An Ensemble Kalman Filter and Smoother for Satellite Data Assimilation

Jonathan R. Stroud, Michael L. Stein, Barry M. Lesht, David J. Schwab, Dmitry Beletsky

George Washington University, University of Chicago, University of Illinois-Chicago, NOAA-GLERL, and University of Michigan

This paper proposes a methodology for combining satellite images with advection-diffusion models for interpolation and prediction of environmental processes. We propose a dynamic state-space model and ensemble Kalman filter and smoothing algorithms for on-line and retrospective state estimation. Our approach adddresses the nonlinearities, high-dimensionality and measurement bias inherent in satellite data. We apply our method to a sequence of SeaWiFS satellite images in Lake Michigan from March 1998, showing the development of a large sediment plume. Using this approach, we combine the images with a sediment transport model to estimate the sediment concentrations and uncertainties over space and time.

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