Application of Gaussian Weight to Improve Perturbation Features of Convection-Permitting Ensemble Forecast Based on Local Breeding of Growing Modes

Local breeding of growing modes(LBGM) is a method used to generate initial condition perturbation(ICP) for convection-permitting ensemble forecasts. Equal weights(EWs) are usually presumed in LBGM during the localization of ICP, without considering different contributions of the grid points within the local radius. To address this problem, Gaussian weights(GWs) are proposed in this study, which can accommodate the varied influences of the grids inside the local radius on the central grid through a Gaussian function. Specifically, two convection-permitting ensemble forecast experiments based on LBGM with GWs and EWs are compared and analyzed respectively for two squall line cases. The results showed that the use of the GWs intensified the local characteristics of the ICP and made the distribution of the ICP fields more flow-dependent. Kinetic energy spectrum of the ICP indicated that there could be more large-scale information in the ICP by using the GWs. In addition, mesoscale information also improved slightly. For forecast of nonprecipitation variables, GWs improved the relationship between the root-mean-square error and the spread and contributed to the forecasting accuracy of wind, temperature, geopotential height, and humidity. For the precipitation forecast, GWs simulated the precipitation structure successfully and provided better probability forecasting during the evolution of the two squall line processes than the EWs.