Preprints
https://doi.org/10.5194/amt-2022-81
https://doi.org/10.5194/amt-2022-81
 
15 Mar 2022
15 Mar 2022
Status: this preprint is currently under review for the journal AMT.

Combining of Mie-Raman and fluorescence observations: a step forward in aerosol classification with lidar technology

Igor Veselovskii1, Qiaoyun Hu2, Philippe Goloub2, Thierry Podvin2, Boris Barchunov1, and Mikhail Korenskii1 Igor Veselovskii et al.
  • 1Prokhorov General Physics Institute of the Russian Academy of Sciences, Moscow, Russia
  • 2Univ. Lille, CNRS, UMR 8518 - LOA - Laboratoire d'Optique Atmosphérique, F-59650 Lille, France

Abstract. The paper presents an innovative approach to reveal variability of aerosol type at high spatio-temporal resolution, by combining fluorescence and Mie-Raman lidar observations. The multi-wavelength Mie-Raman lidar system in operation at the ATOLL platform (ATmospheric Observatory of liLLe), Laboratoire d’Optique Atmosphérique, University of Lille, includes, since 2019, a wideband fluorescence channel allowing the derivation of the fluorescence backscattering coefficient βF. The fluorescence capacity GF, which is the ratio of βF to the aerosol backscattering coefficient, is an intensive particle’s property, strongly changing with aerosol type, thus providing a relevant basis for aerosol classification. In this first single version of the algorithm, only two intensive properties are used for classification: the particle depolarization ratio at 532 nm, and the fluorescence capacity, GF. We applied our new classification approach to ATOLL high performance lidar data obtained during 2020–2021 period, which includes strong smoke, dust and pollen episodes. It is demonstrated that separation of the main particle’s types and their mixtures can be performed with height resolution about 60 m and temporal resolution better than 10 minutes for the current lidar configuration.

Igor Veselovskii et al.

Status: open (extended)

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  • RC1: 'Comment on amt-2022-81', Anonymous Referee #1, 05 May 2022 reply

Igor Veselovskii et al.

Igor Veselovskii et al.

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Short summary
An approach to reveal variability of aerosol type at high spatio-temporal resolution, by combining fluorescence and Mie-Raman lidar data is presented. We applied this new classification scheme to lidar data obtained LOA, University of Lille in 2020–2021. It is demonstrated that separation of the main particle’s types, such as smoke, dust, pollen and urban can be performed with height resolution about 60 m and temporal resolution better than 10 minutes for the current lidar configuration.