APPLICATION OF BAYESIAN STATISTICAL POST-PROCESSING TECHNIQUES TO PROBABILISTIC NOWCASTS OF CEILING HEIGHT AND VISIBILITY
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Authors
Jones, Kellen T.
Subjects
operational nowcasting
cloud forecasting
Bayesian estimation
statistical post-processing
supervised machine learning
ceiling height
visibility
probabilistic weather forecasting
cloud forecasting
Bayesian estimation
statistical post-processing
supervised machine learning
ceiling height
visibility
probabilistic weather forecasting
Advisors
Nuss, Wendell A.
Date of Issue
2018-06
Date
Publisher
Monterey, CA; Naval Postgraduate School
Language
Abstract
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.
Type
Thesis
Description
Series/Report No
Department
Meteorology (MR)
Meteorology (MR)
Organization
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NPS Report Number
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Distribution Statement
Approved for public release; distribution is unlimited.
Rights
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.