final_program_abstracts[1]

11 IMSC Session Program
A comparison study of extreme precipitation from six regional
climate models via spatial hierarchical modeling
Wednesday - Plenary Session 5
Dan Cooley
Department of Statistics, Colorado State University, USA
We analyze output from six regional climate models (RCMs) via a spatial Bayesian
hierarchical model. The primary advantage of this approach is that the model
naturally borrows strength across locations via a spatial model on the parameters of
the generalized extreme value distribution. This is especially important in this
application as the data we analyze have great spatial coverage, but have a relatively
short data record for characterizing extreme behavior. The hierarchical model we
employ is also designed to be computationally efficient as we analyze data from
nearly 12000 locations. The aim of this analysis is to compare the extreme
precipitation as generated by these RCMs. Our results show that, although the RCMs
produce similar spatial patterns for the 100-year return level, their characterizations of
extreme precipitation are quite different. Additionally, we examine the spatial
behavior of the extreme value index and find differing spatial patterns for the point
estimates from model to model. However, these differences may not be significant
given the uncertainty associated with estimating this parameter.
Abstracts 161