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消除系统性观测误差的时空梯度信息同化方法研究
引用本文:王云峰,费建芳,袁炳,韩月琪.消除系统性观测误差的时空梯度信息同化方法研究[J].大气科学,2013,37(1):54-64.
作者姓名:王云峰  费建芳  袁炳  韩月琪
作者单位:解放军理工大学气象海洋学院,南京 211101
基金项目:国家高技术研究发展计划(863计划)2010AA012304,国际科技合作项目2010DFB33880,国家自然科学基金 41005029、41105065、11271195,国家公益性行业(气象)科研专项GYHY201106004,江苏省自然科学基金BK2010128
摘    要:随着气象观测手段的进步,各种气象观测资料在数值预报模式中的应用不断发展.然而由于观测资料存在观测误差,尤其一些非常规资料存在系统性偏差,且难以对此类误差进行充分订正,使得观测资料在数值预报模式同化应用过程中的作用没有被充分发挥.文中提出一种消去此类误差的时间及空间梯度信息变分同化方法,其特点在于不需要知道系统性偏差的具体估计,而是用一个梯度信息算子对原变量进行变换从而隐性回避此类误差.通过浅水波模式四维变分同化理想试验结果表明,此同化方法完全消除平整性系统性偏差对同化结果的影响,本身数值较小的模式变量更能够获得好的同化效果,大数值变量则可通过估算来确定适用范围.由于最优解不唯一性质的存在,同化效果更多的吸收观测场的整体时空梯度分布趋势而非观测量值本身,这对具有较低可信度的观测资料是适用的.

关 键 词:数值预报    系统性观测偏差    时空梯度信息    同化
收稿时间:2011/11/27 0:00:00
修稿时间:2012/8/25 0:00:00

Assimilation of Temporal and Spatial Gradient Information to Eliminate the Systematic Observation Error
WANG Yunfeng,FEI Jianfang,YUAN Bing and HAN Yueqi.Assimilation of Temporal and Spatial Gradient Information to Eliminate the Systematic Observation Error[J].Chinese Journal of Atmospheric Sciences,2013,37(1):54-64.
Authors:WANG Yunfeng  FEI Jianfang  YUAN Bing and HAN Yueqi
Affiliation:Institute of Meteorology and Oceanography PLA University of Science and Technology, Nanjing 211101
Abstract:With the advances of meteorological observation instruments, a variety of meteorological observations can be used in numerical prediction models. However, due to the observational error, especially systematic deviations in unconventional observations, the effect of observational data assimilation has not been fully examined. Thus, a variational assimilation method for temporal and spatial gradient information is proposed to eliminate such errors. The principle is that no a priori estimates of the systematic bias are needed, but a gradient information operator is used to transform the original variables so as to implicitly avoid this systematic error. A series of results of four-dimensional variational assimilation ideal experiments based on a shallow water model shows that this assimilation could completely eliminate the impact of smoothness systematic deviation on the assimilation results. The model could provide a good assimilation effect for the variables having a small value, and could estimate the scope of application for the ones having a large value. Due to the uncertainty of the optimal solution, the assimilation effect absorbs more of the overall temporal and spatial gradient trends of the observation field rather than the observation value itself, which is applicable to observational data with low credibility.
Keywords:Numerical weather prediction  Systematic error  Temporal and spatial gradient information  Assimilation
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