Articles | Volume 27, issue 2
https://doi.org/10.5194/npg-27-307-2020
https://doi.org/10.5194/npg-27-307-2020
Research article
 | 
27 May 2020
Research article |  | 27 May 2020

Correcting for model changes in statistical postprocessing – an approach based on response theory

Jonathan Demaeyer and Stéphane Vannitsem

Viewed

Total article views: 2,920 (including HTML, PDF, and XML)
HTML PDF XML Total Supplement BibTeX EndNote
2,222 616 82 2,920 163 78 64
  • HTML: 2,222
  • PDF: 616
  • XML: 82
  • Total: 2,920
  • Supplement: 163
  • BibTeX: 78
  • EndNote: 64
Views and downloads (calculated since 21 Nov 2019)
Cumulative views and downloads (calculated since 21 Nov 2019)

Viewed (geographical distribution)

Total article views: 2,920 (including HTML, PDF, and XML) Thereof 2,484 with geography defined and 436 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 

Cited

Latest update: 30 Jan 2025
Download
Short summary
Postprocessing schemes used to correct weather forecasts are no longer efficient when the model generating the forecasts changes. An approach based on response theory to take the change into account without having to recompute the parameters based on past forecasts is presented. It is tested on an analytical model and a simple model of atmospheric variability. We show that this approach is effective and discuss its potential application for an operational environment.