Please use this identifier to cite or link to this item: http://hdl.handle.net/2440/118101
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Type: Journal article
Title: A coherent structure approach for parameter estimation in Lagrangian Data Assimilation
Author: Maclean, J.
Santitissadeekorn, N.
Jones, C.K.
Citation: Physica D: Nonlinear Phenomena, 2017; 360:36-45
Publisher: Elsevier
Issue Date: 2017
ISSN: 0167-2789
1872-8022
Statement of
Responsibility: 
John Maclean, Naratip Santitissadeekorn, Christopher K.R.T. Jones
Abstract: We introduce a data assimilation method to estimate model parameters with observations of passive tracers by directly assimilating Lagrangian Coherent Structures. Our approach differs from the usual Lagrangian Data Assimilation approach, where parameters are estimated based on tracer trajectories. We employ the Approximate Bayesian Computation (ABC) framework to avoid computing the likelihood function of the coherent structure, which is usually unavailable. We solve the ABC by a Sequential Monte Carlo (SMC) method, and use Principal Component Analysis (PCA) to identify the coherent patterns from tracer trajectory data. Our new method shows remarkably improved results compared to the bootstrap particle filter when the physical model exhibits chaotic advection.
Keywords: Data assimilation; Lagrangian data; coherent structures
Rights: © 2017 Elsevier B.V. All rights reserved.
RMID: 0030105503
DOI: 10.1016/j.physd.2017.08.007
Appears in Collections:Mathematical Sciences publications

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