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Advanced Data Assimilation for GeosciencesLecture Notes of the Les Houches School of Physics: Special Issue, June 2012$
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Éric Blayo, Marc Bocquet, Emmanuel Cosme, and Leticia F. Cugliandolo

Print publication date: 2014

Print ISBN-13: 9780198723844

Published to Oxford Scholarship Online: March 2015

DOI: 10.1093/acprof:oso/9780198723844.001.0001

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PRINTED FROM OXFORD SCHOLARSHIP ONLINE (oxford.universitypressscholarship.com). (c) Copyright Oxford University Press, 2021. All Rights Reserved. An individual user may print out a PDF of a single chapter of a monograph in OSO for personal use. date: 08 December 2021

Error dynamics in ensemble Kalman-filter systems

Error dynamics in ensemble Kalman-filter systems


(p.255) 10 Error dynamics in ensemble Kalman-filter systems
Advanced Data Assimilation for Geosciences

P. Houtekamer

Oxford University Press

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.

Keywords:   localization, ensemble Kalman filter, toy model, model dynamics, observational network

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