Using Remotely Piloted Aircraft System to study the evolution of the boundary layer related to fog events

Abstract : Over the past decade, the scientific community considers the RPAS (remotely piloted aircraft system) as a tool which can help to improve their knowledge of climate and atmospheric phenomena. RPAS equipped with instruments can now conduct measurements in areas that are too hazardous or remote for a manned plane. RPAS are especially adapted system for observing the atmospheric boundary layer processes at high vertical and temporal resolution. The main objectives of VOLTIGE (Vecteur d'Observation de La Troposphère pour l'Investigation et la Gestion de l'Environnement) are to study the life cycle of fog with micro-RPAS, encourage direct participation of the students on the advancement and development of novel observing systems, and assess the feasibility of deploying RPAS in Météo-France's operational network.The instrumented RPAS flights successfully observed the evolution of the boundary layer and dissipation of a fog event. In one study, profiles show a temperature inversion of a hundred meters which overlaps a cold and wet atmospheric layert. Subsequent profiles show the combination of the arrival of a cool, saturated marine air mass as well as the arrival of a higher level warm pseudofront. In another study, the evolution of the boundary layer stability relates to the dissipation of a fog event. The temperature and the relative humidity profiles show < 1 meter vertical resolution with a difference between ascent and descent profiles within +/- 0.5◦ C and +/-6 % RH. These results comply with the Météo-France standard limits of quality control. The RPAS profiles were compared with those of the Arome forecast model (an operational model at Météo France). The temperature and wind in the Arome model profiles generally agree with those of the RPAS (less for relative humidity profiles). The Arome model also suggests transitions between air masses occurred at a higher level than those measured by RPAS. These results suggest that forecast models may be improved using high resolution and frequent in-situ measurements.