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.

向左移动的超级单体雷暴每年产生大量的恶劣天气报告，包括许多高影响的冰雹事件。雷达代表了研究这些风暴的一种方法。新的偏振雷达技术现在已经运行了足够长的时间，可以开发出大量的LM超级细胞样本。使用这个数据集，该项目将通过检查这些风暴的环境，它们在不同环境条件下的样子，以及如何使用这些风暴的雷达信号来预测相关的恶劣天气事件，来重点研究这些风暴发生的时间。这将是第一次大规模研究LM超级细胞，预计将以一种可能导致改善恶劣天气警告的方式推进对这些风暴的科学理解，从而造福社会。教育部分将为从中学到研究生院的学生提供培训。使用风暴预报中心的数据和对最新雷达数据集的手动分析，将构建一个预计包含600多个极化雷达观测的单个风暴的LM超级单体数据集。这些风暴的环境将使用模型输出数据进行表征，并使用自组织映射进行聚类。上升气流、冰雹和入流区域的极化雷达特征将使用Supercell极化观测研究工具包进行量化。环境集群将被用来描述LM超单体出现的环境，以及它们的雷达呈现和影响在不同环境中是如何变化的。LM超级单体与更常见的右移动风暴有相似之处，并且将把LM数据集中风暴的结构、微物理和时间演化与先前研究过的右移动风暴集合中的这些特征进行比较。LM风暴中的恶劣天气结果将被放在雷达特征演变和环境特征的背景下，从而允许开发描述导致LM超级单体中恶劣天气产生的事件的模型。该项目还将包括为中学生开发一个带有动手实验组件的流动恶劣天气研讨会。该研讨会将由研究生开发，作为其专业发展的一部分，并将由学生和教师颁发给中部大平原地区的农村和美洲原住民学生。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。