File:USING BAYESIAN STATISTICAL POST-PROCESSING TECHNIQUES TO IMPROVE TROPICAL CYCLONE TRACK AND INTENSITY FORECASTS (IA usingbayesiansta1094559641).pdf
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USING BAYESIAN STATISTICAL POST-PROCESSING TECHNIQUES TO IMPROVE TROPICAL CYCLONE TRACK AND INTENSITY FORECASTS
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| Author |
Cummings, Sabrina L. |
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| Title |
USING BAYESIAN STATISTICAL POST-PROCESSING TECHNIQUES TO IMPROVE TROPICAL CYCLONE TRACK AND INTENSITY FORECASTS |
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| Publisher |
Monterey, CA; Naval Postgraduate School |
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| Description |
This 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. Subjects: Bayes; Bayesian; statistics; statistical; tropical cyclone; hurricane; track; intensity; forecast; weather; post-processing. |
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| Language | English | |
| Publication date | June 2018 | |
| Current location |
IA Collections: navalpostgraduateschoollibrary; fedlink |
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| Accession number |
usingbayesiansta1094559641 |
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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. Copyright protection is not available for this work 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. | ||
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| current | 19:42, 25 July 2020 |  | 1,275 × 1,650, 64 pages (1.54 MB) | Fæ (talk | contribs) | FEDLINK - United States Federal Collection usingbayesiansta1094559641 (User talk:Fæ/IA books#Fork8) (batch 1993-2020 #31607) |
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