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Reconstruction of meteorological records with PCA-based analog ensemble methods

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datacite.subject.fosCiências Naturais::Matemáticas
datacite.subject.fosCiências Naturais::Ciências da Computação e da Informação
datacite.subject.fosCiências Naturais::Ciências da Terra e do Ambiente
datacite.subject.sdg04:Educação de Qualidade
dc.contributor.authorBreve,Murilo M.
dc.contributor.authorBalsa, Carlos
dc.contributor.authorRufino, José
dc.date.accessioned2026-03-16T11:32:42Z
dc.date.available2026-03-16T11:32:42Z
dc.date.issued2024
dc.description.abstractThe Analog Ensemble (AnEn) method has been used to reconstruct missing data in time series with base on other correlated time series with full data. As the AnEn method benefits from the use of large volumes of data, there is a great interest in improving its efficiency. In this paper, the Principal Component Analysis (PCA) technique is combined with the classical AnEn method and a K-means cluster-based variant, within the context of reconstructing missing meteorological data at a particular station using information from neighboring stations. This combination allows to reduce the dimension of the number of predictor time series, while ensuring better accuracy and higher computational performance than the AnEn methods: it reduces prediction errors by up to 30% and achieves a computational speedup of up to 2x.por
dc.description.sponsorshipThis work was supported by national funds through FCT/ MCTES (PIDDAC): CeDRI, UIDB/05757/2020 (DOI: 10.54499/UIDB/05757/2020) and UIDP/05757/2020 (DOI: 10.54499/UIDB/05757/2020); and SusTEC, LA/P/0007/2020 (DOI: 10.54499/LA/P/0007/2020)
dc.identifier.citationBreve,Murilo M.; Balsa, Carlos, Rufino, José (2024). Reconstruction of meteorological records with PCA-based analog ensemble methods. In International Conference on Advanced Research in Technologies, Information, Innovation and Sustainability, ARTIIS 2024. 2348, p. 85-96. ISBN 9783031456411. DOI: 10.1007/978-3-031-45642-8_8
dc.identifier.doi10.1007/978-3-031-45642-8_8
dc.identifier.isbn9783031456411
dc.identifier.urihttp://hdl.handle.net/10198/36080
dc.language.isoeng
dc.peerreviewedyes
dc.publisherSpringer Nature Switzerland
dc.relationResearch Centre in Digitalization and Intelligent Robotics
dc.relation0007/2020
dc.relation.hasversionhttps://link.springer.com/chapter/10.1007/978-3-031-45642-8_8
dc.relation.ispartofLecture Notes in Networks and Systems
dc.relation.ispartofInformation Systems and Technologies
dc.relation.ispartofseriesCommunications in Computer and Information Science
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/
dc.subjectMeteorological data reconstruction
dc.subjectAnalogue ensemble
dc.subjectK-means clustering
dc.subjectPrincipal component analysis
dc.subjectMATLAB
dc.subjectR
dc.titleReconstruction of meteorological records with PCA-based analog ensemble methodspor
dc.typeconference object
dspace.entity.typePublication
oaire.awardNumberUIDP/05757/2020
oaire.awardTitleResearch Centre in Digitalization and Intelligent Robotics
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDP%2F05757%2F2020/PT
oaire.citation.endPage96
oaire.citation.issue1
oaire.citation.startPage85
oaire.citation.titleInternational Conference on Advanced Research in Technologies, Information, Innovation and Sustainability, ARTIIS 2024
oaire.citation.volume2348
oaire.fundingStream6817 - DCRRNI ID
oaire.versionhttp://purl.org/coar/version/c_970fb48d4fbd8a85
person.familyNameBalsa
person.familyNameRufino
person.givenNameCarlos
person.givenNameJosé
person.identifier1721518
person.identifier.ciencia-idDE1E-2F7A-AAB1
person.identifier.ciencia-idC414-F47F-6323
person.identifier.orcid0000-0003-2431-8665
person.identifier.orcid0000-0002-1344-8264
person.identifier.ridM-8735-2013
person.identifier.scopus-author-id23391719100
person.identifier.scopus-author-id55947199100
project.funder.identifierhttp://doi.org/10.13039/501100001871
project.funder.nameFundação para a Ciência e a Tecnologia
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relation.isAuthorOfPublication1e24d2ce-a354-442a-bef8-eebadd94b385
relation.isAuthorOfPublication.latestForDiscoveryd0e5ccff-9696-4f4f-9567-8d698a6bf17d
relation.isProjectOfPublicationd0a17270-80a8-4985-9644-a04c2a9f2dff
relation.isProjectOfPublication.latestForDiscoveryd0a17270-80a8-4985-9644-a04c2a9f2dff

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