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集合卡尔曼滤波(EnKF)是一种灵活有效的序贯数据同化方法,解决参数优化问题具有优势:一是可以显式地考虑多源不确定性,从而避免对参数的过度调整来弥补其他来源的误差而产生次优参数;二是实时处理最新更新的观测数据,从而不需要存储和同时处理所有历史数据;三是使用集合和蒙特卡罗方法来表征和预报相关误差统计量,不需要封闭解逼近,易于实施。论文借助一维土壤湿度模型,通过观测系统模拟试验的方式,评估EnKF对水力学函数参数的优化效果。结果表明,敏感参数更易得到最优估值,优化效果不受初始猜测及观测误差设置等的影响。和直观想法相反,增加同化频率可能会使估值结果不稳定。
Ensemble Kalman filter (EnKF) is a flexible and effective method of sequential data assimilation. It has some advantages to solve the problem of parameter optimization. One is that it can explicitly consider the multi-source uncertainty so as to avoid over-adjusting parameters to make up for other sources The second is to deal with the latest updated observations in real time so that it does not need to store and process all the historical data at the same time. The third is to use set and Monte Carlo methods to characterize and forecast the relevant error statistics, without the need of closed Solution approximation, easy to implement. Based on the one-dimensional soil moisture model, the paper evaluates the effect of EnKF on the parameters of hydraulic function by observing the system simulation test. The results show that the sensitive parameters are easier to get the best estimate, the optimization effect is not affected by the initial guess and observation error setting. Contrary to intuitive thinking, increasing the frequency of assimilation may destabilize the valuation.