ATD: Statistical and Machine Learning Methods for Studying the Dynamics of Weather and Climate Extremes

极端天气和气候对人类社会和所有国家的自然环境都产生了深刻影响，无论贫富。近年来，天气灾害造成了大量生命损失，经济损失也大幅增加。2020年初，澳大利亚经历了有史以来最严重的山火季节，这是有史以来最热的一年，土壤和燃料异常干燥。大火烧毁了1000多万公顷土地，造成至少28人死亡，数百万人受到有害烟雾的影响。海洋温度升高使非洲之角地区发生干旱的可能性增加了一倍。严重的干旱使埃塞俄比亚、肯尼亚和索马里的1500万人需要援助，数百万人面临严重的粮食和水短缺。2020年夏天，美国西海岸经历了其现代历史上最强烈的热浪之后，发生了最具灾难性的野火。根据NOAA的报告（2020年），仅在2020年8月，美国就遭受了四次不同的数十亿美元灾难：两次飓风，巨大的野火和一次非同寻常的中西部derecho。虽然极端天气是自然周期的一部分，但最近这些极端天气的凶猛程度和频率的上升证明了气候影响的加速。该项目将在三年的项目中每年资助一名研究生。该项目将开发统计和机器学习方法，从三个不同的角度研究天气和气候极端情况：气候模型验证，极端情况的变点估计，以及多模型气候集合的整合。气候模型是科学家研究气候动态和极端情况的重要工具。因此，验证气候模式模拟真实的极端气候的能力是一项关键任务。这涉及到比较建模和观察到的空间极值，多重检验的调整是比较随机场的关键统计挑战之一。我们将发展最佳的统计技术，比较两个空间极端随机场的回报水平。迄今为止，在极端天气和气候中发现变点和估计中断时间的工作尚未得到应有的重视，但变点可以表明气候系统的临界点，因此对于备灾和启动应对气候风险的适应措施十分重要。我们还将开发一种新的方法，用于估计功能时间序列的空间变化变点，以研究极端气候的突变。最后，一系列的多模式集成的方法已经开发出来，从简单的或加权平均的模型，完全贝叶斯分层模型。贝叶斯模型中的多层次结构促使我们利用神经网络来学习不同气候模型与实际观测之间的复杂关系。最后，我们将开发一种贝叶斯机器学习方法，将模型输出与观测结果相结合，以预测未来的极端气候。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

Weather and climate extremes profoundly impact human society and the natural environment of all countries, rich and poor. Recent years have seen a number of large losses of life as well as a tremendous increase in economic losses from weather hazards. The start of 2020 found Australia amid its worst-ever bushfire season, following on from its hottest year on record which had left soil and fuels exceptionally dry. The fires have burned through more than 10 million hectares, killed at least 28 people, and left millions of people affected by a hazardous smoke haze. Higher sea temperatures have doubled the likelihood of drought in the Horn of Africa region. Severe droughts have left 15 million people in Ethiopia, Kenya and Somalia in need of aid, and millions of people are facing acute food and water shortages. In the summer of 2020, the West Coast of the U.S. saw its most catastrophic wildfires following the arguably most intensive heat waves in its modern history. According to NOAA’s report (2020), just during the month of August in 2020 the U.S. was hit by four different billion-dollar disasters: two hurricanes, huge wildfires, and an extraordinary Midwest derecho. While extreme weather is a part of the natural cycle, the recent uptick in the ferocity and frequency of these extremes is evidence of an acceleration of climate impacts. This project will support one graduate student each year of the three year project. This project will develop statistical and machine learning methods to study weather and climate extremes from three different perspectives: climate model validation, changepoint estimation for extremes, and integration of multi-model climate ensembles. Climate models are vital tools for scientists studying climate dynamics and extremes. Hence, validating climate models in their capacity of mimicking real climate extremes is a critical task. This involves comparing the modeled and observed spatial extremes, and adjustment for multiple testing is one of the key statistical challenges in comparing random fields. We will develop optimal statistical techniques for comparing the return levels of two spatial extremes random fields. The detection of changepoints and estimation of break time in extreme weather and climate have not received due attention to date, yet changepoints can signal a climate system’s tipping point and thus are important for disaster preparedness and activation of adaptation measures against climate risks. We will also develop a novel method for estimating spatially varying changepoints for functional time series to study abrupt changes in climate extremes. Finally, an array of methodology for multi-model ensemble integration has been developed, ranging from simple or weighted averaging of the models to fully Bayesian hierarchical models. The multiple levels of hierarchy in Bayesian models motivated us to take advantage of neural networks to learn the complex relationship between different climate models and actual observations. Finally, we will develop a Bayesian machine learning approach to integrating model outputs with observations to project future climate extremes.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.