Aeolus Lidar Surface Returns (LSR) at 355 nm as a new Aeolus L2A Phase-F product
Authors: Labzovskii Lev D. , van Zadelhoff Gerd-Jan , Donovan David P. , de Kloe Jos , Tilstra L. Gijsbert , Stoffelen Ad , Josset Damien , Stammes Piet 
Published: July 2024

Abstract. The Atmospheric Laser Doppler Instrument (ALADIN) onboard Aeolus was the first spaceborne high-resolution lidar, measuring vertical profiles of aerosol optical properties at 355 nm at an incidence angle of ~ 35°. Although Aeolus had been primarily developed to provide vertical profiles of wind speed, aerosols, and cloud products, its lidar surface returns (LSR) were shown to contain useful information about UV surface reflectivity and agreed well with passive remote sensing reflectance. Within the process to incorporate the LSR algorithm into the Aeolus Level 2A product during the post-commissioning phase of Aeolus, we describe the methodology and evaluate the results of the adopted LSR retrieval. The algorithm combines attenuated backscattering parameters (L2 AEL-PRO data) with the information on the surface bin detection (L1 data) to produce attenuated LSR estimates (e.g. surface integrated attenuated backscatter) for all bins where the ground was detected. The correction for producing final LSR estimates at the original Aeolus resolution is performed using the Aeolus L2 retrievals, namely Aerosol Optical Depth (AOD) and Rayleigh Optical Depth to ensure that LSR is free from effects of atmospheric attenuative features such as optically thick clouds and thick aerosol conditions (AOD > 1.0). The evaluation shows that Aeolus LSR estimates produced from this approach agreed well with the UV Lambertian-Equivalent Reflectivity (LER) from GOME-2 (LERG) and TROPOMI (LERT) climatologies at all spatial scales. For four reference orbits (September 10, 2018; November 30, 2018; January 11, 2019; and May 1, 2019), all cloud and aerosol-free LSR estimates agree well with both LER references with correlation coefficients (r) varying from 0.55 to 0.71. For monthly scales, the agreement was moderate-to-high for LSR-LERT (r = 0.61 – 0.77 depending on the month) and was weak-to-moderate for LSR-LERG comparison (r = 0.44 – 0.64). Globally, the averaged 2.5 × 2.5° LSR estimates exhibit very high agreement with both LERG (0.90) and LERT (0.92) references. In reproducing regional monthly dynamics LSR and LER agree very well in snow/ice-covered regions (r > 0.90), semi-arid regions (r > 0.90), arid regions (r > 0.70), and only some regions with mixed vegetation like Australia (r = 0.94), while no agreement was found for ocean regions due to the Aeolus optical setup, favourable for ocean subsurface, not direct surface backscatter probing. We unveiled four reflectivity clusters of LSR at 2.5 × 2.5 degree grids, manifesting a transition from white to darker surfaces in descending LSR magnitude order: ice, snow, surface without snow, and water. Regionally, the LSR-LER agreement can vary and yields the highest correlation values in regions where snow is present in winter. This pattern is explained by the very good sensitivity of LSR to modelled snow cover we demonstrated (r = 0.62 – 0.74 between these parameters in such regions), while sensitivity to purely vegetation-driven changes of surface is lower, as indicated by the comparison between LSR and NDVI without snow (r < 0.30 in the regional analysis). Overall, our work complemented existing LSR studies that were mostly focused on nadir-looking CALIPSO cases by demonstrating the usability of LSR for scientific applications at non-nadir angles. By taking together CALIPSO and Aeolus experiences, a framework on effective LSR utilization using future lidar missions such as EarthCARE and Aeolus-2 can be effectively designed.