A1106
Title: Modeling high and low extremes with a novel dynamic spatiotemporal model
Authors: Likun Zhang - University of Missouri (United States) [presenting]
Christopher Wikle - University of Missouri (United States)
Abstract: In numerous dynamic systems, significant environmental challenges, including severe weather events and abrupt climate changes, have become prevalent. In order to fully understand the underlying mechanisms and enhance informed decision-making, a flexible model capable of accommodating extremes is necessary. The existing dynamic spatio-temporal models exhibit limitations in capturing extremes when assuming Gaussian error distributions, whereas the current models for spatial extremes are focused on joint upper tails at two or more locations while assuming temporal independence in the copula-based modeling framework. A novel class of dynamic spatiotemporal models are introduced, capable of accommodating both high and low extremes through a mixture of stable distributions with varying tail indices. Due to a redistribution kernel embedded in the hierarchical construct, the model can describe complex advective and diffusive dynamics with relatively few parameters and characterize differing levels of same-tail and opposite-tail extremal dependence, which are non-stationary across space and time. The effectiveness of the methods is demonstrated by applying them to turbulence flow observations that are chaotic and highly irregular.
