An Ensemble Kalman Filter and Smoother for Satellite Data Assimilation

作者：Stroud Jonathan R^*; Stein Michael L; Lesht Barry M; Schwab David J; Beletsky Dmitry

来源：Journal of the American Statistical Association, 2010, 105(491): 978-990.

DOI：10.1198/jasa.2010.ap07636

• Circulant embedding
• Covariance tapering
• Gaussian random field
• Nonlinear state-space model
• Spatial statistics
• Spatio-temporal model
• Variogram

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 an ensemble Kalman filter and smoothing algorithm for on-line and retrospective state estimation. Our approach addresses the high dimensionality, measurement bias, and nonlinearities inherent in satellite data. We apply the method to a sequence of SeaWiFS satellite images in Lake Michigan from March 1998, when a large sediment plume was observed in the images following a major storm event. Using our approach, we combine the images with a sediment transport model to produce maps of sediment concentrations and uncertainties over space and time. We show that our approach improves out-of-sample RMSE by 20%-30% relative to standard approaches. This article has supplementary material online.

• 出版日期2010-9