Space-time extreme value statistics of non-Gaussian random fields

This paper focuses on two new methods for predicting the extreme values of a non-Gaussian random field in both space and time. Both methods rely on the use of scalar time series expressing spatial extremes. These rime series are constructed by sampling the available realizations of the random field over a suitable grid defining the domains in question and extracting the extreme values for each time point. In this way, time series of spatial extremes are produced. The realizations of the random field are obtained from either measurements or Monte Carlo simulations. The obtained time series provide the basis for estimating the extreme value distribution using recently developed techniques for time series, which results in an accurate practical procedure. The proposed prediction methods are applied to two specific cases. One is a second-order random ocean wave field, whose statistics deviate only mildly from the Gaussian, and the other is an example of a random field whose statistics is strongly non-Gaussian. (c) 2011 Elsevier Ltd. All rights reserved.