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

Abstract

The local breeding growth mode (LBGM) method does not consider the difference between the grid points within the local radius. To address this problem, Gaussian weights (GWs) were proposed, which cause the influence of each grid within a local radius to exert an increase in distance with Gaussian decay on the central grid. In this paper, the effects of two different LBGM methods under GWs and equal weights (EWs) in convection-permitting ensemble forecasting are compared and analyzed using two squall line examples. The results showed that the use of the GWs intensified the local characteristics of the initial condition perturbation (ICP) and made the distribution of the ICP more flow-dependent. The result of the kinetic energy spectrum of the ICP indicated that there could be more large-scale information in the ICP using the GWs. In addition, meso-scale information also improved slightly. For nonprecipitation variable forecasting, GWs improved the relationships 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 two squall line processes than the EWs.