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: 1908862
• 负责人: Lixin Lu
• 金额: $ 8.95万
• 依托单位: Colorado State University
• 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 2023-01-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1908862&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场，雷达，卫星观察到风，降雨和洪水来解决飓风登陆的洪水如何影响天气预报模型。为了将观察覆盖范围扩展到政府操作的天气和水文站，科学家将从佛罗伦萨飓风和迈克尔影响的地区的非规范政府经营的气象站收集数据。该项目可能会提供观察证据，以改善天气预报模型，以便在飓风登陆后更好的大雨预测。改善飓风登陆的天气预报模型将使飓风的准备受益，尤其是对于沿海岸线的各州，导致损害损失减少。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博士后研究员一起收集和编译北卡罗来纳州沿海地区的现场，雷达和卫星观察的近乎表面的风数据河，受到佛罗伦萨和迈克尔的影响。此外，他们将使用卫星观测来结合表面水文观测来估计表面粗糙度的变化。通过比较表面粗糙度变化和风的空间变化，他们将研究洪水降临的互动。相互作用的重要性和表面粗糙度变化之间的意义将提供有关是否将这些因素包括在飓风登陆的数值预测模型中的有用信息。该研究将恢复和维护时间关键的风数据集，尤其是私营部门的数据集，这些数据集通常不受很好的维护或广播。调查人员将通过会议和网络广播与科学社区和机构互动，并教育和培训研究生和研究后的研究助理进行研究。该团队将在CSU Web门户网站上发布数据集，在科学会议上介绍他们的结果，与机构，尤其是NOAA合作，以确保预报员意识到数据集的可用性。该奖项反映了NSF的法定任务，并通过使用基金会的知识优点和广泛影响来评估NSF的法定任务，并被认为是值得的。

项目成果

Relative accuracy of HWRF reanalysis and a parametric wind model during the landfall of Hurricane Florence and the impacts on storm surge simulations

• DOI: 10.1007/s11069-022-05702-3
• 发表时间: 2022-11
• 期刊: Natural Hazards
• 影响因子: 3.7
• 作者: M. Rahman;Yu Zhang;Lixin Lu;S. Moghimi;K. Hu;Ali Abdolali