A Hierarchical Bayesian Approach for Aerosol Retrieval Using MISR Data
Atmospheric aerosols can cause serious damage to human health and reduce life expectancy. Using the radiances observed by NASA's Multi-angle Imaging SpectroRadiometer (MISR), the current MISR operational algorithm retrieves aerosol optical depth (AOD) at 17.6 km resolution. A systematic study o...
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ftrepec:oai:RePEc:taf:jnlasa:v:108:y:2013:i:502:p:483-493 2023-05-15T13:06:23+02:00 A Hierarchical Bayesian Approach for Aerosol Retrieval Using MISR Data Yueqing Wang Xin Jiang Bin Yu Ming Jiang http://hdl.handle.net/10.1080/01621459.2013.796834 unknown http://hdl.handle.net/10.1080/01621459.2013.796834 article ftrepec 2020-12-04T13:32:22Z Atmospheric aerosols can cause serious damage to human health and reduce life expectancy. Using the radiances observed by NASA's Multi-angle Imaging SpectroRadiometer (MISR), the current MISR operational algorithm retrieves aerosol optical depth (AOD) at 17.6 km resolution. A systematic study of aerosols and their impact on public health, especially in highly populated urban areas, requires finer-resolution estimates of AOD's spatial distribution. We embed MISR's operational weighted least squares criterion and its forward calculations for AOD retrievals in a likelihood framework and further expand into a hierarchical Bayesian model to adapt to finer spatial resolution of 4.4 km. To take advantage of AOD's spatial smoothness, our method borrows strength from data at neighboring areas by postulating a Gaussian Markov random field prior for AOD. Our model considers AOD and aerosol mixing vectors as continuous variables, whose inference is carried out using Metropolis-within-Gibbs sampling methods. Retrieval uncertainties are quantified by posterior variabilities. We also develop a parallel Markov chain Monte Carlo (MCMC) algorithm to improve computational efficiency. We assess our retrieval performance using ground-based measurements from the AErosol RObotic NETwork (AERONET) and satellite images from Google Earth. Based on case studies in the greater Beijing area, China, we show that 4.4 km resolution can improve both the accuracy and coverage of remotely sensed aerosol retrievals, as well as our understanding of the spatial and seasonal behaviors of aerosols. This is particularly important during high-AOD events, which often indicate severe air pollution. Article in Journal/Newspaper Aerosol Robotic Network RePEc (Research Papers in Economics) |
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Open Polar |
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RePEc (Research Papers in Economics) |
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Atmospheric aerosols can cause serious damage to human health and reduce life expectancy. Using the radiances observed by NASA's Multi-angle Imaging SpectroRadiometer (MISR), the current MISR operational algorithm retrieves aerosol optical depth (AOD) at 17.6 km resolution. A systematic study of aerosols and their impact on public health, especially in highly populated urban areas, requires finer-resolution estimates of AOD's spatial distribution. We embed MISR's operational weighted least squares criterion and its forward calculations for AOD retrievals in a likelihood framework and further expand into a hierarchical Bayesian model to adapt to finer spatial resolution of 4.4 km. To take advantage of AOD's spatial smoothness, our method borrows strength from data at neighboring areas by postulating a Gaussian Markov random field prior for AOD. Our model considers AOD and aerosol mixing vectors as continuous variables, whose inference is carried out using Metropolis-within-Gibbs sampling methods. Retrieval uncertainties are quantified by posterior variabilities. We also develop a parallel Markov chain Monte Carlo (MCMC) algorithm to improve computational efficiency. We assess our retrieval performance using ground-based measurements from the AErosol RObotic NETwork (AERONET) and satellite images from Google Earth. Based on case studies in the greater Beijing area, China, we show that 4.4 km resolution can improve both the accuracy and coverage of remotely sensed aerosol retrievals, as well as our understanding of the spatial and seasonal behaviors of aerosols. This is particularly important during high-AOD events, which often indicate severe air pollution. |
format |
Article in Journal/Newspaper |
author |
Yueqing Wang Xin Jiang Bin Yu Ming Jiang |
spellingShingle |
Yueqing Wang Xin Jiang Bin Yu Ming Jiang A Hierarchical Bayesian Approach for Aerosol Retrieval Using MISR Data |
author_facet |
Yueqing Wang Xin Jiang Bin Yu Ming Jiang |
author_sort |
Yueqing Wang |
title |
A Hierarchical Bayesian Approach for Aerosol Retrieval Using MISR Data |
title_short |
A Hierarchical Bayesian Approach for Aerosol Retrieval Using MISR Data |
title_full |
A Hierarchical Bayesian Approach for Aerosol Retrieval Using MISR Data |
title_fullStr |
A Hierarchical Bayesian Approach for Aerosol Retrieval Using MISR Data |
title_full_unstemmed |
A Hierarchical Bayesian Approach for Aerosol Retrieval Using MISR Data |
title_sort |
hierarchical bayesian approach for aerosol retrieval using misr data |
url |
http://hdl.handle.net/10.1080/01621459.2013.796834 |
genre |
Aerosol Robotic Network |
genre_facet |
Aerosol Robotic Network |
op_relation |
http://hdl.handle.net/10.1080/01621459.2013.796834 |
_version_ |
1766003768552325120 |