【摘要】 随着全球气候变暖，近年来极端降水事件及其引发的洪涝灾害频发，极端降水事件的模拟与精细化研究显得尤为重要。随着区域气象站网的加密建设，为极端降水事件的精细化研究提供可能。为了将区域站短序列数据应用到日极端降水量的研究中，本研究首先基于年最大值法(annual maximum, AM)和超阈值峰值法(peak over threshold, POT)抽样方法与44种概率分布模型，选择最优抽样方法与概率分布模型，并在此基础上提出对于短序列数据计算日极端降水量的订正方案，通过国家站分析论证，优选出最佳订正方案，将该订正方法应用到只有短序列实测数据的区域站中，优选插值参数并比较不同空间插值方法对插值精度的影响，选择最优的插值方法实现日极端降水量的精细化研究。结果表明，POT1抽样方法与广义帕累托模型是最适用于计算河北省日极端降水量的抽样方法与模型；本研究提出的区域站订正与计算日极端降水量方法可行，将区域站考虑进来后与国家站联合插值使得在空间上更加精细。更多还原

【Abstract】 With global warming, extreme precipitation events and flood disasters have become frequent in recent years, so fine research of extreme precipitation events are particularly important. With the construction of regional weather station, it is possible to study extreme precipitation events in detail. In order to apply short series data to the study of daily extreme precipitation, this study firstly select the optimal sampling method and probability distribution model based on AM(annual maximum) and POT1(peak over threshold) sampling methods and 44 probability distribution models. On this basis, an adjusting method for calculating daily extreme precipitation with short series data is proposed. The best adjusting method is chosen through the analysis and demonstration of national stations, and the best adjusting method is applied to regional stations with only short series measured data. The interpolation parameters are optimized and the effects of different spatial interpolation methods on interpolation accuracy are compared. The best interpolation method is selected to realize the fine study of daily extreme precipitation. The results show that POT1 sampling method is the most suitable sampling method and Gen. Pareto model are the most suitable model for calculating daily extreme precipitation in Hebei Province. The regional stations adjust method and daily extreme precipitation calculation method proposed in this study is feasible, and the regional stations are taken into account and combined with national interpolation to make the spatial distribution more precise.更多还原