CAJ | 학술논문

利用浅水方程模式和模式模拟资料进行数值试验比较3种不同的背景误差协方差矩阵处理方法对四维变分(4DVAR)资料同化的影响.3种背景误差协方差矩阵分别是:(1)对单一变量将背景误差协方差矩阵简化为对角矩阵;(2)将背景误差协方差矩阵的作用简化为高斯过滤;(3)由预报集合生成背景误差协方差矩阵并利用奇异值分解技术解决矩阵的求逆.通过一系列数值试验,比较不同观测密度、不同观测误差下3种背景误差协方差处理方法对4DVAR同化效果的影响.结果表明,背景误差协方差的结构对4DVAR有重大影响.当观测资料的空间密度不够高时,采用对角矩阵得不到满意的结果.高斯过滤方案可以明显改善同化结果,但是对背景误差特征长度比较敏感.第3种方法采用的背景误差协方差矩阵是流型依赖的,而且并不以显式的方式出现在目标函数中.避免了对它求逆的复杂运算.由于做了降维处理,在观测点的密度较低和观测误差较大时可望取得较好的同化结果,同化效果较为稳定.
이용천수방정모식화모식모의자료진행수치시험비교3충불동적배경오차협방차구진처리방법대사유변분(4DVAR)자료동화적영향.3충배경오차협방차구진분별시:(1)대단일변량장배경오차협방차구진간화위대각구진;(2)장배경오차협방차구진적작용간화위고사과려;(3)유예보집합생성배경오차협방차구진병이용기이치분해기술해결구진적구역.통과일계렬수치시험,비교불동관측밀도、불동관측오차하3충배경오차협방차처리방법대4DVAR동화효과적영향.결과표명,배경오차협방차적결구대4DVAR유중대영향.당관측자료적공간밀도불구고시,채용대각구진득불도만의적결과.고사과려방안가이명현개선동화결과,단시대배경오차특정장도비교민감.제3충방법채용적배경오차협방차구진시류형의뢰적,이차병불이현식적방식출현재목표함수중.피면료대타구역적복잡운산.유우주료강유처리,재관측점적밀도교저화관측오차교대시가망취득교호적동화결과,동화효과교위은정.
Using the two-dimensional shallow water equation model and model simulated data, a set of numerical experiments were conducted to evaluate the impacts of three different specification schemes of the background error covariance matrix on the four-dimensional variational (4DVAR) data assimilation in the case of different observation densities and observation errors. The three schemes are as follows: (1) for a single control variable, the background error covariance is assumed to be a diagonal matrix; (2) the background error covariance is simplified to a Gaussian form with the homogeneous and isotropic assumptions; (3) the background error covariance is restructured through using the ensemble forecasts and the solving of the inverse of the background error covariance matrix is carried out by using the singular value decomposition (SVD) technique. The results show that the background error covariance plays an important role in 4DVAR data assimilation. When the observational spatial density is not high enough, there is no satisfied analysis available if the background error covariance matrix is simply reduced to a diagonal matrix. The Gaussian filter scheme has the ability to improve the analysis accuracy, but this it is sensitive to the length scale of background error correlations. The third method shows a stable performance. In this method, the background error covariance matrix is calculated implicitly so the computation of the inverse of background error covariance matrix is avoided. When observations are sparse or large errors exist in the observations, the third method will behave better compared to the other two methods.