期刊名称：IOP Conference Series: Earth and Environmental Science
印刷版ISSN：1755-1307
电子版ISSN：1755-1315
出版年度：2019
卷号：310
期号：2
页码：1-6
DOI：10.1088/1755-1315/310/2/022035
出版社：IOP Publishing
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.
