Clustering bivariate dependencies of compound precipitation and wind extremes over Great Britain and Ireland
Clustering bivariate dependencies of compound precipitation and wind extremes over Great Britain and Ireland
复制标题
英国和爱尔兰复合降水和极端风的二变量依赖性聚类
DOI：
10.1016/j.wace.2021.100318
复制
发表时间：
2021
影响因子：
8
通讯作者：
J. Zscheischler
中科院分区：
文献类型：
--
作者：
Edoardo Vignotto;Sebastian Engelke;J. Zscheischler
确定确定具有相似行为特征的子区域的隐藏空间模式是统计气候学的一个中心主题。这项任务通常被称为区域化，它有助于识别所考虑的变量具有类似随机分布的区域，从而有可能降低数据的维数。区域化的例子有很多，从水文学到天气和气候科学。然而，大多数区域化技术侧重于单个感兴趣变量的空间聚类，往往不适合极端情况。极端事件往往具有严重的影响，当与其他变量的极端事件同时发生时，这种影响可能会被放大。考虑到在区域尺度上表征复合极端事件的重要性，本文开发了一种算法来识别均匀的空间子区域，这些子区域在二元分布的尾部具有共同的二元依赖结构。特别是，我们使用了一种新的非参数散度，能够捕捉二元分布尾部行为的相似性和差异性，作为我们聚类过程的核心。我们应用该方法来识别在英国和爱尔兰表现出相似的复合降水和极端风可能性的同质区域。
Identifying hidden spatial patterns that define sub-regions characterized by a similar behaviour is a central topic in statistical climatology. This task, often called regionalization, is helpful for recognizing areas in which the variables under consideration have a similar stochastic distribution and thus, potentially, for reducing the dimensionality of the data. Many examples for regionalization are available, spanning from hydrology to weather and climate science. However, the majority of regionalization techniques focuses on the spatial clustering of a single variable of interest and is often not tailored to extremes. Extreme events often have severe impacts, which can be amplified when co-occurring with extremes in other variables. Given the importance of characterizing compound extreme events at the regional scale, here we develop an algorithm that identifies homogeneous spatial sub-regions that are characterized by a common bivariate dependence structure in the tails of a bivariate distribution. In particular, we use a novel non-parametric divergence able to capture the similarities and differences in the tail behaviour of bivariate distributions as the core of our clustering procedure. We apply the approach to identify homogeneous regions that exhibit similar likelihood of compound precipitation and wind extremes in Great Britain and Ireland.
影响因子：
4.6
作者：
Couasnon, Anais;Eilander, Dirk;Ward, Philip J.
通讯作者：
Ward, Philip J.
影响因子：
6.7
作者：
J. Hillier;R. S. Dixon
通讯作者：
J. Hillier;R. S. Dixon