Stochastic daily precipitation models: 2. A comparison of distributions of amounts
Stochastic daily precipitation models: 2. A comparison of distributions of amounts
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DOI：
10.1029/wr018i005p01461
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发表时间：
1982-10
影响因子：
5.4
通讯作者：
D. Woolhiser;J. Roldan
中科院分区：
文献类型：
--
作者：
D. Woolhiser;J. Roldan
比较了链相关和独立指数分布、伽马分布和混合指数分布作为日降水量分布的模型。每种分布的参数是用最大似然技术估计的14天周期。Akaike信息标准用于为每个时期和全年选择最合适的分布。在所研究的五个美国台站中，基于Akaike信息准则的独立混合指数分布最好，独立伽马和链依赖伽马分别位居第二和第三。用最小二乘法将傅里叶级数与参数进行拟合，为后续傅里叶系数的数值最大似然估计提供起始值。根据Akaike信息准则，混合指数模型的模型参数的傅里叶级数描述优于对每个14天周期的参数描述。
Chain-dependent and independent exponential, gamma, and mixed exponential distributions are compared as models for the distribution of daily precipitation. Parameters for each distribution are estimated by maximum likelihood techniques for 14-day periods. The Akaike information criterion is used to select the most appropriate distribution for each period and for the entire year. For the five U.S. stations studied, the independent mixed exponential distribution was the best on the basis of the Akaike information criterion, and the independent gamma and chain-dependent gamma ranked second and third, respectively. Fourier series are fit to the parameters by least squares to provide starting values for subsequent numerical maximum likelihood estimates of the Fourier coefficients. According to the Akaike information criterion, the Fourier series description of model parameters for the mixed exponential model is superior to the specification of parameters for each 14-day period.