A Hierarchical Bayesian Approach

9 Atmospheric aerosols can cause serious damage to human health and life expectancy. Using the radiances observed by 10 NASA’s Multi-angle Imaging SpectroRadiometer (MISR), the current MISR operational algorithm retrieves Aerosol 11 Optical Depth (AOD) at 17.6 km resolution. A systematic study of aerosols and their impact on public health, espe12 cially in highly-populated urban areas, requires finer-resolution estimates of AOD’s spatial distribution. 13 We embed MISR’s operational weighted least squares criterion and its forward calculations for AOD retrievals 14 in a likelihood framework and further expand into a hierarchical Bayesian model to adapt to finer spatial resolution 15 of 4.4 km. To take advantage of AOD’s spatial smoothness, our method borrows strength from data at neighboring 16 areas by postulating a Gaussian Markov Random Field prior for AOD. Our model considers AOD and aerosol mixing 17 vectors as continuous variables, whose inference is carried out using Metropolis-within-Gibbs sampling methods. 18 Retrieval uncertainties are quantified by posterior variabilities. We also develop a parallel MCMC algorithm to improve 19 computational efficiency. We assess our retrieval performance using ground-based measurements from the AErosol 20 RObotic NETwork (AERONET) and satellite images from Google Earth. 21 ∗Corresponding author: [email protected]. †Xin Jiang is now working at Netease Youdao. 1 Zhongguancun East Road, Haidian District, Beijing, 100084 China. Based on case studies in the greater Beijing area, China, we show that 4.4 km resolution can improve both the 22 accuracy and coverage of remotely-sensed aerosol retrievals, as well as our understanding of the spatial and seasonal 23 behaviors of aerosols. This is particularly important during high-AOD events, which often indicate severe air pollution. 24