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A localized ensemble Kalman smoother

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dc.contributor.author Butala, Mark D.
dc.date.accessioned 2012-12-06T16:47:01Z
dc.date.available 2012-12-06T16:47:01Z
dc.date.issued 2012-08-05
dc.identifier.citation IEEE - 2012 IEEE Statistical Signal Processing Workshop (SSP), Ann Arbor, Michigan, August 5-8, 2012 en_US
dc.identifier.clearanceno 12-2037
dc.identifier.uri http://hdl.handle.net/2014/42495
dc.description.abstract Numerous geophysical inverse problems prove difficult because the available measurements are indirectly related to the underlying unknown dynamic state and the physics governing the system may involve imperfect models or unobserved parameters. Data assimilation addresses these difficulties by combining the measurements and physical knowledge. The main challenge in such problems usually involves their high dimensionality and the standard statistical methods prove computationally intractable. This paper develops and addresses the theoretical convergence of a new high-dimensional Monte-Carlo approach called the localized ensemble Kalman smoother. en_US
dc.description.sponsorship NASA-JPL en_US
dc.language.iso en_US en_US
dc.publisher Pasadena, CA : Jet Propulsion Laboratory, National Aeronautics and Space Administration, 2012. en_US
dc.subject multidimensional signal processing en_US
dc.subject recursive estimation en_US
dc.subject Kalman filter en_US
dc.subject remote sensing en_US
dc.title A localized ensemble Kalman smoother en_US
dc.type Preprint en_US
dc.subject.NASATaxonomy Statistics and Probability en_US


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