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A HIERARCHIAL STOCHASTIC MODEL OF LARGE SCALE ATMOSPHERIC CIRCULATION PATTERNS AND MULTIPLE STATION DAILY PRECIPITATION
Citation:
Wilson, L., d. Lettenmaier, AND E. Skyllingstad. A HIERARCHIAL STOCHASTIC MODEL OF LARGE SCALE ATMOSPHERIC CIRCULATION PATTERNS AND MULTIPLE STATION DAILY PRECIPITATION. U.S. Environmental Protection Agency, Washington, D.C., EPA/600/J-92/287.
Description:
A stochastic model of weather states and concurrent daily precipitation at multiple precipitation stations is described. our algorithms are invested for classification of daily weather states; k means, fuzzy clustering, principal components, and principal components coupled with k-means clustering. emi-Markov model with a geometric distribution for within-class lengths of stay is used to describe the evolution of weather classes. ierarchical modified Polya urn model is used to simulate precipitation conditioned on the regional weather type. n information measure that considers both the probability of climate class occurrence and conditional precipitation probabilities is developed to quantify the extent to which each of the climate classification schemes discriminates the precipitation states at the precipitation stations. valuation of the four algorithms using the information measure shows that principal components is far superior to the other methods tested. recipitation amounts distributions are assumed to be drawn from spatially correlated mixed exponential distributions, whose parameters varied by season and climate class. he model is implemented using National Meteorological Center historical atmospheric observations for the period 1965-88 mapped to 5 degrees x 5 degrees grid cells over the eastern North Pacific, and three precipitation stations west of the Cascade mountain range in the State of Washington. omparison of simulated weather class-station precipitation time series with observational data shows that the model preserved weather class statistics and mean daily precipitation quite well, especially for stations higher in the hierarchy, and for precipitation extremes, and for precipitation extremes, are not as well preserved.