Background‐error covariances for a convective‐scale data‐assimilation system: AROME–France 3D‐Var

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Operational Implementation of the 1D+3D-Var Assimilation Method of Radar Reflectivity Data in the AROME Model (2014, methodology): “…the global model ARPEGE with a 10-km resolution over western Europe, background-error covariances deduced froman ensemble data-assimilation technique (Brousseau et al. 2011) and a larger computational domain (with 750 and 720 physical grid points in the east–west and north– south directions).”
Convection‐permitting models: a step‐change in rainfall forecasting (2016, methodology): “In April 2015, Meteo France introduced an hourly cycling 3D-Var system with a 1.3 km resolution version of their AROME model.”
The evolution of the ECMWF hybrid data assimilation system (2016, background): “…increase, the expected increased heterogeneity and anisotropy of error correlations, the sharper error gradients and the increased flow-dependent aspects of the balance relationships (Brousseau et al., 2011, 2012; Ménétrier et al., 2014) would pose greater challenges to the current B model.”
An extended specification of flow‐dependent background error variances in the Météo‐France global 4D‐Var system (2011, background): “There have already been successful attempts to estimate the static part of regional error covariances (Desroziers et al., 2007; Brousseau et al., 2011), and an extension to represent space- and time-dependent covariances is also investigated (Monteiro and Berre, 2010).”