Stochastic representations of model uncertainties at ECMWF: state of the art and future vision

TLDR

Ensemble forecasts differ because of initial and model uncertainties, and as ensemble methods expand in forecasting and assimilation, there is growing demand for physically consistent perturbation methods. The article reviews recent progress, challenges, and future directions for stochastic representations of model uncertainties at ECMWF, including expanding uncertainty representations to the dynamical core and other Earth system components and improving computational efficiency. The authors synthesize recent developments, challenges, and future directions in stochastic model uncertainty representations at ECMWF. The inclusion of stochastic schemes to represent model uncertainties has improved the probabilistic skill of the ECMWF ensemble by increasing reliability and reducing the error of the ensemble mean.

Abstract

Members in ensemble forecasts differ due to the representations of initial uncertainties and model uncertainties. The inclusion of stochastic schemes to represent model uncertainties has improved the probabilistic skill of the ECMWF ensemble by increasing reliability and reducing the error of the ensemble mean. Recent progress, challenges and future directions regarding stochastic representations of model uncertainties at ECMWF are described in this article. The coming years are likely to see a further increase in the use of ensemble methods in forecasts and assimilation. This will put increasing demands on the methods used to perturb the forecast model. An area that is receiving greater attention than 5–10 years ago is the physical consistency of the perturbations. Other areas where future efforts will be directed are the expansion of uncertainty representations to the dynamical core and other components of the Earth system, as well as the overall computational efficiency of representing model uncertainty.