Abstract
This study compares the impacts of two stochastic parameterization schemes in a convective-scale model. The implementation of the physically based stochastic perturbation (PSP) scheme in the Weather Research and Forecasting (WRF) Model represents uncertainties arising from unresolved boundary layer processes due to the finite grid size. Uncertainty in the microphysical processes is represented by the stochastic parameter perturbation applied to the microphysics parameterization (SPPMP) scheme. We examine the regime dependence of the impacts with 48-h forecasts of days with weakly and strongly forced convection, as well as winter storms. Early in the forecasts, the two stochastic parameterizations have different effects. The PSP scheme responds to boundary layer turbulence and produces strong perturbations that grow in phase with the diurnal cycle of convection. In regions with existing precipitation, SPPMP produces perturbations that grow more slowly with lead time, independent of the time of day. Perturbation growth in the PSP experiments is stronger for convective weather and can increase the total precipitation by triggering new convection, but SPPMP can dominate when precipitation occurs under stable conditions at night or in winter storms. The differences between the two schemes are relatively short-lived, and within a day of simulation, the amplitude and structure of differences introduced by both schemes are similar. This is found to be associated with saturation of perturbation growth on small scales (up to about 50 km). The locations and amplitudes of upscale perturbation growth appear to be determined by the larger-scale dynamics, independent of the details of the stochastic physics.
Significance Statement
This study investigates how a physically based stochastic perturbation (PSP) scheme and a stochastic parameter perturbation to the microphysics parameterization (SPPMP) scheme impact forecast perturbation growth under different weather situations. The findings suggest that while the two stochastic parameterizations yield different impacts in the short term, they demonstrate overlapping effects in longer lead times despite their distinct theoretical foundations. This is due to the fact that once introduced, perturbations naturally grow in regions where the atmospheric flow exhibits moist instabilities. Consequently, sampling the two sources of uncertainty does not result in a proportional spread. This finding elucidates the notable success of ad hoc stochastic perturbation methods since forecast perturbation growth is primarily dominated by the underlying flow rather than the details of the stochastic schemes. The insights gained from this study are valuable for the community, particularly in the realm of developing ensemble systems. Moving forward, it is crucial to evaluate their impact within comprehensive ensemble systems that account for both initial and boundary condition uncertainties.
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