Gauging mixed climate extreme value distributions in tropical cyclone regions.
Gauging mixed climate extreme value distributions in tropical cyclone regions.
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DOI：
10.1038/s41598-022-08382-y
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发表时间：
2022-03-17
影响因子：
4.6
通讯作者：
McInnes KL
中科院分区：
文献类型：
--
作者：
O'Grady JG;Stephenson AG;McInnes KL
在热带气旋（TC）地区，潮汐计或数值后降记录的长度通常不够，无法对足够的气旋进行采样，从而对由TC引起的极端水位事件的气候进行可靠的估计。合成生成的TC总体提供了一种方法来定义更广泛的似是而非的TC事件集，以更好地定义与极端水位事件相关的概率。挑战在于如何将人工生成的TC种群对极端值的估计与观测记录统一起来，这些记录主要包括由潮汐和更频繁发生的大气低气压天气和气候事件引起的非TC极端值。我们发现，在TC地区的多个验潮仪记录中，极端水位测量值（有些超过100年）表现出与两个种群（TC和非TC）组合一致的行为。我们建立了一个方程来模拟单一连续混合气候（MC）极值分布（EVD）中两个极端群体的组合。然后，我们进行了统计模拟，以表明使用MC比使用重尾广义evd可以更好地解释包括历史和合成事件在内的长期记录。这对将合成气旋极端海平面与后降水位结合起来估算极端水位具有重要意义，从而为海岸保护提供可操作的信息。
In tropical cyclone (TC) regions, tide gauge or numerical hindcast records are usually of insufficient length to have sampled sufficient cyclones to enable robust estimates of the climate of TC-induced extreme water level events. Synthetically-generated TC populations provide a means to define a broader set of plausible TC events to better define the probabilities associated with extreme water level events. The challenge is to unify the estimates of extremes from synthetically-generated TC populations with the observed records, which include mainly non-TC extremes resulting from tides and more frequently occurring atmospheric-depression weather and climate events. We find that extreme water level measurements in multiple tide gauge records in TC regions, some which span more than 100 years, exhibit a behaviour consistent with the combining of two populations, TC and non-TC. We develop an equation to model the combination of two populations of extremes in a single continuous mixed climate (MC) extreme value distribution (EVD). We then run statistical simulations to show that long term records including both historical and synthetic events can be better explained using MC than heavy-tailed generalised EVDs. This has implications for estimating extreme water levels when combining synthetic cyclone extreme sea levels with hindcast water levels to provide actionable information for coastal protection.
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影响因子：
9.8
作者：
Bloemendaal, Nadia;Haigh, Ivan D.;Aerts, Jeroen C. J. H.
通讯作者：
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影响因子：
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作者：
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通讯作者：
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DOI：
10.1073/pnas.1913049117
发表时间：
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影响因子：
11.1
作者：
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影响因子：
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作者：
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通讯作者：
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影响因子：
4.6
作者：
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通讯作者：
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