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 Météo‐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.