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For most statistical postprocessing schemes used to correct weatherforecasts, changes to the forecast model induce a considerable reforecastingeffort. We present a new approach based on response theory to cope with slightmodel changes. In this framework, the model change is seen as a perturbation ofthe original forecast model. The response theory allows us then to evaluate thevariation induced on the parameters involved in the statistical postprocessing,provided that the magnitude of this perturbation is not too large. Thisapproach is studied in the context of simple Ornstein-Uhlenbeck models, andthen on a more realistic, yet simple, quasi-geostrophic model. The analyticalresults for the former case help to pose the problem, while the application tothe latter provide a proof-of-concept and assesses the potential performancesof response theory in a chaotic system. In both cases, the parameters of thestatistical postprocessing used - an Error-in-Variables Model Output Statistics(EVMOS) - are appropriately corrected when facing a model change. The potentialapplication in an operational environment is also discussed.

Correcting for Model Changes in Statistical Postprocessing -- An approach based on Response Theory