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dc.contributor.advisorDamien, Paul, 1960-en
dc.creatorCai, Yihuaen
dc.date.accessioned2010-06-04T14:43:16Zen
dc.date.available2010-06-04T14:43:16Zen
dc.date.created2009-08en
dc.date.issued2010-06-04T14:43:16Zen
dc.date.submittedAugust 2009en
dc.identifier.urihttp://hdl.handle.net/2152/ETD-UT-2009-08-293en
dc.descriptiontexten
dc.description.abstractVarious climate models have been developed to analyze and predict climate change; however, model uncertainties cannot be easily overcome. A statistical approach has been presented in this paper to calculate the distributions of future climate change based on an ensemble of the Weather Research and Forecasting (WRF) models. Wavelet analysis has been adopted to de-noise the WRF model output. Using the de-noised model output, we carry out Bayesian analysis to decrease uncertainties in model CAM_KF, RRTM_KF and RRTM_GRELL for each downscaling region.en
dc.format.mimetypeapplication/pdfen
dc.language.isoengen
dc.subjectBayesian analysisen
dc.subjectwavelet analysisen
dc.subjectmultimodel ensemblesen
dc.titleStatistical analysis in downscaling climate models : wavelet and Bayesian methods in multimodel ensemblesen
dc.contributor.committeeMemberMcCulloch, Robert E.en
dc.description.departmentMathematicsen
dc.type.genrethesisen
thesis.degree.departmentMathematicsen
thesis.degree.disciplineStatisticsen
thesis.degree.grantorThe University of Texas at Austinen
thesis.degree.levelMastersen
thesis.degree.nameMaster of Science in Statisticsen


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