Preprint Article Version 1 This version not peer reviewed

Evaluation of Analysis by Cross-Validation. Part II: Diagnostic and Optimization of Analysis Error Covariance

Version 1 : Received: 7 November 2017 / Approved: 7 November 2017 / Online: 7 November 2017 (10:09:42 CET)

A peer-reviewed article of this Preprint also exists.

Ménard, R.; Deshaies-Jacques, M. Evaluation of Analysis by Cross-Validation, Part II: Diagnostic and Optimization of Analysis Error Covariance. Atmosphere 2018, 9, 70. Ménard, R.; Deshaies-Jacques, M. Evaluation of Analysis by Cross-Validation, Part II: Diagnostic and Optimization of Analysis Error Covariance. Atmosphere 2018, 9, 70.

Journal reference: Atmosphere 2018, 9, 70
DOI: 10.3390/atmos9020070

Abstract

We present a general theory of estimation of analysis error covariances based on cross-validation as well as a geometric interpretation of the method. In particular we use the variance of passive observation–minus-analysis residuals and show that the true analysis error variance can be estimated, without relying on the optimality assumption. This approach is used to obtain near optimal analyses that are then used to evaluate the air quality analysis error using several different methods at active and passive observation sites. We compare the estimates according to the method of Hollingsworth-Lönnberg, Desroziers et al., a new diagnostic we developed, and the perceived analysis error computed from the analysis scheme, to conclude that, as long as the analysis is near optimal, all estimates agree within a certain error margin.

Subject Areas

data assimilation; statistical diagnostics of analysis residuals; estimation of analysis error, air quality model diagnostics; Desroziers et al. method; cross-validation

Readers' Comments and Ratings (0)

Leave a public comment
Send a private comment to the author(s)
Rate this article
Views 0
Downloads 0
Comments 0
Metrics 0
Leave a public comment

×
Alerts
Notify me about updates to this article or when a peer-reviewed version is published.