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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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Observation influence diagnostic of a data assimilation system

Observation influence diagnostic of a data assimilation system

Chapter:
(p.137) 5 Observation influence diagnostic of a data assimilation system
Source:
Advanced Data Assimilation for Geosciences
Author(s):

C. Cardinali

Publisher:
Oxford University Press
DOI:10.1093/acprof:oso/9780198723844.003.0005

This chapter presents the concept of the influence matrix. Because of the cost and the possible redundancy of observations, particularly in numerical weather prediction systems, quantifying the influence of individual observations on data assimilation analysis is increasingly important. This chapter describes how the influence matrix is used to examine the influence of individual data on the analysis, the analysis change that would occur by leaving one observation out, and the effective information content (degrees of freedom for the signal) in any subset of the analysed data. A toy model is introduced to illustrate how the observation influence depends on the data assimilation covariance matrices. It is shown, in particular, that this influence strongly depends on the relative nature of the background and observation error covariance matrices.

Keywords:   observation influence, influence matrix, numerical weather prediction, covariance matrices, degrees of freedom for the signal

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