After six years of scientific, technical developments and meteorological validation, the Application of Research to Operations at Mesoscale (AROME-France) convective-scale model became operational at Météo-France at the end of 2008. This paper presents the main characteristics of this new numerical weather prediction system: the nonhydrostatic dynamical model core, detailed moist physics, and the associated three-dimensional variational data assimilation (3D-Var) scheme. Dynamics options settings and variables are explained. The physical parameterizations are depicted as well as their mutual interactions. The scale-specific features of the 3D-Var scheme are shown. The performance of the forecast model is evaluated using objective scores and case studies that highlight its benefits and weaknesses.

The background error covariance plays an important role in modern data assimilation and analysis systems by determining the distribution of the information in the data in space and between variables. A new formulation has been developed for use in the ECMWF system. The non-separable structure functions depend on the horizontal and vertical scales and a generalized linear balance operator to imply multivariate structure functions. The balance operator is incorporated into the definition of the analysis variables to ensure good preconditioning of the problem. The formulation and structure of the background error covariance are presented, and the implications for the analysis increments are examined. This reformulation became the operational ECMWF formulation in 3D

A stochastic physics scheme is tested in the Application of Research to Operations at Mesoscale (AROME) short-range convection-permitting ensemble prediction system. It is an adaptation of ECMWF’s stochastic perturbation of physics tendencies (SPPT) scheme. The probabilistic performance of the AROME model ensemble is found to be significantly improved, when verified against observations over two 2-week periods. The main improvement lies in the ensemble reliability and the spread–skill consistency. Probabilistic scores for several weather parameters are improved. The tendency perturbations have zero mean, but the stochastic perturbations have systematic effects on the model output, which explains much of the score improvement. Ensemble spread is an increasing function of the SPPT space and time correlations. A case study reveals that stochastic physics do not simply increase ensemble spread, they also tend to smooth out high-spread areas over wider geographical areas. Although the ensemble design lacks surface perturbations, there is a significant end impact of SPPT on low-level fields through physical interactions in the atmospheric model.

AROME-France is a convective-scale numerical weather prediction system running operationally at Météo-France since the end of 2008. It uses a 3D-Var assimilation scheme to determine its initial conditions. Climatological background-error covariances of such a system are calculated using differences between forecasts from an AROME ensemble assimilation. These statistics are compared with the lowerresolution ALADIN-France system ones: they provide 3D-Var analysis increments that are more intense and more localized, in accordance with the actual AROME model resolution. AROME ensemble-assimilation (ENS DA) covariances have also been compared with covariances calculated with an AROME ensemble of forecasts run in spin-up mode (ENS SU). On the one hand, ENS SU appears to be a reasonable approximation of ENS DA compared with ALADIN-France covariances, by representing a large part of the small-scale variance increase. On the other hand, ENS DA allows for a fully cycled development of small-scale forecast perturbations, which leads to a further enhancement of small-scale covariances. This aspect is shown to be beneficial in terms of assimilation diagnostics and forecast performance and in a case study.