NONHOMOGENEOUS MARKOV MODEL FOR DAILY PRECIPITATION

This paper presents a one-step nonhomogeneous Markov model for describing daily precipitation at a site. Daily transitions between wet and dry states are considered. The. one-step, 2 x 2 transition-probability matrix is presumed to vary smoothly day by day over the year. The dally transition-probability matrices are estimated nonparametrically. A kernel estimator is used to estimate the transition probabilities through a weighted average of transition counts over a symmetric time interval centered at the day of interest. The precipitation amounts on each wet day are simulated from the kernel probability density estimated fro. m all wet days that fall within a time interval centered on the calendar day interest over all the years of available historical observations. The model is completely data-dnven. An application to data from Utah is presented. Wet- and dry-spell attributes [specifically the historical and simulated probability-mass functions (PMFs) of wet- and dry-spell length] appear to be reproduced in our Monte Carlo simulations. Precipitation amount statistics are also well reproduced.