A non-homogeneous hidden Markov model for precipitation occurrence

被引:303
作者
Hughes, JP [1 ]
Guttorp, P
Charles, SP
机构
[1] Univ Washington, Dept Biostat, Seattle, WA 98195 USA
[2] CSIRO, Wembley, WA, Australia
关键词
climate change; EM algorithm; hidden Markov model; Monte Carlo maximum; likelihood; precipitation model;
D O I
10.1111/1467-9876.00136
中图分类号
O21 [概率论与数理统计]; C8 [统计学];
学科分类号
020208 ; 070103 ; 0714 ;
摘要
A non-homogeneous hidden Markov model is proposed for relating precipitation occurrences at multiple rain-gauge stations to broad scale atmospheric circulation patterns (the so-called 'downscaling problem'). We model a 15-year sequence of winter data from 30 rain stations in south-western Australia. The first 10 years of data are used for model development and the remaining 5 years are used for model evaluation. The fitted model accurately reproduces the observed rainfall statistics in the reserved data despite a shift in atmospheric circulation land, consequently, rainfall) between the two periods. The fitted; model also provides some useful insights into the processes driving rainfall in this region.
引用
收藏
页码:15 / 30
页数:16
相关论文
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