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Activity Number:
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61
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Type:
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Topic Contributed
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Date/Time:
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Sunday, August 2, 2009 : 4:00 PM to 5:50 PM
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Sponsor:
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Section on Bayesian Statistical Science
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| Abstract - #303734 |
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Title:
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Inferring Likelihoods and Climate System Characteristics from Climate Models and Spatio-Temporal Tracer Data
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Author(s):
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K. Sham Bhat*+ and Murali Haran and Klaus Keller and Roman Tonkonojenkov
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Companies:
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Penn State University and Penn State University and Penn State University and Penn State University
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Address:
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109 Green Meadow Ln. Department of Statistics, Port Matilda, PA, 16870,
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Keywords:
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spatiotemporal data ; hierarchical Bayes ; Gaussian process ; computer experiments ; multivariate spatial data ; climate change
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Abstract:
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To understand the current state of the climate system and to predict future behavior, good estimates of key climate system parameters are critical. Due to the difficulty in measuring these parameters directly, we must infer their values based on two sources: (i) spatiotemporal observations of `tracers' that indirectly provide information about these parameters, and (ii) output from computationally expensive climate models run at several climate parameter settings. Here we describe an inferential approach using Gaussian processes to emulate the climate models, thereby establishing a connection between the climate parameters and the multiple tracers. We carry out statistical inference for the climate parameters, accounting for dependence and sources of uncertainty. We also discuss identifiability issues and computational challenges posed by the size of the data.
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