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

预防轨道 2：协作研究：通过将冰雹环境与冰雹过程模型联系起来改进高影响冰雹事件预报

• 批准号: 1855054
• 负责人: John Allen
• 金额: $ 24.81万
• 依托单位: Central Michigan University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-08-01 至 2023-12-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1855054&HistoricalAwards=false
• 关键词: PREEVENTS; Track; Collaborative; Research; Improving

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.

美国每年因损害冰雹风暴而遭受的保险损失数十亿美元，此类风暴的社会和经济成本也在增加。仅在2017年，美国就会造成超过1000万处房地产损害。灾难性冰雹的最新例子包括2017年耗资23亿美元的损失冰雹，2016年4月在圣安东尼奥举行的14亿美元的损失冰雹事件，包括4.5英寸以上的冰雹，2012年在德克萨斯州阿马里洛（Amarillo）产生了2毫米漂移冰雹冲出了道路，使交通停滞不前。不幸的是，这些事件都没有提前预期。该建议将确定哪种类型的环境产生了如此高的影响冰雹事件，以及产生冰雹的物理过程如何受环境过程的影响。对冰雹增长的增长将在国家天气预测模型中纳入冰雹预测系统，以改善冰雹预测，这符合NSF的使命，旨在提高国家健康，繁荣和福利并确保国防。随着短期（1小时）冰雹预测的改善，可以避免立即威胁，例如建议参加户外体育场的与会者或活动避难。改进的中期预测（1-3天）可能会导致建议采取的行动，需要进行更多时间密集型计划，例如在庇护所下移动飞机，通知保险调节器，并与县级应急管理人员合作，以制定应有的计划计划公共户外活动。了解预期类型的​​事件，例如巨型冰雹或许多小型冰雹（或仅仅是冰雹尺寸），还可以使预报员能够更好地为公众做好准备：例如，预测的“暴雪”冰雹活动可能要求城市准备耕作设备，并建议公众避免潜在洪水的低洼地区。此外，了解哪些环境已连接到冰雹事件类型是在较长时间尺度，季节性到季节性规模及以后开发冰雹预测的必要步骤。该建议还将支持获得博士学位的研究生。学位，两名接受硕士学位的研究生学位和三名本科生。为了提高可预测性并降低冰雹对社会的日益显着影响，该提案将实现以下四个目标：1）确定对不同冰雹威胁类别的冰雹生产的环境控制（例如，巨型冰雹，或10厘米或4英寸，大量的小冰雹）并确定政权，季节性和区域差异。2）建立冰雹威胁类别发生与环境条件之间的物理关系。确定哪种胚胎源区域特征增加了不同类别的冰雹威胁的有利冰雹生长轨迹的可能性，以及这些胚胎在现实风暴环境中如何变化。3）验证新近可用的时间变化的雷达雷达风，验证一个具有微物理复杂的冰雹轨迹模型4）整合有关环境控制，三维冰雹轨迹以及胚胎源区域的新知识，以改善冰雹威胁的运营预测。目标1将使用广泛的冰雹质疑从风暴预测中心（SPC）提供的声音数据库可探索环境控制。目标2将使用理想化的模拟来探索对环境条件和微物理过程的敏感性。目标3将使用新开发的雷达风和微物理数据集来驱动冰雹轨迹模型，该模型将通过表面冰雹观测来验证。最后，将根据SPC数据库的冰雹威胁事件的独立子集进行测试，在目标4中开发的改进的冰雹预测模型。这项拟议的研究将提高人们对环境和风暴量表的冰雹增长的基本过程以及在环境中如何变化的基本过程。它将确定哪些环境最有利于不同的冰雹威胁类别（例如冰雹或大量冰雹）或冰雹尺寸。它还超越了纯粹的统计努力，以确保了解每个冰雹威胁类别的环境控制基础的物理过程，包括大型上升量，胚胎来源区域的有利位置以及适当的胚胎规模。使用基金会的知识分子优点和更广泛的审查标准，被认为值得通过评估来支持。

项目成果

Ice from Above: Toward a Better Understanding of Hailstorms

上面的冰：更好地了解冰雹

• DOI: 10.1029/2020eo148818
• 发表时间: 2020
• 期刊: Eos
• 作者: Allen, John;Giammanco, Ian;Kumjian, Matthew;Punge, Heinz;Kunz, Michael;Zhang, Qinghong;Groenemeijer, Pieter
• 通讯作者: Groenemeijer, Pieter

Hodographs and Skew Ts of Hail-Producing Storms

产生冰雹的风暴的均线图和倾斜 T 值

• DOI: 10.1175/waf-d-23-0031.1
• 发表时间: 2023
• 期刊: Weather and Forecasting
• 影响因子: 2.9
• 作者: Nixon, Cameron J.;Allen, John T.;Taszarek, Mateusz
• 通讯作者: Taszarek, Mateusz

Understanding Hail in the Earth System

• DOI: 10.1029/2019rg000665
• 发表时间: 2020-03
• 期刊: Reviews of Geophysics
• 影响因子: 25.2
• 作者: J. Allen;I. Giammanco;M. Kumjian;Heinz Jurgen Punge;Qinghong Zhang;P. Groenemeijer;M. Kunz;K. Ortega

Deriving Severe Hail Likelihood from Satellite Observations and Model Reanalysis Parameters using a Deep Neural Network

• DOI: 10.1175/aies-d-22-0042.1
• 发表时间: 2023-06
• 期刊: Artificial Intelligence for the Earth Systems
• 作者: B. Scarino;K. Itterly;Kristopher Bedka;C. Homeyer;J. Allen;S. Bang;Daniel J. Cecil

Distinguishing between Hodographs of Severe Hail and Tornadoes

区分严重冰雹和龙卷风的速动仪

• DOI: 10.1175/waf-d-21-0136.1
• 发表时间: 2022
• 期刊: Weather and Forecasting
• 影响因子: 2.9
• 作者: Nixon, Cameron J.;Allen, John T.
• 通讯作者: Allen, John T.