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dc.contributor.authorPrieto Herráez, Diego 
dc.contributor.authorFrías-Paredes, Laura
dc.contributor.authorCascón Barbero, José Manuel 
dc.contributor.authorLagüela López, Susana 
dc.contributor.authorGastón Romeo, Martín
dc.contributor.authorAsensio Sevilla, María Isabel 
dc.contributor.authorMartín Nieto, Ignacio 
dc.contributor.authorFernandes Correia, P.M.
dc.contributor.authorLaiz Alonso, Pablo 
dc.contributor.authorCarrasco Díaz, O.F.
dc.contributor.authorSáez Blázquez, Cristina 
dc.contributor.authorHernández, E.
dc.contributor.authorFerragut Canalsd, L.
dc.contributor.authorGonzález Aguilera, Diego 
dc.date.accessioned2022-05-24T11:03:13Z
dc.date.available2022-05-24T11:03:13Z
dc.date.issued2021
dc.identifier.citationPrieto Herráez, D,... et al. (2021). Local wind speed forecasting based on WRF-HDWind coupling. Atmospheric Research, 248, pp. 105219es_ES
dc.identifier.issn0169-8095
dc.identifier.urihttp://hdl.handle.net/10366/149831
dc.description.abstract[EN] Wind speed forecasts obtained by Numerical Weather Prediction models are limited for fine interpretation in heterogeneous terrain, in which different roughnesses and orographies occur. This limitation is derived from the use of low-resolution and grid-box averaged data. In this paper a dynamical downscaling method is presented to increase the local accuracy of wind speed forecasts. The proposed method divides the wind speed forecasting into two steps. In the first one, the mesoscale model WRF (Weather Research and Forecasting) is used for getting wind speed forecasts at specific points of the study domain. On a second stage, these values are used for feeding the HDWind microscale model. HDWind is a local model that provides both a high-resolution wind field that covers the entire study domain and values of wind speed and direction at very located points. As an example of use of the proposed method, we calculate a high-resolution wind field in an urban-interface area from Badajoz, a South-West Spanish city located near the Portugal border. The results obtained are compared with the values read by a weathervane tower of the Spanish State Meteorological Agency (AEMET) in order to prove that the microscale model improves the forecasts obtained by the mesoscale model.es_ES
dc.language.isoenges_ES
dc.publisherElsevieres_ES
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 Internacional*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/*
dc.subjectWind speed forecastinges_ES
dc.subjectWRFes_ES
dc.subjectHDWindes_ES
dc.subjectDynamical downscalinges_ES
dc.subjectLocal wind adjustmentes_ES
dc.titleLocal wind speed forecasting based on WRF-HDWind couplinges_ES
dc.typeinfo:eu-repo/semantics/articlees_ES
dc.relation.publishversionhttps://doi.org/10.1016/j.atmosres.2020.105219es_ES
dc.subject.unesco2511.07 Ingeniería de Sueloses_ES
dc.subject.unesco5506.06 Historia de la Economíaes_ES
dc.subject.unesco12 Matemáticases_ES
dc.identifier.doi10.1016/j.atmosres.2020.105219
dc.rights.accessRightsinfo:eu-repo/semantics/openAccesses_ES
dc.journal.titleAtmospheric Researches_ES
dc.volume.number248es_ES
dc.page.initial105219es_ES
dc.type.hasVersioninfo:eu-repo/semantics/acceptedVersiones_ES


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Attribution-NonCommercial-NoDerivatives 4.0 Internacional
Except where otherwise noted, this item's license is described as Attribution-NonCommercial-NoDerivatives 4.0 Internacional