Statistical single-station short-term forecasting of temperature and probability of precipitation: Area interpolation and NWP combination

Two statistical single-station short-term forecast schemes are introduced a nd applied to real-time weather prediction. A multiple regression model (R model) predicting the temperature anomaly and a multiple regression Markov model (M model) forecasting the probability of precipitation are shown. The following forecast experiments conducted for central European weather stat ions are analyzed: (a) The single-station performance of the statistical mo dels, (b) a linear error minimizing combination of independent forecasts of numerical weather prediction and statistical models, and (c) the forecast representation for a region deduced by applying a suitable interpolation te chnique. This leads to an operational weather forecasting system for the te mperature anomaly and the probability of precipitation; the statistical tec hniques demonstrated provide a potential for future applications in operati onal weather forecasts.