State and parameter estimation of spatiotemporally chaotic systems illustrated by an application to Rayleigh–Bénard convection

Data assimilation refers to the process of estimating a system’s state from a time series of measurements (which may be noisy or incomplete) in conjunction with a model for the system’s time evolution. Here we demonstrate the applicability of a recently developed data assimilation method, the local...

Full description

Saved in:
Bibliographic Details
Published in:Chaos (Woodbury, N.Y.) N.Y.), 2009-03, Vol.19 (1), p.013108-013108-10
Main Authors: Cornick, Matthew, Hunt, Brian, Ott, Edward, Kurtuldu, Huseyin, Schatz, Michael F.
Format: Article
Language:English
Subjects:
Algorithms
Models, Theoretical
Nonlinear Dynamics
Reproducibility of Results
Systems Theory
Temperature
Time Factors
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Data assimilation refers to the process of estimating a system’s state from a time series of measurements (which may be noisy or incomplete) in conjunction with a model for the system’s time evolution. Here we demonstrate the applicability of a recently developed data assimilation method, the local ensemble transform Kalman filter, to nonlinear, high-dimensional, spatiotemporally chaotic flows in Rayleigh–Bénard convection experiments. Using this technique we are able to extract the full temperature and velocity fields from a time series of shadowgraph measurements. In addition, we describe extensions of the algorithm for estimating model parameters. Our results suggest the potential usefulness of our data assimilation technique to a broad class of experimental situations exhibiting spatiotemporal chaos.
ISSN:1054-1500
1089-7682
DOI:10.1063/1.3072780