Abstract: Statistical and balance features of forecast errors are generally incorporated in the background constraint of variational data assimilation. Forecast error covariances are here estimated with a spectral approach and from ...

Abstract: The use of local spatial averaging to estimate and validate background error covariances has received increasing attention recently, in particular in the context of variational data assimilation for global numerical weather ...

Abstract: The use of orthogonal wavelets for the representation of background error covariances over a limited area is studied. Each wavelet function contains both information on position and information on scale: using a diagonal ...

Abstract: The estimation of the background error statistics is a key issue for data assimilation. Their time average is estimated here using an analysis ensemble method. The experiments are performed with the nonstretched version ...

Abstract: An ensemble of limited-area forecasts has been obtained by integrating the Aire Limitée Adaptation Dynamique Développement International (ALADIN) limited-area model, in cold-starting mode, from an ensemble of Action de ...

Abstract: n data assimilation (DA) schemes for numerical weather prediction (NWP) systems, the estimation of forecast error covariances is a key point to get some flow dependency. As shown in previous studies, ensemble data assimilation ...

Abstract: n Part I of this two-part study, a new theory for optimal linear filtering of covariances sampled from an ensemble of forecasts was detailed. This method, especially designed for data assimilation (DA) schemes in numerical ...

Abstract: Real-size assimilation systems basically rely on estimation theory in which two sources of information, background and observations, are combined in an optimal way. However, the optimality of such large problems is not ...