MULTIVARIATE NONPARAMETRIC RESAMPLING SCHEME FOR GENERATION OF DAILY WEATHER VARIABLES

A nonparametric resampling technique for generating daily weather vari ables at a site is presented. The method samples the original data wit h replacement while smoothing the empirical conditional distribution f unction. The technique can be thought of as a smoothed conditional Boo tstrap and is equivalent to simulation from a kernel density estimate of the multivariate conditional probability density function. This imp roves on the classical Bootstrap technique by generating values that h ave not occurred exactly in the original sample and by alleviating the reproduction of fine spurious details in the data. Precipitation is g enerated from the nonparametric wet/dry spell model as described in La ll et al. [1995]. A vector of other variables (solar radiation, maximu m temperature, minimum temperature, average dew point temperature, and average wind speed) is then simulated by conditioning on the vector o f these variables on the preceding day and the precipitation amount on the day of interest. An application of the resampling scheme with 30 years of dairy weather data at Salt Lake City, Utah, USA, is provided.