Statistical analysis in downscaling climate models : wavelet and Bayesian methods in multimodel ensembles

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Statistical analysis in downscaling climate models : wavelet and Bayesian methods in multimodel ensembles

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dc.contributor.advisor Damien, Paul, 1960-
dc.creator Cai, Yihua
dc.date.accessioned 2010-06-04T14:43:16Z
dc.date.available 2010-06-04T14:43:16Z
dc.date.created 2009-08
dc.date.issued 2010-06-04T14:43:16Z
dc.date.submitted August 2009
dc.identifier.uri http://hdl.handle.net/2152/ETD-UT-2009-08-293
dc.description.abstract Various 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.
dc.format.mimetype application/pdf
dc.language.iso eng
dc.subject Bayesian analysis
dc.subject wavelet analysis
dc.subject multimodel ensembles
dc.title Statistical analysis in downscaling climate models : wavelet and Bayesian methods in multimodel ensembles
dc.contributor.committeeMember McCulloch, Robert E.
dc.description.department Mathematics
dc.type.genre thesis
dc.type.material text
thesis.degree.department Mathematics
thesis.degree.discipline Statistics
thesis.degree.grantor The University of Texas at Austin
thesis.degree.level Masters
thesis.degree.name Master of Science in Statistics

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