In this paper, we develop a data-driven methodology to characterize
the likelihood of orographic precipitation enhancement using sequences
of weather radar images and a digital elevation model (DEM). Geographical
locations with topographic characteristics favorable to enforce repeatable
and persistent orographic precipitation such as stationary cells,
upslope rainfall enhancement, and repeated convective initiation
are detected by analyzing the spatial distribution of a set of precipitation
cells extracted from radar imagery. Topographic features such as
terrain convexity and gradients computed from the DEM at multiple
spatial scales as well as velocity fields estimated from sequences
of weather radar images are used as explanatory factors to describe
the occurrence of localized precipitation enhancement. The latter
is represented as a binary process by defining a threshold on the
number of cell occurrences at particular locations. Both two-class
and one-class support vector machine classifiers are tested to separate
the presumed orographic cells from the nonorographic ones in the
space of contributing topographic and flow features. Site-based validation
is carried out to estimate realistic generalization skills of the
obtained spatial prediction models. Due to the high class separability,
the decision function of the classifiers can be interpreted as a
likelihood or susceptibility of orographic precipitation enhancement.
The developed approach can serve as a basis for refining radar-based
quantitative precipitation estimates and short-term forecasts or
for generating stochastic precipitation ensembles conditioned on
the local topography.