Collaborative Research: Understanding Tornado Development and Maintenance in Supercells with an Emphasis on "High-End" Events

合作研究：了解超级单体中龙卷风的发展和维护，重点是“高端”事件

• 批准号: 1832327
• 负责人: Leigh Orf
• 金额: $ 59.04万
• 依托单位: University of Wisconsin-Madison
• 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: 2019-03-15 至 2023-02-28
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1832327&HistoricalAwards=false
• 关键词: Collaborative; Research; Understanding; Tornado; Development

In 2011, tornadoes in the United States killed 553 people and caused $28 billion in property damage. Most of the damage and fatalities were caused by violent tornadoes spawned by supercell thunderstorms, with six tornadoes receiving EF5 rankings, the most violent category of the Enhanced Fujita scale. Last year, several outbreaks occurred with over 140 confirmed tornadoes rated between EF2 and EF4 that resulted in substantial damage and over 30 deaths. While significant advances in our knowledge of supercell thunderstorms and tornadoes have occurred in recent decades, understanding of the fundamental processes occurring within supercells that result in tornadoes remains elusive. This void in knowledge is especially concerning for violent tornadoes that are responsible for a disproportionate number of fatalities and injuries as well as damage. Until these tornado genesis and maintenance processes are understood, our ability to forecast such events is severely limited. The primary goal of this research is to improve the fundamental understanding of processes underlying the formation and maintenance of tornadoes in supercell thunderstorms, especially as these processes relate to violent tornadoes. This knowledge is critical for improving tornado forecasts, tornado warning lead times, and estimations of potential tornado strength and longevity, and is especially applicable to tornadoes of strong and violent intensity. In conjunction with an awarded NSF grant providing continued access to the NSF-funded Blue Waters supercomputer, the research will analyze petabytes of data to study the processes occurring in supercells that result in tornadoes of varying strength, longevity, and structure.Intellectual Merit:Despite advances in observational and modeling/computing technology, much still needs to be understood about the evolution of tornadoes. Few computer simulations exist in which a well-resolved tornado forms within its parent supercell, and this work uses the highest resolution simulations of violently tornadic supercells conducted to date. A control simulation with 30 meter grid spacing has been completed where twenty fields in a volume centered on the tornado during its entire life cycle have been saved to disk every model time step, which will facilitate the most accurate analysis possible. 20 m and 15 m simulations containing long-track EF5 tornadoes have also been completed, and additional runs at these resolutions will be executed as needed. Tools to efficiently interrogate and analyze model data saved every time step will be improved and new tools will be developed. New simulations will be conducted in other environments in which violent tornadoes were observed. By analyzing simulated storms in different environments and comparing tornadogenesis failure cases to cases where violent tornadoes occur, the research team aims to identify processes common within unusually violent supercells involved in the formation and maintenance of tornadoes.Broader Impacts:A better fundamental understanding of tornado development in supercell thunderstorms should help guide future field programs in which new features identified in these numerical simulations are targeted for study, and ultimately lead to improvements in our ability to accurately forecast significant tornadic events and increase tornado warning lead times. Improving our fundamental understanding of tornado maintenance, made possible through analysis of the simulations described, should provide insight critical for National Weather Service forecasters to better predict, once a tornado has developed, how long a particular tornado may last. This type of information is invaluable, especially when pertaining to high-end, long-track events. The improved understanding of tornadoes in addition to cutting-edge visualizations of this research will facilitate science education in forums ranging from formal courses for students to learning venues for the general public.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.

