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dc.creatorCai, Yihuaen_US
dc.date.accessioned2010-06-04T14:43:16Z
dc.date.available2010-06-04T14:43:16Z
dc.date.created2009-08en_US
dc.date.issued2010-06-04T14:43:16Z
dc.date.submittedAugust 2009en_US
dc.identifier.urihttp://hdl.handle.net/2152/ETD-UT-2009-08-293
dc.descriptiontext
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_US
dc.format.mimetypeapplication/pdfen_US
dc.language.isoengen_US
dc.subjectBayesian analysisen_US
dc.subjectwavelet analysisen_US
dc.subjectmultimodel ensemblesen_US
dc.titleStatistical analysis in downscaling climate models : wavelet and Bayesian methods in multimodel ensemblesen_US
dc.description.departmentMathematicsen_US
dc.type.genrethesisen_US
thesis.degree.departmentMathematicsen_US
thesis.degree.disciplineStatisticsen_US
thesis.degree.grantorThe University of Texas at Austinen_US
thesis.degree.levelMastersen_US
thesis.degree.nameMaster of Science in Statisticsen_US


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