Please use this identifier to cite or link to this item: http://hdl.handle.net/20.500.11765/10155
An approach to homogenize daily peak wind gusts: an application to the Australian series
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dc.contributor.authorAzorín Molina, Césares_ES
dc.contributor.authorGuijarro, José Antonioes_ES
dc.contributor.authorMcVicar, Tim R.es_ES
dc.contributor.authorTrewin, Blair C.es_ES
dc.contributor.authorFrost, Andrew J.es_ES
dc.contributor.authorChen, Delianges_ES
dc.date.accessioned2018-12-17T12:42:55Z-
dc.date.available2018-12-17T12:42:55Z-
dc.date.issued2018-
dc.identifier.citationInternational Journal of Climatology. 2018, p. 1-18es_ES
dc.identifier.issn0899-8418-
dc.identifier.issn1097-0088-
dc.identifier.urihttp://hdl.handle.net/20.500.11765/10155-
dc.description.abstractDaily Peak Wind Gust (DPWG) time series are important for the evaluation of wind-related hazard risks to different socioeconomic and environmental sectors. Yet, wind time series analyses can be impacted by several artefacts, both tempo-rally and spatially, which may introduce inhomogeneities that mislead the study of their decadal variability and trends. The aim of this study is to present a strategy in the homogenization of a challenging climate extreme such as the DPWG using 548 time series across Australia for 1941–2016. This automatic homogenization of DPWG is implemented in the recently developed Version 3.1 of the R package Climatol. This approach is an advance in homogenization of climate records as it identifies 353 break points based on monthly data, splits the daily series into homo- geneous subperiods, and homogenizes them without needing the monthly corrections. The major advantages of this homogenization strategy are its ability to: (a) automatically homogenize a large number of DPWG series, including short-term ones and without needing site metadata (e.g., the change in observational equipment in 2010/2011 was correctly identified); (b) use the closest reference series even not sharing a common period with candidate series or presenting missing data; and (c) supply homogenized series, correcting anomalous data (quality control by spatial coherence), and filling in all the missing data. The NCEP/NCAR reanalysis wind speed data were also trialled in aiding homogenization given the station density was very low during the early decades of the record; however, reanalysis data did not improve the homogenization. Application of this approach found a reduced range of DPWG trends based on site data, and an increased negative regional trend of this climate extreme, compared to raw data and homogenized data using NCEP/NCAR. The analysis produced the first homogenized DPWG dataset to assess and attribute long-term variability of extreme winds across Australia.es_ES
dc.description.sponsorshipC.A.-M. received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No. 703733 (STILLING project). This work was also supported by the project “Detection and attribution of changes in extreme wind gusts over land” (2017-03780) funded by the Vetenskapsrådet, and the MULTITEST (Multiple verification of automatic software homogenizing monthly temperature and precipitation series; CGL2014-52901-P) project, funded by the Spanish Ministry of Economy and Competitivity.es_ES
dc.language.isoenges_ES
dc.publisherRoyal Meteorological Society; Wileyes_ES
dc.rightsLicencia CC: Reconocimiento CC BYes_ES
dc.subjectAustraliaes_ES
dc.subjectClimatolV3.1es_ES
dc.subjectDaily peak wind gustses_ES
dc.subjectHomogenizationes_ES
dc.titleAn approach to homogenize daily peak wind gusts: an application to the Australian serieses_ES
dc.typeinfo:eu-repo/semantics/articlees_ES
dc.relation.publisherversionhttps://dx.doi.org/10.1002/joc.5949es_ES
dc.rights.accessRightsinfo:eu-repo/semantics/openAccesses_ES
dc.relation.projectIDinfo:eu-repo/grantAgreement/EC/H2020/703733es_ES
Appears in Collections:Artículos científicos 2015-2018


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