Conditional simulations of the extremal t process: application to fields of extreme precipitation

Abstract : The last decade has seen max-stable processes emerge as a powerful tool for the statistical modeling of spatial extremes and there are increasing works using them in a climate framework. One recent utilization of max-stable processes in this context is for conditional simulations that provide empirical distribution of a spatial field conditioned by observed values in some locations. In this work conditional simulations are investigated for the extremal t process taking benefits of its spectral construction. The methodology of conditional simulations proposed by Dombry et al. (2013) for Brown-Resnick and Schlather models is adapted for the extremal \textsl{t} process with some original improvements which enlarge the possible number of conditional points. A simulation study enables to highlight the role of the different parameters of the model and to emphasize the importance of the steps of the algorithm. An application is performed on precipitation data in the south of France where extreme precipitation events (Cevenol) may generate major floods. This shows that the model and the algorithm perform well provided the stationary assumptions are fulfilled.
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Aurélien Bechler, Liliane Bel, Mathieu Vrac. Conditional simulations of the extremal t process: application to fields of extreme precipitation. 2014. ⟨hal-01053605⟩

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