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Verification Against Perturbed Analyses and Observations : Volume 22, Issue 4 (24/07/2015)

By Bowler, N. E.

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Book Id: WPLBN0004019842
Format Type: PDF Article :
File Size: Pages 9
Reproduction Date: 2015

Title: Verification Against Perturbed Analyses and Observations : Volume 22, Issue 4 (24/07/2015)  
Author: Bowler, N. E.
Volume: Vol. 22, Issue 4
Language: English
Subject: Science, Nonlinear, Processes
Collections: Periodicals: Journal and Magazine Collection (Contemporary), Copernicus GmbH
Publication Date:
Publisher: Copernicus Gmbh, Göttingen, Germany
Member Page: Copernicus Publications


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Piccolo, C., P. Culle, M. J., & Bowler, N. E. (2015). Verification Against Perturbed Analyses and Observations : Volume 22, Issue 4 (24/07/2015). Retrieved from

Description: Met Office, Fitzroy Road, Exeter, EX1 3PB, UK. It has long been known that verification of a forecast against the sequence of analyses used to produce those forecasts can under-estimate the magnitude of forecast errors. Here we show that under certain conditions the verification of a short-range forecast against a perturbed analysis coming from an ensemble data assimilation scheme can give the same root-mean-square error as verification against the truth. This means that a perturbed analysis can be used as a reliable proxy for the truth. However, the conditions required for this result to hold are rather restrictive: the analysis must be optimal, the ensemble spread must be equal to the error in the mean, the ensemble size must be large and the forecast being verified must be the background forecast used in the data assimilation. Although these criteria are unlikely to be met exactly it becomes clear that for most cases verification against a perturbed analysis gives better results than verification against an unperturbed analysis.

We demonstrate the application of these results in a idealised model framework and a numerical weather prediction context. In deriving this result we recall that an optimal (Kalman) analysis is one for which the analysis increments are uncorrelated with the analysis errors.

Verification against perturbed analyses and observations

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