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

该项目是为了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，而超级单体雷暴是最多产的猛烈龙卷风的来源。增强藤田等级中强度最高的龙卷风EF4和EF5是造成大部分死亡的原因，尽管它们是最不常见的，只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，但目前对于是什么决定了超级单体是否会产生龙卷风，以及龙卷风如果形成的话，是弱还是强，是短暂的还是长期的，目前还缺乏了解。这个复杂的问题不仅是自然界的一大谜团，而且对确保公共安全至关重要。该项目将结合观测、数值和分析方法来研究这些问题。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俄克拉荷马州诺曼的国家天气博物馆开发关于龙卷风的教育展览。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。要理解超级单体中猛烈、长轨迹龙卷风的产生和维持，核心挑战是能够量化整个风暴范围的过程，这些过程决定了是否形成了强大、长寿命的龙卷风。这项提议将使用一种名为熵场分解(EFD)的新方法作为统一框架，以整合和量化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、从这些模拟得出的预测雷达特征以及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，它利用贝叶斯分析和场的物理理论中的技术，在大量复杂的、通常是有噪声的数据中识别具有统计意义的风暴“模式”。与机器学习方法相比，不需要训练数据集。相反，来自时空相关性的个体数据中的先验信息被编码在熵谱路径(ESP)理论中，为提取复杂系统的不同的时空模式提供了足够的先验信息。这种方法将被用来研究由高分辨率模拟集合组成的首个此类数据集，这些集合产生丰富的龙卷风和非龙卷风风暴，以了解龙卷风发生、龙卷风形成失败和龙卷风维持的基本控制。这一整体还将使移动雷达观测和龙卷风解析、理想化模拟之间的一些首次详细相互比较成为可能。这一奖项反映了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。

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.