This paper extends the dimension-reduced projection four-dimensional variational assimilation method(DRP-4DVar) by adding a nonlinear correction process,thereby forming the DRP-4DVar with a nonlinear correction, which...This paper extends the dimension-reduced projection four-dimensional variational assimilation method(DRP-4DVar) by adding a nonlinear correction process,thereby forming the DRP-4DVar with a nonlinear correction, which shall hereafter be referred to as the NC-DRP-4DVar. A preliminary test is conducted using the Lorenz-96 model in one single-window experiment and several multiple-window experiments. The results of the single-window experiment show that compared with the adjoint-based traditional 4DVar, the final convergence of the cost function for the NC-DRP-4DVar is almost the same as that using the traditional 4DVar, but with much less computation. Furthermore, the 30-window assimilation experiments demonstrate that the NC-DRP-4DVar can alleviate the linearity approximation error and reduce the root mean square error significantly.展开更多
Soil Moisture and Ocean Salinity （SMOS） Level 3 （L3） sea surface salinity （SSS） products are provided by the Barcelona Expert Centre （BEC）. Strong biases were observed on the SMOS SSS products, thus the data f...Soil Moisture and Ocean Salinity （SMOS） Level 3 （L3） sea surface salinity （SSS） products are provided by the Barcelona Expert Centre （BEC）. Strong biases were observed on the SMOS SSS products, thus the data from the Centre Aval de Traitement des Donnees SMOS （CATDS） were adjusted for biases using a large-scale correction derived from observed differences between the SMOS SSS and World Ocean Atlas （WOA） climatology data. However, this large-scale correction method is not suitable for correcting the large gradient of salinity biases. Here, we present a method for the correction of SSS regional bias of the monthly L3 products. Based on the stable characteristics of the large SSS biases from month to month in some regions, corrected SMOS SSS maps can be obtained from the monthly mean values after removing the regional biases. The accuracy of the SMOS SSS measurements is greatly improved, especially near the coastline, at high latitudes, and in some open ocean regions. The SMOS and ISAS SSS data are also compared with Aquarius SSS to verify the corrected SMOS SSS data. The correction method presented here only corrects annual mean biases. The measurement accuracy of the SSS may be improved by considering the influence of atmospheric and ocean circulation in different seasons and years.展开更多
基金supported by the National Basic Research Program of China (973 Program, Grant No. 2010CB951604)the National Key Technologies Research and Development Program of China (Grant No. 2012BAC22B02)the National Natural Science Foundation of China (Grant No. 41105120)
文摘This paper extends the dimension-reduced projection four-dimensional variational assimilation method(DRP-4DVar) by adding a nonlinear correction process,thereby forming the DRP-4DVar with a nonlinear correction, which shall hereafter be referred to as the NC-DRP-4DVar. A preliminary test is conducted using the Lorenz-96 model in one single-window experiment and several multiple-window experiments. The results of the single-window experiment show that compared with the adjoint-based traditional 4DVar, the final convergence of the cost function for the NC-DRP-4DVar is almost the same as that using the traditional 4DVar, but with much less computation. Furthermore, the 30-window assimilation experiments demonstrate that the NC-DRP-4DVar can alleviate the linearity approximation error and reduce the root mean square error significantly.
基金Supported by the National Natural Science Foundation of China(No.41076117)
文摘Soil Moisture and Ocean Salinity （SMOS） Level 3 （L3） sea surface salinity （SSS） products are provided by the Barcelona Expert Centre （BEC）. Strong biases were observed on the SMOS SSS products, thus the data from the Centre Aval de Traitement des Donnees SMOS （CATDS） were adjusted for biases using a large-scale correction derived from observed differences between the SMOS SSS and World Ocean Atlas （WOA） climatology data. However, this large-scale correction method is not suitable for correcting the large gradient of salinity biases. Here, we present a method for the correction of SSS regional bias of the monthly L3 products. Based on the stable characteristics of the large SSS biases from month to month in some regions, corrected SMOS SSS maps can be obtained from the monthly mean values after removing the regional biases. The accuracy of the SMOS SSS measurements is greatly improved, especially near the coastline, at high latitudes, and in some open ocean regions. The SMOS and ISAS SSS data are also compared with Aquarius SSS to verify the corrected SMOS SSS data. The correction method presented here only corrects annual mean biases. The measurement accuracy of the SSS may be improved by considering the influence of atmospheric and ocean circulation in different seasons and years.
