The initial ensemble perturbations for an ensemble data assimilation system are expected to reasonably sample model uncertainty at the time of analysis to further reduce analysis uncertainty. Therefore, the careful choice of an initial ensemble perturbation method that dynamically cycles ensemble perturbations is required for the optimal performance of the system. Based on the multivariate empirical orthogonal function (MEOF) method, a new ensemble initialization scheme is developed to generate balanced ini...
【英文摘要】
The initial ensemble perturbations for an ensemble data assimilation system are expected to reasonably sample model uncertainty at the time of analysis to further reduce analysis uncertainty. Therefore, the careful choice of an initial ensemble perturbation method that dynamically cycles ensemble perturbations is required for the optimal performance of the system. Based on the multivariate empirical orthogonal function (MEOF) method, a new ensemble initialization scheme is developed to generate balanced ini...
【基金】
supported by the Knowledge Innovation Program of the Chinese Academy of Sciences (Grant No. KZCX1-YW-12-03);
the National Basic Research Program of China (Grant No. 2010CB951901);
the National Natural Science Foundation of China (Grant No. 40805033)
【更新日期】
2011-10-18
【分类号】
P435
【正文快照】
1 Introduction The ensemble Kalman filter (EnKF) that was intro- duced by Evensen (1994, 2003) is a Monte Carlo ap- proximation to the traditional Kalman filter (e.g., Kalman and Bucy, 1961). The EnKF method uses an ensemble of forecasts to estimate the b
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