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Activity Number:
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112
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Type:
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Contributed
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Date/Time:
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Monday, July 30, 2007 : 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 - #309201 |
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Title:
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A Multivariate Semiparametric Bayesian Spatial Modeling Framework for Hurricane Surface Wind Fields
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Author(s):
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Brian Reich*+ and Montserrat Fuentes
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Companies:
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North Carolina State University and North Carolina State University
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Address:
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2501 Founders Drive, Raleigh, NC, 27695-8203,
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Keywords:
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Bayesian ; nonparametric ; multivariate ; spatial ; wind field ; hurricane
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Abstract:
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Numerical ocean models are essential for creating storm surge forecasts for coastal areas. These models are driven primarily by the surface wind forcings. A new nonparametric multivariate spatial modeling framework is introduced combining data with physical knowledge about the wind fields to improve the estimation of the wind vectors. Our model builds on the stick-breaking prior, which is frequently used in Bayesian modeling to capture uncertainty in the parametric form of an outcome. The stick-breaking prior is extended to the spatial setting by assigning each location a different, unknown distribution, and smoothing the distributions in space with a series of kernel functions. This semiparametric spatial model is shown to improve prediction compared to usual Bayesian kriging methods for the wind field of Hurricane Ivan.
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