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

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.