Jump to ContentJump to Main Navigation
Advanced Data Assimilation for GeosciencesLecture Notes of the Les Houches School of Physics: Special Issue, June 2012$
Users without a subscription are not able to see the full content.

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

Show Summary Details
Page of

PRINTED FROM OXFORD SCHOLARSHIP ONLINE (www.oxfordscholarship.com). (c) Copyright Oxford University Press, 2019. All Rights Reserved. Under the terms of the licence agreement, an individual user may print out a PDF of a single chapter of a monograph in OSO for personal use (for details see www.oxfordscholarship.com/page/privacy-policy).date: 22 July 2019

Errors. A posteriori diagnostics

Errors. A posteriori diagnostics

Chapter:
(p.229) 9 Errors. A posteriori diagnostics
Source:
Advanced Data Assimilation for Geosciences
Author(s):

O. Talagrand

Publisher:
Oxford University Press
DOI:10.1093/acprof:oso/9780198723844.003.0009

This chapter discusses three questions concerning the implementation and validation of assimilation algorithms. Objective estimation of the quality of the results of an assimilation system can be done only through comparison against independent data. Objective estimation of the statistics of the errors in the data is discussed in the context of best linear unbiased estimation, which underlies both the Kalman filter and variational assimilation. It is shown that those statistics remain totally undetermined in the absence of independent hypotheses, which cannot be objectively validated just through processing of the data. The most that can be done is to check consistency between the statistics of the innovation, as a priori implied by the specification of the data error and as a posteriori observed. Optimality of a least variance estimation procedure can be objectively evaluated against independent data using the fact that the estimation error must be statistically uncorrelated with the innovation.

Keywords:   objective estimation, statistics of errors, best linear unbiased estimation, innovation, independent data, optimality

Oxford Scholarship Online requires a subscription or purchase to access the full text of books within the service. Public users can however freely search the site and view the abstracts and keywords for each book and chapter.

Please, subscribe or login to access full text content.

If you think you should have access to this title, please contact your librarian.

To troubleshoot, please check our FAQs , and if you can't find the answer there, please contact us .