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	A Hierarchical Bayesian Approach for Aerosol Retrieval Using MISR Data
	Wang, Yueqing ; Jiang, Xin ; Yu, Bin ; Jiang, Ming
	2013
关键词	Fine retrieval resolution Hierarchical Bayesian model MCMC Remote sensing Spatial dependence ATMOSPHERIC TURBIDITY DISTRIBUTIONS METROPOLITAN AERONET CLIMATE
英文摘要	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.; http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000321727700010&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=8e1609b174ce4e31116a60747a720701 ; Statistics & Probability; SCI(E); 2; ARTICLE; 502; 483-493; 108
语种	英语
出处	SCI
出版者	journal of the american statistical association
内容类型	其他
源URL	[http://hdl.handle.net/20.500.11897/222770]
专题	数学科学学院
推荐引用方式 GB/T 7714	Wang, Yueqing,Jiang, Xin,Yu, Bin,et al. A Hierarchical Bayesian Approach for Aerosol Retrieval Using MISR Data. 2013-01-01.

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