Stochastic precipitation generator with hidden state covariates

Abstract

Time series of daily weather such as precipitation, minimum temperature and maximum temperature are commonly required for various fields. Stochastic weather generators constitute one of the techniques to produce synthetic daily weather. The recently introduced approach for stochastic weather generators is based on generalized linear modeling (GLM) with covariates to account for seasonality and teleconnections (e.g., with the El Nino). In general, stochastic weather generators tend to underestimate the observed interannual variance of seasonally aggregated variables. To reduce this overdispersion, we incorporated time series of seasonal dry/wet indicators in the GLM weather generator as covariates. These seasonal time series were local (or global) decodings obtained by a hidden Markov model of seasonal total precipitation and implemented in the weather generator. The proposed method is applied to time series of daily weather from Seoul, Korea and Pergamino, Argentina. This method provides a straightforward translation of the uncertainty of the seasonal forecast to the corresponding conditional daily weather statistics.