Collaborative Research: Detection and Estimation of Multi-Scale Complex Spatiotemporal Processes in Tornadic Supercells from High Resolution Simulations and Multiparameter Radar

合作研究：通过高分辨率模拟和多参数雷达检测和估计龙卷超级单体中的多尺度复杂时空过程

• 批准号: 2114757
• 负责人: Leigh Orf
• 金额: $ 34.15万
• 依托单位: University of Wisconsin-Madison
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-07-15 至 2025-06-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2114757&HistoricalAwards=false
• 关键词: Collaborative; Research; Detection; Estimation; Multi

The project is to understand thunderstorm conditions that trigger tornados. Each year across broad regions of the United States, atmospheric conditions become favorable for the formation of supercell thunderstorms, the most prolific source of violent tornadoes. Tornadoes ranked EF4 and EF5, the top strength categories of the Enhanced Fujita scale, are responsible for the bulk of fatalities, even though they are the least common, comprising less than 1% of observed tornadoes. The death and destruction wrought by supercell tornadoes has motivated much observational, theoretical, and numerical modeling research designed to understand and predict these powerful storms. However, despite the many advances that have resulted from these studies, there is currently poor understanding of what determines whether a supercell will produce a tornado or not, and whether that tornado, should it form at all, will be weak or strong, short-lived or long-lived. This complex question is not only one of the great mysteries of nature but is of critical importance to assuring public safety. The project will investigate these issues by combining observational, numerical, and analytical methods. The project will develop educational exhibits on tornadoes at the Fleet Science Center at Balboa Park, San Diego, CA and the National Weather Museum at Norman, OK. The project will also provide unique research and education opportunities for undergraduate and graduate students in understanding tornado evolution through high-resolution numerical simulations as well as data analysis and visualization. The central challenge for understanding the generation and maintenance of violent, long-track tornadoes in supercells is being able to quantify the storm-wide processes that determine whether strong, long-lived tornadoes form. This proposal will use a novel method called the Entropy Field Decomposition (EFD) as a unifying framework to integrate and quantify the complex dynamics of tornadic supercells produced in high resolution physics-based simulations, predicted radar signatures derived from these simulations, and actual observational data of supercells collected in the field. EFD is a data-agnostic approach to four-dimensional space-time entangled data mining that leverages techniques from Bayesian analysis and the physics theory of fields to identify statistically significant storm “modes" within huge volumes of complex, often noisy, data. In contrast with machine learning approaches, no training datasets are required. Rather, prior information within individual data derived from space-time correlations, codified in the theory of Entropy Spectrum Pathways (ESP), provides sufficient prior information to extract distinct space-time modes of complex systems. This method will be used to study a first-of-its-kind data set comprised of ensembles of high-resolution simulations that yield a rich variety of tornadic and non-tornadic storms to understand fundamental controls of tornadogenesis, tornadogenesis failure, and tornado maintenance. This ensemble will also enable some of the first detailed intercomparisons between mobile radar observations and tornado-resolving, idealized simulations.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.

该项目旨在了解引发龙卷风的雷暴条件。每年，在美国的广大地区，大气条件变得有利于超级单体雷暴的形成，超级单体雷暴是强烈龙卷风的最多产的来源。EF4和EF5级龙卷风是藤田增强龙卷风等级的最高级别，它们造成了大部分的死亡，尽管它们是最不常见的，占观测到的龙卷风的不到1%。超级单体龙卷风造成的死亡和破坏激发了许多观测、理论和数值模拟研究，旨在了解和预测这些强大的风暴。然而，尽管这些研究取得了许多进展，但目前人们对是什么决定了超级单体是否会产生龙卷风，以及龙卷风是否会形成，是弱还是强，是短还是长，知之甚少。这个复杂的问题不仅是自然界最大的谜团之一，而且对确保公共安全至关重要。该项目将结合观测、数值和分析方法来调查这些问题。该项目将在加利福尼亚州圣地亚哥巴尔博亚公园的舰队科学中心和OK诺曼的国家气象博物馆开展龙卷风教育展览。该项目还将为本科生和研究生提供独特的研究和教育机会，通过高分辨率数值模拟以及数据分析和可视化来了解龙卷风的演变。理解超级单体中猛烈的、长轨迹龙卷风的产生和维持的核心挑战是能够量化风暴范围内的过程，这些过程决定了强的、长寿命的龙卷风是否会形成。该提案将使用一种称为熵场分解（EFD）的新方法作为统一框架，整合和量化在高分辨率物理模拟中产生的龙卷风超级单体的复杂动力学，从这些模拟中获得的预测雷达特征，以及在现场收集的超级单体的实际观测数据。EFD是一种与数据无关的四维时空纠缠数据挖掘方法，它利用贝叶斯分析技术和场的物理理论，在大量复杂的、通常嘈杂的数据中识别统计上显著的风暴“模式”。与机器学习方法相比，不需要训练数据集。相反，在熵谱路径（ESP）理论中编纂的来自时空相关性的单个数据中的先验信息，为提取复杂系统的不同时空模式提供了足够的先验信息。该方法将用于研究由高分辨率模拟集合组成的首个同类数据集，这些数据集产生丰富多样的龙卷风和非龙卷风风暴，以了解龙卷风形成、龙卷风形成失败和龙卷风维护的基本控制。这个集合也将使移动雷达观测和龙卷风解析、理想模拟之间的一些首次详细的相互比较成为可能。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。