变分数据同化方法中背景误差协方差矩阵的统计特性研究

详细信息    查看全文 | 推荐本文 |

英文篇名：Statistic characteristics research of background error covariance in variational data assimilation methods 作者：摆玉龙 ; 孟若玉 ; 马真东 ; 柴乾隆 英文作者：BAI Yu-long;MENG Ruo-yu;MA Zhen-dong;CHAI Qian-long;College of Physics and Electrical Engineering,Northwest Normal University; 关键词：背景误差协方差 ; 3D-Var ; NMC方法 ; 统计分析 英文关键词：background error covariance;;3D-Var;;NMC method;;statistical analysis 中文刊名：XBSF 英文刊名：Journal of Northwest Normal University(Natural Science) 机构：西北师范大学物理与电子工程学院; 出版日期：2019-01-15 出版单位：西北师范大学学报(自然科学版) 年：2019 期：v.55;No.204 基金：国家自然科学基金资助项目(41861047,41461078);; 西北师范大学科研能力提升团队资助项目(NWNU-LKQN-1706) 语种：中文; 页：XBSF201901009 页数：5 CN：01 ISSN：62-1087/N 分类号：54-58

摘要

变分数据同化方法中,背景误差协方差矩阵(简称B矩阵)作为反映模型不同状态变量间关系的重要数学量,对保证解的唯一性和分析值的平滑性具有重要意义.文中简述了B矩阵的结构特征和相关性质.针对难以获得真实B矩阵的情况,采用National meteorological center(简称NMC)方法对B矩阵进行了近似构造,以Lorenz63模型和三维变分同化算法进行了同化试验,验证了NMC方法的有效性,讨论了B矩阵的数学特性和分布特征.
        In variational data assimilation method,as an important mathematical quantity that can reflect the relationship between different state variables of model,background error covariance matrix has great significance to guarantee the uniqueness of solution and smoothness of analysis value.In this paper we have briefly described the structure features and properties of B matrix.For the cases where real B matrices are difficult to obtain,we use the national meteorological center(NMC)method to compute an approximation B matrix based on Lorenz-63 model and to verify the effectiveness of NMC method.Then,we discussed the mathematical characteristics and distribution characteristic of B matrix according to the results.

引文

[1]LI Xin,HUANG Chun-lin,CHE Tao,et al.Development of a Chinese land data assimilation system:its progress and prospects[J].Progress in Natural Science,2007,17(8):881.
    [2]摆玉龙,李新,韩旭军.陆面数据同化系统误差问题研究综述[J].地球科学进展,2011,26(8):795.
    [3]PARRISH D F.The national meteorological center's spectral-statistical interpolation analysis system[J].Monthly Weather Review,1992,120(120):62.
    [4]RABIER F,MCNALLY A,ANDERSSON E,et al.The ECMWF implementation of threedimensional variational assimilation(3D-Var).II:Structure functions[J].Quarterly Journal of the Royal Meteorological Society,1998,124:1809.
    [5]JIANDONG G,GANG Z.Accurate estimation and application of 3-D error covariance structures in global data assimilation partⅡ:background error covariance structure adjustments and numerical experiments[J].Acta Meteorologica Sinica,2006,64(6):684.
    [6]曹小群,黄思训,张卫民,等.区域三维变分同化中背景误差协方差的模拟[J].气象科学,2008,28(1):12.
    [7]赵延来,黄思训,张维峰,等.三维变分同化中多变量平衡约束设计[J].大气科学学报,2013,36(3):277.
    [8]LORENC A C.Analysis methods for numerical weather prediction[J].Quarterly Journal of the Royal Meteorological Society,1986,112:1177.
    [9]LORENZ E N.Deterministic nonperiodic flows[J].Journal of Atmospheric Sciences,1963,20(3):130.
    [10]KALMAN R E.A new approach to linear filtering and predication problems[J].Transactions of the ASME-Journal of Basic Engineering Series D,1960,82(2):35.
    [11]KALNAY E.Atmospheric Modeling:Data Assimilation and Predictability[M].Cambridge:Cambridge University Press,2003.