A Nonparametric Renewal Model for Modeling Daily Precipitation
Abstract
A nonparametric wet/dry spell model is developed for describing daily precipitation at a site. The model considers alternating sequences of wet and dry days in a given season of the year. All the probability densities of interest are estimated nonparametrically using kernel probability density estimators. The model is data adaptive, and yields stochastic realizations of daily precipitation sequences for different seasons at a site. Applications of the model to data from rain gauges in Utah indicate good performance of the model.
This paper has been withdrawn.
