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Using analysis state to construct a forecast error covariance matrix in ensemble Kalman filter assimilation 总被引:1,自引:0,他引:1
Correctly estimating the forecast error covariance matrix is a key step in any data assimilation scheme. If it is not correctly estimated, the assimilated states could be far from the true states. A popular method to address this problem is error covariance matrix inflation. That is, to multiply the forecast error covariance matrix by an appropriate factor. In this paper, analysis states are used to construct the forecast error covariance matrix and an adaptive estimation procedure associated with the error covariance matrix inflation technique is developed. The proposed assimilation scheme was tested on the Lorenz-96 model and 2D Shallow Water Equation model, both of which are associated with spatially correlated observational systems. The experiments showed that by introducing the proposed structure of the forecast error covariance matrix and applying its adaptive estimation procedure, the assimilation results were further improved. 相似文献
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沈阳浑河是沈阳市一条非常重要的河流由于其原有护岸被洪水破坏,失去防护能力。基于此,从设计原则、设计水面线计算以及丁坝形式三方面对坝式护岸进行设计分析。提出建立上部为格宾石笼,下部为沉排抛石丁坝的方案。经多年运行证明:该设计对护岸具有良好的保护作用,可为同类工程提供借鉴。 相似文献
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