Activity Number:
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280
- Climate Statistics: Studies on the Physics and Impacts of Climate Change Using Data Science
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Type:
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Topic Contributed
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Date/Time:
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Tuesday, August 1, 2017 : 8:30 AM to 10:20 AM
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Sponsor:
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Section on Physical and Engineering Sciences
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Abstract #323546
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Title:
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The Scale Enhanced Wild Bootstrap Method for Evaluating Climate Models Using Wavelets
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Author(s):
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Ansu Chatterjee* and Amy Braverman and Megan Heyman and Noel Cressie
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Companies:
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University of Minnesota and Jet Propulsion Laboratory and Rose-Hulman Institute of Technology and University of Wollongong
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Keywords:
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Wavelet ;
resampling ;
Indian monsoon
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Abstract:
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There is ambiguity about how to transform weather data to climate signals, which in turn creates ambiguity about how to assess the performance of Physics-based climate models and reanalysis objects in modeling observed climate phenomena. We propose a definition of climate signal based on the coarse levels from a wavelet decomposition of weather data. Using that definition, we propose a new resampling-based methodology for testing if a Physics-model or reanalysis object captures the observed climate signal. We establish consistency of the resampling procedure for the hypothesis testing problem. Simulation results, and a real data application on precipitation in the Indian subcontinent, are provided to illustrate the performance and efficacy of our proposal.
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Authors who are presenting talks have a * after their name.