热带气旋
气候学
风暴
极值理论
震级(天文学)
重现期
环境科学
比例(比率)
降水
气象学
公制(单位)
极端天气
地理
统计
数学
地图学
气候变化
大洪水
地质学
海洋学
考古
经济
物理
天文
运营管理
作者
C. Bosma,Daniel B. Wright,Phu Nguyen,James P. Kossin,Derrick Herndon,J. Marshall Shepherd
标识
DOI:10.1175/bams-d-19-0075.1
摘要
Abstract Recent tropical cyclones (TCs) have highlighted the hazards that TC rainfall poses to human life and property. These hazards are not adequately conveyed by the commonly used Saffir–Simpson scale. Additionally, while recurrence intervals (or, their inverse, annual exceedance probabilities) are sometimes used in the popular media to convey the magnitude and likelihood of extreme rainfall and floods, these concepts are often misunderstood by the public and have important statistical limitations. We introduce an alternative metric—the extreme rain multiplier (ERM), which expresses TC rainfall as a multiple of the climatologically derived 2-yr rainfall value. ERM allows individuals to connect (“anchor,” in cognitive psychology terms) the magnitude of a TC rainfall event to the magnitude of rain events that are more typically experienced in their area. A retrospective analysis of ERM values for TCs from 1948 to 2017 demonstrates the utility of the metric as a hazard quantification and communication tool. Hurricane Harvey (2017) had the highest ERM value during this period, underlining the storm’s extreme nature. ERM correctly identifies damaging historical TC rainfall events that would have been classified as “weak” using wind-based metrics. The analysis also reveals that the distribution of ERM maxima is similar throughout the eastern and southern United States, allowing for both the accurate identification of locally extreme rainfall events and the development of regional-scale (rather than local-scale) recurrence interval estimates for extreme TC rainfall. Last, an analysis of precipitation forecast data for Hurricane Florence (2018) demonstrates ERM’s ability to characterize Florence’s extreme rainfall hazard in the days preceding landfall.
科研通智能强力驱动
Strongly Powered by AbleSci AI