Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
5500227 | Physica D: Nonlinear Phenomena | 2017 | 10 Pages |
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.
Related Topics
Physical Sciences and Engineering
Mathematics
Applied Mathematics
Authors
John Maclean, Naratip Santitissadeekorn, Christopher K.R.T. Jones,