两种耦合模糊控制的局地化方法研究
收稿日期: 2020-10-09
修回日期: 2021-01-16
网络出版日期: 2021-04-19
基金资助
国家自然科学基金项目“中国西北干旱区均匀下垫面上稳定边界层非湍运动和湍流相互作用的观测研究”(41875009)
Two Localization Methods Based on Fuzzy Control
Received date: 2020-10-09
Revised date: 2021-01-16
Online published: 2021-04-19
Supported by
the National Natural Science Foundation of China “Observational study on the interaction between nonturbulence and turbulence in the stable boundary layer over the uniform underlying surface in the arid region of Northwest China”(41875009)
在集合数据同化过程中，由于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。此外，由于集合数的限制，容易引起欠采样和协方差被低估等现象，使得滤波效果欠佳。因此，提出模糊控制算法，模糊控制算法主要用于判断观测点与状态更新点之间的距离来匹配相应的观测权重，进而调整局地化系数来更新背景误差协方差和观测误差协方差矩阵，从而得到有效的状态估计。基于背景误差协方差局地化方法和观测误差协方差局地化方法，耦合模糊控制，形成了新的算法—模糊控制的背景误差协方差局地化方法和模糊控制的观测误差协方差局地化方法。利用Lorenz-96模型，在小集合数和局地化半径下，得出模糊控制的背景误差协方差局地化方法和模糊控制的观测误差协方差局地化方法有较好的同化性能。通过分析泰勒图谱甄别出新算法与观测点具有高度的相关性以及较小的空间变异性。最后，在不同维数的模糊控制器下，新算法的有效性进一步得到验证。为今后数据同化误差处理方面提供了良好的研究平台。
关键词： 数据同化; 模糊控制; 背景误差协方差局地化方法（BL）; 观测误差协方差局地化方法（RL）
常明恒 , 左洪超 , 摆玉龙 , 段济开 . 两种耦合模糊控制的局地化方法研究[J]. 地球科学进展, 2021 , 36(2) : 185 -197 . DOI: 10.11867/j.issn.1001-8166.2021.014
In the process of ensemble data assimilation， due to the false correlation between the remote observation and the assimilation state， which affects the performance of DA， more attention has been paid to the localization methods. In addition， because of the limited ensemble size， it is easy to cause phenomena such as under-sampling and underestimation of covariance， which makes the filtering effect divergent. The fuzzy control algorithm is proposed， which is mainly used to judge the distance between the observation point and the state update point to assign the corresponding observation weight to the observation point， and then adjust the localization coefficient to update the background error covariance and observation error covariance， respectively. Thus， an effective state estimation is obtained. Based on BL and RL method， coupled with fuzzy control， the Background Covariance Fuzzy （BCF） and Fuzzy Observation Covariance （FOC） were proposed. We conducted an experiment on the Lorenz-96 model， and the BCF and the FOC method exhibited better assimilation effect with the small ensemble size and localization radius. By analyzing the Taylor diagram， it was found that the new algorithms had a high correlation with the observation point and small spatial variability. Finally， the robustness of BCF and FOC algorithms was further verified under the different dimensional fuzzy controller. It will provide a good research platform for data assimilation error processing in the future.
Key words： Data assimilation; Fuzzy control; B localization; R localization
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