A coherent structure approach for parameter estimation in Lagrangian Data Assimilation

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dc.contributor.authorMaclean, J.
dc.contributor.authorSantitissadeekorn, N.
dc.contributor.authorJones, C.K.
dc.date.issued2017
dc.description.abstractWe 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.
dc.description.statementofresponsibilityJohn Maclean, Naratip Santitissadeekorn, Christopher K.R.T. Jones
dc.identifier.citationPhysica D: Nonlinear Phenomena, 2017; 360:36-45
dc.identifier.doi10.1016/j.physd.2017.08.007
dc.identifier.issn0167-2789
dc.identifier.issn1872-8022
dc.identifier.orcidMaclean, J. [0000-0002-5533-0838]
dc.identifier.urihttp://hdl.handle.net/2440/118101
dc.language.isoen
dc.publisherElsevier
dc.rights© 2017 Elsevier B.V. All rights reserved.
dc.source.urihttps://doi.org/10.1016/j.physd.2017.08.007
dc.subjectData assimilation; Lagrangian data; coherent structures
dc.titleA coherent structure approach for parameter estimation in Lagrangian Data Assimilation
dc.typeJournal article
pubs.publication-statusPublished

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