  Ice storms are extreme weather events that can have devastating implications
for the sustainability of natural ecosystems as well as man made
infrastructure. Ice storms are caused by a complex mix of atmospheric
conditions and are among the least understood of severe weather events. Our
ability to model ice storms and characterize storm features will go a long way
towards both enabling support systems that offset storm impacts and increasing
our understanding of ice storms. In this paper, we present a holistic
computational framework to answer key questions of interest about ice storms.
We model ice storms as a function of relevant surface and atmospheric
variables. We learn these models by adapting and applying supervised and
unsupervised machine learning algorithms on data with missing or incorrect
labels. We also include a knowledge representation module that reasons with
domain knowledge to revise the output of the learned models. Our models are
trained using reanalysis data and historical records of storm events. We
evaluate these models on reanalyis data as well as Global Climate Model (GCM)
data for historical and future climate change scenarios. Furthermore, we
discuss the use of appropriate bias correction approaches to run such modeling
frameworks with GCM data.
