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大气科学  2010 

A Stochastic Precipitation Generator Based on Generalized Linear Models and NCEP Reanalysis Data
基于广义线性模型和NCEP资料的降水随机发生器

Keywords: generalized linear models,weather generator,downscaling,NCEP reanalysis data,genetic algorithms
广义线性模型,天气发生器,降尺度,NCEP再分析资料,遗传算法

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Abstract:

Weather generators were useful for generating incomplete history weather records and weather scenarios in future. Recently they were used to study downscaling climate variables and provide weather forcings for hydrological and ecological simulations. Generalized linear models (GLM) are powerful tools for relating large-scale weather variables with high resolution variables at the earth surface. The application of generators based on GLM is promising. In this paper, by using five variables derived from single grid NCEP reanalysis data, such as air temperature, 500-hPa geopotential height, potential temperature, relative humidity, sea-level pressure, as large-scale independent variables that affect precipitation variations, GLM for downscaling and simulating daily precipitation were constructed. For determination of precipitation occurrence probability, the logistic model was used, and for simulating daily precipitation amounts, the Gamma, exponential, normal, and lognormal distributions were used respectively. The observed daily precipitation series and the selected NCEP reanalysis data were used for parameter-estimation and simulation. The maximum likelihood estimate of the model parameters was performed by genetic algorithms. The results of estimation showed that the fitting effect of the gamma distribution-based models was the best, the lognormal distribution was the second, the exponential distribution was the third, and the fitting effect of the normal distribution-based models was the worst; on the other hand, the fitting effect of the models with their parameters estimated for each month separately was a little better than that without separation of months. Both the observed and simulated daily precipitation-occurrence probabilities were added for each month and the corresponding precipitation amounts were also averaged for each month in order to verify the models ability. Results showed that the precipitation-occurrence probability was underestimated slightly by the logistic model, and precipitation-amounts expectations were underestimated by the exponential and gamma distribution-based GLM but were overestimated by the lognormal distribution-based GLM when monthly total precipitation amounts were large. The stochastic simulation of precipitation showed that the lognormal distribution-based GLM overestimated the yearly averaged monthly total precipitation and the exponential or gamma distribution-based GLM had a good simulation effect. On the whole, these stochastic models based on GLM and the NCEP reanalysis data are powerful tools for explaining and simulating the occurrence probability and amounts of precipitation.

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