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