Bayesian Model Averaging for Deterministic Simulation Models

• Activity Number: 223
• Type: Invited
• Date/Time: Tuesday, August 5, 2003 : 10:30 AM to 12:20 PM
• Sponsor: IMS
• Author(s): Adrian E. Raftery*+ and Fadoua Balabdaoui and Tilmann Gneiting
• Companies: University of Washington and University of Washington and University of Washington
• Keywords: weather forecasting ; deterministic simulation models ; Bayesian model averaging ; calibrated forecasts ; sharpness of forecasts ; temperature

Abstract

We consider the problem of calibrated and sharp probabilistic forecasting of a future meteorological quantity. By calibrated, we mean that if we define a predictive interval, such as a 90% probability interval, then on average in the long run, 90% of such intervals contain the true value. By "sharp," we mean that the distribution is more concentrated than forecast distributions from climatology alone. Mass has developed an ensemble mesoscale forecasting system based on a set of weather forecasting deterministic simulation models. He has established a clear relationship between between-model variability and forecast errors, but his forecast intervals are generally not calibrated; they are too narrow. We apply Bayesian model averaging to develop probability forecasts using Mass's ensemble. The theory of Bayesian model averaging explains both of Mass's main empirical findings: the spread-error relationship, and the fact that the intervals from the Mass ensemble are too narrow on average. We develop Bayesian model averaging forecasts and apply them to Puget Sound winter temperatures. The resulting forecasts are well calibrated and sharp.