OVERDISPERSION PHENOMENON IN STOCHASTIC MODELING OF PRECIPITATION

Simple stochastic models Rt to time series of daily precipitation amou nt have a marked tendency to underestimate the observed (or interannua l) variance of monthly (or seasonal) total precipitation. By consideri ng extensions of one particular class of stochastic model known as a c hain-dependent process, the extent to which this ''overdispersion'' ph enomenon is attributable to an inadequate model for high-frequency var iation of precipitation is examined. For daily precipitation amount in January at Chico, California, fitting more complex stochastic models greatly reduces the underestimation of the variance of monthly total p recipitation. One source of overdispersion, the number of wet days, ca n be completely eliminated through the use of a higher-order Markov ch ain for daily precipitation occurrence. Nevertheless, some of the obse rved variance remains unexplained and could possibly be attributed to low-frequency variation (sometimes termed ''potential predictability'' ). Of special interest is the fact that these more complex stochastic models still underestimate the monthly variance, more so than does an alternative approach, in which the simplest form of chain-dependent pr ocess is conditioned on an index of large-scale atmospheric circulatio n.