A statistical model for minimum temperature forecasting at Dhahran, Saudi Arabia

Abstract

One-day forecasting techniques, based on classical statistical methods and general linear modelling, are described for the particular case of predicting the minimum temperature. The resulting models are designed to run in real time on a computer-driven automatic weather station. Two years of data have been used to develop the minimum temperature model. Variable selection is carried out by using Mallow's C p criterion, as well as stepping regression methods. The problem of collinearity between the regressors is investigated. Model adequacy is examined by using the residual analysis technique. The final model involves parameters of mean air temperature, mean wind speed, mean total (global) solar radiation, mean relative humidity, and maximum relative humidity. Model validation was carried out by using an independent data set of 243 observations. During this period, the forecasts achieved a 67% hit rate and exhibited absolute and algebraic mean errors of 1.5 and 0.7 °C, respectively. Further, no large forecast errors occurred. The model is a useful adjunct tool for subjective forecasting. This statistical forecasting technique is now being applied to nowcasting and 1 2 h forecasting of off-shore conditions.