13C.6 Process-Based Evaluation of Stochastic Perturbed Parameterization Tendencies on Convective-Permitting Ensemble Forecasts of the 1–2 June 2017 Taiwan Heavy Rainfall Event

Thursday, 16 January 2020: 2:45 PM
258B (Boston Convention and Exhibition Center)
Kevin Lupo, Univ. at Albany, SUNY, Albany, NY; and R. D. Torn and S. C. Yang

Heavy rainfall events can be associated with significant societal disruption; therefore, it is imperative to understand the dynamical processes and predictability associated with these events. Predictability can be examined using ensemble prediction systems (EPSs), which should account for errors or uncertainty both in the initial state and the model formulation. Stochastic perturbed parameterization tendencies (SPPT) and independent SPPT (iSPPT) represent model error associated with subgrid-scale parameterization schemes by randomly perturbing the output physics tendencies of these schemes at each time model step using a spatially and temporally correlated stochastic pattern. While this method has been applied extensively within operational EPSs and has been tested in regional convection-permitting experiments, less attention has been given to the physical processes by which SPPT and iSPPT influence rainfall forecast variability. As a consequence, the goal of this research is to understand how stochastic perturbations to the radiation, microphysics, and boundary layer schemes impact the physical processes that determine the predictability of heavy rainfall in the WRF model. Generally speaking, iSPPT perturbations to microphysics tendencies yield the greatest rainfall forecast variability, while perturbations to the radiation temperature tendency are associated with the smallest ensemble spread. Examination of a mei-yu front-related heavy rainfall event over northern Taiwan during 1–2 June 2017 suggests that the impact of the stochastic perturbations to the microphysics and PBL schemes lasts much longer than the timescale of the perturbations themselves. Moreover, higher rainfall totals are associated with greater low-level convergence and moisture, as well as greater rates of warm rain processes and cloud-layer condensation when SPPT perturbations to the microphysics scheme.
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