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: 5,106 (including HTML, PDF, and XML)
HTML PDF XML Total Supplement BibTeX EndNote
4,054 922 130 5,106 242 164 226
  • HTML: 4,054
  • PDF: 922
  • XML: 130
  • Total: 5,106
  • Supplement: 242
  • BibTeX: 164
  • EndNote: 226
Views and downloads (calculated since 21 Nov 2019)
Cumulative views and downloads (calculated since 21 Nov 2019)

Viewed (geographical distribution)

Total article views: 5,106 (including HTML, PDF, and XML) Thereof 4,605 with geography defined and 501 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 

Cited

Saved (final revised paper)

Latest update: 26 Jul 2026
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.
Share