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http://dx.doi.org/10.3741/JKWRA.2002.35.1.109

A Stochastic Model for Precipitation Occurrence Process of Hourly Precipitation Series  

Lee, Jae-Jun (금오공과대학교 토목, 환경 및 건축공학부)
Lee, Jeong-Sik (금오공과대학교 토목, 환경 및 건축공학부)
Publication Information
Journal of Korea Water Resources Association / v.35, no.1, 2002 , pp. 109-124 More about this Journal
Abstract
This study is an effort to develop a stochastic model of precipitation series that preserves the pattern of occurrence of precipitation events throughout the year as well as several characteristics of the duration, amount, and intensity of precipitation events. In this study an event cluster model is used to describe the occurrence of precipitation events. A logarithmic negative mixture distribution is used to describe event duration and separation. The number of events within each cluster is also described by the Poisson cluster process. The duration of each event within a cluster and the separation of events within a single cluster are described by a logarithmic negative mixture distribution. The stochastic model for hourly precipitation occurrence process is fitted to historical precipitation data by estimating the model parameters. To allow for seasonal variations in the precipitation process, the model parameters are estimated separately for each month. an analysis of thirty-four years of historical and simulated hourly precipitation data for Seoul indicates that the stochastic model preserves many features of historical precipitation. The seasonal variations in number of precipitation events in each month for the historical and simulated data are also approximately identical. The marginal distributions for event characteristics for the historical and simulated data were similar. The conditional distributions for event characteristics for the historical and simulated data showed in general good agreement with each other.
Keywords
Marginal distribution; Hourly precipitation series; Precipitation occurrence process; Poisson cluster process; LNMD; Conditional distribution;
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