Flow-dependent background-error covariances for a convective-scale data assimilation system

AROME-France is a convective-scale numerical weather prediction system which has been running operationally at Meteo-France since the end of 2008. It uses a 3D-Var assimilation scheme in order to determine its initial conditions. Static background-error covariances are calculated for the 3D-Var using differences between AROME forecasts from an ensemble data assimilation. In this study, the covariance calculation is generalized in order to estimate time-dependent background-error covariances. A six-member ensemble is shown to provide robust covariance estimates in the context of the considered homogeneous covariance formulation. There is significant day-to-day variability in the variances, autocorrelations, and cross-correlations of background errors. This variability is linked to the meteorological conditions over the AROME-France model domain. The benefits of using flow-dependent background-error covariances, instead of static ones, are shown using assimilation diagnostics and measures of forecast performance. Copyright (c) 2011 Royal Meteorological Societyw