Probabilistic forecasts of wind speed are becoming critical as interest grows in wind as a clean and renewable source of energy, in addition to a wide range of other uses, from aviation to recreational boating. Statistical approaches to wind forecasting offer two particular challenges: the distribution of wind speeds is highly skewed, and wind observations are reported to the nearest whole knot, a much coarser discretization than is seen in other weather quantities. The prevailing paradigm in weather forecasting is to issue deterministic forecasts based on numerical weather prediction models. Uncertainty can then be assessed through ensemble forecasts, where multiple estimates of the current state of the atmosphere are used to generate a collection of deterministic predictions. Ensemble forecasts are often uncalibrated, however, and Bayesian model averaging (BMA) is a statistical way of postprocessing these forecast ensembles to create calibrated predictive probability density functions (PDFs). It represents the predictive PDF as a weighted average of PDFs centered on the individual bias-corrected forecasts, where the weights reflect the forecasts’ relative contributions to predictive skill over a training period. In this paper we extend BMA to provide probabilistic forecasts of wind speed, taking account of the skewness of the predictive distributions and the discreteness of the observations. The BMA method is applied to 48-hour ahead forecasts of maximum wind speed over the North American Pacific Northwest in 2003 using the University of Washington mesoscale ensemble, and is shown to provide calibrated and sharp probabilistic forecasts. Comparisons are made between a number of formulations that account for the discretization of the observations.