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

TL;DR

This paper introduces a response theory-based method to adjust statistical postprocessing parameters in weather forecasting models after slight model changes, reducing reforecasting efforts and maintaining accuracy.

Contribution

It presents a novel application of response theory to correct postprocessing parameters following small model perturbations in weather forecasts.

Findings

Response theory effectively predicts parameter changes due to model perturbations.

The method works on simple Ornstein-Uhlenbeck and quasi-geostrophic models.

Potential for operational implementation in weather forecasting is discussed.

Abstract

For most statistical postprocessing schemes used to correct weather forecasts, changes to the forecast model induce a considerable reforecasting effort. We present a new approach based on response theory to cope with slight model changes. In this framework, the model change is seen as a perturbation of the original forecast model. The response theory allows us then to evaluate the variation induced on the parameters involved in the statistical postprocessing, 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 help to pose the problem, while the application to the latter provide a proof-of-concept and assesses the potential performances of response theory in a chaotic system. In both cases, the parameters of the statistical postprocessing used - an Error-in-Variables Model Output Statistics (EVMOS) - are appropriately corrected when facing a model change. The potential application in an operational environment is also discussed.