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Nonlinear Processes in Geophysics An interactive open-access journal of the European Geosciences Union
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Discussion papers
https://doi.org/10.5194/npg-2019-57
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/npg-2019-57
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.

Submitted as: research article 21 Nov 2019

Submitted as: research article | 21 Nov 2019

Review status
This discussion paper is a preprint. It is a manuscript under review for the journal Nonlinear Processes in Geophysics (NPG).

Correcting for Model Changes in Statistical Post-Processing – An approach based on Response Theory

Jonathan Demaeyer1,2 and Stéphane Vannitsem1,2 Jonathan Demaeyer and Stéphane Vannitsem
  • 1Institut Royal Météorologique de Belgique, Avenue Circulaire, 3, 1180 Brussels, Belgium
  • 2European Meteorological Network, Avenue Circulaire, 3, 1180 Brussels, Belgium

Abstract. For most statistical post-processing schemes used to correct weather forecasts, changes to the forecast model induce a considerable reforcasting effort. We present a new approach based on response theory to cope with slight model change. In this framework, the model change is seen as a perturbation of the original forecast model. The response theory allows then to evaluate the variation induced on the averages involved in the statistical post-processing, provided that the magnitude of this perturbation is not too large. This approach is studied in the context of simple Ornstein-Uhlenbeck models, and then on a more realistic, yet simple, quasi-geostrophic model. The analytical results for the former case allow for posing the problem, while the application to the latter provide a proof-of-concept of the potential performances of response theory in a chaotic system. In both cases, the parameters of the statistical post-processing used – an Error-in-Variables Model Output Statistics (EVMOS) – are appropriately corrected when facing a model change. The potential application in a more operational environment is also discussed.

Jonathan Demaeyer and Stéphane Vannitsem
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Jonathan Demaeyer and Stéphane Vannitsem
Jonathan Demaeyer and Stéphane Vannitsem
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Short summary
Post-processing 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.
Post-processing schemes used to correct weather forecasts are no longer efficient when the model...
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