Stochastically Perturbed Parameterizations in an HRRR-Based Ensemble

A stochastically perturbed parameterization (SPP) approach that spatially and temporally perturbs parameters and variables in the Mellor-Yamada-Nakanishi-Niino planetary boundary layer scheme (PBL) and introduces initialization perturbations to soil moisture in the Rapid Update Cycle land surface model was developed within the High-Resolution Rapid Refresh convection-allowing ensemble. This work is a follow-up study to a work performed using the Rapid Refresh (RAP)-based ensemble. In the present study, the SPP approach was used to target the performance of precipitation and low-level variables (e.g., 2-m temperature and dewpoint, and 10-m wind). The stochastic kinetic energy backscatter scheme and the stochastic perturbation of physics tendencies scheme were combined with the SPP approach and applied to the PBL to target upper-level variable performance (e.g., improved skill and reliability). The three stochastic experiments (SPP applied to PBL only, SPP applied to PBL combined with SKEB and SPPT, and stochastically perturbed soil moisture initial conditions) were compared to a mixed-physics ensemble. The results showed a positive impact from initial condition soil moisture perturbations on precipitation forecasts; however, it resulted in an increase in 2-m dewpoint RMSE. The experiment with perturbed parameters within the PBL showed an improvement in low-level wind forecasts for some verification metrics. The experiment that combined the three stochastic approaches together exhibited improved RMSE and spread for upper-level variables. Our study demonstrated that, by using the SPP approach, forecasts of specific variables can be improved. Also, the results showed that using a single-physics suite ensemble with stochastic methods is potentially an attractive alternative to using multiphysics for convection allowing ensembles.