A machine learning approach for aerosol classification using sun photometer and lidar data

Open Access

Issue		EPJ Web Conf. Volume 362, 2026 31^st International Laser Radar Conference (ILRC 31) Held Together with the 22^nd Coherent Laser Radar Conference (CLRC 22)


Article Number		10003
Number of page(s)		4
Section		Airborne Lidar Investigations, Large Scale Field Experiments, and Synergistic Use of Lidar and Other Instruments
DOI		https://doi.org/10.1051/epjconf/202636210003
Published online		09 April 2026

Masson-Delmotte, V., P. Zhai, A. Pirani, S.L. Connors, C. Péan, S. Berger, N. Caud, Y. Chen, L. Goldfarb, M.I. Gomis, M. Huang, K. Leitzell, E. Lonnoy, J.B.R. Matthews, T.K. Maycock, T. Waterfield, O. Yelekçi, R. Yu, and B. Zhou (eds.)], IPCC, 2021: Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change, Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, 2391 pp, 2021. [Google Scholar]
https://www.who.int/news-room/fact-sheets/detail/ambient-(outdoor)-air-quality-and-health, last access March 2024. [Google Scholar]
B.N. Holben, T.F. Eck, I. Slutsker, A. Smirnov, A. Sinyuk, J. Schafer, D. Giles, O. Dubovik, AERONET’s version 2.0 quality assurance criteria, Remote Sensing of the Atmosphere and Clouds, Proc. SPIE, 6408, p. 64080Q, 2006. [Google Scholar]
A.H. Omar, J.-G. Won, D.M. Winker, S.-C. Yoon, O. Dubovick, M.P. McCormick, Development of global aerosol models using cluster analysis of Aeronet Robotic Network (AERONET) measurements, J. Geophys. Res., 110, 2005. [Google Scholar]
C. Toledano, M. Wiegner, S. Gross, V. Freudenthaler, J. Gasteiger, D. Muller, T. Muller, A. Schladitz, B. Weinzierl, B. Torres, N.T. O’Neill, Optical properties of aerosol mixtures derived from sun-sky radiometry during SAMUM-2, Tellus, Ser. B, 63, pp. 635–648, 2011. [Google Scholar]
Patrick Hamill, Marco Giordano, Carolyne Ward, David Giles, Brent Holben, An AERONET-based aerosol classification using the Mahalanobis distance, Atmospheric Environment, Volume 140, Pages 213–233, ISSN 1352-2310, 2016. [Google Scholar]
Vijayakumar, K., P.C.S. Devara, S.M. Sowbane, D.M. Giles, B.N. Holben, S.V.B. Rao and C.J. Shankar, Solar radiometer sensing of multi-year aerosol features over a tropical urban station: direct-Sun and inversion products, Atmos. Meas. Tech., 13, 5569–5593, 2020. [Google Scholar]
Groß, S., Freudenthaler, V., Wirth, M., and Weinzierl, B.,Towards an aerosol classification scheme for future EarthCARE lidar observations and implications for research needs, Atmos. Sci. Lett., 16, 77–82, https://doi.org/10.1002/asl2.524, 2015. [Google Scholar]
Mona, L., Liu, Z., Müller, D., Omar, A., Papayannis, A., Pappalardo, G., Sugimoto, N., and Vaughan, M.,Lidar Measurements for Desert Dust Characterization: An Overview, Adv. Meteorol., 2012, 356265, 2012. [Google Scholar]
Nicolae, D., Vasilescu, J., Talianu, C., Binietoglou, I., Nicolae, V., Andrei, S., and Antonescu, B., A neural network aerosol-typing algorithm based on lidar data, Atmos. Chem. Phys., 18, 14511–14537, 2018. [Google Scholar]
Mohan, A. S., Manisekaran, A., & Kumar, L. S., Aerosol classification using machine learning algorithms. Indian Journal of Radio & Space Physics, 50(4), 217–223, 2021. [Google Scholar]
C. Cattrall, J. Reagan, K. Thome, O. Dubovik, Variability of aerosol and spectral lidar and backscatter and extinction ratios of key aerosol types derived from selected aerosol robotic network locations, J. Geophys. Res., 110, 2005. [Google Scholar]
D.M. Giles, B.N. Holben, T.F. Eck, A. Sinyuk, A. Smirnov, I. Slutsker, R.R. Dickenson, A.M. Thompson, J.S. Schafer An analysis of AERONET aerosol absorption properties and classifications representative of aerosol source regions J. Geophys. Res., 117, 2012. [Google Scholar]
P.C. Mahalanobis, On the generalized distance in statistics, Proc. Natl. Inst. Sci. India, 2, pp. 49–55, 1936. [Google Scholar]
A.M. Sayer, A. Smirnov, N.C. Hsu, B.N. Holben, A pure marine aerosol model for use in remote sensing applications, J. Geophys. Res., 117, 2012. [Google Scholar]
Osborne, M., Malavelle, F. F., Adam, M., Buxmann, J., Sugier, J., Marenco, F., and Haywood, J.: Saharan dust and biomass burning aerosols during ex-hurricane Ophelia: observations from the new UK lidar and sun-photometer network, Atmos. Chem. Phys., 19, 3557–3578, 2019. [Google Scholar]
Johannes Speidel and Hannes Vogelmann, Correct(ed) Klett–Fernald algorithm for elastic aerosol backscatter retrievals: a sensitivity analysis, Appl. Opt. 62, 861–868, 2023. [Google Scholar]

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.