Algorithm based on local breeding of growing modes for convection-allowing ensemble forecasting

作     者：Chaohui CHEN Xiang LI Hongrang HE Jie XIANG Shenjia MA 

作者机构：College of Meteorology and OceanographyNational University of Defense Technology 

出 版 物：《Science China Earth Sciences》 (中国科学（地球科学英文版）)

年 卷 期：2018年第61卷第4期

页      面：462-472页

核心收录：

学科分类：07[理学] 070601[理学-气象学] 0706[理学-大气科学] 

基　　金：supported by the Natural Science Foundation of Nanjing Joint Center of Atmospheric Research(Grant Nos.NJCAR2016MS02 and NJCAR2016ZD04) the National Natural Science Foundation of China(Grant Nos.41205073 and41675007) the National Key Research and Development Program of China(Grant No.2017YFC1501800) 

主　　题：Convection allowing ensemble forecasting Local breeding of growing modes Perturbation structure Spread Root mean square error of forecast 

摘      要：We propose a method based on the local breeding of growing modes(LBGM) considering strong local weather characteristics for convection-allowing ensemble forecasting. The impact radius was introduced in the breeding of growing modes to develop the LBGM method. In the local breeding process, the ratio between the root mean square error(RMSE) of local space forecast at each grid point and that of the initial full-field forecast is computed to rescale perturbations. Preliminary evaluations of the method based on a nature run were performed in terms of three aspects: perturbation structure, spread,and the RMSE of the forecast. The experimental results confirm that the local adaptability of perturbation schemes improves after rescaling by the LBGM method. For perturbation physical variables and some near-surface meteorological elements, the LBGM method could increase the spread and reduce the RMSE of forecast,improving the performance of the ensemble forecast *** addition, different from those existing methods of global orthogonalization approach, this new initial-condition perturbation method takes into full consideration the local characteristics of the convective-scale weather system, thus making convectionallowing ensemble forecast more accurate.