严重对流天气警报的地理空间验证方法:对当前和未来预警方法的影响

Gregory J. Stumpf, Sarah M. Stough
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引用次数: 0

摘要

国家气象局的传统验证技术在应用于当前的静态强对流预警时表现出局限性,特别是在考虑预警和强对流事件的精确空间和时间方面。因此,这些技术并不特别适合应用于 "环境威胁连续预报(FACETs)"计划中考虑的一些国家气象局未来预警发布方法。这些方法包括 "移动威胁"(TIM)和 "概率危害信息"(PHI)。"移动威胁 "和 "概率危害信息 "的概念是通过快速更新的概率羽流来增强预警。我们开发并评估了一种新的地理空间验证方法,通过这种方法,预警和观测结果被置于共同参考框架内的等效网格上,每个网格单元在每分钟内被表示为命中、未命中、误报或正确空。我们利用 2011 年 4 月 27 日的龙卷风事件,应用 TIM 和 PHI 预警技术,展示了快速更新预警区域的好处,展示了地理空间验证方法在这种新型预警框架中的应用,并强调了不同概率预警阈值对预警性能的影响。此外,还对基于风暴的预警数据集(2008-2022 年)进行了地理空间验证方法测试,以得出年度、月度和小时统计数据。
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A Geospatial Verification Method for Severe Convective Weather Warnings: Implications for Current and Future Warning Methods
Legacy National Weather Service verification techniques, when applied to current static severe convective warnings, exhibit limitations, particularly in accounting for the precise spatial and temporal aspects of warnings and severe convective events. Consequently, they are not particularly well-suited for application to some proposed future National Weather Service warning delivery methods considered under the Forecasting a Continuum of Environmental Threats (FACETs) initiative. These methods include Threats-In-Motion (TIM), wherein warning polygons move nearly continuously with convective hazards, and Probabilistic Hazard Information (PHI), a concept that involves augmenting warnings with rapidly updating probabilistic plumes. A new geospatial verification method was developed and evaluated, by which warnings and observations are placed on equivalent grids within a common reference frame, with each grid cell being represented as a hit, miss, false alarm, or correct null for each minute. New measures are computed, including false alarm area, and location-specific lead time, departure time, and false alarm time. Using the 27 April 2011 tornado event, we applied the TIM and PHI warning techniques to demonstrate the benefits of rapidly updating warning areas, showcase the application of the geospatial verification method within this novel warning framework, and highlight the impact of varying probabilistic warning thresholds on warning performance. Additionally, the geospatial verification method was tested on a storm-based warning dataset (2008-2022) to derive annual, monthly, and hourly statistics.
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