A Climatology of Quasi-Linear Convective Systems and Their Hazards in the United States
A Climatology of Quasi-Linear Convective Systems and Their Hazards in the United States
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DOI：
10.1175/waf-d-19-0014.1
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发表时间：
2019-12-01
影响因子：
2.9
通讯作者：
Strohm, Jacob
中科院分区：
文献类型：
--
作者：
Ashley, Walker S.;Haberlie, Alex M.;Strohm, Jacob
本研究使用雷达反射率马赛克上的图像分类和机器学习方法对美国22年的准线性对流系统（QLCSs）进行分割、分类和跟踪。算法使用雷达衍生的空间和强度信息进行训练和验证，这些信息来自数千个手动标记的QLCS和非QLCS事件切片。然后，该算法被用于自动识别和跟踪超过3000个qlcs，具有很高的准确性，提供了qlcs的第一个，系统的，长期的气候学。将程序确定的对流区域用作强雷暴观测报告的时空滤波焦点；这样就可以估计由于这种形态造成的严重风暴灾害的数量。结果显示，近32%的MCSs被归类为qlcs。平均每年发生139次qlcs，其中大多数事件集中在4月至8月的大平原东部和密西西比河中下游和俄亥俄河谷。qlcs对严重灾害报告的时空变化比例负责，在俄亥俄州西部和密西西比河流域中部，qlcs报告的归因最大（30%-42%）。超过21%的龙卷风、28%的强风和10%的严重冰雹报告都是由美国中部和东部的qlcs造成的。与qlcs相关的龙卷风和强风报告的比例在夜间和凉爽季节最大，在某些地区超过50%的龙卷风和大风报告是由qlcs引起的。这项研究说明了自动风暴模式分类系统在产生广泛、系统的现象气候学方面的效用，减少了研究者手动分配形态分类的耗时和时空限制方法的需要。
This research uses image classification and machine learning methods on radar reflectivity mosaics to segment, classify, and track quasi-linear convective systems (QLCSs) in the United States for a 22-yr period. An algorithm is trained and validated using radar-derived spatial and intensity information from thousands of manually labeled QLCS and non-QLCS event slices. The algorithm is then used to automate the identification and tracking of over 3000 QLCSs with high accuracy, affording the first, systematic, long-term climatology of QLCSs. Convective regions determined by the procedure to be QLCSs are used as foci for spatiotemporal filtering of observed severe thunderstorm reports; this permits an estimation of the number of severe storm hazards due to this morphology. Results reveal that nearly 32% of MCSs are classified as QLCSs. On average, 139 QLCSs occur annually, with most of these events clustered from April through August in the eastern Great Plains and central/lower Mississippi and Ohio River Valleys. QLCSs are responsible for a spatiotemporally variable proportion of severe hazard reports, with a maximum in QLCS-report attribution (30%-42%) in the western Ohio and central Mississippi River Valleys. Over 21% of tornadoes, 28% of severe winds, and 10% of severe hail reports are due to QLCSs across the central and eastern United States. The proportion of QLCS-affiliated tornado and severe wind reports maximize during the overnight and cool season, with more than 50% of tornadoes and wind reports in some locations due to QLCSs. This research illustrates the utility of automated storm-mode classification systems in generating extensive, systematic climatologies of phenomena, reducing the need for time-consuming and spatiotemporal-limiting methods where investigators manually assign morphological classifications.