耦合模糊控制算法的数据同化观测误差处理方法
发布时间：2019-01-22 19:11
【摘要】：针对数据同化过程中集合数目有限情形下的虚假相关问题,通过模糊控制算法判断观测点与状态更新点之间的距离,构造观测位置等价权重,与集合转换卡尔曼滤波方法相结合,提出一种新的数据同化方法。利用经典的Lorenz-96混沌模型,比较分析集合转换卡尔曼滤波(ETKF),局地化集合转换卡尔曼滤波(LETKF)和模糊控制数据同化算法(FETKF)在不同参数变化时的性能,由此探讨3种方法的优劣。研究结果表明:新方法能够使每一步状态更新获得更有效的观测信息,减小因观测数据难以得到有效利用而带来的误差,同时避免了同化过程中的虚假相关问题,从而提高滤波精度。
[Abstract]:In order to solve the problem of false correlation in the case of finite number of sets in the process of data assimilation, the distance between observation point and state update point is judged by fuzzy control algorithm, and the equivalent weight of observation position is constructed. A new data assimilation method is proposed by combining with the set transform Kalman filtering method. The classical Lorenz-96 chaotic model is used to compare and analyze the performance of set converted Kalman filter (ETKF),) local set converted Kalman filter (LETKF) and fuzzy control data assimilation algorithm (FETKF) under different parameters. The advantages and disadvantages of the three methods are discussed. The results show that the new method can obtain more effective observation information for each step state update, reduce the error caused by the difficulty of using the observed data effectively, and avoid the false correlation problem in the assimilation process. Thus, the filtering accuracy is improved.
【作者单位】： 西北师范大学物理与电子工程学院;
【基金】：国家自然科学基金项目(41461078、41061038) 兰州市科技计划项目(2015-3-34)资助
【分类号】：O415.5;TP273.4
，
本文编号：2413485
[Abstract]:In order to solve the problem of false correlation in the case of finite number of sets in the process of data assimilation, the distance between observation point and state update point is judged by fuzzy control algorithm, and the equivalent weight of observation position is constructed. A new data assimilation method is proposed by combining with the set transform Kalman filtering method. The classical Lorenz-96 chaotic model is used to compare and analyze the performance of set converted Kalman filter (ETKF),) local set converted Kalman filter (LETKF) and fuzzy control data assimilation algorithm (FETKF) under different parameters. The advantages and disadvantages of the three methods are discussed. The results show that the new method can obtain more effective observation information for each step state update, reduce the error caused by the difficulty of using the observed data effectively, and avoid the false correlation problem in the assimilation process. Thus, the filtering accuracy is improved.
【作者单位】： 西北师范大学物理与电子工程学院;
【基金】：国家自然科学基金项目(41461078、41061038) 兰州市科技计划项目(2015-3-34)资助
【分类号】：O415.5;TP273.4
，
本文编号：2413485
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