Toumelin, Isabelle Gouttevin, Clovis Galiez, and Nora Helbig

Viewed

Total article views: 1,798 (including HTML, PDF, and XML)
HTML PDF XML Total Supplement BibTeX EndNote
1,437 312 49 1,798 84 39 24
  • HTML: 1,437
  • PDF: 312
  • XML: 49
  • Total: 1,798
  • Supplement: 84
  • BibTeX: 39
  • EndNote: 24
Views and downloads (calculated since 10 May 2023)
Cumulative views and downloads (calculated since 10 May 2023)

Viewed (geographical distribution)

Total article views: 1,798 (including HTML, PDF, and XML) Thereof 1,747 with geography defined and 51 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 

Cited

Latest update: 26 Jul 2024
Download
Short summary
Forecasting wind fields over mountains is of high importance for several applications and particularly for understanding how wind erodes and disperses snow. Forecasters rely on operational wind forecasts over mountains, which are currently only available on kilometric scales. These forecasts can also be affected by errors of diverse origins. Here we introduce a new strategy based on artificial intelligence to correct large-scale wind forecasts in mountains and increase their spatial resolution.
Read more