EGU2020-5664
https://doi.org/10.5194/egusphere-egu2020-5664
EGU General Assembly 2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

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

Jonathan Demaeyer and Stéphane Vannitsem
Jonathan Demaeyer and Stéphane Vannitsem
  • Royal Meteorological Institute, Brussels, Belgium

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 a simple quasi-geostrophic model. It provides a proof-of-concept of the potential performances of response theory in a chaotic system. 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.

How to cite: Demaeyer, J. and Vannitsem, S.: Correcting for Model Changes in Statistical Post-Processing - An approach based on Response Theory, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-5664, https://doi.org/10.5194/egusphere-egu2020-5664, 2020

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