A hierarchical Bayesian model is proposed to quantify the magnitude of extreme surges on the Atlantic coast of Canada with limited data. Generalized extreme value distributions are fitted to surges derived from water levels measured at 21 buoys along the coast. The parameters of these distributions are linked together through a Gaussian field whose mean and variance are driven by atmospheric sea‐level pressure and the distance between stations, respectively. This allows for information sharing across the original stations and for interpolation anywhere along the coast. The use of a copula at the data level of the hierarchy further accounts for the dependence between locations, allowing for inference beyond a site‐by‐site basis. It is shown how the extreme surges derived from the model can be combined with the tidal process to predict potentially catastrophic water levels.

中文翻译：

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使用基于 copula 的分层贝叶斯空间模型从稀疏数据中预测极端浪涌

提出了一个分层贝叶斯模型，用有限的数据量化加拿大大西洋沿岸极端浪涌的幅度。广义极值分布适用于源自沿海 21 个浮标测量的水位的浪涌。这些分布的参数通过高斯场联系在一起，其均值和方差分别由大气海平面压力和站间距离驱动。这允许在原始站之间共享信息并在沿海任何地方进行插值。在层次结构的数据级别使用 copula 进一步说明了位置之间的依赖关系，允许在逐个站点的基础上进行推断。