  Recent research in Aerosol Optical Depth (AOD) retrieval algorithms for
Multi-angle Imaging SpectroRadiometer (MISR) proposed a hierarchical Bayesian
model. However the inference algorithm used in their work was Markov Chain
Monte Carlo (MCMC), which was reported prohibitively slow. The poor speed of
MCMC dramatically limited the production feasibility of the Bayesian framework
if large scale (e.g. global scale) of aerosol retrieval is desired.
  In this paper, we present an alternative optimization method to mitigate the
speed problem. In particular we adopt Maximize a Posteriori (MAP) approach, and
apply a gradient-free "hill-climbing" algorithm: the coordinate-wise
stochastic-search. Our method has shown to be much (about 100 times) faster
than MCMC, easier to converge, and insensitive to hyper parameters. To further
scale our approach, we parallelized our method using Apache Spark, which
achieves linear speed-up w.r.t number of CPU cores up to 16. Due to these
efforts, we are able to retrieve AOD at much finer resolution (1.1km) with a
tiny fraction of time consumption compared with existing methods.
  During our research, we find that in low AOD levels, the Bayesian network
tends to produce overestimated retrievals. We also find that high absorbing
aerosol types are retrieved at the same time. This is likely caused by the
Dirichlet prior for aerosol types, as it is shown to encourage selecting
absorbing types in practice. After changing Dirichlet to uniform, the AOD
retrievals show excellent agreement with ground measurement in all levels.
