A modified iterative ensemble Kalman filter data assimilation method

2nd International Conference on Materials Science, Energy Technology and Environmental Engineering

材料科学;能源学;生态环境科学

Xu, Baoxiong^1 ; Bai, Yulong^1 ; Wang, Yizhao^1 ; Li, Zhe^1 ; Ma, Boyang^1

College of Physics and Electrical Engineering, Northwest Normal University, Lanzhou

730070, China^1

关键词: Comparative research;    Data assimilation methods;    Data assimilation systems;    Ensemble Kalman Filter;    Ensemble-based method;    Gauss-Newton iteration;    Global conver-gence;    Strongly nonlinear system;   

Others  :  https://iopscience.iop.org/article/10.1088/1755-1315/81/1/012197/pdf
DOI  :  10.1088/1755-1315/81/1/012197

来源: IOP

【 摘 要 】

High nonlinearity is a typical characteristic associated with data assimilation systems. Additionally, iterative ensemble based methods have attracted a large amount of research attention, which has been focused on dealing with nonlinearity problems. To solve the local convergence problem of the iterative ensemble Kalman filter, a modified iterative ensemble Kalman filter algorithm was put forward, which was based on a global convergence strategy from the perspective of a Gauss-Newton iteration. Through self-adaption, the step factor was adjusted to enable every iteration to approach expected values during the process of the data assimilation. A sensitivity experiment was carried out in a low dimensional Lorenz-63 chaotic system, as well as a Lorenz-96 model. The new method was tested via ensemble size, observation variance, and inflation factor changes, along with other aspects. Meanwhile, comparative research was conducted with both a traditional ensemble Kalman filter and an iterative ensemble Kalman filter. The results showed that the modified iterative ensemble Kalman filter algorithm was a data assimilation method that was able to effectively estimate a strongly nonlinear system state.