  The identification of precipitation regimes is important for many purposes
such as agricultural planning, water resource management, and return period
estimation. Since precipitation and other related meteorological data typically
exhibit spatial dependency and different characteristics at different time
scales, clustering such data presents unique challenges. In this paper, we
develop a flexible model-based approach to cluster multi-scale spatial
functional data to address such problems. The underlying clustering model is a
functional linear model , and the cluster memberships are assumed to be a
realization from a Markov random field with geographic covariates. The
methodology is applied to a precipitation data from China to identify
precipitation regimes.
