Creating extreme weather time series through a quantile regression ensemble
Creating extreme weather time series through a quantile regression ensemble
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DOI：
10.1016/j.envsoft.2018.03.007
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发表时间：
2018-12
期刊：
影响因子：
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通讯作者：
M. Herrera;A. Ramallo-González;M. Eames;A. Ferreira;D. Coley
中科院分区：
文献类型：
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作者：
M. Herrera;A. Ramallo-González;M. Eames;A. Ferreira;D. Coley
热浪导致的死亡率比其他与天气有关的自然灾害高出一个数量级。不幸的是，由于气候变化，预计全球范围内热浪的严重程度和幅度都将增加。因此，气象服务越来越需要识别这样的时段，以设置警报，而研究人员和行业需要具有代表性的未来热浪来研究风险。本文介绍了一种新的针对特定地点的死亡风险的热波定义，以及一个新的数学框架，用于创建代表热波的时间序列。它的重点是确定白天和夜间温度较高的时期，因为这种巧合与死亡率密切相关。该方法使用来自巴西和英国的观测数据进行了测试。与以前方法的比较表明，这种新方法代表着一项重大进步，可以在全球范围内被政府、研究人员和工业界采用。
Heat waves give rise to order of magnitude higher mortality rates than other weather-related natural disasters. Unfortunately both the severity and amplitude of heat waves are predicted to increase worldwide as a consequence of climate change. Hence, meteorological services have a growing need to identify such periods in order to set alerts, whilst researchers and industry need representative future heat waves to study risk. This paper introduces a new location-specific mortality risk focused definition of heat waves and a new mathematical framework for the creation of time series that represents them. It focuses on identifying periods when temperatures are high during the day and night, as this coincidence is strongly linked to mortality. The approach is tested using observed data from Brazil and the UK. Comparisons with previous methods demonstrate that this new approach represents a major advance that can be adopted worldwide by governments, researchers and industry.