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Extreme wave analysis based on atmospheric pattern classification: an application along the Italian coast

机译:基于大气模式分类的极端波分析:沿意大利海岸的应用

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摘要

This paper provides a methodology for classifying samples of significant wave-height peaks in homogeneous subsets in terms of the atmospheric circulation patterns behind the observed extreme wave conditions. Then, a methodology is given for the computation of the overall extreme value distribution by starting from the distributions fitted to each single subset. To this end, the k-means clustering technique is used to classify the shape of the wind fields that occurred simultaneously to and prior to the occurrences of the extreme wave events. This results in a small number of characteristic circulation patterns related to as many subsets of extreme wave values. After fitting an extreme value distribution to each subset, bootstrapping is used to reconstruct the omni-circulation pattern's extreme value distribution. The methodology is applied to several locations along the Italian buoy network, and it is concluded from the obtained results that it yields a two-fold advantage: first, it is capable of identifying clearly differentiated subsets driven by homogeneous circulation patterns; second, it allows one to estimate high-return-period quantiles consistent with those resulting from the usual extreme value analysis. In particular, the circulation patterns highlighted are analyzed in the context of the Mediterranean Sea's atmospheric climatology and are shown to be due to well-known cyclonic systems typically crossing the Mediterranean basin.
机译:本文提供了一种在观察到的极端波条件后面的大气循环模式方面对均匀亚群中的显着波高峰的样本进行分类方法。然后,通过从装配到每个单个子集的分布开始,给出一种方法来计算整体极值分布。为此,k-means聚类技术用于对极端波事件的出现发生同时发生的风场的形状。这导致少量与极端波值的多个子集相关的特征循环模式。在拟合到每个子集的极值分布后,使用自举来重建全循环模式的极值分布。该方法应用于意大利浮标网络的几个位置,从所得结果结束,即它产生的两倍优势:首先,它能够识别由均匀循环模式驱动的明显分化的亚群;其次,它允许一个估计与通常极值分析产生的那些一致的高回报周期定量。特别地,在地中海大气气候学的背景下分析了突出的循环模式,并且由于通常穿过地中海盆地的众所周知的旋风系统而被证明是由于众所周知的旋风系统。

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