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 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.
Cornick
Matthew
M
University of Maryland, College Park, Maryland 20742, USA.
Hunt
Brian
B
Ott
Edward
E
Kurtuldu
Huseyin
H
Schatz
Michael F
MF