Stochastic Precipitation Generation Based on a Multivariate Autoregression Model

Abstract

The problem of stochastic precipitation generation has long been of interest. A good generator should produce time series with statistical properties to match those of the real precipitation. Here, a multivariate autoregression model designed to capture the covariance and lag-1 cross-covariance structure of the precipitation measurements is presented. A truncated and power-transformed normal distribution is used to simultaneously model both occurrences and amounts of daily precipitation. The methodology is illustrated using daily rain gauge datasets for three areas in the continental United States.