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Diagnosis of observation, background and analysis‐error statistics in observation space

Desroziers, G., Berre, L., Chapnik, B., Poli, P.·2005·Quarterly Journal of the Royal Meteorological Society

Abstract

Most operational assimilation schemes rely on linear estimation theory. Under this assumption, it is shown how simple consistency diagnostics can be obtained for the covariances of observation, background and estimation errors in observation space. Those diagnostics are shown to be nearly cost‐free since they only combine quantities available after the analysis, i.e. observed values and their background and analysis counterparts in observation space. A first application of such diagnostics is presented on analyses provided by the French 4D‐Var assimilation. A procedure to refine background and observation‐error variances is also proposed and tested in a simple toy analysis problem. The possibility to diagnose cross‐correlations between observation errors is also investigated in this same simple framework. A spectral interpretation of the diagnosed covariances is finally presented, which allows us to highlight the role of the scale separation between background and observation errors. Copyright © 2005 Royal Meteorological Society

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Metadata

Authors
Desroziers, G., Berre, L., Chapnik, B., & Poli, P.
Venue
Quarterly Journal of the Royal Meteorological Society
Publisher
Wiley
Published
2005-10
Volume
131
Issue
613
Pages
3385-3396
ISSN
  • 0035-9009 (ISSN-L)
  • 1477-870X
OpenAlex
W2166317254
Cited by
971
References
0
FWCI
3.41
Type
journal article
Language
en
Open access
closed

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CoChat. (2026). Citation check: Diagnosis of observation, background and analysis‐error statistics in observation space. https://app.cochat.ai/papers/10.1256/qj.05.108
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Desroziers, G., Berre, L., Chapnik, B., & Poli, P. (2005). Diagnosis of observation, background and analysis‐error statistics in observation space. Quarterly Journal of the Royal Meteorological Society. https://doi.org/10.1256/qj.05.108

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Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.