资源环境科技发展态势分析平台

The design of convection-permitting ensemble prediction systems capable of producing accurate forecasts of disruptive events is an extraordinarily challenging effort. The difficulties associated with the detection of extreme events found at these scales motivates the research of methodologies that efficiently sample relevant uncertainties. This study investigates the potential of multiple techniques to account for model uncertainty. The performance of various stochastic schemes is assessed for an exceptional heavy precipitation episode which occurred in eastern Spain. In particular, the stochastic strategies are compared to a multiphysics approach in terms of both spread and skill. The analyzed techniques include stochastic parameterization perturbation tendency and perturbations to influential parameters within the microphysics scheme. The introduction of stochastic perturbations to the microphysics processes results in a larger ensemble spread throughout the entire simulation. Conversely, modifications to microphysics parameters generate small-scale perturbations that rapidly grow over areas with high convective instability, in contrast to the other methods, which produce more widespread perturbations. A conclusion of specific interest for the western Mediterranean, where deep moist convection and local orography play an important role, is that stochastic methods are shown to outperform a multiphysics-based ensemble for this case, indicating the potential positive impact of stochastic parameterizations for the forecast of extreme events in the region.