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1979-2017热浪和高温事件的大规模数据记录发表

 2018/11/7 8:55:03 《最新论文》 作者:Scientific Data 我有话说(0人评论) 字体大小:+

论文标题:GHWR, a multi-method global heatwave and warm-spell record and toolbox

期刊:Scientific Data

作者:Ehsan Raei, Mohammad Reza Nikoo, Amir AghaKouchak, Omid Mazdiyasni, Mojtaba Sadegh

发表时间:2018/10/30

数字识别码: 10.1038/sdata.2018.206

原文链接:http://t.cn/EwQP93u

微信链接:https://mp.weixin.qq.com/s/dwocZRNPuFOsxPkg7Xtxbg

《科学数据》论文GHWR, a multimethod global heatwave and warm-spell record and toolbox近日在线发表了“全球热浪和高温记录”(Global Heatwave and Warm-spell Record,GHWR),这一数据库对当前和既往热浪的研究提供了新见解。GHWR数据库向研究人员开放,将促进对热浪以及热浪对人类和环境影响的研究。

图1:不同热浪定义下的1995年7月美国的热浪天数。图源:Raei等

热浪一般是指长时间的持续高温和过度炎热。虽然全球变暖可能会增加热浪频次和严重程度,但目前缺乏对热浪形成的统一定义。这不仅阻碍了研究发展,也让比较过去和现在热浪的难度更大。热浪被称为大自然的“无声杀手”,在某些地区是最致命的自然灾害之一,会导致基础设施受损,作物产量下降,并给卫生系统造成压力。

美国博伊西州立大学的Mojtaba Sadegh及同事报告了1979年至2017年期间发生的热浪和高温事件的大规模数据记录,包括强度、持续时间和频次方面的信息。作者收集了美国国家海洋和大气管理局(NOAA)地球系统研究实验室的数据,并建立了一套用来识别热浪和高温的标准化指标。加入高温数据的重要性在于高温事件也能产生和热浪一样的重大影响,但对其的研究却很少。研究人员可以利用GHWR比较不同地区或时期的数据,并对各种不同热浪定义进行比较。

图2:2015年6月20日的全球温度和热浪情况。图源:Raei等

作者认为,GHWR提供了有价值的基线资源,或有助于增进对未来热浪的理解和预测,并能通过预警和有效干预帮助受到影响的人群。

摘要:Heatwaves are extended periods of unusually high temperatures with significant societal and environmental impacts. Despite their significance, there is not a generalized definition for heatwaves. In this paper, we introduce a multi-method global heatwave and warm-spell data record and analysis toolbox (named GHWR). In addition to a comprehensive long-term global data record of heatwaves, GHWR allows processing and extracting heatwave records for any location efficiently. We use traditional constant temperature threshold methods, as well as spatially and temporally localized threshold approaches to identify heatwaves. GHWR includes binary (0/1) occurrence records of heatwaves/warm-spells, and annual summary files with detailed information on their frequency, duration, magnitude and amplitude. GHWR also introduces the standardized heat index (SHI) as a generalized statistical metric to identify heatwave/warm-spells. SHI has direct association with the probability distribution function of long-term daily temperatures for any given calendar day and spatial grid. Finally, GHWR offers a unique opportunity for users to select the type of heatwave/warm-spell information from a plethora of methods based on their needs and applications.

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来源:Scientific Data