由于在数据同化过程中远距离的观测与同化状态之间存在着虚假相关,局地化方法受到广泛关注。同时,在集合数目较少的同化情况下,观测数据难以得到有效利用,使得同化效果欠佳。因此,提出了一种新的模糊控制局地化同化方法,通过模糊控制算法判断观测点与状态更新点之间的距离,构造观测位置模糊权重。利用非线性Lorenz-96模型,比较分析模糊控制局地化同化(FLETKF)算法与模糊控制同化(FETKF)方法、局地化分析同化(LETKF)算法和集合转换卡尔曼滤波(ETKF)算法在非线性强迫参数变化时的性能,同时探讨了4种算法在不同强度下的优劣。研究结果表明,新方法能够获得更有效的观测权重,避免了远距离观测与状态变量之间的虚假相关,减小由于观测数据难以得到有效利用而带来的误差,在不同观测误差协方差情况下,随着集合数的增加,4种算法中FLETKF能够保持较好的鲁棒性,在观测误差协方差较大时,FLETKF方法的均方根误差(RMSE)相对FETKF方法的RMSE值减小98.2%,提高了同化精度,但在同化所需时间上,由于模糊控制局地化同化方法在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需要较长的额外时间,因此,并行计算的性能需进一步研究。

Due to the fake correlation between distance-observations and assimilation-states during data assimilation, more attention has been paid to the localization method. Meanwhile, in the case of assimilation with a small number of sets, the observation data is difficult to be used effectively, which makes the assimilation effect not good enough. Therefore, a new fuzzy control was proposed to analyze the local method. The fuzzy control algorithm was used to judge the distance between the observation point and the status update point and to construct the fuzzy weight of the observation position. The study aimed to make use of the nonlinear Lorenz-96 model to compare the Fuzzy control combine Local Analysis algorithm (FLETKF) and Fuzzy control combine Ensemble Transform Kalman Filter method (FETKF), local Ensemble Transform Kalman Fliter (LETKF) and Ensemble Transform Kalman Filter algorithm (ETKF) when the nonlinear forced parameter changed. In addition, the strengths and weaknesses of four algorithms were discussed by different intensities. The results show that the new method can obtain more effective observation weights, avoiding the false correlation between long-distance observations and state variables, reducing the errors caused by the observation data which is difficult to be used effectively. Under different assimilation strength, FLETKF can maintain good robustness. However, in terms of assimilation time, the construction of the equivalent weight of the observation position requires additional time because the localization assimilation method of fuzzy control determines the distance between the observation point and the status update point. Parallel computing performance needs further study.