A Comparative Study of Localization Methods in EnkF Data Assimilation

2019 2nd International Conference of Green Buildings and Environmental Management

生态环境科学

Bai, Yulong^1 ; Ma, Xiaoyan^1 ; Tang, Lihong^1

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

730070, China^1

关键词: Background-error covariances;    Comparative studies;    Ensemble Kalman Filter;    Error covariance matrix;    Localization method;    Numerical experiments;    Power spectrum density;    Science applications;   

Others  :  https://iopscience.iop.org/article/10.1088/1755-1315/310/2/022035/pdf
DOI  :  10.1088/1755-1315/310/2/022035

学科分类：环境科学（综合）

来源: IOP

【 摘 要 】

This study investigates the relation between some common localization methods in ensemble Kalman filter (EnKF) systems including covariance localization (CL) and local analysis (LA), which are popular used in large-scale Geo-science applications. Two fuzzy-based localization methods, named covariance fuzzy (CF) and fuzzy analysis (FA), are formulated in terms of tapering of ensemble covariance in a fuzzy logic way. To explore the effects of all algorithms on the error covariance matrix, numerical experiments are designed using a classical nonlinear model (i.e., the Lorenz-96 model) by determining the Power Spectrum Density (PSD) of the corresponding systems. The experiments show that the new algorithms can eliminate spurious correlation of the background error covariance matrix. The results of PSD demonstrate that the new fuzzy based methods have more robust performance than the CL or LA algorithm.