Journal
CHAOS
Volume 19, Issue 1, Pages -Publisher
AMER INST PHYSICS
DOI: 10.1063/1.3072780
Keywords
chaos; convection; data assimilation; Kalman filters; Rayleigh-Benard instability; spatiotemporal phenomena
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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-Benard 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.
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