Synoptic-Scale Influences on Outbreaks of Severe Convection

强雷暴及其产生的破坏性天气（包括大冰雹、强直线风和龙卷风）本质上是小尺度现象，只占据狭窄的空间和时间。因此，局部极端事件，如龙卷风，即使是先进的中尺度数值模式也无法解决。尽管如此，最近的证据表明，可以在区域基础上预测这类恶劣天气事件（称为“暴发”）的群集，提前时间为2-3天或更长，尽管在破坏性风暴最频繁的春季，模式区分龙卷风暴发与主要是非龙卷风暴发的能力明显减弱。本研究将调查可预测性的限制（即最大提前时间），在此范围内，MM5和WRF（天气研究和预报）社区模型仅使用大气结构的空间粗糙天气尺度信息初始化时，可以可靠地预测不同的爆发类型。将通过主客观统计技术相结合的方式分析导出的气象协变量（用于评估对比爆发类型的可能性的摘要参数）。本分析将涵盖1970-2003年期间。初步结果表明，在这些长期预报中，包括切变相关量（如风暴相对螺旋度）在内的动力学测量比基于对流有效势能（CAPE）的热力学测量更受青睐。这就需要进一步探索为什么某些途径可能更适合于将大尺度大气影响传递到更小的单个雷暴的尺度。将进一步探讨辨别龙卷风与非龙卷风爆发能力的季节性调节的证据，以及基于单一模式运行和对计算要求更高的集合运行的预测技能。还将评估模式分辨风暴特性（如上升气流强度）在判别爆发类型方面的效用。这项工作的智力价值在于提高了运行和解释中尺度预报模型的能力，从而可靠地预测严重天气的爆发，以几天的提前期辨别这些爆发的性质，并提高了对连接大尺度和小尺度的大气过程的理解。这项工作的更广泛影响将包括，通过与NOAA风暴预测中心和澳大利亚气象局的业务预报员的合作和协作，以及更多的传统联系和研究生教育，通过这些进展的即时沟通，为公众准备危险天气事件增加了准备时间。

Severe thunderstorms and the damaging weather they produce (including large hail, strong straight-line winds and tornadoes) are by nature small scale phenomena occupying narrow segments of space and time. As such, local extreme events such as tornadoes are not resolved even by advanced mesoscale numerical models. Recent evidence nonetheless suggests that clusters of such severe weather events (termed "outbreaks") may be anticipated on a regional basis with lead times of 2-3 days or more, although the ability of models to discriminate tornadic outbreaks from primarily non-tornadic ones is apparently diminished during the spring season when damaging storms are most frequent. This study will investigate the limits of predictability (viz. maximum lead times) over which contrasting outbreak types may be reliably forecast by the MM5 and WRF (Weather Research & Forecasting) community models when initialized using only spatially coarse synoptic-scale information on atmospheric structure. Derived meteorological covariates (summary parameters used to evaluate the potential for contrasting outbreak types) will be analyzed through a combination of subjective and objective statistical techniques. This analysis will cover the period 1970-2003. Initial results suggest that dynamical measures including shear-related quantities such as storm-relative helicity are favored over thermodynamically-based measures such as Convective Available Potential Energy (CAPE) in these long-range forecasts. This invites further exploration of why certain pathways may be preferred for communicating large-scale atmospheric influences down to more the scale of individual thunderstorms. Evidence of seasonal modulation of ability to discern tornadic vs. non-tornadic outbreaks will be further explored, as will forecast skill based both upon single model runs and more computationally demanding ensemble runs. The utility of model-resolved storm properties such as updraft strength in discriminating outbreak type will also be evaluated.The intellectual merit of this work rests on improved ability to run and interpret mesoscale forecast models to reliably anticipate major outbreaks of severe weather, to discern the nature of these outbreaks with lead times of several days, and to gain improved understanding of atmospheric processes connecting large and small scales. Broader impacts of this work will include benefits from increased lead time for the public to prepare for hazardous weather events, through immediate communication of these advances through cooperation and collaboration with operational forecasters at NOAA's Storm Prediction Center and Australian Bureau of Meteorology, as well as more traditional links and graduate student education.