E. Cosme
- Published in print:
- 2014
- Published Online:
- March 2015
- ISBN:
- 9780198723844
- eISBN:
- 9780191791185
- Item type:
- chapter
- Publisher:
- Oxford University Press
- DOI:
- 10.1093/acprof:oso/9780198723844.003.0004
- Subject:
- Physics, Geophysics, Atmospheric and Environmental Physics
This chapter describes the use of smoothers in data assimilation. The filtering problem in data assimilation consists in estimating the state of a system based on past and present observations. In ...
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This chapter describes the use of smoothers in data assimilation. The filtering problem in data assimilation consists in estimating the state of a system based on past and present observations. In contrast to filters, amoothers implement Bayesian data assimilation using future observations. Smoothing problems can be posed in different ways. The main formulations in geophysics are fixed-point, fixed-interval, and fixed-lag smoothers. In this chapter, these problems are first introduced in a Bayesian framework, and the most straightforward Bayesian solutions are formulated. Common linear, Gaussian implementations, many of which are based on the classical Kalman filter, are then derived, followed by their ensemble counterparts, based on the usual ensemble Kalman filter techniques. Finally, the pros and cons, as well as the computational complexities, of all the schemes are discussed.Less
This chapter describes the use of smoothers in data assimilation. The filtering problem in data assimilation consists in estimating the state of a system based on past and present observations. In contrast to filters, amoothers implement Bayesian data assimilation using future observations. Smoothing problems can be posed in different ways. The main formulations in geophysics are fixed-point, fixed-interval, and fixed-lag smoothers. In this chapter, these problems are first introduced in a Bayesian framework, and the most straightforward Bayesian solutions are formulated. Common linear, Gaussian implementations, many of which are based on the classical Kalman filter, are then derived, followed by their ensemble counterparts, based on the usual ensemble Kalman filter techniques. Finally, the pros and cons, as well as the computational complexities, of all the schemes are discussed.
