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An ensemble-based smoother with retrospectively updated weights for highly nonlinear systems

Show simple item record Chin, T. M. Turmon, M. J. Jewell, J. B. Ghil, M. 2007-08-16T22:51:58Z 2007-08-16T22:51:58Z 2006-07-12
dc.identifier.citation Monthly Weather Review Vol. 135 pp 186-202 DOI: 10.1175/MWR3353.1 en
dc.identifier.clearanceno 05-2243
dc.description.abstract Monte Carlo computational methods have been introduced into data assimilation for nonlinear systems in order to alleviate the computational burden of updating and propagating the full probability distribution. By propagating an ensemble of representative states, algorithms like the ensemble Kalman filter (EnKF) and the resampled particle filter (RPF) rely on the existing modeling infrastructure to approximate the distribution based on the evolution of this ensemble. This work presents an ensemble-based smoother that is applicable to the Monte Carlo filtering schemes like EnKF and RPF. At the minor cost of retrospectively updating a set of weights for ensemble members, this smoother has demonstrated superior capabilities in state tracking for two highly nonlinear problems: the double-well potential and trivariate Lorenz systems. The algorithm does not require retrospective adaptation of the ensemble members themselves, and it is thus suited to a streaming operational mode. The accuracy of the proposed backward-update scheme in estimating non-Gaussian distributions is evaluated by comparison to the more accurate estimates provided by a Markov chain Monte Carlo algorithm. en
dc.description.sponsorship NASA/JPL en
dc.format.extent 1529538 bytes
dc.format.mimetype application/pdf
dc.language.iso en_US en
dc.publisher American Meteorological Society en
dc.subject Monte Carlo en
dc.subject filtering en
dc.subject algorithm en
dc.title An ensemble-based smoother with retrospectively updated weights for highly nonlinear systems en
dc.type Article en

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