DOI: 10.1002/2014MS000373
Scopus记录号: 2-s2.0-85027957176
论文题名: A modified ensemble Kalman particle filter for non-Gaussian systems with nonlinear measurement functions
作者: Shen Z ; , Tang Y
刊名: Journal of Advances in Modeling Earth Systems
ISSN: 19422466
出版年: 2015
卷: 7, 期: 1 起始页码: 50
结束页码: 66
语种: 英语
英文关键词: Bayesian networks
; Covariance matrix
; Gaussian distribution
; Gaussian noise (electronic)
; Kalman filters
; Monte Carlo methods
; Nonlinear systems
; Bayesian estimations
; Data assimilation
; Ensemble Kalman Filter
; Non-Gaussian
; Particle filter
; Algorithms
; algorithm
; computer simulation
; data assimilation
; Gaussian method
; Kalman filter
; measurement method
; nonlinearity
; numerical model
; observational method
英文摘要: The ensemble Kalman particle filter (EnKPF) is a combination of two Bayesian-based algorithms, namely, the ensemble Kalman filter (EnKF) and the sequential importance resampling particle filter (SIR-PF). It was recently introduced to address non-Gaussian features in data assimilation for highly nonlinear systems, by providing a continuous interpolation between the EnKF and SIR-PF analysis schemes. In this paper, we first extend the EnKPF algorithm by modifying the formula for the computation of the covariance matrix, making it suitable for nonlinear measurement functions (we will call this extended algorithm nEnKPF). Further, a general form of the Kalman gain is introduced to the EnKPF to improve the performance of the nEnKPF when the measurement function is highly nonlinear (this improved algorithm is called mEnKPF). The Lorenz '63 model and Lorenz '96 model are used to test the two modified EnKPF algorithms. The experiments show that the mEnKPF and nEnKPF, given an affordable ensemble size, can perform better than the EnKF for the nonlinear systems with nonlinear observations. These results suggest a promising opportunity to develop a non-Gaussian scheme for realistic numerical models. © 2014. The Authors. Citation statistics:
资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/76065
Appears in Collections: 影响、适应和脆弱性 气候变化与战略
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作者单位: State Key Laboratory of Satellite Ocean Environment Dynamics, Second Institute of Oceanography, State Oceanic Administration, Hangzhou, China; Environmental Science and Engineering, University of Northern British Columbia, Prince George, BC, Canada
Recommended Citation:
Shen Z,, Tang Y. A modified ensemble Kalman particle filter for non-Gaussian systems with nonlinear measurement functions[J]. Journal of Advances in Modeling Earth Systems,2015-01-01,7(1)