Paper Info
Title
An Ensemble Kalman-Particle Predictor-Corrector Filter for Non-Gaussian Data Assimilation
Abstract
An Ensemble Kalman Filter (EnKF, the predictor) is used make a large change in the state, followed by a Particle Filer (PF, the corrector), which assigns importance weights to describe a non-Gaussian distribution. The importance weights are obtained by nonparametric density estimation. It is demonstrated on several numerical examples that the new predictor-corrector filter combines the advantages of the EnKF and the PF and that it is suitable for high dimensional states which are discretizations of solutions of partial differential equations.
Year
DOI
Venue
2009
10.1007/978-3-642-01973-9_53
ICCS (2)
Keywords
Field
DocType
non-gaussian distribution,new predictor-corrector filter,high dimensional state,non-gaussian data assimilation,nonparametric density estimation,large change,partial differential equation,ensemble kalman filter,particle filer,ensemble kalman-particle predictor-corrector filter,numerical example,importance weight,statistical computing,gaussian distribution,data assimilation
Mathematical optimization,Extended Kalman filter,Particle filter,Kalman filter,Gaussian,Data assimilation,Ensemble Kalman filter,Statistics,Partial differential equation,Predictor–corrector method,Mathematics
Conference
Volume
ISSN
Citations 
5545
Lecture Notes in Computer Science 5545, 470-478, 2009
5
PageRank 
References 
Authors
0.78
2
2
Name
Order
Citations
PageRank
Jan Mandel144469.36
Jonathan D. Beezley210114.55