Show simple item record

dc.contributor.advisorNuss, Wendell A.
dc.contributor.authorWendt, Robert D. T.
dc.dateSep-17
dc.date.accessioned2017-11-07T23:41:13Z
dc.date.available2017-11-07T23:41:13Z
dc.date.issued2017-09
dc.identifier.urihttp://hdl.handle.net/10945/56188
dc.description.abstractPrevious research in statistical post-processing has found systematic deficiencies in deterministic forecast guidance. As a result, ensemble forecasts of sensible weather variables often manifest biased central tendencies and anomalous dispersion. In this way, the numerical weather prediction community has largely focused on upgrades to upstream model components to improve forecast performance--that is, innovations in data assimilation, governing dynamics, numerical techniques, and various parameterizations of subgrid-scale processes. However, this dissertation explores the efficacy of statistical post-processing methods downstream of these dynamical model components with a hierarchical multivariate Bayesian approach to ensemble model output statistics. This technique directly parameterizes meteorological phenomena with probability distributions that describe the intrinsic structure of observable data. Bayesian posterior beliefs in model parameter were conditioned on previous observations and dynamical predictors available outside of the parent ensemble. An adaptive variant of the random-walk Metropolis algorithm was used to complete the inference scheme with block-wise multiparameter updates. This produced calibrated multivariate posterior predictive distributions (PPD) for 24-hour forecasts of diurnal extrema in surface temperature and wind speed. These Bayesian PPDs reliably characterized forecast uncertainty and outperformed the parent ensemble and a classical least-squares approach to multivariate multiple linear regression using both measures-oriented and distributions-oriented scoring rules.en_US
dc.description.urihttp://archive.org/details/ahierarchicalmul1094556188
dc.publisherMonterey, California: 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.titleA hierarchical multivariate Bayesian approach to ensemble model output statistics in atmospheric predictionen_US
dc.typeThesisen_US
dc.contributor.departmentMeteorology
dc.subject.authorensemble model output statisticsen_US
dc.subject.authorstatistical post-processingen_US
dc.subject.authormultivariate multiple linear regressionen_US
dc.subject.authorBayesian data analysisen_US
dc.subject.authorBayesian hierarchical modelingen_US
dc.subject.authorMarkov chain Monte Carlo methodsen_US
dc.subject.authorMetropolis algorithmen_US
dc.subject.authormachine learningen_US
dc.subject.authoratmospheric predictionen_US
dc.description.serviceLieutenant Commander, United States Navyen_US
etd.thesisdegree.nameDoctor of Philosophy in Meteorologyen_US
etd.thesisdegree.levelDoctoralen_US
etd.thesisdegree.disciplineMeteorologyen_US
etd.thesisdegree.grantorNaval Postgraduate Schoolen_US
dc.description.distributionstatementApproved for public release; distribution is unlimited.


Files in this item

Thumbnail

This item appears in the following Collection(s)

Show simple item record