This study investigates whether probabilistic ozone forecasts from an ensemble can be made with skill: i.e., high verification resolution and reliability. Twenty-eight ozone forecasts were generated over the Lower Fraser Valley, British Columbia, Canada, for the 5-day period 11--15 August 2004 and compared with 1-hour averaged measurements of ozone concentrations at five stations. The forecasts were obtained by driving the Community Multiscale Air Quality Model (CMAQ) model with four meteorological forecasts and seven emission scenarios: a control run, ¿50% NOx, ¿50% volatile organic compounds (VOC), and ¿50% NOx combined with VOC. Probabilistic forecast quality is verified using relative operating characteristic curves, Talagrand diagrams, and a new reliability index. Results show that both meteorology and emission perturbations are needed to have a skillful probabilistic forecast system: the meteorology perturbation is important to capture the ozone temporal and spatial distribution and the emission perturbation is needed to span the range of ozone concentration magnitudes. Emission perturbations are more important than meteorology perturbations for capturing the likelihood of high ozone concentrations. Perturbations involving NOx resulted in a more skillful probabilistic forecast for the episode analyzed, and therefore the 50% perturbation values appear to span much of the emission uncertainty for this case. All of the ensembles analyzed show a high ozone concentration bias in the Talagrand diagrams, even when the biases from the unperturbed emissions forecasts are removed from all ensemble members. This result indicates nonlinearity in the ensemble, which arises from both ozone chemistry and its interaction with input from particular meteorological models.