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Activity Number:
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307
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Type:
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Topic Contributed
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Date/Time:
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Tuesday, August 5, 2008 : 2:00 PM to 3:50 PM
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Sponsor:
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Section on Statistics and the Environment
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| Abstract - #301361 |
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Title:
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Spatial Hierarchical Modeling of Weather Extremes from a Regional Climate Model
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Author(s):
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Dan Cooley*+ and Stephan Sain
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Companies:
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Colorado State University and National Center for Atmospheric Research
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Address:
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Department of Statistics, Fort Collins, CO, 80523-1877,
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Keywords:
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climate change ; extremes ; precipitation
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Abstract:
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Regional climate models (RCMs) are tools which allow scientists to begin to understand how different forcings may affect climate. There has been some statistical work done to characterize the difference in mean behavior between control and future scenarios as predicted by RCMs. The goal of this work is to characterize the extremes as produced by a RCM and to additionally examine the difference in extremes between a control and future scenario. To characterize the spatial behavior of extremes we construct a hierarchical model. The data level is formed by the point process representation of extremes, and the process level is based on a conditional autoregressive (CAR) model since our data are on a regular lattice. To our knowledge, this is the first work which spatially models the shape parameter of the extreme value distribution.
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