Process-Based Evaluation of Stochastic Perturbed Microphysics Parameterization Tendencies on Ensemble Forecasts of Heavy Rainfall Events

Abstract

Stochastic model error schemes, such as the stochastic perturbed parameterization tendencies (SPPT) and independent SPPT (iSPPT) schemes, have become an increasingly accepted method to represent model error associated with uncertain subgrid-scale processes in ensemble prediction systems (EPSs). While much of the current literature focuses on the effects of these schemes on forecast skill, this research examines the physical processes by which iSPPT perturbations to the microphysics parameterization scheme yield variability in ensemble rainfall forecasts. Members of three 120-member Weather Research and Forecasting (WRF) Model ensemble case studies, including two distinct heavy rain events over Taiwan and one over the northeastern United States, are ranked according to an area-averaged accumulated rainfall metric in order to highlight differences between high- and low-precipitation forecasts. In each case, high-precipitation members are characterized by a damping of the microphysics water vapor and temperature tendencies over the region of heaviest rainfall, while the opposite is true for low-precipitation members. Physically, the perturbations to microphysics tendencies have the greatest impact at the cloud level and act to modify precipitation efficiency. To this end, the damping of tendencies in high-precipitation forecasts suppresses both the loss of water vapor due to condensation and the corresponding latent heat release, leading to grid-scale supersaturation. Conversely, amplified tendencies in low-precipitation forecasts yield both drying and increased positive buoyancy within clouds.