Synoptic-Scale Influences on Outbreaks of Severe Convection

天气尺度对强对流爆发的影响

• 批准号: 0831359
• 负责人: Lance Leslie
• 金额: $ 47.93万
• 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: 2009
• 资助国家: 美国
• 起止时间: 2009-01-01 至 2012-12-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0831359&HistoricalAwards=false
• 关键词: Synoptic; Scale; Influences; Outbreaks; Severe

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.

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）本质上是占据狭窄空间和时间段的小规模现象。因此，即使是先进的中尺度数值模型也无法解决龙卷风等局部极端事件。然而，最近的证据表明，尽管模型区分龙卷风爆发和主要非龙卷风爆发的能力在破坏性风暴最频繁的春季明显减弱，但可能会在区域范围内预计会出现2-3天或更长时间的此类严重天气事件（称为“爆发”）的集群。本研究将调查可预测性的限制（即最大提前时间），当仅使用大气结构的空间粗略天气尺度信息进行初始化时，MM5 和 WRF（天气研究与预报）社区模型可以可靠地预测对比爆发类型。将通过主观和客观统计技术的结合来分析派生的气象协变量（用于评估对比爆发类型的可能性的汇总参数）。本分析涵盖 1970 年至 2003 年期间。初步结果表明，在这些长期预测中，包括与切变相关的量（例如风暴相对螺旋度）在内的动力学测量比基于热力学的测量（例如对流可用势能（CAPE））更受青睐。这需要进一步探索为什么某些路径可能更适合将大规模大气影响传达到更大范围的单个雷暴。将进一步探索辨别龙卷风与非龙卷风爆发的能力的季节性调节证据，以及基于单一模型运行和计算要求更高的集合运行的预测技能。还将评估模型解析的风暴属性（例如上升气流强度）在区分爆发类型中的效用。这项工作的智力价值在于提高运行和解释中尺度预报模型的能力，以可靠地预测恶劣天气的重大爆发，在几天的提前时间内辨别这些爆发的性质，并更好地了解连接大尺度和小尺度的大气过程。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业务预报员的合作与协作以及更传统的联系和研究生教育来立即交流这些进展，从而增加公众为危险天气事件做好准备的准备时间。