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Non-stationary statistical modeling of extreme wind speed series with exposure correction

机译:具有暴露校正的极端风速序列的非平稳统计建模

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摘要

Extreme wind speed analysis has been carried out conventionally by assuming the extreme series data is stationary. However, time-varying trends of the extreme wind speed series could be detected at many surface meteorological stations in China. Two main reasons, exposure change and climate change, were provided to explain the temporal trends of daily maximum wind speed and annual maximum wind speed series data, recorded at Hangzhou (China) meteorological station. After making a correction on wind speed series for time varying exposure, it is necessary to perform non-stationary statistical modeling on the corrected extreme wind speed data series in addition to the classical extreme value analysis. The generalized extreme value (GEV) distribution with time-dependent location and scale parameters was selected as a non-stationary model to describe the corrected extreme wind speed series. The obtained non-stationary extreme value models were then used to estimate the non-stationary extreme wind speed quantiles with various mean recurrence intervals (MRIs) considering changing climate, and compared to the corresponding stationary ones with various MRIs for the Hangzhou area in China. The results indicate that the non-stationary property or dependence of extreme wind speed data should be carefully evaluated and reflected in the determination of design wind speeds.
机译:通常,通过假设极端序列数据是固定的来进行极端风速分析。但是,在中国许多地面气象站都可以检测到极端风速序列的时变趋势。提供了两个主要原因,即暴露变化和气候变化,以解释在杭州(中国)气象站记录的每日最大风速和年度最大风速系列数据的时间趋势。对时变暴露的风速序列进行校正后,除了经典的极值分析之外,还需要对校正后的极端风速数据序列执行非平稳统计模型。选择具有随时间变化的位置和比例参数的广义极值(GEV)分布作为非平稳模型来描述校正后的极端风速序列。然后,将获得的非平稳极值模型用于考虑气候变化的各种平均重复间隔(MRI)的非平稳极风速分位数,并将其与中国杭州地区具有各种MRI的相应平稳极风分位数进行比较。结果表明,应谨慎评估极端风速数据的非平稳性或依赖性,并反映在设计风速的确定中。

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