Optimization of the Assimilation of Radar Data at the Convective Scale Using Specific Background Error Covariances in Precipitation

Abstract

his study focuses on the impact of using specific background error covariances in precipitating areas in the Application of Research to Operations at Mesoscale (AROME-France) numerical weather prediction (NWP) system that considers reflectivities and radial velocities in its assimilation system. Such error covariances are deduced from the application of geographical masks on forecast differences generated from an ensemble assimilation of various precipitating cases. The retrieved forecast error covariances are then applied in an incremental three-dimensional variational data assimilation (3D-Var) specifically in rainy areas, in addition to the operational climatological background error covariances that are used elsewhere. Such heterogeneous formulation gives better balanced and more realistic analysis increments, as retrieved from the assimilation of radar data. For instance, midlevel humidification allows for the reinforcement of the low-level cooling and convergence, the warming in clouds, and high-level divergence. Smaller forecast error horizontal lengths explain the smaller-scale structures of the increments and render possible the increase of data densities in rainy areas. Larger error variances for the dynamical variables give more weight to wind observations such as radial winds. A reduction of the spinup is also shown and is positively correlated to the size of the area where rainy forecast error covariances are applied. Positive forecast scores on cumulated rain and on low-level temperature and humidity are finally displayed.