PREEVENTS Track 2: Collaborative Research: Improving High-Impact Hail Event Forecasts by Linking Hail Environments and Modeled Hailstorm Processes

GSTDTAP

项目编号	1855100
	PREEVENTS Track 2: Collaborative Research: Improving High-Impact Hail Event Forecasts by Linking Hail Environments and Modeled Hailstorm Processes
	Conrad Ziegler (Principal Investigator)
主持机构	University of Oklahoma Norman Campus
项目开始年	2019
	2019-08-01
项目结束日期	2022-07-31
资助机构	US-NSF
项目类别	Continuing grant
项目经费	168629(USD)
国家	美国
语种	英语
英文摘要	The United States suffers billions of dollars in insured losses each year from damaging hail storms and the societal and economic costs of such storms have been increasing. Over 10 million properties in the U.S. were damaged by hail in 2017 alone. Recent examples of catastrophic hailstorms include a $2.3 billion loss hail event impacting Denver in 2017, a $1.4 billion loss hail event in San Antonio in April 2016 that included 4.5+ inch hailstones, and a 2012 hailstorm in Amarillo, TX that produced 2-m drifts of hail that washed out roads and brought traffic to a standstill. Unfortunately, none of these events were anticipated ahead of time. This proposal will identify what type of environments produce such high impact hail events, and how the physical processes that produce hail are affected by environmental processes. The improved understanding of hail growth will be incorporated into a hail forecasting system within national weather prediction models to improve hail forecasts, which is in line with NSF's mission to advance national health, prosperity and welfare and to secure the national defense. With improved short-term (< 1 h) hail forecasts, immediate threats could be avoided, such as recommending that attendees at outdoor stadiums or events take shelter. Improved intermediate-term forecasts (1-3 days) could result in recommended action that required more time-intensive planning, such as moving aircraft under shelter, notifying insurance adjusters, and working with county-level emergency managers to have contingency plans in place for public outdoor events. Knowledge of the expected type of event, such as giant hail or lots of small hail (or "blizzard" hail), in addition to merely hail size, would allow forecasters to better prepare the public: for example, a forecasted "blizzard" hail event might require a city to ready its plowing equipment and advise the public to avoid low-lying areas that could potentially flood. Additionally, knowledge of which environments are connected to which hail events types is a necessary step for developing hail forecasts on longer time scales, of subseasonal to seasonal scale and beyond. This proposal will also support a graduate student receiving a Ph.D. degree, two graduate students receiving M.S. degrees, and three undergraduate students. In order to advance predictability and reduce the increasingly significant impact of hail on society, this proposal will accomplish the following four goals: 1) Identify environmental controls on hail production for different hail threat classes (e.g., giant hail or >10 cm or 4 in, large amounts of small hail) and identify regime, seasonal and regional differences. 2) Establish the physical relationship between hail threat class occurrence and environmental conditions. Determine what embryo source region characteristics increase the probability of favorable hail growth trajectories for different classes of hail threats and how these vary across realistic storm environments. 3) Validate a microphysically complex hail trajectory model in light of newly available time-varying radar-retrieved wind and buoyancy fields and surface hail observations. 4) Integrate new knowledge about environmental controls, three-dimensional hail trajectories, and embryo source regions into the CAM-HAILCAST hail model to improve operational forecasts of hail threats. Objective 1 will use an extensive hailstorm proximity sounding database available from the Storm Prediction Center (SPC) to explore environmental controls. Objective 2 will use idealized simulations to explore sensitivity to both environmental conditions and microphysical processes. Objective 3 will use a newly-developed radar-derived wind and microphysical dataset to drive a hail trajectory model which will be validated with surface hail observations. Finally, the improved hail forecasting model developed in Objective 4 will be tested against an independent subset of hail threat events from the SPC database. This proposed research will improve understanding of the basic processes underlying hail growth on both environmental- and storm-scales and how those vary across environments. It will determine which environments are most favorable to different hail threat classes (such as giant hail or large amounts of small hail) or hail sizes. It also moves beyond a purely statistical endeavor to ensure the physical processes underlying the environmental controls for each hail threat class are understood, including large updraft volumes, favorable placement of embryo source regions, and appropriate embryo sizes. 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/213414
专题	环境与发展全球科技态势
推荐引用方式 GB/T 7714	Conrad Ziegler .PREEVENTS Track 2: Collaborative Research: Improving High-Impact Hail Event Forecasts by Linking Hail Environments and Modeled Hailstorm Processes.2019.