Aerosol Typing from Linear Estimations for the Analytical Separation (ATLAS) of Complex Aerosol Mixtures and Improved Identification of Microphysical Parameters from Multiwavelength Lidar Data, Part 2: Case Studies

We developed a new methodology for the improved identification of particle microphysical parameters (PMPs) from multiwavelength lidar measurements. The underlying problem is underdetermined and relates to the class of ill-posed problems. In this study, we apply our new methodology to lidar measurements. We investigate how results obtained for typical aerosol mixtures (AMs) in the atmosphere can be improved if information about aerosol types and the number of aerosol types in such an AM is available. We have developed a methodology of Aerosol Typing from Linear estimations for the Analytical Separation (ATLAS) of complex aerosol mixtures in the first part of our study. ATLAS allows us to decompose a complex AM into individual aerosol types in terms of optical data measured by lidar. Optical data derived for individual aerosol types are then separately considered and inverted into Algorithm). We apply our new two-stage (ATLAS-TiARA) synergetic methodology to three lidar-measurement cases corresponding to two-, three-, and four-component AMs. The measurements we use for this study were carried out in the frameworks of the ORACLES-2016 and SHADOW field campaigns and lidar observations at the University of Lille (France), respectively. Results of the new methodology agree with results obtained with data collected by in situ instruments during the ORACLES-2016 campaign. Deviations of number concentration and single-scattering albedo at 532 nm retrieved with the new methodology from respective in situ measurements do not exceed 25% and 0.05, respectively. We find both fine- and coarse-mode particles from all three measurement cases. Fine-mode particles are represented by urban and smoke (haze), whereas coarse-mode particles can be attributed to dust, marine, and pollen aerosols. In summary, the methodology allows us to obtain a more detailed insight into microphysical particle properties. (c) 2025 Optica Publishing Group. All rights, including for text and data mining (TDM), Artificial Intelligence (AI) training, and similar technologies, are reserved.