Estimation of Environmental Contours Using a Block Resampling Method
Estimation of Environmental Contours Using a Block Resampling Method
复制标题
使用块重采样方法估计环境轮廓
DOI：
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发表时间：
2020
期刊：
影响因子：
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通讯作者：
Philip Jonathan
中科院分区：
文献类型：
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作者：
E. Mackay;Philip Jonathan
提出了一种估计环境变量联合分布的新方法。与以往方法的关键区别在于，只对风暴峰值参数的联合分布进行建模，而不是将一个模型拟合到所有观测值。这为使用渐近极值模型提供了更有力的理由，因为所考虑的数据是近似独立的。所有数据的联合分布是通过重新采样和重新缩放风暴历史来恢复的，以峰值为条件。这简化了分析，因为许多复杂的依赖结构被重新采样，而不是显式建模。风暴历史是通过将时间序列划分为离散块来定义的，分界点定义为相邻最大值之间变量的最小值。风暴是根据每个离散块内的每个参数的峰值来表征的，它们不需要在时间上重合。关键的假设是，在峰值变化不大的情况下，重新调整测量到的风暴历史会得到一个同样真实的时间序列。考虑了两个二元分布的例子：有效波高（Hs）和零上行周期（Tz）的联合分布以及Hs和风速的联合分布。结果表明，风暴重采样方法给出的环境等高线估计与观测值吻合较好，并提供了一种估计极值的严格方法。
A new method for estimating joint distributions of environmental variables is presented. The key difference to previous methods is that the joint distribution of only storm-peak parameters is modelled, rather than fitting a model to all observations. This provides a stronger justification for the use of asymptotic extreme value models, as the data considered are approximately independent. The joint distribution of all data is recovered by resampling and rescaling storm histories, conditional on the peak values. This simplifies the analysis as much of the complex dependence structure is resampled, rather than modelled explicitly. The storm histories are defined by splitting the time series into discrete blocks, with the dividing points defined as the minimum value of a variable between adjacent maxima. Storms are characterised in terms of the peak values of each parameter within each discrete block, which need not coincide in time. The key assumption is that rescaling a measured storm history results in an equally realistic time series, provided that the change in peak values is not large. Two examples of bivariate distribution are considered: the joint distribution of significant wave height (Hs) and zero up-crossing period (Tz) and the joint distribution of Hs and wind speed. It is shown that the storm resampling method gives estimates of environmental contours that agree well with the observations and provides a rigorous method for estimating extreme values.