Preprints
https://doi.org/10.5194/amt-2024-17
https://doi.org/10.5194/amt-2024-17
12 Feb 2024
 | 12 Feb 2024
Status: a revised version of this preprint was accepted for the journal AMT and is expected to appear here in due course.

Retrieval and analysis of the composition of an aerosol mixture through Mie-Raman-Fluorescence lidar observations

Igor Veselovskii, Boris Barchunov, Qiaoyun Hu, Philippe Goloub, Thierry Podvin, Mikhail Korenskii, Gaël Dubois, William Boissiere, and Nikita Kasianik

Abstract. In the atmosphere, aerosols can originate from numerous sources, leading to the mixing of different particle types. This paper introduces an approach to the partitioning of aerosol mixtures in terms of backscattering coefficients. The method utilizes data collected from the Mie-Raman-fluorescence lidar, with the primary input information being the aerosol backscattering coefficient, particle depolarization ratio (δ), and fluorescence capacity (GF). The fluorescence capacity is defined as the ratio of the fluorescence backscattering coefficient to the particle backscattering coefficient at the laser wavelength. By solving a system of equations that model these three properties (bF, δ and GF), it is possible to characterize a three-component aerosol mixture. Specifically, the paper assesses the contributions of smoke, urban, and dust aerosols to the overall backscattering coefficient at 532 nm. It is important to note that aerosol properties (δ and GF) may exhibit variations even within a specified aerosol type. To estimate the associated uncertainty, we employ the Monte Carlo technique, which assumes that GF and δ are random values uniformly distributed within predefined intervals. In each Monte Carlo run, a solution is obtained. Rather than relying on a singular solution, an average is computed across the whole set of solutions, and their dispersion serves as a metric for method uncertainty. This methodology was tested using observations conducted at the ATOLL observatory, Laboratoire d'Optique Atmosphérique, University of Lille, France.

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Igor Veselovskii, Boris Barchunov, Qiaoyun Hu, Philippe Goloub, Thierry Podvin, Mikhail Korenskii, Gaël Dubois, William Boissiere, and Nikita Kasianik

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on amt-2024-17', Anonymous Referee #1, 15 Mar 2024
    • AC1: 'Reply on RC1', Philippe Goloub, 05 May 2024
  • RC2: 'Comment on amt-2024-17', Sergei Bobrovnikov, 28 Mar 2024
    • AC2: 'Reply on RC2', Philippe Goloub, 05 May 2024
  • RC3: 'Comment on amt-2024-17', Anonymous Referee #3, 04 Apr 2024
    • AC3: 'Reply on RC3', Philippe Goloub, 05 May 2024

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on amt-2024-17', Anonymous Referee #1, 15 Mar 2024
    • AC1: 'Reply on RC1', Philippe Goloub, 05 May 2024
  • RC2: 'Comment on amt-2024-17', Sergei Bobrovnikov, 28 Mar 2024
    • AC2: 'Reply on RC2', Philippe Goloub, 05 May 2024
  • RC3: 'Comment on amt-2024-17', Anonymous Referee #3, 04 Apr 2024
    • AC3: 'Reply on RC3', Philippe Goloub, 05 May 2024
Igor Veselovskii, Boris Barchunov, Qiaoyun Hu, Philippe Goloub, Thierry Podvin, Mikhail Korenskii, Gaël Dubois, William Boissiere, and Nikita Kasianik
Igor Veselovskii, Boris Barchunov, Qiaoyun Hu, Philippe Goloub, Thierry Podvin, Mikhail Korenskii, Gaël Dubois, William Boissiere, and Nikita Kasianik

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Short summary
The paper presents a new method that categorizes atmospheric aerosols by analyzing their optical properties with a Mie-Raman-fluorescence lidar. The research specifically looks into understanding the presence of smoke, urban, and dust aerosols in the mixtures identified by this lidar. The reliability of the results is evaluated using the Monte Carlo technique. The effectiveness of this approach is successfully demonstrated through testing in ATOLL, an observatory influenced by diverse aerosols.