气候变化领域集成服务门户(CCPortal): Aerosol type classification analysis using EARLINET multiwavelength and depolarization lidar observations

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DOI	10.5194/acp-21-2211-2021
	Aerosol type classification analysis using EARLINET multiwavelength and depolarization lidar observations
	Mylonaki M.; Giannakaki E.; Papayannis A.; Papanikolaou C.-A.; Komppula M.; Nicolae D.; Papagiannopoulos N.; Amodeo A.; Baars H.; Soupiona O.
发表日期	2021
ISSN	1680-7316
起始页码	2211
结束页码	2227
卷号	21 期号:3
英文摘要	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 3 C2 C1 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 (3 C2 C1) 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 %). © 2021 BMJ Publishing Group. All rights reserved.
语种	英语
来源期刊	Atmospheric Chemistry and Physics
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/168762
作者单位	Laser Remote Sensing Unit, Department of Physics, National and Technical University of Athens, Zografou, 15780, Greece; Department of Environmental Physics and Meteorology, Faculty of Physics, National and Kapodistrian University of Athens, Athens, Greece; Finnish Meteorological Institute, P.O. Box 1627, Kuopio, 70211, Finland; National Institute of R and D for Optoelectronics (INOE), Magurele, Romania; Consiglio Nazionale delle Ricerche, Istituto di Metodologie per l'Analisi Ambientale (CNR-IMAA), C.da S. Loja, Tito Scalo (PZ), 85050, Italy; CommSensLab, Dept. of Signal Theory and Communications, Universitat Politècnica de Catalunya, Barcelona, Spain; Leibniz Institute for Tropospheric Research, Leipzig, Germany
推荐引用方式 GB/T 7714	Mylonaki M.,Giannakaki E.,Papayannis A.,et al. Aerosol type classification analysis using EARLINET multiwavelength and depolarization lidar observations[J],2021,21(3).
APA	Mylonaki M..,Giannakaki E..,Papayannis A..,Papanikolaou C.-A..,Komppula M..,...&Soupiona O..(2021).Aerosol type classification analysis using EARLINET multiwavelength and depolarization lidar observations.Atmospheric Chemistry and Physics,21(3).
MLA	Mylonaki M.,et al."Aerosol type classification analysis using EARLINET multiwavelength and depolarization lidar observations".Atmospheric Chemistry and Physics 21.3(2021).