A Comparison of Four Precipitation Distribution Models Used in Daily Stochastic Models |
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| Authors: | LIU Yonghe ZHANG Wanchang SHAO Yuehong and ZHANG Kexin |
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| Institution: | Key Laboratory of Regional Climate-Environment Research for Temperate East Asia (RCE-TEA), Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, Institute of Resources and Environment, Henan Polytechnic University, Jiaozuo 454000, Graduate University of Chinese Academy of Sciences, Beijing 100049,Center for Hydrosciences Research, Nanjing University, Nanjing 210093,Applied Hydrometeorological Research Institute, Nanjing University of Information Science and Technology, Nanjing 210044,Linyi Meteorological Bureau, Shandong Province, Linyi 276004 |
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| Abstract: | Stochastic weather generators are statistical models that produce random
numbers that resemble the observed weather data on which they have been
fitted; they are widely used in meteorological and hydrological simulations.
For modeling daily precipitation in weather generators, first-order Markov
chain--dependent exponential, gamma, mixed-exponential, and lognormal
distributions can be used. To examine the performance of these four
distributions for precipitation simulation, they were fitted to observed
data collected at 10 stations in the watershed of Yishu River. The
parameters of these models were estimated using a maximum-likelihood
technique performed using genetic algorithms. Parameters for each calendar
month and the Fourier series describing parameters for the whole year were
estimated separately. Bayesian information criterion, simulated monthly
mean, maximum daily value, and variance were tested and compared to evaluate
the fitness and performance of these models. The results indicate that the
lognormal and mixed-exponential distributions give smaller BICs, but their
stochastic simulations have overestimation and underestimation respectively,
while the gamma and exponential distributions give larger BICs, but their
stochastic simulations produced monthly mean precipitation very well. When
these distributions were fitted using Fourier series, they all
underestimated the above statistics for the months of June, July and August. |
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| Keywords: | weather generators gamma distribution mixed-exponential distribution Markov chain Fourier series |
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