Detecting Synoptic-Scale Precursors of Tornado Outbreaks

知识价值-历史上，天气尺度信号在区分龙卷风暴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。需要回答的一个问题是：龙卷风的爆发在多大程度上可归因于天气尺度的过程，而不是中尺度的过程？为了探讨这个问题，将从平滑的天气尺度初始条件开始进行一系列数值模拟。这些模拟将运行一到三天的准备时间。为了确定天气尺度信号与龙卷风爆发之间的关系，有必要排除中尺度观测数据。将使用两个中尺度数值模式。这些模式将使用来自NCEP/NCAR再分析数据的复合网格场进行初始化，水平网格间距约为200公里。将使用经验正交函数对数据进行过滤，从而只保留主要的天气尺度模式，从而开发出一系列这样的合成。一系列气象协变量，包括对流有效势能(CAPE)、低层风切变、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，因为即使是最复杂的中尺度模式目前也可以预测超级单体的形成和运动，但不能明确和常规地预测龙卷风。首席调查员将调查与龙卷风暴发病例有关的模拟场与主要涉及非龙卷风恶劣天气的病例之间的空间和时间相关性。对暴发和非暴发病例的统计探索将加强对天气环境和龙卷风暴发之间关系的实际了解。暴发和未暴发病例之间的高度统计相关性将意味着，即使是最重要的龙卷风事件也主要在次天气尺度上得到控制。这样的发现将对作战观测战略和研究计划产生明显的影响。或者，如果发现低相关性，那么旨在诊断那些将天气尺度与龙卷风爆发联系起来的过程的进一步研究可能会被证明是非常有成效的。更广泛的影响-这一努力将促进科学研究，同时促进研究生培训，研究成果将被纳入广泛学科领域的教学课程，包括数值天气预报、统计学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界和业务界。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。

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.