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A coherent structure approach for parameter estimation in Lagrangian Data Assimilation

Maclean, J, Santitissadeekorn, Naratip and Jones, CKRT (2017) A coherent structure approach for parameter estimation in Lagrangian Data Assimilation Physica D: Nonlinear Phenomena, 360. pp. 36-45.

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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.

Item Type: Article
Divisions : Faculty of Engineering and Physical Sciences > Mathematics
Authors :
NameEmailORCID
Maclean, JUNSPECIFIEDUNSPECIFIED
Santitissadeekorn, Naratipn.santitissadeekorn@surrey.ac.ukUNSPECIFIED
Jones, CKRTUNSPECIFIEDUNSPECIFIED
Date : 5 September 2017
Identification Number : 10.1016/j.physd.2017.08.007
Copyright Disclaimer : © 2017. This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/
Uncontrolled Keywords : Data assimilation; Lagrangian data; Coherent structures
Depositing User : Melanie Hughes
Date Deposited : 21 Sep 2017 12:27
Last Modified : 13 Oct 2017 14:12
URI: http://epubs.surrey.ac.uk/id/eprint/842365

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