Evaluation of stochastic perturbed parameterization tendencies on convective-permitting ensemble forecasts of heavy rainfall events in New York and Taiwan

The representation of model error in ensemble prediction systems (EPSs) can be limited by the assumptions within parameterization schemes. Stochastic perturbed parameterization tendencies (SPPT) is one representation of model error that randomly perturbs parameterized physical tendencies using a spatially and temporally correlated red-noise field. This research investigates the sensitivity of ensemble rainfall forecasts produced by the Weather Research and Forecasting (WRF) Model to the configuration of SPPT and independent SPPT (iSPPT) for three meso–synoptic-scale heavy rainfall events over the United States and Taiwan, primarily focusing on the ensemble mean and standard deviation as well as forecast skill. Thirty-two 20-member ensembles, which represent a combination of eight configurations of the stochastic perturbation time scale, length scale, and amplitude scale, and four perturbed parameterization schemes, as well as an unperturbed control simulation, are examined for each event. In each case, rainfall standard deviation is most sensitive to the perturbation time scale and amplitude scale. Moreover, microphysics tendency perturbations are associated with the largest standard deviation in two of the three events, followed by perturbations to the total (nonmicrophysics), turbulent mixing, and radiation parameterized tendencies. Additionally, microphysics tendency perturbations are associated with an increase in the areal coverage of heavy rainfall compared to the control forecast, regardless of whether the control forecast over or underrepresents the observed rainfall distribution.