A stochastic cluster model of daily rainfall sequences
A stochastic cluster model of daily rainfall sequences
复制标题
日降雨序列的随机聚类模型
DOI：
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发表时间：
1981
期刊：
影响因子：
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通讯作者：
J. Delleur
中科院分区：
文献类型：
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作者：
M. L. Kavvas;J. Delleur
在时间维上，建立了一个给定雨量站降雨发生的两水平点随机模型。该模型是一个Neyman-Scott型的聚类过程。该模型以事件生成机制为主要层次，以由这些机制生成的事件为次要层次。它使用了无限的重叠，并有一个非常灵活的依赖结构。该模型是适合在印第安纳州的日降雨量序列后，这些是平稳的转换。模型的拟合，然后测试其相关性和边际概率特性。Neyman-Scott团簇模型的当前形式是时间齐次的。因此，Neyman-Scott过程，如本文所提出的，可能是实际使用的模拟平稳降雨发生。
A two-level point stochastic model for the rainfall occurrences at a given rainfall station is constructed in the time dimension. The model is a cluster process of the Neyman-Scott type. The model has the rainfall-generating mechanisms as its primary level and the rainfalls that are generated by these mechanisms as the secondary level. It uses infinite superposition of rainfalls and has a very flexible dependence structure. The model is fitted to daily rainfall sequences in Indiana after these are stationarized by a transformation. The fit of the model is then tested in terms of its correlation and marginal probability characteristics. The present form of the Neyman-Scott cluster model is time homogeneous. Therefore the Neyman-Scott process, as presented in this paper, may be of practical use only for modeling the stationary rainfall occurrences.