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A random map implementation of implicit filters
"... Implicit particle filters for data assimilation generate highprobability samples by representing each particle location as a separate function of a common reference variable. This representation requires that a certain underdetermined equation be solved for each particle and at each time an observa ..."
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Cited by 23 (13 self)
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Implicit particle filters for data assimilation generate highprobability samples by representing each particle location as a separate function of a common reference variable. This representation requires that a certain underdetermined equation be solved for each particle and at each time an observation becomes available. We present a new implementation of implicit filters in which we find the solution of the equation via a random map. As examples, we assimilate data for a stochastically driven Lorenz system with sparse observations and for a stochastic KuramotoSivashinski equation with observations that are sparse in both space and time.
Implicit particle filters for data assimilation
, 2010
"... Implicit particle filters for data assimilation update the particles by first choosing probabilities and then looking for particle locations that assume them, guiding the particles one by one to the high probability domain. We provide a detailed description of these filters, with illustrative exampl ..."
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Cited by 21 (14 self)
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Implicit particle filters for data assimilation update the particles by first choosing probabilities and then looking for particle locations that assume them, guiding the particles one by one to the high probability domain. We provide a detailed description of these filters, with illustrative examples, together with new, more general, methods for solving the algebraic equations and with a new algorithm for parameter identification. 1
CSU Account #: 533023
, 2008
"... This research project has produced a novel methodology for ensemble data assimilation (EnsDA) based on control theory, named the Maximum Likelihood Ensemble Filter (MLEF). This methodology extends the applicability of current nonlinear filters to arbitrary nonlinear observation operators, with impor ..."
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This research project has produced a novel methodology for ensemble data assimilation (EnsDA) based on control theory, named the Maximum Likelihood Ensemble Filter (MLEF). This methodology extends the applicability of current nonlinear filters to arbitrary nonlinear observation operators, with important implications to remote sensing and other nonlinear observations. In addition to the main goal of developing an ensemble data assimilation system based on control theory, at least two major new results were produced by this research: (1) nonGaussian framework for data assimilation was first formulated within this project, and is now gaining a worldwide recognition, and (2) new nondifferentiable unconstrained minimizations algorithms are formulated and tested within this project. In addition, the issue of insufficient number of degrees of freedom (DOF) was addressed during last year (20072008), resulting in an improvement of currently used error covariance localization techniques. To date, this project produced 13 papers (10 published, 2 submitted, 1 to be submitted, referenced below). Two postdoctoral researchers are trained under this project; one at Colorado State
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"... INFORMATION TO ALL USERS The quality of this reproduction is dependent on the quality of the copy submitted. In the unlikely event that the author did not send a complete manuscript and there are missing pages, these will be noted. Also, if material had to be removed, a note will indicate the delet ..."
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INFORMATION TO ALL USERS The quality of this reproduction is dependent on the quality of the copy submitted. In the unlikely event that the author did not send a complete manuscript and there are missing pages, these will be noted. Also, if material had to be removed, a note will indicate the deletion. All rights reserved. This edition of the work is protected against
NSF Collaboration in Mathematical Geosciences Research
, 2008
"... This research project has produced a novel methodology for ensemble data assimilation (EnsDA) based on control theory, named the Maximum Likelihood Ensemble Filter (MLEF). This methodology extends the applicability of current nonlinear filters to arbitrary nonlinear observation operators, with impor ..."
Abstract
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This research project has produced a novel methodology for ensemble data assimilation (EnsDA) based on control theory, named the Maximum Likelihood Ensemble Filter (MLEF). This methodology extends the applicability of current nonlinear filters to arbitrary nonlinear observation operators, with important implications to remote sensing and other nonlinear observations. In addition to the main goal of developing an ensemble data assimilation system based on control theory, at least two major new results were produced by this research: (1) nonGaussian framework for data assimilation was first formulated within this project, and is now gaining a worldwide recognition, and (2) new nondifferentiable unconstrained minimizations algorithms are formulated and tested within this project. In addition, the issue of insufficient number of degrees of freedom (DOF) was addressed during last year (20072008), resulting in an improvement of currently used error covariance localization techniques. To date, this project produced 13 papers (10 published, 2 submitted, 1 to be submitted, referenced below). Two postdoctoral researchers are trained under this project; one at Colorado State