Dynamics of Extreme Precipitation in the Northeast United States in Observations and Models

极端降水及其相关影响，特别是洪水，导致重大的生命损失、财产和基础设施破坏、交通中断和暴雨水污染，每年给美国带来超过80亿美元的经济损失。 该项目将进行基础研究，以了解美国东北部极端降水的原因。这是该国经济最发达和人口最稠密的地区。改进这些过程的模型表示是实现改进对这些高成本事件的预测和预测从而减轻其影响这一总体目标的关键一步。 将确定导致美国东北部每日至每周极端降水的过程。目前的气候模式再现这些过程的能力将被审查。 两个激励性的问题是：什么类型的风暴导致东北部的极端降水？ 目前的模式是否正确地再现了这些风暴类型及其与极端降水的关系？ 利用观测数据，将通过应用先进的分析技术来确定与极端降水有关的风暴类型。将确定急流的特征模式和与极端降水有关的其他风暴特征。 然后将对气候模型输出进行分析，以确定模型中产生的风暴类型，并将模拟类型与观测类型进行比较。 这些差异将被强调用于模型开发和为模型预测和预测提供背景。 还将研究每种风暴类型中产生极端降水的物理过程。将在每种风暴类型中审查已知影响降水的不同因素的相对强度，如低层大气中的水分含量。在确定关键因素后，将通过改变单个因素的强度并检查模拟的降水量如何响应变化，在区域高分辨率模型中进一步测试它们的影响。

Extreme precipitation and its related impacts, especially flooding, result in significant loss of life, property and infrastructure damage, transportation disruption, and storm water pollution, and have economic costs of more than $8 billion per year for the US. This project will undertake fundamental research into understanding the causes of extreme precipitation in the Northeast US. This is the most economically developed and densely populated region of the country. Improved model representation of these processes is a crucial step in the overall goal of improving forecasts and projections of these high-cost events, thereby mitigating their impacts. Processes that cause extreme precipitation over daily to weekly periods in the Northeast US will be identified. The ability of current climate models to reproduce these processes will be examined. The two motivating questions are: What types of storms cause extreme precipitation in the Northeast? Do current models correctly reproduce these storms types and their relationship to extreme precipitation? Using observational data, storm types associated with extreme precipitation will be identified by applying advanced analytic techniques. Characteristic patterns in the jet stream and other storm features that occur in association with extreme precipitation will be identified. This analysis will then be undertaken on the climate model output to identify the storm types that are produced in the models and compare the modeled types to the observed types. The differences will be highlighted for use in model development and for providing context for model forecasts and projections. The physical processes by which the extreme precipitation are generated within each storm type will also be investigated. The relative strength of different factors that are known to influence precipitation, such as the amount of moisture in the lower atmosphere, will be examined within each storm type. After the key factors are identified, their influence will then be further tested in a regional, high resolution model by changing the strength of individual factors and examining how the modeled precipitation changes in response.