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Activity Number:
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503
- Climate and Meteorological Statistics
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Type:
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Contributed
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Date/Time:
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Thursday, August 6, 2020 : 10:00 AM to 2:00 PM
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Sponsor:
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Section on Statistics and the Environment
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Abstract #313540
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Title:
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Bayesian Hierarchical Models for Statistical Downscaling of Climate Models
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Author(s):
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Ayesha Kumari Ekanayaka Katugoda Gedara* and Emily Kang and Peter Kalmus and Amy Braverman
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Companies:
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University of Cincinnati and University of Cincinnati and NASA Jet Propulsion Laboratory and Jet Propulsion Laboratory, California Institute of Technology
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Keywords:
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Bayesian hierarchical modeling;
Climate models;
Downscaling;
Massive data;
spatio-temporal statistics
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Abstract:
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We propose a Bayesian Hierarchical Modeling framework to downscale output from multiple climate models using fine-resolution remote sensing data. The model possesses the capabilities of analyzing massive data and taking account of cross-climate-model variability and heterogeneous spatio-temporal dependence structures. Numerical results will be presented to demonstrate how ensembles of global climate models and high-resolution remote sensing datasets are synthesized in a principled way to produce fine-resolution downscaled sea surface temperature (SST) projections.
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Authors who are presenting talks have a * after their name.
