Mesoscale Assimilation of Radial Velocities from Doppler Radars in a Preoperational Framework

Abstract

Abstract This paper presents the results of a preoperational assimilation of radial velocities from Doppler radars of the French Application Radar la Météorologie InfraSynoptique (ARAMIS) network in the nonhydrostatic model, the Application of Research to Operations at Mesoscale (AROME). For this purpose, an observation operator, which allows the simulation of radial winds from the model variables, is included in the three-dimensional variational data assimilation (3DVAR) system. Several data preprocessing procedures are applied to avoid as much as possible erroneous measurements (e.g., due to dealiasing failures) from entering the minimization process. Quality checks and other screening procedures are discussed. Daily monitoring diagnostics are developed to check the status and the quality of the observations against their simulated counterparts. Innovation biases in amplitude and in direction are studied by comparing observed and simulated velocity–azimuth display (VAD) profiles. Experiments over 1 month are performed. Positive impacts on the analyses and on precipitation forecasts are found. Scores against conventional data show mostly neutral results because of the much-localized impact of radial velocities in space and in time. Significant improvements of low-level divergence analysis and on the resulting forecast are found when specific sampling conditions are met: the closeness of convective systems to radars and the orientation of the low-level horizontal wind gradient with respect to the radar beam. Focus on a frontal rainband case study is performed to illustrate this point.