Abstract
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) the Mahalanobis distance automatic aerosol type classification (MD) and (2) a 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 aerosol layers 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 method independent of optical properties, is not affected by 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 single (elastic) lidar signals in the case of lidar stations that 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 a lower percentage of unclassified layers (4 %), while MD has a higher percentage of unclassified layers (50 %) and a lower percentage of cases classified as aerosol mixtures (5 %).
Highlights
Aerosol particles directly affect the Earth’s radiation budget by interacting mainly with solar radiation through absorption and scattering (Hobbs, 1993)
The other category consists of cases that network aerosol typing algorithm (NATALI) marked as aerosol type/cloudcontaminated, and the unknown category (Fig. 6, yellow) consists of the cases for which the method was unable to identify the source of the observed aerosol layers
It can be concluded that NATALI (Fig. 6a) is able to classify the highest number of cases (94 cases), while MD (Fig. 6b) failed to classify a high number of cases (46 %) with a lower percentage of aerosols classified as “mixture” types (5 %), which is a reasonable outcome given that the MD scheme considers only two aerosol mixtures, while NA
Summary
Aerosol particles directly affect the Earth’s radiation budget by interacting mainly with solar radiation through absorption and scattering (aerosol–radiation interaction – ARI) (Hobbs, 1993). Multiwavelength Raman and depolarization lidars can be used for aerosol detection and characterization (i.e., dust, smoke, continental) They can provide vertically resolved information on extensive and intensive aerosol optical properties (Freudenthaler et al, 2009; Nicolae et al, 2006; Burton et al, 2012; Groß et al, 2013; Giannakaki et al, 2016; Soupiona et al, 2018, 2019). The algorithm, called Source Classification Analysis (SCAN), is based on the amount of time that the air parcel spends above an already characterized aerosol source region and a number of additional criteria This algorithm, being independent of aerosol optical properties, provides the advantage that its classification process is not affected by overlapping values of optical properties representing more than one aerosol type (e.g., clean continental, continental polluted, smoke).
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