暴雨灾害 (Jun 2023)
Comparative evaluation of different stochastic perturbation schemes within the convection-allowing ensemble forecast of rainstorm in WRF model
Abstract
Because of the small disturbance energy of random parameter in the rainstorm convective-allowing ensemble forecast model, a convective-allowing ensemble forecasting test based on WRFv3.9 model was carried out for a heavy rainstorm case in the Yangtze River Basin in China. The disturbance characteristics of various hybrid perturbation schemes of stochastically perturbed parameterization (SPP), stochastically perturbed parameterization tendencies (SPPT), and stochastic kinetic-energy backscatter (SKEB) were compared and analyzed, and the prediction effects of these schemes were evaluated. The main conclusions are as follows: the spread of meteorological elements of multi-stochastic physical disturbance in rainstorm convective-allowing ensemble forecast increases comparing to the single SPP scheme. The spread of SPP+SPPT+SKEB is the largest and the overall score is the best. The spread of surface meteorological elements of SPP+SPPT is larger than that of SPP+SKEB, while the spread of meteorological elements in the upper air (especially in the wind field) of SPP+SKEB is larger than that of SPP+SPPT. In the SPP+SPPT test, the spread of meteorological elements increased significantly at the initial stage of integration time, while the spread of meteorological elements at each altitude increased more prominently with the extension of integration time in the SPP+SKEB test. In terms of ensemble average, spread distribution and probability prediction skills, the hybrid perturbation scheme of multi-stochastic disturbance are better than those of the single SPP scheme. Overall, the SPP+SPPT+SKEB scheme performs the best. The SPP+SPPT+SKEB scheme integrate the advantages of SPP, SPPT and SKEB, and achieve the best spread of meteorological elements in the whole integration period of each altitude layer, because of the complementary effects of the hybrid perturbation of multi-stochastic disturbance schemes.
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