2023 年 8 月毛伊岛野火的气象学特征

IF 3 3区 地球科学 Q2 METEOROLOGY & ATMOSPHERIC SCIENCES
Clifford F. Mass, David Ovens
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引用次数: 0

摘要

2023 年 8 月 8 日,一场由风引起的野火席卷了夏威夷西茂宜岛的拉海纳市,造成至少 100 人死亡,估计经济损失达 40-6 亿美元。拉海纳野火与强劲、干燥的下坡风有关,阵风高达 31-41 毫秒-1(60-80 千吨),大风破坏了电力基础设施,引发了火灾。大火在拉海纳上坡废弃农田中生长的入侵草丛中迅速蔓延。本文介绍了与此次事件相关的同步和中尺度气象学及其可预测性。强于正常水平的东北信风,伴随着西茂宜山脉峰顶附近的稳定层,导致了高振幅的山波响应和强烈的下坡暴风。中尺度模型对强风的位置、强度和时间的预测非常准确。飓风多拉在茂宜岛以南约 1300 公里处经过,似乎对野火事件相关风的发生和强度没有产生重大影响。毛伊岛野火发生之前,冬季比正常天气湿润,夏季接近正常。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
The Meteorology of the August 2023 Maui Wildfire
On 8 August 2023, a wind-driven wildfire pushed across the city of Lahaina, located in West Maui, Hawaii, resulting in at least 100 deaths and an estimated economic loss of 4-6 billion dollars. The Lahaina wildfire was associated with strong, dry downslope winds gusting to 31-41 ms−1 (60-80 kt) that initiated the fire by damaging power infrastructure. The fire spread rapidly in invasive grasses growing in abandoned agricultural land upslope from Lahaina. This paper describes the synoptic and mesoscale meteorology associated with this event, as well as its predictability. Stronger than normal northeast trade winds, accompanied by a stable layer near the crest level of the West Maui Mountains, resulted in a high-amplitude mountain wave response and a strong downslope windstorm. Mesoscale model predictions were highly accurate regarding the location, strength, and timing of the strong winds. Hurricane Dora, which passed approximately 1300 km to the south of Maui, does not appear to have had a significant impact on the occurrence and intensity of the winds associated with the wildfire event. The Maui wildfire was preceded by a wetter-than-normal winter and near-normal summer conditions.
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来源期刊
Weather and Forecasting
Weather and Forecasting 地学-气象与大气科学
CiteScore
5.20
自引率
17.20%
发文量
131
审稿时长
6-12 weeks
期刊介绍: Weather and Forecasting (WAF) (ISSN: 0882-8156; eISSN: 1520-0434) publishes research that is relevant to operational forecasting. This includes papers on significant weather events, forecasting techniques, forecast verification, model parameterizations, data assimilation, model ensembles, statistical postprocessing techniques, the transfer of research results to the forecasting community, and the societal use and value of forecasts. The scope of WAF includes research relevant to forecast lead times ranging from short-term “nowcasts” through seasonal time scales out to approximately two years.
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