The importance of data assimilation components for initial conditions and subsequent error growth

作     者：Zhongrui WANG Haohao SUN Lili LEI Zhe-Min TAN Yi ZHANG 

作者机构：Key Laboratory of Mesoscale Severe WeatherMinistry of Educationand School of Atmospheric SciencesNanjing UniversityNanjing 210023China Frontiers Science Center for Critical Earth Material CyclingNanjing UniversityNanjing 210023China 

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

年 卷 期：2024年第67卷第1期

页      面：105-116页

核心收录：

学科分类：07[理学] 0706[理学-大气科学] 

基　　金：This work was supported by the National Natural Science Foundation of China(Grant Nos.42192553,41922036&41775057) the Frontiers Science Center for Critical Earth Material Cycling Fund(Grant No.JBGS2102) the Fundamental Research Funds for the Central Universities(Grant No.0209-14380097). 

主　　题：Data assimilation Atmospheric predictability Background error covariances Ensemble forecasts 

摘      要：Despite a specific data assimilation method,data assimilation(DA)in general can be decomposed into components of the prior information,observation forward operator that is given by the observation type,observation error covariances,and background error covariances.In a classic Lorenz model,the influences of the DA components on the initial conditions(ICs)and subsequent forecasts are systematically investigated,which could provide a theoretical basis for the design of DA for different scales of interests.The forecast errors undergo three typical stages:a slow growth stage from 0 h to 5 d,a fast growth stage from 5 d to around 15 d with significantly different error growth rates for ensemble and deterministic forecasts,and a saturation stage after 15 d.Assimilation strategies that provide more accurate ICs can improve the predictability.Cycling assimilation is superior to offline assimilation,and a flow-dependent background error covariance matrix(Pf)provides better analyses than a static background error covariance matrix(B)for instantaneous observations and frequent time-averaged observations;but the opposite is true for infrequent time-averaged observations,since cycling simulation cannot construct informative priors when the model lacks predictive skills and the flow-dependent Pf cannot effectively extract information from low-informative observations as the static B.Instantaneous observations contain more information than time-averaged observations,thus the former is preferred,especially for infrequent observing systems.Moreover,ensemble forecasts have advantages over deterministic forecasts,and the advantages are enlarged with less informative observations and lower predictive-skill model priors.