A comparison of four precipitation distribution models used in daily stochastic models

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• 作者：Yonghe Liu (1) (5) (6)
  Wanchang Zhang (2)
  Yuehong Shao (3)
  Kexin Zhang (4)
  
• 关键词：weather generators ; gamma distribution ; mixed ; exponential distribution ; Markov chain ; Fourier series
• 刊名：Advances in Atmospheric Sciences
• 出版年：2011
• 出版时间：July 2011
• 年：2011
• 卷：28
• 期：4
• 页码：809-820
• 全文大小：388KB
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• 作者单位：Yonghe Liu (1) (5) (6)
  Wanchang Zhang (2)
  Yuehong Shao (3)
  Kexin Zhang (4)
  
  1. Key Laboratory of Regional Climate-Environment Research for Temperate East Asia (RCE-TEA), Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, 100029, China
  5. Institute of Resources and Environment, Henan Polytechnic University, Jiaozuo, 454000, China
  6. Graduate University of Chinese Academy of Sciences, Beijing, 100049, China
  2. Center for Hydrosciences Research, Nanjing University, Nanjing, 210093, China
  3. Applied Hydrometeorological Research Institute, Nanjing University of Information Science and Technology, Nanjing, 210044, China
  4. Linyi Meteorological Bureau, Shandong Province, Linyi, 276004, China
  
• ISSN：1861-9533

文摘

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.