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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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Background error covariances

Background error covariances

estimation and specification

(p.184) (p.185) 7 Background error covariances
Advanced Data Assimilation for Geosciences

L. Berre

Oxford University Press

This chapter deals with the estimation and specification of realistic background error covariances, which is a key issue in data assimilation, since these covariances are used to filter and propagate observations. The underlying equations of error evolution are summarized, and associated simulation techniques are also presented, based on either ensemble techniques or the so-called NMC method. Another important source of information on background error covariances corresponds to innovation-based estimates, which may be combined with ensemble data assimilation to estimate model error covariances, for instance. Moreover, because the covariance matrix is huge, diagnostics of its main components and salient features have to be employed. The possibility of modelling this matrix using a sequence of sparse operators is then reviewed, in addition to filtering methods that account for the finite ensemble size and associated sampling noise effects.

Keywords:   background error, error evolution, ensemble techniques, NMC method, innovation-based estimates, covariance matrix, filtering

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