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Open AccessArticle

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

Air Quality Research Division, Environment and Climate Change Canada, 2121 Transcanada Highway, Dorval, QC H9P 1J3, Canada
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Atmosphere 2018, 9(2), 70; https://doi.org/10.3390/atmos9020070
Received: 7 November 2017 / Revised: 19 January 2018 / Accepted: 13 February 2018 / Published: 15 February 2018
(This article belongs to the Special Issue Air Quality Monitoring and Forecasting)
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. View Full-Text
Keywords: data assimilation; statistical diagnostics of analysis residuals; estimation of analysis error; air quality model diagnostics; Desroziers et al. method; cross-validation data assimilation; statistical diagnostics of analysis residuals; estimation of analysis error; air quality model diagnostics; Desroziers et al. method; cross-validation
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Ménard, R.; Deshaies-Jacques, M. Evaluation of Analysis by Cross-Validation, Part II: Diagnostic and Optimization of Analysis Error Covariance. Atmosphere 2018, 9, 70.

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