
Contents
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10.1 Motivation 10.1 Motivation
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10.2 Estimation of scalars and matrices 10.2 Estimation of scalars and matrices
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10.3 Assimilation of one observation 10.3 Assimilation of one observation
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10.4 Experiments with the Lorenz III model 10.4 Experiments with the Lorenz III model
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10.4.1 Sparse and randomly located inaccurate observations 10.4.1 Sparse and randomly located inaccurate observations
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10.4.2 Dense and randomly located accurate observations 10.4.2 Dense and randomly located accurate observations
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10.5 Discussion 10.5 Discussion
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References References
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10 Error dynamics in ensemble Kalman-filter systems: localization
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Published:October 2014
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Abstract
In this chapter, an experimental environment built around the Lorenz III toy model is used to demonstrate some points concerning localization. In an ensemble Kalman filter, localization is almost always necessary because of restrictions on the size of the ensembles. In fact, localization is the key technique that makes the ensemble approximation to the Kalman filter computationally feasible. How localization is best applied depends on aspects of the model dynamics and the observational network. A reasonable choice often leads to a substantial improvement in performance. Fortunately, as shown in this chapter, the statistics from the ensemble itself can provide guidance in the selection of a reasonable localization method.
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