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Regression models for outlier identification (Hurricanes and typhoons) in wave hindcast databases

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dc.affiliation.dptoUC3M. Departamento de Estadísticaes
dc.contributor.authorMínguez Solana, Roberto
dc.contributor.authorReguero, B. C.
dc.contributor.authorLuceño, A.
dc.contributor.authorMéndez, F.J.
dc.date.accessioned2022-05-27T14:08:06Z
dc.date.available2022-05-27T14:08:06Z
dc.date.issued2012-02-01
dc.description.abstractThThe development of numerical wave prediction models for hindcast applications allows a detailed description of wave climate in locations where long-term instrumental records are not available. Wave hindcast databases (WHDBs) have become a powerful tool for the design of offshore and coastal structures, offering important advantages for the statistical characterization of wave climate all over the globe (continuous time series, wide spatial coverage, constant time span, homogeneous forcing, and more than 60-yr-long time series). However, WHDBs present several deficiencies reported in the literature. One of these deficiencies is related to typhoons and hurricanes, which are inappropriately reproduced by numerical models. The main reasons are (i) the difficulty of specifying accurate wind fields during these events and (ii) the insufficient spatiotemporal resolution used. These difficulties make the data related to these events appear as ‘‘outliers’’ when compared with instrumental records. These bad data distort results from calibration and/or correction techniques. In this paper, several methods for detecting the presence of typhoons and/or hurricane data are presented, and their automatic outlier identification capabilities are analyzed and compared. All the methods are applied to a global wave hindcast database and results are compared with existing hurricane and buoy databases in the Gulf of Mexico, Caribbean Sea, and North Atlantic Ocean.en
dc.identifier.bibliographicCitationMínguez, R., Reguero, B. G., Luceño, A., & Méndez, F. J. (2012). Regression Models for Outlier Identification (Hurricanes and Typhoons) in Wave Hindcast Databases. Journal of Atmospheric and Oceanic Technology, 29 (2), pp. 267-285.en
dc.identifier.doihttps://doi.org/10.1175/JTECH-D-11-00059.1
dc.identifier.issn0739-0572
dc.identifier.publicationfirstpage267es
dc.identifier.publicationissue2es
dc.identifier.publicationlastpage285es
dc.identifier.publicationtitleJOURNAL OF ATMOSPHERIC AND OCEANIC TECHNOLOGYen
dc.identifier.publicationvolume29es
dc.identifier.urihttps://hdl.handle.net/10016/34922
dc.identifier.uxxiAR/0000029964
dc.language.isoengen
dc.publisherAmerican Meteorological Societyen
dc.rights© 2012 American Meteorological Societyen
dc.rights.accessRightsopen accessen
dc.subject.ecienciaEstadísticaes
dc.subject.otherError analysisen
dc.subject.otherOcean modelsen
dc.subject.otherRegression analysisen
dc.subject.otherStatistical techniquesen
dc.titleRegression models for outlier identification (Hurricanes and typhoons) in wave hindcast databasesen
dc.typeresearch article*
dc.type.hasVersionVoR*
dspace.entity.typePublication
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