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Activity Number:
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99
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Type:
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Contributed
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Date/Time:
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Monday, August 4, 2008 : 8:30 AM to 10:20 AM
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Sponsor:
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Section on Statistics and the Environment
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| Abstract - #301501 |
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Title:
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Probabilistic Wind Forecasting Using Bayesian Model Averaging
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Author(s):
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J. McLean Sloughter*+
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Companies:
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University of Washington
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Address:
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2600 Eastlake Ave E #204, Statistics, Seattle, WA, 98102,
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Keywords:
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ensemble ; forecasting ; BMA ; wind
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Abstract:
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Bayesian model averaging (BMA) is a statistical way of post-processing forecast ensembles to create predictive PDFs for weather quantities. It represents the predictive PDF as a weighted average of PDFs based on the individual forecasts. It was developed initially for quantities whose PDFs can be approximated by normal distributions, such as temperature and sea level pressure, and has been extended to skewed distributions for modeling precipitation. In this study BMA is extended to another case of skewed distributions for modeling wind speed. The method was applied to daily 48-h forecasts of maximum wind speed in the North American Pacific Northwest in 2003-04 using the University of Washington mesoscale ensemble, and is shown to provide calibrated and sharp probabilistic forecasts. Comparisons are made between a number of potential formulations for the BMA model.
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- The address information is for the authors that have a + after their name.
- Authors who are presenting talks have a * after their name.
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