An Ensemble Kalman Filter and Smoother for Satellite Data Assimilation

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. O...

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Bibliographic Details
Published in:	Journal of the American Statistical Association Vol. 105; no. 491; pp. 978 - 990
Main Authors:	Stroud, Jonathan R., Stein, Michael L., Lesht, Barry M., Schwab, David J., Beletsky, Dmitry
Format:	Journal Article
Language:	English
Published:	Alexandria, VA Taylor & Francis 01.09.2010 American Statistical Association Assoc Taylor & Francis Ltd
Subjects:	Algorithms Applications Applications and Case Studies Artificial satellites Assimilation Bias Biology, psychology, social sciences Circulant embedding Computational methods Covariance Covariance matrices Covariance tapering Data analysis Data assimilation Data smoothing Diffusion models Distribution theory Environmental monitoring Exact sciences and technology Forecasting models Gaussian random field General topics Imagery Interpolation Kalman filters Lakes Mathematical vectors Mathematics Measurement Modeling Nonlinear state-space model Numerical analysis Numerical analysis. Scientific computation Numerical approximation Prediction models Probability and statistics Regression analysis Satellite imagery Satellites Sciences and techniques of general use Sediment concentration Sediment transport Sediments Simulation Spatial models Spatial statistics Spatio-temporal model State estimation State space models Statistical data Statistical forecasts Statistical methods Statistics Variogram Spatio-temporal model Statistical distribution Bias Kalman filter Prediction theory Embedding State space Variogram Multivariate analysis Stochastic process Spatial statistics Numerical approximation Random field Covariance Approximation theory Dynamic model State estimation Smoothing methods Nonlinear state-space model Covariance tapering Space time Statistical estimation State space method Algorithm Statistical method Interpolation Image analysis Smoothing Non linear model Gaussian field Environment Nonlinearity Circulant embedding Gaussian random field Biased estimation
ISSN:	0162-1459, 1537-274X
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Summary:	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.
Bibliography:	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 ObjectType-Feature-1 ObjectType-Article-2 content type line 23
ISSN:	0162-1459 1537-274X
DOI:	10.1198/jasa.2010.ap07636