Abstract Details
Activity Number:
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91
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Type:
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Invited
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Date/Time:
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Sunday, August 9, 2015 : 9:30 PM to 10:15 PM
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Sponsor:
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Section on Statistics and the Environment
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Abstract #317358
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Title:
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Quantifying Numerical Uncertainty in Dynamical Climate Models
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Author(s):
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Oksana Chkrebtii* and David A. Campbell and Mark Girolami and Ben Calderhead
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Companies:
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The Ohio State University and Simon Fraser University and University of Warwick and Imperial College London
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Keywords:
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Uncertainty quantification ;
Dynamic Systems ;
Chaotic Dynamics ;
Bayesian Function Estimation ;
Gaussian Processes ;
Fluid Dynamics
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Abstract:
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Climate models can be very sensitive to small perturbations, including those arising from truncation error in the numerical solution of systems of ODEs or PDEs defining the system states. We present a new formalism for characterizing and propagating this source of uncertainty through the statistical inverse problem of inference. Our Bayesian approach is illustrated on classical chaotic systems and allows the trade-off between accuracy and computational expenditure arising from the choice of discretization grid in a principled way.
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Authors who are presenting talks have a * after their name.
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