File:A hierarchical multivariate Bayesian approach to ensemble model output statistics in atmospheric prediction (IA ahierarchicalmul1094556188).pdf
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Summary[edit]
A hierarchical multivariate Bayesian approach to ensemble model output statistics in atmospheric prediction ( ) | ||
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Author |
Wendt, Robert D. T. |
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Title |
A hierarchical multivariate Bayesian approach to ensemble model output statistics in atmospheric prediction |
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Publisher |
Monterey, California: Naval Postgraduate School |
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Description |
Previous 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. Subjects: ensemble model output statistics; statistical post-processing; multivariate multiple linear regression; Bayesian data analysis; Bayesian hierarchical modeling; Markov chain Monte Carlo methods; Metropolis algorithm; machine learning; atmospheric prediction |
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Language | English | |
Publication date | September 2017 | |
Current location |
IA Collections: navalpostgraduateschoollibrary; fedlink |
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Accession number |
ahierarchicalmul1094556188 |
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Source | ||
Permission (Reusing this file) |
This publication is a work of the U.S. Government as defined in Title 17, United States Code, Section 101. As such, it is in the public domain, and under the provisions of Title 17, United States Code, Section 105, it may not be copyrighted. |
Licensing[edit]
Public domainPublic domainfalsefalse |
This file is a work of a sailor or employee of the U.S. Navy, taken or made as part of that person's official duties. As a work of the U.S. federal government, it is in the public domain in the United States.
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This file has been identified as being free of known restrictions under copyright law, including all related and neighboring rights. |
https://creativecommons.org/publicdomain/mark/1.0/PDMCreative Commons Public Domain Mark 1.0falsefalse
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Short title | A hierarchical multivariate Bayesian approach to ensemble model output statistics in atmospheric prediction |
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Author | Wendt, Robert D. T. |
Date and time of digitizing | 03:21, 6 September 2017 |
Software used | Microsoft® Word 2016 |
File change date and time | 06:30, 20 October 2017 |
Date metadata was last modified | 06:30, 20 October 2017 |
Conversion program | Microsoft® Word 2016 |
Encrypted | no |
Page size | 612 x 792 pts (letter) |
Version of PDF format | 1.4 |