| 资料同化中背景场位势高度误差统计分析的研究 |
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| 引用本文： | 庄照荣,薛纪善,庄世宇,朱国富.资料同化中背景场位势高度误差统计分析的研究[J].大气科学,2006,30(3):533-544. |
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| 作者姓名： | 庄照荣 薛纪善 庄世宇 朱国富 |
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| 作者单位： | 1.中国气象科学研究院,北京,100081;中国科学院研究生院,北京,100049 |
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| 基金项目： | 中国科学院资助项目;国家"火炬计划" |
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| 摘 要： | 在客观分析中,背景误差协方差对观测信息的传播和平滑、反映不同变量之间的关系有着非常重要的作用.构造合理的背景误差协方差矩阵对于同化系统至关重要,甚至会决定同化分析的好坏.作者主要利用观测余差方法,用T213预报资料和无线电探空观测资料统计我国区域的背景位势高度误差协方差样本,分析背景误差协方差场的结构特征和拟合误差场的空间分布.
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| 关 键 词： | 背景误差协方差 特征尺度 变分同化 观测余差方法 |
| 文章编号： | 1006-9895（2006）03-0533-12 |
| 收稿时间： | 2005-03-14 |
| 修稿时间： | 2005-03-142005-07-11 |
A Study of the Statistical Analysis of the Geopotential Height Background Errors in the Data Assimilation |
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| ZHUANG Zhao-Rong,XUE Ji-Shan,ZHUANG Shi-Yu and ZHU Guo-Fu.A Study of the Statistical Analysis of the Geopotential Height Background Errors in the Data Assimilation[J].Chinese Journal of Atmospheric Sciences,2006,30(3):533-544. |
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| Authors: | ZHUANG Zhao-Rong XUE Ji-Shan ZHUANG Shi-Yu and ZHU Guo-Fu |
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| Institution: | 1 Chinese Academy of Meteorological Sciences, Beijing 100081;2 Graduate University of Chinese Academy of Sciences, Beijing 100049 |
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| Abstract: | 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) The resulting three-dimensional covariances are approximated by a combination of Gauss function and SOAR function. |
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| Keywords: | background error covariance characteristic length variational data assimilation the innovation vector method |
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