Aerosol type classification analysis using EARLINET multiwavelength and depolarization lidar observations

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 %).