A Hierarchical Max-Infinitely Divisible Spatial Model for Extreme Precipitation
A Hierarchical Max-Infinitely Divisible Spatial Model for Extreme Precipitation
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DOI：
10.1080/01621459.2020.1750414
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发表时间：
2020-05-06
影响因子：
3.7
通讯作者：
Huser, Raphael
中科院分区：
文献类型：
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作者：
Bopp, Gregory P.;Shaby, Benjamin A.;Huser, Raphael
了解极端降水的空间范围对于确定洪水风险和充分设计基础设施（例如，雨水管）以承受这种危险。虽然环境现象通常在越来越极端的水平上表现出减弱的空间依赖性，但块最大值的极限最大稳定过程模型具有刚性依赖结构，无法捕获这种类型的行为。我们提出了一个灵活的贝叶斯模型，从一个更广泛的家庭（有条件）最大无限可分的过程，允许削弱空间依赖性在越来越极端的水平，由于分层表示的可能性随机效应，我们的推理方法规模大的数据集。因此，我们的模型不仅具有灵活的依赖结构，而且还允许在高维中进行快速，完全的贝叶斯推理，预测和条件模拟。所提出的模型是使用灵活的随机基函数，估计从数据，允许直接检查的极端的主要空间模式。此外，所描述的过程具有（条件）的最大稳定性作为一个特殊的情况下，使推断的尾部依赖类可能。我们将我们的模型应用于美国东北部的极端降水，并表明所提出的模型充分捕捉了数据的极端行为。有趣的是，我们发现，从我们的模型估计的空间变化的主要模式类似于观测到的模式，在极端降水事件发生沿着海岸（例如，有局部的热带气旋和对流风暴）和山脉边界。我们的模型可以很容易地适应其他类型的环境数据集，因此对于识别极端天气模式和风险区域非常有用。包括可用于复制作品的材料的标准化描述，可作为在线补充。
Understanding the spatial extent of extreme precipitation is necessary for determining flood risk and adequately designing infrastructure (e.g., stormwater pipes) to withstand such hazards. While environmental phenomena typically exhibit weakening spatial dependence at increasingly extreme levels, limiting max-stable process models for block maxima have a rigid dependence structure that does not capture this type of behavior. We propose a flexible Bayesian model from a broader family of (conditionally) max-infinitely divisible processes that allows for weakening spatial dependence at increasingly extreme levels, and due to a hierarchical representation of the likelihood in terms of random effects, our inference approach scales to large datasets. Therefore, our model not only has a flexible dependence structure, but it also allows for fast, fully Bayesian inference, prediction and conditional simulation in high dimensions. The proposed model is constructed using flexible random basis functions that are estimated from the data, allowing for straightforward inspection of the predominant spatial patterns of extremes. In addition, the described process possesses (conditional) max-stability as a special case, making inference on the tail dependence class possible. We apply our model to extreme precipitation in North-Eastern America, and show that the proposed model adequately captures the extremal behavior of the data. Interestingly, we find that the principal modes of spatial variation estimated from our model resemble observed patterns in extreme precipitation events occurring along the coast (e.g., with localized tropical cyclones and convective storms) and mountain range borders. Our model, which can easily be adapted to other types of environmental datasets, is therefore useful to identify extreme weather patterns and regions at risk. for this article, including a standardized description of the materials available for reproducing the work, are available as an online supplement.