An Efficient State–Parameter Filtering Scheme Combining Ensemble Kalman and Particle Filters
KAUST DepartmentPhysical Sciences and Engineering (PSE) Division
Earth Science and Engineering Program
Permanent link to this recordhttp://hdl.handle.net/10754/627424
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AbstractThis work addresses the state-parameter filtering problem for dynamical systems with relatively large-dimensional state and low-dimensional parameters' vector. A Bayesian filtering algorithm combining the strengths of the particle filter (PF) and the ensemble Kalman filter (EnKF) is proposed. At each assimilation cycle of the proposed EnKF-PF, the PF is first used to sample the parameters' ensemble followed by the EnKF to compute the state ensemble conditional on the resulting parameters' ensemble. The proposed scheme is expected to be more efficient than the traditional state augmentation techniques, which suffer from the curse of dimensionality and inconsistency that is particularly pronounced when the state is a strongly nonlinear function of the parameters. In the new scheme, the EnKF and PF interact via their ensembles' members, in contrast with the recently introduced two-stage EnKF-PF (TS-EnKF-PF), which exchanges point estimates between EnKF and PF while requiring almost double the computational load. Numerical experiments are conducted with the Lorenz-96 model to assess the behavior of the proposed filter and to evaluate its performances against the joint PF, joint EnKF, and TS-EnKF-PF. Numerical results suggest that the EnKF-PF performs best in all tested scenarios. It was further found to be more robust, successfully estimating both state and parameters in different sensitivity experiments.
CitationAit-El-Fquih B, Hoteit I (2018) An Efficient State–Parameter Filtering Scheme Combining Ensemble Kalman and Particle Filters. Monthly Weather Review 146: 871–887. Available: http://dx.doi.org/10.1175/MWR-D-16-0485.1.
SponsorsResearch reported in this publication was supported by King Abdullah University of Science and Technology (KAUST).
PublisherAmerican Meteorological Society
JournalMonthly Weather Review