This paper proposes a smooth copula-based Generalized Extreme Value (GEV) model to map and predict extreme rainfall in Central Eastern Canada. The considered data contains a large portion of missing values, and one observes several nonconcomitant record periods at different stations. The proposed two-step approach combines GEV parameters' smooth functions in space through the use of spatial covariates and a flexible hierarchical copula-based model to take into account dependence between the recording stations. The hierarchical copula structure is detected via a clustering algorithm implemented with an adapted version of the copula-based dissimilarity measure recently introduced in the literature. Finally, we compare the classical GEV parameter interpolation approaches with the proposed smooth copula-based GEV modeling approach.

中文翻译：

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基于平滑copula的加拿大中东部极端降雨广义极值模型和空间插值

本文提出了一种基于平滑 copula 的广义极值 (GEV) 模型来绘制和预测加拿大中东部的极端降雨。所考虑的数据包含很大一部分缺失值，并且可以在不同站点观察到多个非伴随记录期。所提出的两步法通过使用空间协变量和灵活的基于分层 copula 的模型来结合 GEV 参数在空间中的平滑函数，以考虑记录站之间的依赖性。分层 copula 结构是通过聚类算法检测的，该算法使用最近在文献中引入的基于 copula 的差异度量的改编版本来实现。最后，我们将经典的 GEV 参数插值方法与所提出的基于平滑 copula 的 GEV 建模方法进行了比较。