美国强雷暴环境的时空极值
Space-time extremes of severe US thunderstorm environments
AI总结：
本文针对美国强雷暴环境指标的时空极值，提出以ENSO和时间为协变量的max-stable Brown–Resnick模型及bootstrap检验与选择方法，揭示极值在夏季更局地化且受ENSO影响。
摘要：
强雷暴在美国造成巨大的经济和人员损失。对流有效位能（CAPE）和风暴相对螺旋度（SRH）同时出现高值有利于强天气发生，二者以及复合变量 $\mathrm{PROD}=\sqrt{\mathrm{CAPE}} \times \mathrm{SRH}$ 都可用作强雷暴活动的指标。由于季节性和厄尔尼诺—南方涛动（ENSO）等大尺度大气信号的影响，它们的极值空间依赖性表现出时间非平稳性。为研究这一问题，我们引入一个基于 max-stable Brown–Resnick 场的时空模型，其范围通过张量积样条依赖于 ENSO 和时间。我们还提出一种基于经验似然和 bootstrap 的 max-stability 检验。由于模型较为复杂，边际参数和相依参数必须分开估计，我们开发了一种基于 bootstrap 的模型选择准则，在选择相依模型时考虑边际不确定性。在案例研究中，我们的模型样本外表现良好。我们发现 PROD、CAPE 和 SRH 的极值在夏季通常更加局地化，而在某些区域，厄尔尼诺和拉尼娜事件期间局地化程度较低，并对这些现象给出了气象学解释。
英文摘要：
Severe thunderstorms cause substantial economic and human losses in the United States. Simultaneous high values of convective available potential energy (CAPE) and storm relative helicity (SRH) are favorable to severe weather, and both they and the composite variable $\mathrm{PROD}=\sqrt{\mathrm{CAPE}} \times \mathrm{SRH}$ can be used as indicators of severe thunderstorm activity. Their extremal spatial dependence exhibits temporal non-stationarity due to seasonality and large-scale atmospheric signals such as El Niño-Southern Oscillation (ENSO). In order to investigate this, we introduce a space-time model based on a max-stable, Brown--Resnick, field whose range depends on ENSO and on time through a tensor product spline. We also propose a max-stability test based on empirical likelihood and the bootstrap. The marginal and dependence parameters must be estimated separately owing to the complexity of the model, and we develop a bootstrap-based model selection criterion that accounts for the marginal uncertainty when choosing the dependence model. In the case study, the out-sample performance of our model is good. We find that extremes of PROD, CAPE and SRH are generally more localized in summer and, in some regions, less localized during El Niño and La Niña events, and give meteorological interpretations of these phenomena.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
