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dc.contributor.authorBarcons, Jordi
dc.contributor.authorAvila, Matias
dc.contributor.authorFolch, Arnau
dc.contributor.otherBarcelona Supercomputing Center
dc.date.accessioned2018-05-08T13:21:13Z
dc.date.available2018-05-08T13:21:13Z
dc.date.issued2018-02-06
dc.identifier.citationBarcons, J.; Avila, M.; Folch, A. A wind field downscaling strategy based on domain segmentation and transfer functions. "Wind Energy", 6 Febrer 2018, vol. 21, núm. 6, p. 409-425.
dc.identifier.issn1095-4244
dc.identifier.urihttp://hdl.handle.net/2117/117019
dc.description.abstractThis paper presents a novel methodology for mesoscale‐to‐microscale downscaling of near‐surface wind fields. The model chain consists on the Weather Research and Forecast mesoscale model and the Alya‐CFDWind microscale model (assuming neutral stability). The downscaling methodology combines precomputed microscale simulations with a mesoscale forecast using a domain segmentation technique and transfer functions. As a result, the downscaled wind field preserves the mesoscale pattern but, at the same time, incorporates local mesoscale subgrid terrain effects, particularly at valleys and channelling zones. The methodology has been validated over a 9‐month period on a very complex terrain site instrumented with a dense observational network of meteorological masts. With respect to mesoscale results, the global skills of the downscaled wind at masts improve for wind direction and remain similar for wind velocity. However, a substantial improvement occurs under stable and neutral conditions and for high wind velocity regimes.
dc.description.sponsorshipThis work has been partially funded by the High Performance Computing for Energy (HPC4E) project (call H2020-EUB-2015, Topic: EUB-2-2015, type of action RIA, Grant Agreement number 689772) and the SEDAR ("Simulación eólica de alta resolución") project. It has also been partially supported by the Energy-oriented Centre of Excellence (EoCoE) (Grant Agreement number 676629, funded within the H2020 framework of the EuropeanUnion). J.B. is grateful to a PhD fellowship from the IndustrialDoctorates Plan of the Government of Catalonia (Ref. eco/2497/2013). We also thank Daniel Paredes and Luis Prieto from Iberdrola Renovables S.A. for providing us access to met masts data for validation.
dc.format.extent17 p.
dc.language.isoeng
dc.publisherWiley
dc.rightsAttribution-NonCommercial-NoDerivs 4.0 Spain
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/es/
dc.subjectÀrees temàtiques de la UPC::Energies
dc.subject.lcshWind energy
dc.subject.otherComplex terrain
dc.subject.otherDownscaling
dc.subject.otherHigh-resolution
dc.subject.otherNear-surface winds
dc.subject.otherTransfer functions
dc.subject.otherWind forecast
dc.titleA wind field downscaling strategy based on domain segmentation and transfer functions
dc.typeArticle
dc.subject.lemacEnergia eòlica
dc.identifier.doi10.1002/we.2169
dc.description.peerreviewedPeer Reviewed
dc.relation.publisherversionhttps://onlinelibrary.wiley.com/doi/full/10.1002/we.2169
dc.rights.accessOpen Access
dc.description.versionPostprint (published version)
dc.relation.projectidinfo:eu-repo/grantAgreement/EC/H2020/689772/EU/HPC for Energy/HPC4E
dc.relation.projectidinfo:eu-repo/grantAgreement/EC/H2020/676629/EU/Energy oriented Centre of Excellence for computer applications/EoCoE
upcommons.citation.publishedtrue
upcommons.citation.publicationNameWind Energy
upcommons.citation.volume21
upcommons.citation.number6
upcommons.citation.startingPage409
upcommons.citation.endingPage425


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