摘要
在模拟逐日降水方面,近些年来的一种通用方法是耦合应用马尔科夫链和伽玛分布函数建立随机模拟模型,其中马尔科夫链用于描述降水日的发生,而伽玛分布函数则用于产生降水日的降水量,这种方法在国外很大的环境范围内被证明是有效的。应用哈尔滨48a实测的逐日降水资料,验证了国外的相关经验公式在哈尔滨地区并不完全适用,而基于实测降水资料确定转移概率和伽玛分布参数,继而建立的随机模型的模拟精度很高,为模拟寒区的逐日降水过程提供了一种简便方法。
In modeling and simulation for daily rainfall, a combination of Markov chain and gamma to distribution function has been recognized as a popular method and demonstrated to be effective in generating daily rainfall data for large range of environment abroad in recent years. In this method, Markov chain is to describe the occurrence of daily rainfall, and gamma distribution function is to fit the amount of rainfall for a rainy day. Based on 48-year daily rainfall data of Harbin, the research results show that the method can be used to generate daily rainfall data in Harbin based on the model parameters estimated with historical rainfall records and its accuracy is high, but the empirical formula presented abroad is not applicable in Harbin area, a simple method for modeling and simulation daily rainfall in cold region was developed then.
出处
《黑龙江大学工程学报》
2012年第4期1-4,共4页
Journal of Engineering of Heilongjiang University
基金
黑龙江省普通高校新世纪优秀人才培养计划项目(1155-NCET-004)
关键词
随机模型
转移概率
伽玛分布
stochastic model
transitional probabilities
gamma distribution
