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Predictability of short-range forecasting: a multimodel approach
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dc.contributor.authorGarcía-Moya, José Antonioes_ES
dc.contributor.authorCallado Pallarés, Alfonses_ES
dc.contributor.authorEscribà, Paues_ES
dc.contributor.authorSantos Burguete, Carloses_ES
dc.contributor.authorSantos Muñoz, Danieles_ES
dc.contributor.authorSimarro Grande, Juan Pabloes_ES
dc.date.accessioned2016-03-22T09:33:39Z-
dc.date.available2016-03-22T09:33:39Z-
dc.date.issued2011-
dc.identifier.citationTellus A. 2011, 63(3), p. 550-563es_ES
dc.identifier.issn0280-6495-
dc.identifier.issn1600-0870-
dc.identifier.urihttp://hdl.handle.net/20.500.11765/1382-
dc.description.abstractNumerical weather prediction (NWP) models (including mesoscale) have limitations when it comes to dealing with severe weather events because extreme weather is highly unpredictable, even in the short range. A probabilistic forecast based on an ensemble of slightly differentmodel runs may help to address this issue. Among other ensemble techniques, Multimodel ensemble prediction systems (EPSs) are proving to be useful for adding probabilistic value to mesoscale deterministic models. A Multimodel Short Range Ensemble Prediction System (SREPS) focused on forecasting the weather up to 72 h has been developed at the panishMeteorological Service (AEMET). The system uses five different limited area models (LAMs), namely HIRLAM (HIRLAM Consortium), HRM (DWD), the UM (UKMO), MM5 (PSU/NCAR) and COSMO (COSMO Consortium). These models run with initial and boundary conditions provided by five different global deterministic models, namely IFS (ECMWF), UM (UKMO), GME (DWD), GFS (NCEP) and CMC (MSC). AEMET-SREPS (AE) validation on the large-scale flow, using ECMWF analysis, shows a consistent and slightly underdispersive system. For surface parameters, the system shows high skill forecasting binary events. 24-h precipitation probabilistic forecasts are verified using an up-scaling grid of observations from European high-resolution precipitation networks, and compared with ECMWF-EPS (EC).es_ES
dc.formatapplication/pdf-
dc.language.isoenges_ES
dc.publisherTaylor & Francises_ES
dc.rightsLicencia CC: Reconocimiento CC BYes_ES
dc.subjectShort-range forecastinges_ES
dc.subjectNumerical weather predictiones_ES
dc.subjectEnsemble Prediction Systemes_ES
dc.subjectMesoscale modeles_ES
dc.titlePredictability of short-range forecasting: a multimodel approaches_ES
dc.typeinfo:eu-repo/semantics/articlees_ES
dc.relation.publisherversionhttps://dx.doi.org/10.1111/j.1600-0870.2010.00506.xes_ES
dc.rights.accessRightsinfo:eu-repo/semantics/openAccesses_ES
Colecciones: Artículos científicos 2010-2014


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