Effect of Providing the Uncertainty Information About a Tornado Occurrence on the Weather Recipients' Cognition and Protective Action: Probabilistic Hazard Information Versus Deterministic Warnings.

RISK ANALYSIS(2019)

引用 13|浏览7
暂无评分
摘要
Currently, a binary alarm system is used in the United States to issue deterministic warning polygons in case of tornado events. To enhance the effectiveness of the weather information, a likelihood alarm system, which uses a tool called probabilistic hazard information (PHI), is being developed at National Severe Storms Laboratory to issue probabilistic information about the threat. This study aims to investigate the effects of providing the uncertainty information about a tornado occurrence through the PHI's graphical swath on laypeople's concern, fear, and protective action, as compared with providing the warning information with the deterministic polygon. The displays of color-coded swaths and deterministic polygons were shown to subjects. Some displays had a blue background denoting the probability of any tornado formation in the general area. Participants were asked to report their levels of concern, fear, and protective action at randomly chosen locations within each of seven designated levels on each display. Analysis of a three-stage nested design showed that providing the uncertainty information via the PHI would appropriately increase recipients' levels of concern, fear, and protective action in highly dangerous scenarios, with a more than 60% chance of being affected by the threat, as compared with deterministic polygons. The blue background and the color-coding type did not have a significant effect on the people's cognition of the threat and reaction to it. This study shows that using a likelihood alarm system leads to more conscious decision making by the weather information recipients and enhances the system safety.
更多
查看译文
关键词
Likelihood alarm systems,probabilistic hazard information,probability matching,risk communication,WarnGen
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要