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• W2165744558 abstract "Abstract Stochastic weather generators are commonly used to simulate time series of dailyweather, especially precipitation amount. Recently, a generalized linear model (GLM)has been proposed as a convenient approach to tting these weather generators. In thispaper, a stochastic weather generator is considered to model the time series of dailyprecipitation at Seoul in South Korea. As a covariate, global temperature is introducedto relate long-term temporal scale predictor to short-term temporal predictands. One ofthe limitations of stochastic weather generators is a marked tendency to underestimatethe observed interannual variance of monthly, seasonal, or annual total precipitation.To reduce this phenomenon, we incorporate time series of seasonal total precipitationin the GLM weather generator as covariates. It is veri ed that the addition of thesecovariates does not distort the performance of the weather generator in other respects.Keywords: Generalized linear model, overdispersion, precipitation, stochastic weathergenerator." @default.
• W2165744558 created "2016-06-24" @default.
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• W2165744558 date "2012-11-30" @default.
• W2165744558 modified "2023-09-27" @default.
• W2165744558 title "Stochastic precipitation modeling based on Korean historical data" @default.
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• W2165744558 doi "https://doi.org/10.7465/jkdi.2012.23.6.1309" @default.
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