论文标题
针对大量派生表面温度返回值的大量数据集的非平稳贝叶斯建模
Nonstationary Bayesian modeling for a large data set of derived surface temperature return values
论文作者
论文摘要
长时间高温引起的热浪对全球人类健康构成了重大风险。考虑到极端温度的观察到的局限性，气候模型通常用于表征全球极端温度，从中可以得出诸如返回值之类的数量，以总结任意地理位置的低概率事件的幅度。但是，尽管这些派生的数量本身是有用的，但是将空间统计模型应用于此类数据通常也很重要，例如，以了解回报率的空间依赖性属性在空间上的空间依赖性属性如何变化，并模拟气候模型以生成具有相应统计学统计学的其他空间场。对于这些目标，在建模全球数据时，使用非组织协方差函数至关重要。此外，鉴于现代全球气候模型的输出可以按$ \ MATHCAL {O}（10^4）$的顺序，因此使用近似高斯过程方法来启用推理很重要。在本文中，我们证明了在Risser和Turek（2020）中引入的方法的应用，以对来自全球气候模型集团运行的全体20年回报率的大量数据集进行非平稳的贝叶斯分析，该数据集超过50,000个空间位置。该分析使用了R。
Heat waves resulting from prolonged extreme temperatures pose a significant risk to human health globally. Given the limitations of observations of extreme temperature, climate models are often used to characterize extreme temperature globally, from which one can derive quantities like return values to summarize the magnitude of a low probability event for an arbitrary geographic location. However, while these derived quantities are useful on their own, it is also often important to apply a spatial statistical model to such data in order to, e.g., understand how the spatial dependence properties of the return values vary over space and emulate the climate model for generating additional spatial fields with corresponding statistical properties. For these objectives, when modeling global data it is critical to use a nonstationary covariance function. Furthermore, given that the output of modern global climate models can be on the order of $\mathcal{O}(10^4)$, it is important to utilize approximate Gaussian process methods to enable inference. In this paper, we demonstrate the application of methodology introduced in Risser and Turek (2020) to conduct a nonstationary and fully Bayesian analysis of a large data set of 20-year return values derived from an ensemble of global climate model runs with over 50,000 spatial locations. This analysis uses the freely available BayesNSGP software package for R.
