File:APPLICATION OF BAYESIAN STATISTICAL POST-PROCESSING TECHNIQUES TO PROBABILISTIC NOWCASTS OF CEILING HEIGHT AND VISIBILITY (IA applicationofbay1094559693).pdf
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Summary
APPLICATION OF BAYESIAN STATISTICAL POST-PROCESSING TECHNIQUES TO PROBABILISTIC NOWCASTS OF CEILING HEIGHT AND VISIBILITY
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Author |
Jones, Kellen T. |
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Title |
APPLICATION OF BAYESIAN STATISTICAL POST-PROCESSING TECHNIQUES TO PROBABILISTIC NOWCASTS OF CEILING HEIGHT AND VISIBILITY |
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Publisher |
Monterey, CA; Naval Postgraduate School |
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Description |
Nowcasting is a modern technique in weather prediction that seeks to produce highly accurate analysis and short-term forecasts of up to six hours. Challenges to nowcasting include numerical forecasting spatial and temporal resolution and data availability, especially in data-denied or limited regions. Nowcasting cloud ceiling height and horizontal visibility is a specific example of a challenging nowcasting problem. A nowcast system is applied and tested on summertime conditions from June to August 2017 over the Monterey Regional Airport in California. The system post-processes 12 km North American Mesoscale Model (NAM) data from a local grid point to produce short-term multivariate probabilistic predictions of ceiling of height and visibility. Bayesian Estimation (BE) and Monte Carlo Markov Chain (MCMC) methods are used to train the system from a set of past predictor variables and observations. The approach demonstrates error reduction and skill improvement over the raw NAM ceiling height and visibility forecasts. The computationally cheap system also explicitly communicates uncertainty and requires a relatively limited training data set compared to other statistical post-processing techniques. Using short training periods and/or analog techniques, this system can be used to nowcast in regions with limited or no observational data availability. Subjects: operational nowcasting; cloud forecasting; Bayesian estimation; statistical post-processing; supervised machine learning; ceiling height; visibility; probabilistic weather forecasting |
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Language | English | |
Publication date | June 2018 | |
Current location |
IA Collections: navalpostgraduateschoollibrary; fedlink |
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Accession number |
applicationofbay1094559693 |
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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 | 16:27, 14 July 2020 | ![]() | 1,275 × 1,650, 96 pages (1.71 MB) | Fæ | FEDLINK - United States Federal Collection applicationofbay1094559693 (User talk:Fæ/IA books#Fork8) (batch 1993-2020 #7871) |
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Author | localadmin |
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Short title | APPLICATION OF BAYESIAN STATISTICAL POST-PROCESSING TECHNIQUES TO PROBABILISTIC NOWCASTS OF CEILING HEIGHT AND VISIBILITY |
File change date and time | 02:55, 11 July 2018 |
Date and time of digitizing | 07:55, 20 June 2018 |
Date metadata was last modified | 02:55, 11 July 2018 |
Software used | PScript5.dll Version 5.2.2 |
Conversion program | Acrobat Distiller 11.0 (Windows) |
Encrypted | no |
Page size | 612 x 792 pts (letter) |
Version of PDF format | 1.4 |