  In climate change study, the infrared spectral signatures of climate change
have recently been conceptually adopted, and widely applied to identifying and
attributing atmospheric composition change. We propose a Bayesian hierarchical
model for spatial clustering of the high-dimensional functional data based on
the effects of functional covariates and local features. We couple the
functional mixed-effects model with a generalized spatial partitioning method
for: (1) producing spatially contiguous clusters for the high-dimensional
spatio-functional data; (2) improving the computational efficiency via parallel
computing over subregions or multi-level partitions; and (3) capturing the
near-boundary ambiguity and uncertainty for data-driven partitions. We propose
a generalized partitioning method which puts less constraints on the shape of
spatial clusters. Dimension reduction in the parameter space is also achieved
via Bayesian wavelets to alleviate the increasing model complexity introduced
by clusters. The model well captures the regional effects of the atmospheric
and cloud properties on the spectral radiance measurements. The results
elaborate the importance of exploiting spatially contiguous partitions for
identifying regional effects and small-scale variability.
