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An Ensemble Kalman Filter and Smoother for Satellite Data Assimilation.
- Source :
-
Journal of the American Statistical Association . Sep2010, Vol. 105 Issue 491, p978-990. 13p. 1 Chart, 1 Graph. - Publication Year :
- 2010
-
Abstract
- 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. [ABSTRACT FROM AUTHOR]
- Subjects :
- *MARINE sediments
*KALMAN filtering
*ESTIMATION theory
Subjects
Details
- Language :
- English
- ISSN :
- 01621459
- Volume :
- 105
- Issue :
- 491
- Database :
- Academic Search Index
- Journal :
- Journal of the American Statistical Association
- Publication Type :
- Academic Journal
- Accession number :
- 54493598
- Full Text :
- https://doi.org/10.1198/jasa.2010.ap07636