Influence of Stochastically Perturbed Parameterization on Ensemble Forecasting of Winter Precipitation in China

Precipitation ensemble forecasting is characterized by great uncertainty, and the uncertainty of the parameters in the physical that is closely related to the precipitation forecast is one of the sources of its numerical prediction error. As a frontier research field in international ensemble forecasting, the stochastically perturbed parameterization (SPP) method has been developed to address the uncertainty of representative model precipitation forecasts. To determine whether this method can reflect the uncertainty of numerical predictions of winter precipitation in China and provide a scientific basis for business applications, we used the China Meteorological Administration’s Global/Regional Assimilation and Prediction System (GRAPES) mesoscale regional ensemble prediction model and selected 16 key parameters from four parameterization schemes. These parameters, e.g., cumulus convection, cloud microphysics, boundary layer, and near-surface layer, greatly influence the uncertainty of model precipitation forecasts. In this paper, we introduce the stochastically perturbed parameterization (SPP) method and describe the results of an ensemble prediction experiment conducted from December 12, 2018 to January 12, 2019, a total of 31 days. We compare and analyze the effect of the SPP method on the winter weather situation and precipitation ensemble prediction. The results show that with the addition of a test for the SPP method, the results of probability prediction techniques for precipitation and isobaric elements are better than the control predictions without the SPP method, and the improvement of low-level and near-surface elements is better that of the iso-surface elements in the middle or upper floors. The precipitation prediction results obtained superior scores to those of the control prediction test, but because the improvement did not pass the test of significance, the differences were not statistically significant. The above results indicate that under the influence of the East Asian winter monsoon, the SPP method demonstrates no obvious improvement on the current prediction technique used for winter precipitation in China. The reason for this may be that the SPP method mainly represents the uncertainty of convective precipitation forecasting, whereas the winter precipitation process in China is mainly one characterized by the development of baroclinic instability. Because model precipitation is dominated by large-scale grid precipitation, and less convective precipitation, improvement in the winter precipitation forecast is not obvious. Thus, there is a scientific basis for applying the SPP method to the operation ensemble forecasting model.