Stochastic Model of Conditional Non-stationary Time Series of the Wind Chill Index in West Siberia

Abstract

In this paper, we propose a stochastic model of the conditional time series of the wind chill index. The model is based on the inverse distribution function method and on the normalization method for simulation of the non-Gaussian non-stationary random processes as well as on the method of conditional distributions for simulation of the conditional Gaussian processes. In the framework of the approach considered, two types of conditions (point conditions and interval conditions) are imposed on the time series. The model in question was verified using the real data collected at the weather stations located in West Siberia (Russia). It is shown that the simulated trajectories are close in their statistical properties to the real time series. The model proposed was used for stochastic forecasting of the wind chill index and the results of the numerical experiments have shown that the accuracy of the short-term forecasts is high enough.