An Initial Perturbation Method for the Multiscale Singular Vector in Global Ensemble Prediction

作     者：Xin LIU Jing CHEN Yongzhu LIU Zhenhua HUO Zhizhen XU Fajing CHEN Jing WANG Yanan MA Yumeng HAN Xin LIU;Jing CHEN;Yongzhu LIU;Zhenhua HUO;Zhizhen XU;Fajing CHEN;Jing WANG;Yanan MA;Yumeng HAN

作者机构：School of Atmospheric SciencesNanjing University of Information Science&TechnologyNanjing 210044China Chinese Academy of Meteorological SciencesBeijing 100081China CMA Earth System Modeling and Prediction Centre(CEMC)Beijing 100081China Key Laboratory of Earth System Modeling and Prediction of China Meteorological AdministrationBeijing 100081China Tianjin Meteorological ObservatoryTianjin 300074China China Meteorological Administration Xiong’an Atmospheric Boundary Layer Key LaboratoryHebei 071799China 

出 版 物：《Advances in Atmospheric Sciences》 (大气科学进展（英文版）)

年 卷 期：2024年第41卷第3期

页      面：545-563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 Joint Funds of the Chinese National Natural Science Foundation (NSFC)(Grant No.U2242213) the National Key Research and Development (R&D)Program of the Ministry of Science and Technology of China(Grant No. 2021YFC3000902) the National Science Foundation for Young Scholars (Grant No. 42205166) 

主　　题：multiscale uncertainty singular vector initial perturbation global ensemble prediction system 

摘      要：Ensemble prediction is widely used to represent the uncertainty of single deterministic Numerical Weather Prediction(NWP) caused by errors in initial conditions(ICs). The traditional Singular Vector(SV) initial perturbation method tends only to capture synoptic scale initial uncertainty rather than mesoscale uncertainty in global ensemble prediction. To address this issue, a multiscale SV initial perturbation method based on the China Meteorological Administration Global Ensemble Prediction System(CMA-GEPS) is proposed to quantify multiscale initial uncertainty. The multiscale SV initial perturbation approach entails calculating multiscale SVs at different resolutions with multiple linearized physical processes to capture fast-growing perturbations from mesoscale to synoptic scale in target areas and combining these SVs by using a Gaussian sampling method with amplitude coefficients to generate initial perturbations. Following that, the energy norm,energy spectrum, and structure of multiscale SVs and their impact on GEPS are analyzed based on a batch experiment in different seasons. The results show that the multiscale SV initial perturbations can possess more energy and capture more mesoscale uncertainties than the traditional single-SV method. Meanwhile, multiscale SV initial perturbations can reflect the strongest dynamical instability in target areas. Their performances in global ensemble prediction when compared to single-scale SVs are shown to(i) improve the relationship between the ensemble spread and the root-mean-square error and(ii) provide a better probability forecast skill for atmospheric circulation during the late forecast period and for short-to medium-range precipitation. This study provides scientific evidence and application foundations for the design and development of a multiscale SV initial perturbation method for the GEPS.