Vertical profiling of aerosol optical, microphysical, and chemical properties using elastic-Raman-LIF lidars and in situ aerosol measurements during the 2024–2025 CHOPIN campaign

The Cleancloud Helmos OrograPhic site experimeNt (CHOPIN) campaign took place at mount Helmos, Greece (37.98°N, 22.2°E; 1700-2314 m a.s.l.) to  study the aerosol-cloud interactions during two distinct periods: autumn/winter (October–November 2024) and spring (April–May 2025). In situ aerosol sampling at the Helmos Atmospheric Aerosol and Climate Change Station (HAC)² was performed at 2314 m a.s.l. along with aerosol lidar vertical measurements. (HAC)² is located on a strategic site at a crossroad of different air masses containing various aerosol types (wildfire smoke, mineral dust, continental pollution, marine aerosols, and biogenic particles). Two lidar systems were deployed: the AIAS depolarization lidar (532 nm parallel and cross, 1064 nm) and the ATLAS-NEF multi-wavelength elastic-Raman-LIF lidar (355, 387, 407 and 420-520 nm). The vertically resolved aerosol optical properties (extinction and backscatter coefficient, lidar ratio, Ångström exponent, particle depolarization) and water vapor mixing ratios, alongside with fluorescence backscatter profiles, were provided from near-ground up to 5-7 km a.s.l. Lidar-inversion algorithms were used to retrieve the aerosol microphysical properties (effective radius, single scattering albedo, and complex refractive index). The aerosol chemical composition was retrieved using the ISORROPIA thermodynamic model. The aerosol fluorescence measurements highlighted enhanced presence of bioaerosols in selected cases. Saharan dust particles exhibited high depolarization ratios (δ₅₃₂ ~0.20–0.25) and lidar ratios (LR ~40–55 sr), while biomass burning plumes showed distinct microphysical and chemical signatures. Comparison of in situ and lidar-derived optical, microphysical and chemical properties at 2.314 m a.s.l. was found to be quite satisfactory, paving the way for a novel synergistic approach to further elucidate the aerosols’ role in cloud formation and radiative forcing. These lidar data are used to improve Machine Learning algorithms in the frame of the F-LIDAR-M project.

Funding: The research project, entitled “Real-time detection/Speciation of bio-aerosols profiling using Fluorescence LiDAR techniques and Machine Learning (F-LIDAR-M)” is implemented in the framework of H.F.R.I call “3rd Call for H.F.R.I.’s Research Projects to Support Faculty Members & Researchers” (H.F.R.I. Project Number: 25096).