摘要
This paper presents an evaluation of the observational impacts on blended sea surface winds from a two- dimensional variational data assimilation （2D-Var） scheme. We begin by briefly introducing the analysis sensitivity with respect to observations in variational data assimilation systems and its relationship with the degrees of freedom for signal （DFS）, and then the DFS concept is applied to the 2D-Var sea surface wind blending scheme. Two meth- ods, a priori and a posteriori, are used to estimate the DFS of the zonal （u） and meridional （v） components of winds in the 2D-Var blending scheme. The a posteriori method can obtain almost the same results as the a priori method. Because only by-products of the blending scheme are used for the a posteriori method, the computation time is re- duced significantly. The magnitude of the DFS is critically related to the observational and background error statistics. Changing the observational and background error variances can affect the DFS value. Because the observation error variances are assumed to be uniform, the observational influence at each observational location is related to the background error variance, and the observations located at the place where there are larger background error variances have larger influences. The average observational influence of u and v with respect to the analysis is about 40%, implying that the background influence with respect to the analysis is about 60%.
This paper presents an evaluation of the observational impacts on blended sea surface winds from a two- dimensional variational data assimilation （2D-Var） scheme. We begin by briefly introducing the analysis sensitivity with respect to observations in variational data assimilation systems and its relationship with the degrees of freedom for signal （DFS）, and then the DFS concept is applied to the 2D-Var sea surface wind blending scheme. Two meth- ods, a priori and a posteriori, are used to estimate the DFS of the zonal （u） and meridional （v） components of winds in the 2D-Var blending scheme. The a posteriori method can obtain almost the same results as the a priori method. Because only by-products of the blending scheme are used for the a posteriori method, the computation time is re- duced significantly. The magnitude of the DFS is critically related to the observational and background error statistics. Changing the observational and background error variances can affect the DFS value. Because the observation error variances are assumed to be uniform, the observational influence at each observational location is related to the background error variance, and the observations located at the place where there are larger background error variances have larger influences. The average observational influence of u and v with respect to the analysis is about 40%, implying that the background influence with respect to the analysis is about 60%.
基金
Supported by the National Natural Science Foundation of China(41275113,41206163,41475021,41605075,and U1406404)
China Meteorological Administration Special Public Welfare Research Fund(GYHY201106036)
