Detecting Synoptic-Scale Precursors of Tornado Outbreaks

检测龙卷风爆发的天气规模前兆

• 批准号: 0527934
• 负责人: Lance Leslie
• 金额: $ 35.23万
• 依托单位: University of Oklahoma Norman Campus
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2006
• 资助国家: 美国
• 起止时间: 2006-01-01 至 2009-12-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0527934&HistoricalAwards=false
• 关键词: Detecting; Synoptic; Scale; Precursors; Tornado

Intellectual Merit - Historically, synoptic-scale signals have played an elusive role in discriminating between tornado outbreak days and severe thunderstorm days without substantial tornadic activity. A question that needs to be answered is: To what extent are tornado outbreaks attributable to processes on the synoptic-scale rather than on the mesoscale? To explore this question, a series of numerical simulations will be performed that commence from smoothed, synoptic-scale, initial conditions. These simulations will be run for lead-times of one to three days. The exclusion of mesoscale observational data is necessary to establish a baseline for determining the relationship between synoptic-scale signals and tornado outbreaks. Two mesoscale numerical models will be utilized. These models will be initialized using composite gridded fields from the NCEP/NCAR reanalysis data, which has a horizontal grid spacing of about 200 km. A family of such composites will be developed using Empirical Orthogonal Functions that filter the data such that only the dominant synoptic-scale modes are retained. A range of meteorological covariates, including Convective Available Potential Energy (CAPE), low-level wind shear, storm-relative helicity, relative vorticity, and relative humidity will be used as proxy variables for the occurrence of tornadoes. The covariates are necessary, as even the most sophisticated mesoscale models currently can predict supercell formation and motion but are incapable of explicitly and routinely predicting tornadoes. The Principal Investigator will investigate the spatial and temporal correlations between the simulated fields associated with tornado outbreak cases and cases involving primarily non-tornadic severe weather. Statistical exploration of the outbreak and non-outbreak cases will enhance physical understanding of the relationships between the synoptic environment and tornado outbreaks. A high statistical correlation between the outbreak and non-outbreak cases will imply that even the most important tornado events are controlled primarily at sub-synoptic scales. Such a finding would have clear implications for operational observing strategies and for research programs. Alternatively, if low correlations are found, then further study aimed at diagnosis of those processes that connect the synoptic scales to tornado outbreaks is likely to prove very fruitful. Broader Impacts -This effort will advance scientific research while promoting graduate training and the results of the research will be incorporated into teaching courses in a wide range of subject areas including numerical weather prediction, statistics, and advanced forecasting skills classes. The modeling advances and databases created by the Investigators will be made directly available to the broader scientific and operational communities. The scientific discoveries will generate societal benefits, as they will assist in refining the ability to predict severe weather, particularly tornadic supercells.

知识价值-历史上，天气尺度信号在区分龙卷风爆发日和没有实质性龙卷风活动的严重雷暴日方面发挥了难以捉摸的作用。 一个需要回答的问题是：龙卷风的爆发在多大程度上是天气尺度过程而不是中尺度过程造成的？ 为了探讨这个问题，将进行一系列的数值模拟，从平滑，天气尺度，初始条件。 这些模拟将在一至三天的筹备时间内进行。 排除中尺度观测数据对于确定天气尺度信号与龙卷风爆发之间关系的基线是必要的。 将使用两个中尺度数值模式。 这些模型将使用NCEP/NCAR再分析数据的复合网格场进行初始化，该数据的水平网格间距约为200公里。 将使用经验正交函数开发一系列这样的复合材料，这些函数过滤数据，以便只保留占主导地位的天气尺度模式。 一系列气象协变量，包括对流可用势能（CAPE），低空风切变，风暴相对螺旋度，相对涡度和相对湿度将被用作龙卷风发生的代理变量。 协变量是必要的，因为即使是目前最复杂的中尺度模型也可以预测超级单体的形成和运动，但无法明确和常规地预测龙卷风。首席研究员将调查与龙卷风爆发案例和主要涉及非龙卷风恶劣天气的案例相关的模拟场之间的空间和时间相关性。 对爆发和非爆发情况的统计探索将增强对天气环境和龙卷风爆发之间关系的物理理解。 爆发和非爆发病例之间的高度统计相关性意味着即使是最重要的龙卷风事件也主要在次天气尺度上受到控制。 这一发现将对实际观测战略和研究计划产生明确的影响。 或者，如果发现低相关性，那么进一步的研究，旨在诊断这些过程的天气尺度连接到龙卷风爆发可能会证明是非常富有成效的。更广泛的影响-这项工作将促进科学研究，同时促进研究生培训，研究结果将纳入广泛学科领域的教学课程，包括数值天气预报，统计和高级预报技能课程。 研究人员创建的建模进展和数据库将直接提供给更广泛的科学和操作社区。 这些科学发现将产生社会效益，因为它们将有助于提高预测恶劣天气的能力，特别是龙卷风超级单体。