Development and Investigation of GridRad-Severe, a Multiyear Severe Event Radar Dataset
Development and Investigation of GridRad-Severe, a Multiyear Severe Event Radar Dataset
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多年严重事件雷达数据集 GridRad-Severe 的开发和研究
DOI：
10.1175/mwr-d-23-0017.1
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发表时间：
2023
影响因子：
3.2
通讯作者：
Allen, Kiley Q.
中科院分区：
文献类型：
--
作者：
Murphy, Amanda M.;Homeyer, Cameron R.;Allen, Kiley Q.
许多研究旨在结合地面雷达观测和恶劣报告来识别新的风暴特征，这些特征表明当前或未来潜在的恶劣天气。然而，由于这个过程非常耗时，这通常是使用有限的案例研究（大约数十到数百个风暴）小规模完成的。在此，我们介绍了 GridRad-Severe 数据集，该数据库包括每年约 100 个恶劣天气日和 2010 年至 2019 年客观跟踪的超过 130 万个风暴。使用 GridRad 合成技术为每个选定的日期创建跨越客观确定的、以报告为中心的域的复合雷达量，并使用定义的报告阈值客观确定日期，以捕获每年最严重的恶劣天气日，均匀分布在所有严重天气中报告类型（龙卷风、强冰雹和强风）。每个事件的时空域范围是客观确定的，以涵盖大多数报告和对流启动时间。恶劣天气报告与使用雷达数据客观跟踪的风暴相匹配，因此可以评估风暴单元的演变及其恶劣天气的产生。在这里，我们将风暴模式（单细胞、多细胞或中尺度对流系统风暴）和右移超级细胞分类技术应用于数据集，并重新审视过去工作中提出和评估的有关强风暴及其总体特征的各种问题。对该数据集的其他应用进行了审查，以供未来可能的研究使用。
Many studies have aimed to identify novel storm characteristics that are indicative of current or future severe weather potential using a combination of ground-based radar observations and severe reports. However, this is often done on a small scale using limited case studies on the order of tens to hundreds of storms due to how time-intensive this process is. Herein, we introduce the GridRad-Severe dataset, a database including ∼100 severe weather days per year and upward of 1.3 million objectively tracked storms from 2010 to 2019. Composite radar volumes spanning objectively determined, report-centered domains are created for each selected day using the GridRad compositing technique, with dates objectively determined using report thresholds defined to capture the highest-end severe weather days from each year, evenly distributed across all severe report types (tornadoes, severe hail, and severe wind). Spatiotemporal domain bounds for each event are objectively determined to encompass both the majority of reports and the time of convection initiation. Severe weather reports are matched to storms that are objectively tracked using the radar data, so the evolution of the storm cells and their severe weather production can be evaluated. Herein, we apply storm mode (single-cell, multicell, or mesoscale convective system storms) and right-moving supercell classification techniques to the dataset, and revisit various questions about severe storms and their bulk characteristics posed and evaluated in past work. Additional applications of this dataset are reviewed for possible future studies.