Developing a Structural, Morphological, and Microphysical Understanding of Left-moving Supercells

发展对左移超级细胞的结构、形态和微观物理理解

• 批准号: 2218623
• 负责人: Matthew Van Den Broeke
• 金额: $ 47.35万
• 依托单位: University of Nebraska-Lincoln
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-08-01 至 2025-07-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2218623&HistoricalAwards=false
• 关键词: Developing; Structural; Morphological; Microphysical; Understanding

Left-moving (LM) supercell thunderstorms produce a large number of severe weather reports every year, including many high-impact hail events. Radar represents a way to study these storms. New polarimetric radar technology has now been in operation long enough to allow development of a large sample of LM supercells. Using this dataset, the project will focus on when these storms occur through examination of their environments, what they look like under different environmental conditions, and how these storms’ radar signatures might be used to anticipate associated severe weather events. This will be the first large-scale study of LM supercells, and is anticipated to advance scientific understanding of these storms in a way that may lead to improved severe weather warnings for the benefit of society. An educational component will provide training to students from middle school through graduate school. A LM supercell dataset anticipated to contain 600+ individual storms for which polarimetric radar observations are available will be constructed using data from the Storm Prediction Center and manual analysis of recent radar datasets. Environments of these storms will be characterized using model output data and clustered using self-organizing maps. Polarimetric radar characteristics of the updraft, hail, and inflow regions will be quantified using the Supercell Polarimetric Observation Research Kit. Environmental clustering will be used to characterize the environments in which LM supercells occur and how their radar presentation and impacts vary across environments. LM supercells have similarities to more common right-moving storms, and the structure, microphysics, and temporal evolution of storms in the LM dataset will be compared to these characteristics in sets of right-moving storms which have been studied prior. Severe weather outcomes in LM storms will be put in the context of radar signature evolution and environmental characteristics, allowing development of models describing events which lead to severe weather production in LM supercells. The project will also include development of a mobile severe weather workshop with hands-on experimental components for middle school students. This workshop will be developed by graduate students as part of their professional development, and will be presented by students and faculty to rural and Native American students in the central Great Plains region.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.

左转（LM）超级电池雷暴每年会产生大量恶劣天气报告，包括许多高影响力的冰雹事件。雷达代表了研究这些风暴的一种方法。现在，新的极化雷达技术已经运行了足够长的时间，以允许开发大量的LM超级单元。使用此数据集，该项目将集中在这些风暴通过检查环境，在不同环境条件下的样子以及这些风暴的雷达签名如何用于预测相关的恶劣天气事件时的样子。这将是对LM超级电池的首次大规模研究，并预计将以一种可能导致社会利益的恶劣天气警告的方式来提高对这些风暴的科学理解。教育组成部分将通过研究生院从中学的学生提供培训。预计将使用Storm预测中心中的数据以及对最近的雷达数据集的手动分析来构建一个可获得偏光雷达观测值的LM超级细胞数据集。这些风暴的环境将使用模型输出数据来表征，并使用自组织图聚集。升级，冰雹和流入区域的极化雷达特征将使用超级偏光观察研究试剂盒进行量化。环境聚类将用于表征LM超级电池发生的环境，以及它们的雷达呈现和影响如何在环境中变化。 LM超级电池与更常见的右旋风暴具有相似之处，在LM数据集中的风暴的结构，微观物理学和临时演变将与这些特征进行比较。 LM风暴中的恶劣天气结果将置于雷达标志进化和环境特征的背景下，从而开发了描述事件的模型，这些事件导致LM超级电池的恶劣天气产生。该项目还将包括为中学学生提供动手实验组件的移动严重天气研讨会。该研讨会将由研究生开发，作为其专业发展的一部分，并将由学生和教职员工向中部大平原地区的农村和美洲原住民学生展示。该奖项反映了NSF的法定任务，并被认为是通过基金会的知识分子优点和更广泛的影响来评估的珍贵的支​​持。