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Abstract Details
Activity Number:
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420
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Type:
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Contributed
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Date/Time:
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Tuesday, August 2, 2011 : 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 - #302501 |
Title:
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A Hierarchical Bayesian Approach for Aerosol Retrieval Using MISR Data
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Author(s):
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Yueqing Wang*+ and Xin Jiang and Ming Jiang and Bin Yu
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Companies:
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University of California at Berkeley and Peking University and Peking University and University of California at Berkeley
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Address:
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2 Panoramic Way Apt 105, Berkeley, CA, 94704, United States
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Keywords:
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Aerosol retrieval ;
Multi-angle Imaging SpectroRadiometer ;
Hierarchical Bayesian mode ;
Markov Chain Monte Carlo ;
Maximum a Posteriori ;
Remote sensing
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Abstract:
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Atmospheric aerosols can cause serious damage to human health. Using observations by NASA's Multi-angle Imaging SpectroRadiometer (MISR), the current operational algorithm retrieves Aerosol Optical Depth (AOD) at a resolution of 17.6 km. A systematic study of aerosols and their impact on public health, especially in highly-populated urban areas, requires a finer-resolution estimate of AOD. We embed MISR's operational weighted least squares criterion and forward simulations for AOD retrieval in a likelihood framework and expand it into a Bayesian hierarchical model to adapt to a finer spatial scale of 4.4 km. Our method borrows strength from data at neighboring pixels by postulating a Gaussian Markov Random Field prior for AOD. Our model considers both AOD and aerosol mixing vectors as continuous variables. Their inference is carried out using Metropolis-within-Gibbs sampling methods. We also implement a parallel MCMC algorithm to reduce computational cost. Based on case studies in the greater Beijing area, we show that a 4.4 km resolution can improve the accuracy and coverage of remotely-sensed AOD retrievals and our understanding of the spatial and seasonal behaviors of aerosols.
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Authors who are presenting talks have a * after their name.
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