Articles | Volume 18, issue 20
https://doi.org/10.5194/amt-18-5729-2025
https://doi.org/10.5194/amt-18-5729-2025
Research article
 | 
23 Oct 2025
Research article |  | 23 Oct 2025

FLARE-GMM: an automatic aerosol typing model based on Mie–Raman–fluorescence lidar measurements with LILAS

Robin Miri, Olivier Pujol, Qiaoyun Hu, Philippe Goloub, Igor Veselovskii, Thierry Podvin, and Fabrice Ducos

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Cited articles

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Ansmann, A., Riebesell, M., Wandinger, U., Weitkamp, C., Voss, E., Lahmann, W., and Michaelis, W.: Combined raman elastic-backscatter LIDAR for vertical profiling of moisture, aerosol extinction, backscatter, and LIDAR ratio, Appl. Phys. B Photophysics Laser Chem., 55, 18–28, https://doi.org/10.1007/BF00348608, 1992. 
Bishop, C. M.: Pattern recognition and machine learning, Springer, New York, 738 pp., 2006. 
Bohlmann, S., Shang, X., Vakkari, V., Giannakaki, E., Leskinen, A., Lehtinen, K. E. J., Pätsi, S., and Komppula, M.: Lidar depolarization ratio of atmospheric pollen at multiple wavelengths, Atmos. Chem. Phys., 21, 7083–7097, https://doi.org/10.5194/acp-21-7083-2021, 2021. 
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Short summary
We developed a new method to automatically identify types of particles in the air, such as smoke, dust, or pollution, using a specialized laser system. This helps monitor air quality more efficiently and in greater detail. Our method uses real data collected over 3 years in northern France and can detect changes caused by weather conditions. It offers a faster and more accurate way to understand what is in the air we breathe.
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