Probabilistic Wind Speed Forecasting Using Ensembles and Bayesian Model Averaging

Abstract

The current weather forecasting paradigm is deterministic, based on numerical models. Multiple estimates of the current state of the atmosphere are used to generate an ensemble of deterministic predictions. Ensemble forecasts, while providing information on forecast uncertainty, are often uncalibrated. Bayesian model averaging (BMA) is a statistical ensemble postprocessing method that creates calibrated predictive probability density functions (PDFs). Probabilistic wind forecasting offers two challenges: a skewed distribution, and observations that are coarsely discretized. We extend BMA to wind speed, taking account of these challenges. This method provides calibrated and sharp probabilistic forecasts. Comparisons are made between several formulations.