2011年，美国龙卷风造成553人死亡，造成280亿美元的财产损失。大部分破坏和死亡是由超级单体雷暴引发的猛烈龙卷风造成的，有六个龙卷风获得了EF5级，这是增强的藤田等级中最猛烈的类别。去年，发生了几次龙卷风暴发，140多场龙卷风被评为2级至4级龙卷风，造成重大破坏和30多人死亡。虽然近几十年来，我们对超级单体雷暴和龙卷风的认识取得了重大进展，但对超级单体内部发生的导致龙卷风的基本过程的理解仍然难以捉摸。这一知识空白尤其令人担忧，因为猛烈的龙卷风造成了不成比例的伤亡和破坏。在了解这些龙卷风的发生和维护过程之前，我们预测这类事件的能力受到严重限制。这项研究的主要目标是改善对超级单体雷暴中龙卷风形成和维持的基本过程的基本理解，特别是当这些过程与猛烈的龙卷风有关时。这一知识对于改进龙卷风预报、龙卷风预警提前时间以及对潜在龙卷风强度和寿命的估计至关重要，尤其适用于强度较大和猛烈的龙卷风。这项研究将与美国国家科学基金会授予的一项拨款相结合，继续使用NSF资助的蓝色水域超级计算机，该研究将分析数PB的数据，以研究超级单体中发生的导致不同强度、寿命和结构的龙卷风的过程。智力优势：尽管在观测和建模/计算技术方面取得了进步，但对于龙卷风的演变仍有许多需要了解的地方。很少有计算机模拟在其母超单体内形成分辨率良好的龙卷风，这项工作使用了迄今为止对猛烈的龙卷风超级单体进行的最高分辨率的模拟。已经完成了网格间距为30米的控制模拟，在龙卷风的整个生命周期中，每个模型时间步都将以龙卷风为中心的体积中的20个场保存到磁盘中，这将有助于进行最准确的分析。还完成了包含长路径EF5龙卷风的20米和15米龙卷风模拟，并将根据需要执行这些分辨率的额外运行。将改进有效查询和分析每一时间步保存的模型数据的工具，并将开发新的工具。新的模拟将在其他观察到猛烈龙卷风的环境中进行。通过分析不同环境中的模拟风暴，并将龙卷风发生失败的案例与猛烈龙卷风发生的案例进行比较，研究团队旨在确定异常猛烈的超级单体内部共同参与龙卷风形成和维持的过程。广泛影响：对超级单体雷暴中龙卷风发展的更基本了解应有助于指导未来的现场计划，在这些现场计划中，这些数值模拟中确定的新特征将成为研究的目标，并最终提高我们准确预测重大龙卷风事件和增加龙卷风预警提前期的能力。通过对所描述的模拟的分析，提高我们对龙卷风维护的基本理解，应该会为国家气象局的预报员提供至关重要的洞察力，以便更好地预测龙卷风一旦形成，特定龙卷风可能会持续多久。这种类型的信息是无价的，特别是在与高端、长赛道赛事有关的时候。除了这项研究的前沿可视化之外，对龙卷风的更好的理解将促进从学生的正式课程到普通公众的学习场所的论坛中的科学教育。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

Modeling the World’s Most Violent Thunderstorms

模拟世界上最猛烈的雷暴

• DOI: 10.1109/mcse.2021.3069771
• 发表时间: 2021
• 期刊: Computing in Science & Engineering
• 影响因子: 2.1
• 作者: Orf, Leigh

VAPOR: A Visualization Package Tailored to Analyze Simulation Data in Earth System Science

• DOI: 10.3390/atmos10090488
• 发表时间: 2019-07
• 期刊: Atmosphere
• 影响因子: 2.9
• 作者: Shaomeng Li;Stanislaw Jaroszynski;S. Pearse;Leigh Orf;J. Clyne

A Violently Tornadic Supercell Thunderstorm Simulation Spanning a Quarter-Trillion Grid Volumes: Computational Challenges, I/O Framework, and Visualizations of Tornadogenesis

• DOI: 10.3390/atmos10100578
• 发表时间: 2019-09
• 期刊: Atmosphere
• 影响因子: 2.9
• 作者: Leigh Orf

Hydraulic jump dynamics above supercell thunderstorms

• DOI: 10.1126/science.abh3857
• 发表时间: 2021-09-10
• 期刊: SCIENCE
• 影响因子: 56.9
• 作者: O'Neill, Morgan E.;Orf, Leigh;Halbert, Kelton
• 通讯作者: Halbert, Kelton

Tracking the Centre of Asymmetric Vortices Using Wind Velocity Vector Data Fields

• DOI: 10.1007/s10546-022-00739-0
• 发表时间: 2022-09
• 期刊: Boundary-Layer Meteorology
• 影响因子: 4.3
• 作者: Niall Bannigan;Leigh Orf;E. Savory