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

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Latest update: 13 Dec 2024
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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.