Process‐oriented stochastic perturbations applied to the parametrization of turbulence and shallow convection for ensemble prediction

Abstract

AbstractWeather forecasting nowadays often requires some estimation of uncertainties associated with the output of meteorological models, in order to better inform decision making, especially in the context of intense weather events. Ensemble prediction systems provide such information through sets of possible scenarios which are designed to represent various uncertainty sources, including model uncertainties. A wide variety of methods have been proposed to estimate model uncertainties, among which perturbation methods targeting uncertain processes are a promising research field. In this study, we focus on the representation of small‐scale variability by process‐oriented perturbation schemes applied to two key physical processes, namely turbulence and shallow convection. The perturbations are applied to a single‐column version of the convection‐permitting AROME model, in three idealized boundary‐layer cases. Large‐eddy simulations (LESs) of the same cases serve as a reference for the subgrid variability that has to be represented, and the results are also compared to those given by the Stochastically Perturbed Parametrization Tendencies (SPPT) method, which is a method commonly used by weather forecast centres to represent model uncertainty. The spread produced by our process‐oriented perturbations of turbulence and shallow convection does not represent all the small‐scale variability implied by the LESs for temperature and humidity. However, it is of a similar order of magnitude for the wind, thanks to perturbations generated by the stochastic turbulence scheme. The dispersion is structurally different from what is obtained with SPPT. It is non‐negligible in the lower levels, where SPPT perturbations are usually suppressed because of numerical instabilities, indicating a possible complementarity between the schemes.