Modeling Waves of Extreme Temperature: The Changing Tails of Four Cities
极端温度波动建模：四座城市变化中的尾部AI 解读 ↓
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AI 根据所提供的论文材料归类：摘要明确使用时间序列模型处理趋势、季节性和序列相关，并用变点与时变参数研究变化。
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极端温度波动建模：四座城市变化中的尾部
- 研究问题
- 如何在日最高温度的趋势、季节性和序列相关之外，刻画上尾不同于主体的非平稳性，并估计持续热浪的重现概率？
- 方法与思路
- 作者先建立日最高温度时间序列模型，再对标准化残差进行高阈值超越建模，用变点识别季节区间并引入时变形状参数。
- 材料陈述的贡献
- 摘要称模型在四座城市的样本内外拟合良好，估计显示五十年来长且强烈热浪的概率增加，增幅随城市和季节变化。
- 阅读关注点
- 应核对标准化过程、阈值选择、变点识别与时变尾部参数的估计，并检查连续超越概率计算及不确定性的处理。
- 阅读边界
- 依据版本未注明的 OpenAlex 索引摘要；不能推断刊本变化。摘要未给出概率增幅、置信区间及阈值敏感性，应用对象是气温。
原文摘要
Heat waves are a serious threat to society, the environment, and the economy. Estimates of the recurrence probabilities of heat waves may be obtained following the successful modeling of daily maximum temperature, but working with the latter is difficult as we have to recognize, and allow for, not only a time trend but also seasonality in the mean and in the variability, as well as serial correlation. Furthermore, as the extreme values of daily maximum temperature have a different form of nonstationarity from the body, additional modeling is required to completely capture the realities. We present a time series model for the daily maximum temperature and use an exceedance over high thresholds approach to model the upper tail of the distribution of its scaled residuals. We show how a change-point analysis can be used to identify seasons of constant crossing rates and how a time-dependent shape parameter can then be introduced to capture a change in the distribution of the exceedances. Daily maximum temperature series for Des Moines, New York, Portland, and Tucson are analyzed. In-sample and out-of-sample goodness-of-fit measures show that the proposed model is an excellent fit to the data. The fitted model is then used to estimate the recurrence probabilities of runs over seasonally high temperatures, and we show that the probability of long and intense heat waves has increased considerably over 50 years. We also find that the increases vary by city and by time of year.