Session 12.6 Radiative impact of Saharan dust on precipitation forecast over western Europe during COPS

Friday, 2 July 2010: 11:45 AM
Pacific Northwest Ballroom (DoubleTree by Hilton Portland)
Jean-Pierre Chaboureau, University of Toulouse/CNRS, Toulouse, France; and E. Richard, J. P. Pinty, C. Flamant, P. Di Girolamo, C. Kiemle, A. Behrendt, H. Chepfer, M. Chiriaco, and V. Wulfmeyer

Presentation PDF (324.3 kB)

A Saharan dust event affected the Rhine valley in southwestern Germany and eastern France on 1 August 2007 during the Convective and Orographically-induced Precipitation Study (COPS) experiment. Prior to an episode of intense convection, a layer of dry, clean air capped by a moist, dusty layer was observed using Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO), airborne and ground-based lidar observations from North Africa to western Europe. The origin of the different layers was investigated using the regional model Meso-NH. For the purpose of modeling evaluation, a lidar simulator was developed for directly comparing observed and simulated vertical structures of the lidar backscatter signal. Overall, the model reproduced the vertical structure of dust probed at several times by the different lidar systems during its long-range transport. From Lagrangian back trajectories it was found that the dust was mobilized from sources in Mauritania six days earlier, while the dry layer subsided over the north Atlantic. Off the Moroccan coasts, the dry layer folded down beneath the dusty airmass and the two-layer structure was advected to the Rhine valley in about two days. By heating the atmosphere, the dust layer changed the static stability of the atmosphere and thus the occurrence of convection. A sensitivity study to the radiative effect of dust indeed shows a better prediction of precipitation when a dust prognostic scheme was used rather than climatology or when dust effects were ignored. This result suggests that dust episodes that occur prior to convective events might be important for quantitative precipitation forecasts.
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