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.

该项目是了解触发龙卷风的雷暴条件。每年在美国的广泛地区，大气条件都有利于形成超级电池雷暴，这是最多产的暴力龙卷风来源。龙卷风排名为EF4和EF5，这是增强的富士表量表的最高强度类别，是造成大部分死亡人数的原因，即使它们是最不常见的，但少于观察到的龙卷风的1％。 Supercell Tornadoes周围的死亡和破坏融合了旨在理解和预测这些强大风暴的观察，理论和数值建模研究。但是，尽管这些研究取得了许多进步，但目前对确定超级电池是否会产生龙卷风的原因有很差的理解，以及该龙卷风是否是否形成，是否会形成龙卷风，将是弱或强，短暂的，短暂的或长寿的。这个复杂的问题不仅是自然界的巨大奥秘之一，而且对于确保公共安全至关重要。该项目将通过结合观测，数值和分析方法来研究这些问题。该项目将在加利福尼亚州圣地亚哥的巴尔博亚公园和俄克拉荷马州诺曼市的国家气象博物馆举行的龙卷风上进行教育展览。该项目还将通过高分辨率的数值模拟以及数据分析和可视化来为本科和研究生提供独特的研究和教育机会。超级电池中的长途龙卷风要了解暴力的产生和维持的主要挑战是能够量化整个雨水的过程，这些过程决定了强大的，长寿的龙卷风是否形成。该提案将使用一种称为熵场分解（EFD）的新方法作为统一的框架，以整合和量化在基于高分辨率物理学的模拟中产生的龙卷风超细胞的复杂动力学，预测了从这些模拟和收集在该领域收集的超级电池的实际观察数据的雷达签名。 EFD是一种数据不足的方法，用于四维时空纠缠的数据挖掘，利用贝叶斯分析的技术和物理学的田地理论来识别统计学上具有重要意义的风暴“模式”在复杂的，通常是嘈杂的数据中。与机器学习方法相反，不需要培训数据集。相反，在熵光谱途径理论（ESP）中编码的从时空相关性得出的单个数据中的先前信息提供了足够的先验信息，以提取复杂系统的不同时空模式。该方法将用于研究高分辨率模拟的集合包含的首个数据集，这些数据集产生了各种各样的龙卷风和非龙骨风暴，以了解龙卷风和非龙卷风风暴的基本控制，以了解龙卷力生成的基本控制，龙核病发生失败，龙卷风的作用，和龙卷风维持。该合奏还将实现移动雷达观察与龙卷风解决，理想化的模拟之间的第一个详细截面。该奖项反映了NSF的法定任务，并认为值得通过基金会的知识分子优点和更广泛的影响审查标准通过评估来进行评估。