Nonlinear Processes in Geophysics
Nonlinear Processes in Geophysics
NPG
Articles | Volume 30, issue 4
Articles | Volume 30, issue 4
https://doi.org/10.5194/npg-30-503-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/npg-30-503-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 30, issue 4
Research article
 | 
20 Nov 2023
Research article |  | 20 Nov 2023

Robust weather-adaptive post-processing using model output statistics random forests

Thomas Muschinski, Georg J. Mayr, Achim Zeileis, and Thorsten Simon

Viewed

Total article views: 1,652 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
1,297 289 66 1,652 72 76
  • HTML: 1,297
  • PDF: 289
  • XML: 66
  • Total: 1,652
  • BibTeX: 72
  • EndNote: 76
Views and downloads (calculated since 30 May 2023)
Cumulative views and downloads (calculated since 30 May 2023)

Viewed (geographical distribution)

Total article views: 1,652 (including HTML, PDF, and XML) Thereof 1,579 with geography defined and 73 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 

Cited

Latest update: 10 Jul 2025
Download
Short summary
Statistical post-processing is necessary to generate probabilistic forecasts from physical numerical weather prediction models. To allow for more flexibility, there has been a shift in post-processing away from traditional parametric regression models towards modern machine learning methods. By fusing these two approaches, we developed model output statistics random forests, a new post-processing method that is highly flexible but at the same time also very robust and easy to interpret.
Read more
Share