Exploiting Aeolus level-2b winds to better characterize atmospheric motion vector bias and uncertainty

Abstract. The need for highly accurate atmospheric wind observations is a high priority in the science community, particularly numerical weather prediction (NWP). To address this need, study leverages Aeolus lidar level-2B data provided by European Space Agency (ESA) as potential comparison standard to better characterize motion vector (AMV) bias and uncertainty. AMV products from geostationary (GEO) low Earth orbiting (LEO) satellites are compared with reprocessed horizontal line-of-sight (HLOS) global winds observed August–September 2019. Winds two observing modes AMVs, namely Rayleigh-clear (RAY; derived molecular scattering signal) Mie-cloudy (MIE; particle signal). Quality-controlled (QC'd) co-located QC'd AMVs space time, projected onto HLOS direction. Mean co-location differences (MCDs) deviation (SD) of those (SDCDs) determined analyzed. As shown other studies, level agreement between velocities (HLOSVs) varies type, geographic region, height winds, well mode. In terms statistics, HLOSVs correlated both modes. MIE have great value co-locations generally exhibit smaller biases uncertainties RAY co-locations. contribute substantial fraction total SDCDs presence clouds where co-location/representativeness errors also large. Stratified comparisons consistent known uncertainty tropics, NH extratropics, Arctic, at mid- upper-levels clear cloudy scenes. SH/Antarctic larger-than-expected MCDs SDCDs, most probably due larger assignment speeds strong vertical shear, comparisons.