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. We introduce an automated aerosol type classificationmethod, called Source Classification Analysis (SCAN). SCAN is based onpredefined and characterized aerosol source regions, the time that the airparcel spends above each geographical region, and a number of additionalcriteria. The output of SCAN is compared with two independent aerosolclassification methods, which use the intensive optical parameters fromlidar data: (1) the Mahalanobis distance automatic aerosol typeclassification (MD) and (2) a neural network aerosol typing algorithm(NATALI). In this paper, data from the European Aerosol Research LidarNetwork (EARLINET) have been used. A total of 97 free tropospheric aerosollayers from four 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, as a methodindependent of optical properties, is not affected by overlappingoptical values of different aerosol types. Furthermore, SCAN has nolimitations concerning its ability to classify different aerosol mixtures.Additionally, it is a valuable tool to classify aerosol layers based oneven single (elastic) lidar signals in the case of lidar stations thatcannot provide a full data set (3β+2α+1δ) ofaerosol optical properties; therefore, it can work independently of thecapabilities of a lidar system. Finally, our results show that NATALI hasa lower percentage of unclassified layers (4 %), while MD has a higherpercentage of unclassified layers (50 %) and a lower percentage of casesclassified as aerosol mixtures (5 %).

Aerosol type classification analysis using EARLINET multiwavelength and depolarization lidar observations