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Reconstruction of meteorological records by methods based on dimension reduction of the predictor dataset

2023Journal article Open access
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dc.contributor.authorBalsa, Carlos
dc.contributor.authorBreve, Murilo Montanini
dc.contributor.authorRodrigues, Carlos Veiga
dc.contributor.authorRufino, José
dc.date.accessioned2021-11-17T16:14:38Z
dc.date.available2021-11-17T16:14:38Z
dc.date.issued2023
dc.description.abstractThe reconstruction or prediction of meteorological records through the Analog Ensemble (AnEn) method is very efficient when the number of predictor time series is small. Thus, in order to take advantage of the richness and diversity of information contained in a large number of predictors, it is necessary to reduce their dimensions. This study presents methods to accomplish such reduction, allowing the use of a high number of predictor variables. In particular, the techniques of Principal Component Analysis (PCA) and Partial Least Squares (PLS) are used to reduce the dimension of the predictor dataset without loss of essential information. The combination of the AnEn and PLS techniques results in a very efficient hybrid method (PLSAnEn) for reconstructing or forecasting unstable meteorological variables, such as wind speed. This hybrid method is computationally demanding but its performance can be improved via parallelization or the introduction of variants in which all possible analogs are previously clustered. The multivariate linear regression methods used on the new variables resulting from the PCA or PLS techniques also proved to be efficient, especially for the prediction of meteorological variables without local oscillations, such as the pressure.pt_PT
dc.description.sponsorshipThe authors wish to thank the financial support of FEDER through the COMPETE Program and the national funds from FCT—Science and Technology Portuguese Foundation for financing the DOUROZONE project (PTDC/AAG-MAA/3335/2014; POCI-01-0145-FEDER-016778). Thanks is also due for the financial support to the PhD grant of A. Ascenso (SFRH/BD/136875/2018). Thanks is due to FCT/MCTES for the financial support to CESAM (UIDP/50017/2020+UIDB/50017/2020), through national funds.pt_PT
dc.description.versioninfo:eu-repo/semantics/publishedVersionpt_PT
dc.identifier.citationBalsa, Carlos; Breve, Murilo Montanini; Rodrigues, Carlos Veiga; Rufino, José (2023). Reconstruction of meteorological records by methods based on dimension reduction of the predictor dataset. Computation. eISSN 2079-3197. 11:5, p. 1-23pt_PT
dc.identifier.doi10.3390/computation11050098
dc.identifier.eissn2079-3197
dc.identifier.urihttp://hdl.handle.net/10198/24224
dc.language.isoengpt_PT
dc.peerreviewedyespt_PT
dc.publisherMDPI
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/pt_PT
dc.subjectHindcasting; forecastingpt_PT
dc.subjectAnalog ensemblept_PT
dc.subjectPrincipal component analysispt_PT
dc.subjectPartial least squarept_PT
dc.subjectMultivariate regressionpt_PT
dc.titleReconstruction of meteorological records by methods based on dimension reduction of the predictor datasetpt_PT
dc.typejournal article
dspace.entity.typePublication
oaire.citation.titleComputationpt_PT
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
rcaap.rightsopenAccesspt_PT
rcaap.typearticlept_PT
relation.isAuthorOfPublicationd0e5ccff-9696-4f4f-9567-8d698a6bf17d
relation.isAuthorOfPublication1e24d2ce-a354-442a-bef8-eebadd94b385
relation.isAuthorOfPublication.latestForDiscoveryd0e5ccff-9696-4f4f-9567-8d698a6bf17d

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