%0 Journal Article
%T A Study of the Statistical Analysis of the Geopotential Height Background Errors in the Data Assimilation<br>资料同化中背景场位势高度误差统计分析的研究
%A ZHUANG Zhao-Rong
%A XUE Ji-Shan
%A ZHUANG Shi-Yu
%A ZHU Guo-Fu
%A <br>庄照荣
%A 薛纪善
%A 庄世宇
%A 朱国富
%J 大气科学
%D 2006
%I 
%X Background error covariance is very important to govern the amount of smoothing and spreading of the observed information and to decide the relationships between different variables in variational data assimilation.Because of the existence of a balance in the reality and in the model state,there is a version of the balance that exists in the background error covariances.Background error covariances depend on the uncertainty of the previous analysis and forecast.To a large extent,the form of this background error covariance governs the resulting objective analysis.With the development of data assimilation,the methods to estimate the forecast error correlation structure have been reported in many literatures.However there is a little work about background error covariance in our country and the work is needed in the operational data assimilation system and GRAPES(Global and Regional Assimilation and PrEdiction System) 3D Var(three-Dimensional Variational data assimilation) research.So the statistical structure of background error covariance is studied in this paper.It is difficult to directly get error covariances,which can only be estimated in a statistical sense.In order to get the height background error covariance,the innovation vector method is used in this paper.The data consist of innovation data(12 h and 24 h predicted height of T213 model minus radiosonde measurements) at 0000 UTC and 1200 UTC.Horizontal characteristic length,prediction error variance and observation error variance are obtained using Gauss correlation function approximation in a particular level.The straightforward way and the empirical thickness method are used to get the approximate function in interlevel values.In the vertical direction,vertical covariance approximation is obtained by the second-order autoregressive(SOAR) correlation function and distance transformation method.The resulting three-dimensional approximation function is partially separable,which is the product of the horizontal covariance function and the vertical correlation function.The major products of the analysis include:(i) In regions of sufficiently dense data coverage,the statistical analysis of innovation vectors can be employed. With homogeneous and isotropic assumption,it is a reasonable approximation to fit the horizontal covariances with Gauss function.(ii) It may be better way to calculate the covariances between the levels with empirical thickness method.(iii) The range of correlation distance parameter for prediction error is from 500 to 700 km and for synoptic scale prediction error is from 450 to 650 km in the troposphere.It shows that the range of the influence of large-scale prediction error is large.(iv) The prediction and observation error standard deviations of the values obtained by the T213 data are different from that of LAFS.Observation error is a little smaller than prediction error for the lower troposphere and is larger for the middle and upper levels of the troposphere.(v) T
%K background error covariance
%K characteristic length
%K variational data assimilation
%K the innovation vector method<br>背景误差协方差
%K 特征尺度
%K 变分同化
%K 观测余差方法
%U http://www.alljournals.cn/get_abstract_url.aspx?pcid=E62459D214FD64A3C8082E4ED1ABABED5711027BBBDDD35B&cid=28A2F569B2458C17&jid=46874A5A102033D774D00D819E91CD68&aid=4A71BE4BE78EB94E&yid=37904DC365DD7266&vid=340AC2BF8E7AB4FD&iid=38B194292C032A66&sid=FF58680609C9D068&eid=B34BDD6A690A04C0&journal_id=1006-9895&journal_name=大气科学&referenced_num=7&reference_num=23