A simplified MEV formulation to model extremes emerging from multiple nonstationary underlying processes
A simplified MEV formulation to model extremes emerging from multiple nonstationary underlying processes
复制标题
用于模拟多个非平稳基础过程中出现的极端情况的简化 MEV 公式
DOI：
10.1016/j.advwatres.2019.04.002
复制
发表时间：
2019
影响因子：
4.7
通讯作者：
E. Morin
中科院分区：
文献类型：
--
作者：
Francesco Marra;D. Zoccatelli;Moshe Armon;E. Morin
本文提出了一种简化的元统计极值公式（SMEV），能够模拟多个潜在过程中出现的水文气象极端情况。该公式明确包括过程的平均强度和发生概率，允许对这些量的变化进行简约建模，以量化极端发生概率的变化。 SMEV 允许 (a) 对多个基础过程中出现的极端值进行频率分析，以及 (b) 对极端分位数对基础过程特征变化的敏感性进行计算有效的分析；此外，(c) 它为解释模型、非平稳频率分析和气候预测提供了一个强大的框架。该方法适用于地中海东部长期记录站的每日降水数据，使用威布尔分布对两类天气系统产生的每日降水量进行建模。 SMEV 的现场应用提供了空间上一致的极端分位数估计，与区域 GEV 估计一致，并且通常具有减少不确定性的特点。研究了极端分位数对不同天气类别产生的事件的强度和每年发生率的变化和不确定性的敏感性，并提供了 SMEV 在预测未来极端事件中的应用。
This paper presents a Simplified Metastatistical Extreme Value formulation (SMEV) able to model hydro-meteorological extremes emerging from multiple underlying processes. The formulation explicitly includes the average intensity and probability of occurrence of the processes allowing to parsimoniously model changes in these quantities to quantify changes in the probability of occurrence of extremes. SMEV allows (a) frequency analyses of extremes emerging from multiple underlying processes and (b) computationally efficient analyses of the sensitivity of extreme quantiles to changes in the characteristics of the underlying processes; moreover, (c) it provides a robust framework for explanatory models, nonstationary frequency analyses, and climate projections.The methodology is applied to daily precipitation data from long recording stations in the eastern Mediterranean, using Weibull distributions to model daily precipitation amounts generated by two classes of synoptic systems. At-site application of SMEV provides spatially consistent estimates of extreme quantiles, in line with regional GEV estimates and generally characterized by reduced uncertainties. The sensitivity of extreme quantiles to changes and uncertainty in the intensity and yearly occurrences of events generated by different synoptic classes is examined, and an application of SMEV for the projection of future extremes is provided.
用于了解美国每小时降水极端事件尾部气候学的诊断框架
影响因子：
5.4
作者：
Papalexiou, Simon Michael;AghaKouchak, Amir;Foufoula-Georgiou, Efi
通讯作者：
Foufoula-Georgiou, Efi