Collaborative Research: RAPID--Forensic Analysis of Flood-Wind-Rainfall Interactions during Hurricanes Florence and Michael

合作研究：RAPID——佛罗伦斯和迈克尔飓风期间洪水-风-降雨相互作用的法证分析

• 批准号: 1909367
• 负责人: Yu Zhang
• 金额: $ 3.06万
• 依托单位: University of Texas at Arlington
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-02-01 至 2020-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1909367&HistoricalAwards=false
• 关键词: Collaborative; Research; RAPID; Forensic; Analysis

This research project will address how flood from hurricane landfalls impacts weather forecast models by collecting and analyzing field, radar, satellite observations of wind, rainfall, and flood. To extend the observation coverage beyond the government operated weather and hydrological stations, the scientists will collect data from non-regular government-operated weather stations in the area that was affected by Hurricanes Florence and Michael. The project could potentially provide observational evidences to improve weather forecast models for better heavy rainfall forecasts after hurricane landfalls. Improvement of weather forecast models for hurricane landfalls will benefit hurricane preparation especially for the states along the coast lines, leading to reduced damages. A team of researchers at Colorado State University and University of Texas at Arlington will work together with a graduate student and a postdoctoral fellow to collect and compile near-surface wind data from in situ, radar, and satellite observations present along the North Carolina coast, and in particular the areas surrounding Albemarle Sound and Pamlico Sound, which encompasses the downstream estuaries of Chowan River, Pamlico River, and Neuse River, and was impacted by both Florence and Michael. In addition, they will use satellite observations in combination of the surface hydrological observations to estimate surface roughness changes. By comparing spatial variations of surface roughness changes and wind, they will investigate flood-rainfall-wind interactions. The significance of the interaction and the magnitude of the surface roughness change between pre-inundation and maximum inundation will provide useful information on whether these factors will be included in numerical forecast models for hurricane landfalls. The study will recover and maintain time-critical wind data sets especially datasets in private sectors which are often not well maintained or broadcasted. The investigators will engage science community and agencies through meetings and web broadcast, and educating and training a graduate student and a post-doc research associate on conducting researches. The team will publish the data set in the CSU web portal, present their results at science conferences, work with agencies, in particular NOAA, to ensure that the forecasters are aware of the availability of the dataset.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.

该研究项目将通过收集和分析现场、雷达、卫星对风、降雨和洪水的观测，研究飓风登陆带来的洪水如何影响天气预报模型。为了将观测范围扩大到政府运营的气象站和水文站之外，科学家们将从受飓风佛罗伦萨和迈克尔影响的地区的政府运营的非正规气象站收集数据。该项目可能为改进天气预报模型提供观测证据，以便在飓风登陆后更好地预报强降雨。飓风登陆天气预报模型的改进将有利于飓风的准备工作，特别是对沿海各州，从而减少损失。科罗拉多州立大学和德克萨斯大学阿灵顿分校的一组研究人员将与一名研究生和一名博士后合作，从现场、雷达和卫星观测中收集和汇编北卡罗莱纳州海岸的近地面风数据，特别是阿尔伯马尔海湾和帕姆利科海湾周围的地区，包括乔万河、帕姆利科河和纽斯河的下游河口。受到弗洛伦斯和迈克尔的双重影响此外，他们将利用卫星观测结合地表水文观测来估计地表粗糙度的变化。通过比较地表粗糙度变化和风的空间变化，他们将研究洪水-降雨-风的相互作用。在预淹没和最大淹没之间相互作用的重要性和表面粗糙度变化的大小将为这些因素是否将包括在飓风登陆的数值预报模式中提供有用的信息。这项研究将恢复和维护时间紧迫的风力数据集，特别是私营部门的数据集，这些数据集往往没有得到很好的维护或传播。研究人员将通过会议和网络广播与科学界和机构接触，并教育和培训一名研究生和一名博士后研究助理进行研究。该小组将在科罗拉多州立大学的门户网站上发布数据集，在科学会议上展示他们的结果，并与各机构（特别是NOAA）合作，以确保预报员了解数据集的可用性。该奖项反映了美国国家科学基金会的法定使命，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。