A Tale of Two Hazards: Studying Broadcast Meteorologist Communication of Simultaneous Tornado and Flash Flood (TORFF) Events

IF 0.8 Q4 METEOROLOGY & ATMOSPHERIC SCIENCES
Sean R. Ernst, J. Ripberger, Julie Krutz, Carol L. Silva, H. Jenkins‐Smith, Anna Wanless, David Nowicki, Kimberly E. Klockow-McClain, Kodi L. Berry, Holly B. Obermeier, Makenzie J. Krocak
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Abstract

Broadcast meteorologists are the primary source of weather information for the public, and thus are key to messaging the multiple weather hazards that can occur during simultaneous tornado and flash flood, or TORFF, events. Due in part to the challenge and cost needed to study broadcast coverage, there has been limited study into how broadcasters present these hazards to their viewers during TORFF events. To begin to address this knowledge gap, we developed the Coding Algorithm for Storm coverage Transcripts, or CAST. Bot, a simple algorithm that can efficiently and inexpensively compare the mentions of tornado and flash flood hazards made by meteorologists during on-air coverage. For this study, we used CAST.Bot to quickly analyze 39 segments of coverage from eight TORFF events. Findings suggest that broadcasters generally favor mentions of tornadoes more than flash flooding during TORFF events with many tornado warnings, with more balanced coverage identified during events with similar numbers of tornado and flash flood warnings. Additional study of two cases, 1) the El Reno/Oklahoma City, Oklahoma, tornado and flash flood on 31 May 2013, and 2) Hurricane Harvey in Houston, Texas, on 26 August 2017, suggests that TORFF event coverage on television is subject to differences across stations and the way that the tornado and flash flood hazards in a TORFF unfold. Future work should seek to better understand how changes in the focus of messaging for TORFF events can impact viewers decisions and identify how context can influence TORFF message content. Options for use of the CAST.Bot algorithm to aid broadcasters during multi-hazard event coverage are also discussed.
两种灾害的故事:研究广播气象学家对同时发生的龙卷风和山洪(TORFF)事件的传播情况
广播气象学家是公众获取天气信息的主要来源,因此,他们是在龙卷风和山洪暴发(或称 TORFF)同时发生时传播多种天气危害信息的关键。部分由于研究广播报道所面临的挑战和所需的成本,对广播公司如何在 TORFF 事件期间向观众展示这些危害的研究一直很有限。为了填补这一知识空白,我们开发了风暴覆盖转录编码算法(CAST.它是一种简单的算法,可以高效、低成本地比较气象学家在广播报道中提到的龙卷风和山洪灾害。在这项研究中,我们使用 CAST.Bot 快速分析了八个 TORFF 事件的 39 个报道片段。研究结果表明,在龙卷风警报较多的 TORFF 事件中,广播人员一般更倾向于提及龙卷风,而不是山洪暴发,在龙卷风和山洪暴发警报数量相近的事件中,我们发现了更均衡的报道。对两个案例(1)2013 年 5 月 31 日俄克拉荷马州埃尔雷诺/俄克拉荷马城龙卷风和山洪暴发,以及 2)2017 年 8 月 26 日德克萨斯州休斯顿哈维飓风)的额外研究表明,电视上对 TORFF 事件的报道受制于不同电视台以及 TORFF 中龙卷风和山洪暴发方式的差异。未来的工作应力求更好地了解 TORFF 事件信息重点的变化如何影响观众的决定,并确定背景如何影响 TORFF 信息内容。此外,还讨论了使用 CAST.Bot 算法帮助广播公司报道多种灾害事件的方案。
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来源期刊
Journal of Operational Meteorology
Journal of Operational Meteorology METEOROLOGY & ATMOSPHERIC SCIENCES-
CiteScore
2.40
自引率
0.00%
发文量
4
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