  The statistical modeling of space-time extremes in environmental applications
is key to understanding complex dependence structures in original event data
and to generating realistic scenarios for impact models. In this context of
high-dimensional data, we propose a novel hierarchical model for high threshold
exceedances defined over continuous space and time by embedding a space-time
Gamma process convolution for the rate of an exponential variable, leading to
asymptotic independence in space and time. Its physically motivated anisotropic
dependence structure is based on geometric objects moving through space-time
according to a velocity vector. We demonstrate that inference based on weighted
pairwise likelihood is fast and accurate. The usefulness of our model is
illustrated by an application to hourly precipitation data from a study region
in Southern France, where it clearly improves on an alternative censored
Gaussian space-time random field model. While classical limit models based on
threshold-stability fail to appropriately capture relatively fast joint tail
decay rates between asymptotic dependence and classical independence, strong
empirical evidence from our application and other recent case studies motivates
the use of more realistic asymptotic independence models such as ours.
