A spatiotemporal model for downscaling precipitation occurrence and amounts

A stochastic model that relates synoptic atmospheric data to daily precipit ation at a network of gages is presented. The model extends the nonhomogene ous hidden Markov model (NHMM) of Hughes et al, by incorporating precipitat ion amounts. The NHMM assumes that multisite, daily precipitation occurrenc e patterns are driven by a finite number of unobserved weather states that evolve temporally according to a first-order Markov chain. The state transi tion probabilities are a function of observed or modeled synoptic scale atm ospheric variables such as mean sea level pressure. For each weather state we evaluate the joint distribution of daily precipitation amounts at n site s through the specification of n conditional distributions. The conditional distributions consist of regressions of transformed amounts at a given sit e on precipitation occurrence at neighboring sites within a set radius. Res ults for a network of 30 daily precipitation gages and historical atmospher ic circulation data in southwestern Australia indicate that the extended NH MM accurately simulates the wet-day probabilities, survival curves for dry- and wet-spell lengths, daily precipitation amount distributions at each si te, and intersite correlations for daily precipitation amounts over the 15 year period from 1978 to 1992.