Show simple item record

dc.contributor.authorPapagiannopoulos, Nikolaos
dc.contributor.authorAlados Arboledas, Lucas 
dc.contributor.authorGuerrero-Rascado, Juan Luis
dc.date.accessioned2019-10-31T08:54:56Z
dc.date.available2019-10-31T08:54:56Z
dc.date.issued2018-11-06
dc.identifier.citationPapagiannopoulos, N., Mona, L., Amodeo, A., D'Amico, G., Gumà Claramunt, P., Pappalardo, G., ... & Apituley, A. (2018). An automatic observation-based aerosol typing method for EARLINET. Atmospheric Chemistry and Physics, 18(21), 15879-15901.es_ES
dc.identifier.urihttp://hdl.handle.net/10481/57632
dc.description.abstractWe present an automatic aerosol classification method based solely on the European Aerosol Research Lidar Network (EARLINET) intensive optical parameters with the aim of building a network-wide classification tool that could provide near-real-time aerosol typing information. The presented method depends on a supervised learning technique and makes use of the Mahalanobis distance function that relates each unclassified measurement to a predefined aerosol type. As a first step (training phase), a reference dataset is set up consisting of already classified EARLINET data. Using this dataset, we defined 8 aerosol classes: clean continental, polluted continental, dust, mixed dust, polluted dust, mixed marine, smoke, and volcanic ash. The effect of the number of aerosol classes has been explored, as well as the optimal set of intensive parameters to separate different aerosol types. Furthermore, the algorithm is trained with literature particle linear depolarization ratio values. As a second step (testing phase), we apply the method to an already classified EARLINET dataset and analyze the results of the comparison to this classified dataset. The predictive accuracy of the automatic classification varies between 59% (minimum) and 90% (maximum) from 8 to 4 aerosol classes, respectively, when evaluated against pre-classified EARLINET lidar. This indicates the potential use of the automatic classification to all network lidar data. Furthermore, the training of the algorithm with particle linear depolarization values found in the literature further improves the accuracy with values for all the aerosol classes around 80 %. Additionally, the algorithm has proven to be highly versatile as it adapts to changes in the size of the training dataset and the number of aerosol classes and classifying parameters. Finally, the low computational time and demand for resources make the algorithm extremely suitable for the implementation within the single calculus chain (SCC), the EARLINET centralized processing suite.es_ES
dc.description.sponsorshipThe research leading to these results has received funding from the European Union’s Horizon 2020 research and innovation program under grant agreement no. 602014 (project ECARS – East European Centre for Atmospheric Remote Sensing) and from the European Union’s Horizon 2020 research program for societal challenges – smart, green and integrated transport under grant agreement no. 723986 (project EUNADICS-AV – European Natural Disaster Coordination and Information System for Aviation).es_ES
dc.language.isoenges_ES
dc.publisherEuropean Geosciences Uniones_ES
dc.relationinfo:eu-repo/grantAgreement/EC/FP7/602014es_ES
dc.relationinfo:eu-repo/grantAgreement/EC/FP7/723986es_ES
dc.rightsAtribución 3.0 España*
dc.rights.urihttp://creativecommons.org/licenses/by/3.0/es/*
dc.titleAn automatic observation-based aerosol typing method for EARLINETes_ES
dc.typeinfo:eu-repo/semantics/articlees_ES
dc.rights.accessRightsinfo:eu-repo/semantics/openAccesses_ES
dc.identifier.doi10.5194/acp-18-15879-2018


Files in this item

[PDF]

This item appears in the following Collection(s)

Show simple item record

Atribución 3.0 España
Except where otherwise noted, this item's license is described as Atribución 3.0 España