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A non-homogeneous hidden Markov model for precipitation occurrence

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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 (and, consequently, rainfall) between the two periods. The fitted model also provides some useful insights into the processes driving rainfall in this region.
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Keywords: Climate change; EM algorithm; Hidden Markov model; Monte Carlo maximum likelihood; Precipitation model

Document Type: Research Article

Affiliations: 1: University of Washington, Seattle, USA 2: Commonwealth Scientific and Industrial Research Organisation, Wembley, Australia

Publication date: 1999-01-01

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