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https://doi.org/10.5194/acp-2020-865
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/acp-2020-865
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

  27 Oct 2020

27 Oct 2020

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This preprint is currently under review for the journal ACP.

Aerosol type classification analysis using EARLINET multiwavelength and depolarization lidar observations

Maria Mylonaki1, Elina Giannakaki2,3, Alexandros Papayannis1, Christina-Anna Papanikolaou1, Mika Komppula3, Doina Nicolae4, Nikolaos Papagiannopoulos5,6, Aldo Amodeo5, Holger Baars7, and Ourania Soupiona1 Maria Mylonaki et al.
  • 1Laser Remote Sensing Unit, Department of Physics, National and Technical University of Athens, Zografou, 15780, Greece
  • 2Department of Environmental Physics and Meteorology, Faculty of Physics, National and Kapodistrian University of Athens, National and Kapodistrian University of Athens
  • 3Finnish Meteorological Institute, P.O.Box 1627, 70211 Kuopio, Finland
  • 4National Institute of R&D for Optoelectronics (INOE), Magurele, Romania
  • 5Consiglio Nazionale delle Ricerche, Istituto di Metodologie per l’Analisi Ambientale (CNR-IMAA), C.da S. Loja, Tito Scalo (PZ), 85050, Italy
  • 6CommSensLab, Dept. of Signal Theory and Communications, Universitat Politècnica de Catalunya, Barcelona, Spain
  • 7Leibniz Institute for Tropospheric Research, Leipzig

Abstract. We introduce an automated aerosol type classification method, called Source Classification ANalysis (SCAN). SCAN is based on predefined and characterized aerosol source regions, the time that the air parcel spends above each geographical region and a number of additional criteria. The output of SCAN is compared with two independent aerosol classification methods, which use the intensive optical parameters from lidar data: (1) Mahalanobis distance automatic aerosol type classification (MD) and (2) Neural Network Aerosol Typing Algorithm (NATALI). In this paper, data from the European Aerosol Research Lidar Network (EARLINET) have been used. A total of 97 free tropospheric (FT) aerosol layers from 4 typical EARLINET stations (i.e., Bucharest, Kuopio, Leipzig and Potenza) in the period 2014–2018 were classified based on a 3β+2α+1δ lidar configuration. We found that SCAN, being an optical property independent method, is not affected by the overlapping optical values of different aerosol types. Furthermore, SCAN has no limitations concerning its ability to classify different aerosol mixtures. Additionally, it is a valuable tool to classify aerosol layers, based on even to single (elastic) lidar signals, in case of lidar stations which cannot provide a full data set (3β+2α+1δ) of aerosol optical properties, therefore it can work independently of the capabilities of a lidar system. Finally, our results show that NATALI has the lower percentage of unclassified layers (4 %), while MD has the percentage of unclassified layers (50 %) and the lower percentage of cases classified as aerosol mixtures (5 %).

Maria Mylonaki et al.

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Maria Mylonaki et al.

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Short summary
We introduce an automated aerosol type classification method, SCAN. The output of SCAN is compared with two aerosol classification methods: (1) Mahalanobis distance automatic aerosol type classification and Neural Network Aerosol Typing Algorithm. A total of 97 free tropospheric aerosol layers from 4 EARLINET stations in the period 2014–2018 were classified.
We introduce an automated aerosol type classification method, SCAN. The output of SCAN is...
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