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.
The manuscript is available in PDF format.