Validation of Aeolus wind profiles using ground-based lidar and radiosonde observations at La Réunion Island and the Observatoire de Haute Provence

International audienceEuropean Space Agency's (ESA) Aeolus satellite mission is the first Doppler wind lidar in space, operating in orbit for more than three years since August 2018 and providing global wind profiling throughout the entire troposphere and the lower stratosphere. The Observatoire de Haute Provence (OHP) in southern France and the Observatoire de Physique de l'Atmosphère à La Réunion (OPAR) are equipped with ground-based Doppler Rayleigh-Mie lidars, which operate on similar principles to the Aeolus lidar, and are among essential instruments within ESA Aeolus Cal/Val program. This study presents the validation results of the L2B Rayleigh-clear HLOS winds from September 2018 to January 2022. The point-by-point validation exercise relies on a series of validation campaigns at both observatories: AboVE (Aeolus Validation Experiment) that were held in September 2019 and June 2021 at OPAR, and in January 2019 and December 2021 at OHP. The campaigns involved time-coordinated lidar acquisitions and radiosonde ascents collocated with the nearest Aeolus overpasses. During AboVE-2, Aeolus was operated in a campaign mode with an extended range bin setting allowing inter-comparisons up to 28.7 km. We show that this setting suffers from larger random error in the uppermost bins, exceeding the estimated error, due to lack of backscatter at high altitudes. To evaluate the long-term evolution in Aeolus wind product quality, twice-daily routine Météo-France radiosondes and regular lidar observations were used at both sites. This study evaluates the long-term evolution of the satellite performance along with punctual collocation analyses. On average, we find a systematic error (bias) of-0.92 ms-1 and-0.79 ms-1 and a random error (scaled MAD) of 6.49 ms-1 and 5.37 ms-1 for lidar and radiosondes, respectively. 1 Introduction Wind velocity is one of the fundamental meteorological variables describing the atmospheric state. Assimilating atmospheric wind observations into numerical weather prediction (NWP) models is crucial to understand the evolution and structure of weather dynamics, air quality monitoring, forecasting, and climate and meteorological studies. Accurate NWPs are essentia