The use of an ensemble approach to study the background error covariance in a global
L'usage d'une approche ensembliste pour étudier la covariance des erreurs de base dans un modèle global
Belo Pereira, M. ; Berre, L.
The estimation of the background error statistics is a key issue for
data assimilation. Their time average is estimated here using an <br>analysis ensemble method. The experiments are performed with the <br>nonstretched version of the Action de Recherche Petite Echelle Grande <br>Echelle global model, in a perfect-model context. The global (spatially <br>averaged) correlation functions are sharper in the ensemble method than <br>in the so-called National Meteorological Center (NMC) method. This is <br>shown to be closely related to the differences in the analysis step <br>representation. The local (spatially varying) variances appear to <br>reflect some effects of the data density and of the atmospheric <br>variability. The resulting geographical contrasts are found to be partly<br> different from those that are visible in the operational variances and <br>in the NMC method. An economical estimate is also introduced to <br>calculate and compare the local correlation length scales. This allows <br>for the diagnosis of some existing heterogeneities and anisotropies. <br>This information can also be useful for the modeling of heterogeneous <br>covariances based, for example, on wavelets. The implementation of the <br>global covariances and of the local variances, which are provided by the<br> ensemble method, appears moreover to have a positive impact on the <br>forecast quality.
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