Characterization of Ensemble Generation Strategies: Application to Three Illustrative Examples of Mediterranean High-Impact Weather
Predictability of high-impact weather constitutes a major challenge. In this study, we contribute to this relevant topic by analyzing the characteristics of multiple ensemble prediction systems aimed at sampling initial and/or model uncertainties for three exemplary heavy precipitation systems occurred over the western Mediterranean. In particular, initial condition perturbations are gener- ated by (i) directly downscaling the ensemble members from a global ensemble and (ii) creating perturbations by sampling a wide range of scales using an adaptation of the breeding methodology. Model error is sampled by applying stochastic perturbations to physical parameterizations by combining stochasti- cally perturbed physics tendencies with stochastic perturbations to microphysics parameters. A positive impact in terms of ensemble diversity is obtained when initial condition perturbations across a broad range of scales are applied, es- pecially at low levels and for cases in which local factors are more relevant. Regarding stochastic model perturbations, they display very localized pertur- bations with low amplitude, which are insufficient to capture extreme scenarios, except when local factors play a dominant role. However, when combined with initial condition perturbations, they generally increase diversity and perturba- tion amplitude over areas characterized by deep moist convection. This outcome exhibits the positive impact of sampling both uncertainty sources, even in the extremely nonlinear regime that defines convective-scale phenomena.