Nonlinear Processes in Geophysics
Nonlinear Processes in Geophysics
NPG
Articles | Volume 29, issue 2
Articles | Volume 29, issue 2
https://doi.org/10.5194/npg-29-171-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/npg-29-171-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 29, issue 2
Research article
 | Highlight paper
 | 
02 May 2022
Research article | Highlight paper |  | 02 May 2022

Using neural networks to improve simulations in the gray zone

Raphael Kriegmair, Yvonne Ruckstuhl, Stephan Rasp, and George Craig

Viewed

Total article views: 3,617 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
2,918 613 86 3,617 66 56
  • HTML: 2,918
  • PDF: 613
  • XML: 86
  • Total: 3,617
  • BibTeX: 66
  • EndNote: 56
Views and downloads (calculated since 17 May 2021)
Cumulative views and downloads (calculated since 17 May 2021)

Viewed (geographical distribution)

Total article views: 3,617 (including HTML, PDF, and XML) Thereof 3,360 with geography defined and 257 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 

Cited

Latest update: 20 Nov 2024
Download
Short summary
Our regional numerical weather prediction models run at kilometer-scale resolutions. Processes that occur at smaller scales not yet resolved contribute significantly to the atmospheric flow. We use a neural network (NN) to represent the unresolved part of physical process such as cumulus clouds. We test this approach on a simplified, yet representative, 1D model and find that the NN corrections vastly improve the model forecast up to a couple of days.
Read more