Objective Classification of Tornadic and Nontornadic Severe Weather Outbreaks
Objective Classification of Tornadic and Nontornadic Severe Weather Outbreaks
复制标题
龙卷风和非龙卷风灾害性天气爆发的客观分类
DOI：
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发表时间：
2009
期刊：
影响因子：
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通讯作者：
M. B. Richman
中科院分区：
文献类型：
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作者：
A. Mercer;C. Shafer;C. Doswell;L. Leslie;M. B. Richman
龙卷风经常作为孤立事件发生，但许多龙卷风是龙卷风大规模爆发的一部分。强对流风暴的非龙卷风爆发在美国更为常见，但与龙卷风爆发相关的威胁不同。这项工作的主要目标是通过对用天气尺度数据初始化的数值天气预报输出使用统计建模技术，客观地区分这些爆发类型的重要实例。天气尺度结构包含可用于通过统计方法区分两种类型的恶劣天气爆发的信息。天气研究和预报模型 (WRF) 使用天气尺度输入数据（NCEP-NCAR 再分析数据集）对一组 50 次重大龙卷风爆发和 50 次非龙卷风恶劣天气爆发进行初始化。目标分类中使用 18 公里网格间距的 WRF 输出。模型预测的爆发前后的个别恶劣天气参数是根据爆发前 24、48 和 72 小时初始化的模拟进行分析的。预计与严重风暴相关的 15 个变量的初始候选集被减少到 6 或 7 个变量集，具体取决于交付时间，通过排列测试拥有最大的分类能力。这些变量作为支持向量机和逻辑回归这两种统计方法的输入，以对爆发类型进行分类。每种技术都是根据意外统计的引导置信极限进行评估的。对减少的变量集进行额外的向后选择，以确定哪种变量组合提供最佳的意外事件统计数据。鉴别能力验证的应急统计结果在24小时时最好； 48 小时后，出现适度的降解。到 72 小时，应急统计数据下降高达 15%。总体而言，结果令人鼓舞，在 24 小时交付时间内，检测概率值通常超过 0.8，Heidke 技能得分超过 0.7。
Tornadoes often strike as isolated events, but many occur as part of a major outbreak of tornadoes. Nontornadic outbreaks of severe convective storms are more common across the United States but pose differentthreats thando thoseassociated with atornadooutbreak. Themaingoalof this workisto distinguish between significant instances of these outbreak types objectively by using statistical modeling techniques on numerical weather prediction output initialized with synoptic-scale data. The synoptic-scale structure contains information that can be utilized to discriminate between the two types of severe weather outbreaks through statistical methods. The Weather Research and Forecast model (WRF) is initialized with synopticscale input data (the NCEP‐NCAR reanalysis dataset) on a set of 50 significant tornado outbreaks and 50 nontornadic severe weather outbreaks. Output from the WRF at 18-km grid spacing is used in the objective classification. Individual severe weather parameters forecast by the model near the time of the outbreak are analyzed from simulations initialized at 24, 48, and 72 h prior to the outbreak. An initial candidate set of 15 variables expected to be related to severe storms is reduced to a set of 6 or 7, depending on lead time, that possess the greatest classification capability through permutation testing. These variables serve as inputs into two statistical methods, support vector machines and logistic regression, to classify outbreak type. Each technique is assessed based on bootstrap confidence limits of contingency statistics. An additional backward selection of the reduced variable set is conducted to determine which variable combination provides the optimal contingency statistics. Results for the contingency statistics regarding the verification of discrimination capability are best at 24 h; at 48 h, modest degradation is present. By 72 h, the contingency statistics decline by up to 15%. Overall, results are encouraging, with probability of detection values often exceeding 0.8 and Heidke skill scores in excess of 0.7 at 24-h lead time.