姚博, 全涌, 顾明, 聂铭. 混合气候地区极值风速分析方法研究[J]. 工程力学, 2018, 35(5): 86-92. DOI: 10.6052/j.issn.1000-4750.2016.12.1000
引用本文: 姚博, 全涌, 顾明, 聂铭. 混合气候地区极值风速分析方法研究[J]. 工程力学, 2018, 35(5): 86-92. DOI: 10.6052/j.issn.1000-4750.2016.12.1000
YAO Bo, QUAN Yong, GU Ming, NIE Ming. STUDY ON THE ANALYSIS METHOD OF EXTREME WIND SPEED IN MIXED CLIMATE AREAS[J]. Engineering Mechanics, 2018, 35(5): 86-92. DOI: 10.6052/j.issn.1000-4750.2016.12.1000
Citation: YAO Bo, QUAN Yong, GU Ming, NIE Ming. STUDY ON THE ANALYSIS METHOD OF EXTREME WIND SPEED IN MIXED CLIMATE AREAS[J]. Engineering Mechanics, 2018, 35(5): 86-92. DOI: 10.6052/j.issn.1000-4750.2016.12.1000

混合气候地区极值风速分析方法研究

STUDY ON THE ANALYSIS METHOD OF EXTREME WIND SPEED IN MIXED CLIMATE AREAS

  • 摘要: 在混合风气候地区,不同类型风气候(如台风、良态风)的极值风速概率分布特性各不相同,如果直接采用单一概率分布模型对混合极值风速进行拟合,往往会造成较大的分析误差。目前常采用将台风和良态风数据分离后分别分析的方法进行极值风速分析,但这种方法在分析良态风时存在难以将台风记录从气象部门长期观测数据中分离出来的问题,在台风蒙特卡罗模拟研究中存在台风参数概率分布信息收集困难的问题。该文基于混合函数构建混合风气候地区极值风速的概率密度函数,采用加权最小二乘法拟合分布函数和权重函数的参数,并对各参数进行优化得到更为精确的计算结果。最后采用蒙特卡罗模拟结果和长期观测数据验证了该文方法的准确性和实用性。

     

    Abstract: The probability distribution of extreme wind speed for different wind climate, such as typhoon and normal wind, are different from each other in the region of mixed wind climate. Large analytical error/deviation would be generated if only one probability distribution model is used to fit the extreme wind speed of mixed wind climate. At present, in the analysis of extreme wind speed for mixed climate, the method to separate the wind data into typhoon and normal wind and analyze them individually is commonly applied. However, when using this method, it is difficult to distinguish the typhoon records from the long-term observation wind data of meteorological department. Moreover, it is hard work to collect the probability distribution information of the key parameters of typhoon for the Monte Carlo simulation. In present study, a probability density function of extreme wind speed for mixed wind climate is proposed based on a mixed function. The parameters of the distribution function and the weight function are fitted through the weighted least square method. Then optimization has been made for these parameters to obtain more accurate simulation results. Finally, the simulation results from Monte Carlo method, the results of the proposed method and the long-term observation data are compared to verify the accuracy and practicability of the proposed method.

     

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