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

合作研究：美国本土遭遇最极端暴雨的原因是什么？

• 批准号: 2337381
• 负责人: Aaron Hill
• 金额: $ 30.67万
• 依托单位: University of Oklahoma Norman Campus
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2024
• 资助国家: 美国
• 起止时间: 2024-02-01 至 2027-01-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2337381&HistoricalAwards=false
• 关键词: Collaborative; Research; What; Drives; Most

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.

极端降水及其经常造成的山洪泛滥，每年造成许多死亡和重大破坏。对极端降雨的准确预测仍然难以捉摸，这部分原因是对哪些大气因素的不完全了解将最极端的降水事件与产生重量但不那么极端的降雨量的事件区分开来。 该项目将使用观测值，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特定条件，从而改善山洪事件的先进警告。 该项目还具有重大的教育和外展成分，其目的是提高公共科学素养和参与度。在这里定义为降雨，其平均复发间隔（ARI）为1000年，在美国几乎所有案件中都会产生重大的社会影响。 该项目将着重于提高对美国大陆最极端降雨事件的理解，以及这些事件与沉重但极端降水的区别。 该项目有三个主要目标。 研究小组最初将开发一个数据库，其中包括多个降水数据集的最极端降雨事件，重点是超过6日，100年和1000年的ARIS，在6日，24日，48小时和72小时的积累期内。 对于目标1中确定的事件，研究人员将从大气重新分析数据集中收集有关环境条件的数据，并使用自我组织地图，一种机器学习技术，以确定哪些大气参数与越来越多的极端降雨事件相关。 在项目的最后一步中，将使用云模型1（CM1）进行理想化的数值建模，以测试大气水分，中尺度上升，垂直风剪切和其他分析所识别的其他组件的作用，以确定对中镜对流系统的影响，即对中央我们中部中常见的大型thundenderstorm复合物的影响。该项目由物理和动态气象计划共同资助，并启发竞争性研究的既定计划（EPSCOR）。该奖项反映了NSF的法定任务，并被认为是通过基金会的知识分子和更广泛影响的评估审查标准来评估值得通过评估来支持的。