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. This article investigates the potential of W-band radar reflectivity toimprove the quality of analyses and forecasts of heavy precipitation eventsin the Mediterranean area. The “1D+3DVar” assimilation method,operationally employed to assimilate ground-based precipitation radar data inthe Meteo-France kilometre-scale numerical weather prediction (NWP)model AROME, has been adapted to assimilate the W-band reflectivity measuredby the airborne cloud radar RASTA (Radar Airborne System Tool for Atmosphere)during a 2-month period over the Mediterranean area. After applying a biascorrection, vertical profiles of relative humidity are first derived via a1-D Bayesian retrieval, andthen used as relative humidity pseudo-observations in the 3DVar assimilationsystem of AROME. The efficiency of the 1-D Bayesian method in retrievinghumidity fields is assessed using independent in-flight humiditymeasurements. To complement this study, the benefit brought by consistentthermodynamic and dynamic cloud conditions has been investigated byseparately and jointly assimilating the W-band reflectivity and horizontalwind measurements collected by RASTA in the 3 h 3DVar assimilation system ofAROME. The data assimilation experiments are conducted for a single heavy precipitation eventand then also for 32 cases. Results indicate that the W-band reflectivity has a largerimpact on the humidity, temperature and pressure fields in the analyses compared to theassimilation of RASTA wind data alone. Besides, the analyses get closer to independenthumidity observations if the W-band reflectivity is assimilated alone or jointly withRASTA wind data. Nonetheless, the impact of the W-band reflectivity decreases morerapidly as the forecast range increases when compared to the assimilation of RASTA winddata alone. Generally, the joint assimilation of the W-band reflectivity with wind dataresults in the best improvement in the rainfall precipitation forecasts. Consequently,results of this study indicate that consistent thermodynamic and dynamic cloud conditionsin the analysis leads to an improvement of both model initial conditions and forecasts.Even though to a lesser extent, the assimilation of the W-band reflectivity alone alsoresults in a slight improvement of the rainfall precipitation forecasts.

natural hazards and earth system sciences

Impact of airborne cloud radar reflectivity data assimilation on kilometre-scale numerical weather prediction analyses and forecasts of heavy precipitation events