USING BAYESIAN STATISTICAL POST-PROCESSING TECHNIQUES TO IMPROVE TROPICAL CYCLONE TRACK AND INTENSITY FORECASTS

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dc.contributor.advisorNuss, Wendell A.
dc.contributor.authorCummings, Sabrina L.
dc.contributor.departmentMeteorology (MR)
dc.contributor.secondreaderHendricks, Eric A.
dc.date.accessioned2018-08-24T22:35:03Z
dc.date.available2018-08-24T22:35:03Z
dc.date.issued2018-06
dc.description.abstractThis thesis examines the use of statistical post-processing techniques involving Bayesian estimation and Markov Chain Monte Carlo methods to aid in the reduction or elimination of tropical cyclone track and intensity forecast errors. The results of this research showed an improvement in the forecasts for intensity and total track error over the European Centre for Medium-Range Weather Forecasts (ECMWF) ensemble mean for all forecast times. These findings indicate that applying Bayesian statistical post-processing to forecasts made by the ECMWF ensemble can reduce the overall track and intensity error and result in more accurate forecasts. The most significant forecast improvement resulted from larger sample sizes and creative grouping schemes. By increasing the number of storms used and altering the manner in which the data is grouped, a more accurate forecast can be obtained. Future research using a larger sample size that spans several decades is indicated, but any significant physics alterations to the models over time, as well as more specific ways of grouping the data, must be taken into consideration.en_US
dc.description.distributionstatementApproved for public release; distribution is unlimited.
dc.description.serviceLieutenant Commander, United States Navyen_US
dc.description.urihttp://archive.org/details/usingbayesiansta1094559641
dc.identifier.thesisid29431
dc.identifier.urihttps://hdl.handle.net/10945/59641
dc.publisherMonterey, CA; Naval Postgraduate Schoolen_US
dc.rightsThis publication is a work of the U.S. Government as defined in Title 17, United States Code, Section 101. Copyright protection is not available for this work in the United States.en_US
dc.subject.authorBayesen_US
dc.subject.authorBayesianen_US
dc.subject.authorstatisticsen_US
dc.subject.authorstatisticalen_US
dc.subject.authortropical cycloneen_US
dc.subject.authorhurricaneen_US
dc.subject.authortracken_US
dc.subject.authorintensityen_US
dc.subject.authorforecasten_US
dc.subject.authorweatheren_US
dc.subject.authorpost-processing.en_US
dc.titleUSING BAYESIAN STATISTICAL POST-PROCESSING TECHNIQUES TO IMPROVE TROPICAL CYCLONE TRACK AND INTENSITY FORECASTSen_US
dc.typeThesisen_US
dspace.entity.typePublication
etd.thesisdegree.disciplineMeteorology and Physical Oceanographyen_US
etd.thesisdegree.grantorNaval Postgraduate Schoolen_US
etd.thesisdegree.levelMastersen_US
etd.thesisdegree.nameMaster of Science in Meteorology and Physical Oceanographyen_US
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