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Abstract Details
Activity Number:
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463
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Type:
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Contributed
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Date/Time:
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Wednesday, August 1, 2012 : 8:30 AM to 10:20 AM
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Sponsor:
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Section on Physical and Engineering Sciences
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Abstract - #305666 |
Title:
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Filtering Partially Observed Multiscale Systems with Heterogeneous-Multiscale-Methods-Based Reduced Models
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Author(s):
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Emily Lei Kang*+ and John Harlim
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Companies:
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University of Cincinnati and North Carolina State University
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Address:
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3585 Springlake Circle, Loveland, OH, 45140, United States
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Keywords:
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ensemble Kalman filter ;
method-of-Moments ;
multiscale system ;
posteior ;
prior
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Abstract:
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A fast filtering strategy for assimilating multiscale systems in the presence of observations of only the macroscopic (or large scale) variables will be presented. This filtering strategy is built through a reduced model based on the heterogeneous multiscale methods (HMM), and is not restricted to any analysis (or Bayesian updating) step from various ensemble-based filters. This filtering strategy can be applied even when separation of scales is not apparent as typically observed in geophysical turbulent systems, since it incorporates an additional procedure to reinitialize microscopic variables to statistically reflect pseudo-observations that are constructed based on the estimates of the macroscopic variables. The proposed method has been shown to be comparable to the more expensive standard filter through direct numerical simulation (DNS), on a stringent test bed, the two-layer Lorenz'96 model, in various regimes of scale separation, including the not so apparent one. Its high filtering skill is even robust in the presence of additional model errors through inconsistent pseudo-observations and when macroscopic observations are spatially incomplete.
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