Collaborative Research: What Drives the Most Extreme Rainstorms in the Contiguous United States (US)?

极端降水及其经常导致的山洪暴发每年都造成大量死亡和重大损失。极端降雨的准确预报仍然难以实现，部分原因是不完全了解哪些大气因素将最极端的降水事件与产生大量但不极端的降水事件区分开来。 该项目将使用观测、机器学习和数值模型模拟来确定支持这些事件的环境条件的关键差异。 该项目的结果应有助于天气预报员更好地确定导致极端降雨的具体条件，从而改善山洪暴发事件的预警。 此外，该项目有一个重要的教育和推广组件，以提高公众的科学素养和engagement.Extreme降水，这里定义为降雨事件的平均重现时间间隔（ARI）为1000年，在美国几乎所有的情况下，造成重大的社会影响。 该项目将侧重于提高对美国大陆最极端降雨事件的理解，以及这些事件与强降水但不太极端的降水的区别。 该项目有三个主要目标。 该研究小组将首先从多个降水数据集开发一个最极端降雨事件的数据库，重点关注10年、100年和1000年阿里斯在6小时、24小时、48小时和72小时累积期的持续性。 对于目标1中确定的事件，研究人员将从大气再分析数据集中收集有关环境条件的数据，并使用自组织地图（一种机器学习技术）来确定哪些大气参数与更多和更少的极端降雨事件相关。 在该项目的最后一步，将使用云模型1（CM1）进行理想化的数值模拟，以测试大气湿度，中尺度上升，垂直风切变和早期分析确定的其他成分的作用，以确定对中尺度对流系统的影响，美国中部经常发生的大雷暴复合体。该项目由物理和动力气象学计划和激励竞争研究的既定计划（EPSCoR）共同资助。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。

Extreme precipitation, and the flash flooding it often causes, is responsible for numerous fatalities and significant damage every year. Accurate forecasts of extreme rainfall remain elusive, and this stems in part from incomplete understanding of what atmospheric factors distinguish the most extreme precipitation events from those that produce heavy, but not as extreme, rainfall. This project will use observations, machine learning, and numerical model simulations to identify key differences in environmental conditions that support these events. The project results should help weather forecasters better identify the specific conditions that lead to extreme rainfall, thereby improving the advanced warning for flash flooding events. The project additionally has a significant education and outreach component with the goal of increased public scientific literacy and engagement.Extreme precipitation, defined here as rainfall events with an average recurrence interval (ARI) of 1000 years, causes significant societal impacts in almost all cases in the United States. This project will focus on improving understanding of the most extreme rainfall events in the continental United States, and what distinguishes these events from heavy, but less extreme precipitation. The project has three main objectives. The research team will initially develop a database of the most extreme rainfall events from multiple precipitation datasets, focusing on the exceedance of the 10-, 100-, and 1000-year ARIs for 6-, 24-, 48-, and 72-hour accumulation periods. For the events identified in Objective 1, the researchers will collect data about the environmental conditions from atmospheric reanalysis datasets and use Self Organizing Maps, a machine learning technique, to identify which atmospheric parameters are associated with more and less extreme rainfall events. In the final step of the project, idealized numerical modeling will be conducted using Cloud Model 1 (CM1) to test the roles of atmospheric moisture, mesoscale ascent, vertical wind shear, and other components identified by the earlier analyses to determine the impacts on mesoscale convective systems, the large thunderstorm complexes that are frequent in the central US. This project is jointly funded by the Physical and Dynamic Meteorology program and the Established Program to Stimulate Competitive Research (EPSCoR).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.