Variational Quality Control of Non-Gaussian Innovations in the GRAPES m3DVAR System: Mass Field Evaluation of Assimilation Experiments

作     者：Jie HE Xulin MA Xuyang GE Juanjuan LIU Wei CHENG Man-Yau CHAN Ziniu XIAO Jie HE;Xulin MA;Xuyang GE;Juanjuan LIU;Wei CHENG;Man-Yau CHAN;Ziniu XIAO

作者机构：Collaborative Innovation Center on Forecast and Evaluation of Meteorological DisastersKey Laboratory of Meteorological DisasterNanjing University of Information Science and TechnologyNanjing 210044China State Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid DynamicsInstitute of Atmospheric PhysicsChinese Academy of SciencesBeijing 100029China Beijing Institute of Applied MeteorologyBeijing 100029China Department of Meteorology and Atmospheric Scienceand Center for Advanced Data Assimilation and Predictability TechniquesThe Pennsylvania State UniversityUniversity ParkPA 16801USA 

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

年 卷 期：2021年第38卷第9期

页      面：1510-1524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 China Scholarship Council primarily sponsored by the National Key R&D Program of China (Grant No.2018YFC1506702 and Grant No.2017YFC1502000) 

主　　题：variational quality control non-Gaussian distribution innovation outlier data assimilation 

摘      要：The existence of outliers can seriously influence the analysis of variational data *** control allows us to effectively eliminate or absorb these outliers to produce better analysis *** particular,variational quality control(VarQC) can process gray zone outliers and is thus broadly used in variational data assimilation *** this study,governing equations are derived for two VarQC algorithms that utilize different contaminated Gaussian distributions(CGDs): Gaussian plus flat distribution and Huber norm *** such,these VarQC algorithms can handle outliers that have non-Gaussian ***,these VarQC algorithms are implemented in the Global/Regional Assimilation and PrEdiction System(GRAPES) model-level three-dimensional variational data assimilation(m3 DVAR) *** using artificial observations indicate that the VarQC method using the Huber distribution has stronger robustness for including outliers to improve posterior analysis than the VarQC method using the Gaussian plus flat ***,real observation experiments show that the distribution of observation analysis weights conform well with theory,indicating that the application of VarQC is effective in the GRAPES m3 DVAR *** case study and longperiod data assimilation experiments show that the spatial distribution and amplitude of the observation analysis weights are related to the analysis increments of the mass field(geopotential height and temperature).Compared to the control experiment,VarQC experiments have noticeably better posterior mass ***,the VarQC method using the Huber distribution is superior to the VarQC method using the Gaussian plus flat distribution,especially at the middle and lower levels.