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

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项目编号	1909367
	Collaborative Research: RAPID--Forensic Analysis of Flood-Wind-Rainfall Interactions during Hurricanes Florence and Michael
	Yu Zhang (Principal Investigator)
主持机构	University of Texas at Arlington
项目开始年	2019
	2019-02-01
项目结束日期	2020-01-31
资助机构	US-NSF
项目类别	Standard Grant
项目经费	30611(USD)
国家	美国
语种	英语
英文摘要	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.
文献类型	项目
条目标识符	http://119.78.100.173/C666/handle/2XK7JSWQ/213599
专题	环境与发展全球科技态势
推荐引用方式 GB/T 7714	Yu Zhang .Collaborative Research: RAPID--Forensic Analysis of Flood-Wind-Rainfall Interactions during Hurricanes Florence and Michael.2019.