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Assessment of extreme wind speeds from regional climate models - Part 1: Estimation of return values and their evaluation

机译：通过区域气候模型评估极端风速-第1部分：返回值的估算及其评估

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摘要

Frequency and intensity of gust wind speeds associated with severe mid-latitude winter storms are estimated by applying extreme value statistics to data sets from regional climate models (RCM). Maximum wind speeds related to probability are calculated with the classical peaks over threshold method, where a statistical distribution function is fitted to the reduced sample describing the tail of the distribution function. From different sensitivity studies it is found that the Generalized Pareto Distribution in combination with a Maximum-Likelihood estimator provide the most reliable and robust results. For a reference period from 1971 to 2000, the ability of the RCMs to realistically simulate extreme wind speeds is investigated. For this purpose, data from three RCM scenarios, including the REMO-UBA simulations at 10 km resolution and the so-called consortial runs performed with the CCLM at 18 km resolution (two runs), are evaluated with observations and a pre-existing storm hazard map for Germany. It is found that all RCMs tend to underestimate the magnitude of the gusts in a range between 10 and 30% for a 10-year return period. Averaged over the investigation area, the underestimation is higher for CCLM compared to REMO. The spatial distribution of the gusts, on the other hand, is well reproduced, in particular by REMO.
机译：通过将极端值统计应用于区域气候模型（RCM）的数据集，可以估计与严重的中纬度冬季风暴相关的阵风的频率和强度。与概率有关的最大风速是通过经典的阈值峰值法计算的，其中将统计分布函数拟合到描述分布函数尾部的简化样本。从不同的敏感性研究中可以发现，广义帕累托分布与最大似然估计器的组合提供了最可靠，最可靠的结果。 在1971年至2000年的参考期间，研究了RCM实际模拟极端风速的能力。为此，通过观测和预先存在的暴风雨来评估来自三种RCM场景的数据，包括10 km分辨率的REMO-UBA模拟和CCLM以18 km分辨率进行的所谓的联合运行（两次运行）。德国的危险地图。结果发现，在十年的回归期内，所有RCM都低估了阵风的幅度在10％到30％之间。在调查区域中平均而言，与REMO相比，CCLM的低估率更高。另一方面，阵风的空间分布被很好地再现，特别是通过REMO。

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