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dc.contributor.authorPapagiannopoulos, N.
dc.contributor.authorMona, Lucia
dc.contributor.authorAmodeo, Aldo
dc.contributor.authorD'Amico, Giuseppe
dc.contributor.authorComerón Tejero, Adolfo
dc.contributor.authorRodríguez Gómez, Alejandro Antonio
dc.contributor.authorSicard, Michaël
dc.contributor.otherUniversitat Politècnica de Catalunya. Departament de Teoria del Senyal i Comunicacions
dc.date.accessioned2018-11-28T18:58:22Z
dc.date.available2018-11-28T18:58:22Z
dc.date.issued2018-11-06
dc.identifier.citationPapagiannopoulos, N., Mona, L., Amodeo, A., D'Amico, G., Comeron, A., Rodriguez-Gomez, A., Sicard, M. An automatic observation-based aerosol typing method for EARLINET. "Atmospheric chemistry and physics", 6 Novembre 2018, vol. 18, núm. 21, p. 15879-15901.
dc.identifier.issn1680-7316
dc.identifier.urihttp://hdl.handle.net/2117/125208
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.
dc.format.extent23 p.
dc.language.isoeng
dc.publisherEuropean Geosciences Union (EGU)
dc.rightsAttribution-NonCommercial-NoDerivs 3.0 Spain
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/3.0/es/
dc.subjectÀrees temàtiques de la UPC::Enginyeria de la telecomunicació::Radiocomunicació i exploració electromagnètica::Teledetecció
dc.subject.lcshAtmospheric physics
dc.subject.lcshRemote sensing
dc.titleAn automatic observation-based aerosol typing method for EARLINET
dc.typeArticle
dc.subject.lemacFísica atmosfèrica
dc.subject.lemacTeledetecció
dc.contributor.groupUniversitat Politècnica de Catalunya. RSLAB - Grup de Recerca en Teledetecció
dc.identifier.doi10.5194/acp-18-15879-2018
dc.description.peerreviewedPeer Reviewed
dc.relation.publisherversionhttps://www.atmos-chem-phys.net/18/15879/2018/
dc.rights.accessOpen Access
local.identifier.drac23536550
dc.description.versionPostprint (published version)
dc.relation.projectidinfo:eu-repo/grantAgreement/EC/H2020/654109/EU/Aerosols, Clouds, and Trace gases Research InfraStructure/ACTRIS-2
local.citation.authorPapagiannopoulos, N.; Mona, L.; Amodeo, A.; D'Amico, G.; Comeron, A.; Rodriguez-Gomez, A.; Sicard, M.
local.citation.publicationNameAtmospheric chemistry and physics
local.citation.volume18
local.citation.number21
local.citation.startingPage15879
local.citation.endingPage15901


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