关于极端日降水量长周期回归值的不确定性

IF 3.3 Q2 ENVIRONMENTAL SCIENCES
Michael F. Wehner, Margaret L. Duffy, M. Risser, C. J. Paciorek, Dáithí A. Stone, P. Pall
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引用次数: 0

摘要

利用大型气候模式模拟集合中美国西部和加拿大西南部的日降水率,比较了计算极端降水回归值及其不确定性的方法。针对不同的回归期,对回归值估算程序和样本大小在不确定性中的作用进行了评估。我们比较了两种不同的广义极值(GEV)参数估计技术,即 L-moments 和最大似然法(MLE),以及经验技术。即使对于非常大的数据集,使用 GEV 技术计算出的置信区间也比使用经验方法计算出的置信区间要窄。此外,更有效的 L-moments 参数估计技术在小样本量时的置信区间也比 MLE 参数估计技术窄,但最佳估计值却相似。需要注意的是,我们并没有说任何一种参数拟合技术都比另一种技术更适合估计长期回报值。虽然非稳态 MLE 方法可用于估算 GEV 参数,但 L-moments 方法却不适用。对不确定性量化方法进行比较后发现,在样本量较小的情况下,估算结果会有显著差异,但随着样本量的增加,结果会趋于相似。最后,就气候模式集合模拟的长度和规模以及统计方法的选择提出了实用建议,以稳健地估算极端日降水量统计的长期回归值并量化其不确定性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
On the uncertainty of long-period return values of extreme daily precipitation
Methods for calculating return values of extreme precipitation and their uncertainty are compared using daily precipitation rates over the Western U.S. and Southwestern Canada from a large ensemble of climate model simulations. The roles of return-value estimation procedures and sample size in uncertainty are evaluated for various return periods. We compare two different generalized extreme value (GEV) parameter estimation techniques, namely L-moments and maximum likelihood (MLE), as well as empirical techniques. Even for very large datasets, confidence intervals calculated using GEV techniques are narrower than those calculated using empirical methods. Furthermore, the more efficient L-moments parameter estimation techniques result in narrower confidence intervals than MLE parameter estimation techniques at small sample sizes, but similar best estimates. It should be noted that we do not claim that either parameter fitting technique is better calibrated than the other to estimate long period return values. While a non-stationary MLE methodology is readily available to estimate GEV parameters, it is not for the L-moments method. Comparison of uncertainty quantification methods are found to yield significantly different estimates for small sample sizes but converge to similar results as sample size increases. Finally, practical recommendations about the length and size of climate model ensemble simulations and the choice of statistical methods to robustly estimate long period return values of extreme daily precipitation statistics and quantify their uncertainty.
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来源期刊
Frontiers in Climate
Frontiers in Climate Environmental Science-Environmental Science (miscellaneous)
CiteScore
4.50
自引率
0.00%
发文量
233
审稿时长
15 weeks
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