This talk will describe a methodology for combining multiple forecasts of different types (e.g. deterministic and probabilistic forecasts) into a single forecast of airspace capacity, as well as providing a dynamic measure of forecast confidence. To begin, relevant features are extracted from individual weather forecasts and are used to translate the forecasts into an ensemble of predictions for airspace impact. Machine learning techniques are then used to combine this ensemble of translated forecasts into a posterior distribution of impact for up to 12 hours into the future. An impact forecast and its corresponding uncertainty are then computed from these posterior distributions. Four forecast models are used in this work: the FAA's Corridor Integrated Weather System (CIWS) extrapolation forecast, NOAA's High Resolution Rapid Refresh (HRRR) model, NOAA's Short Range Ensemble Forecast (SREF) Calibrated Thunderstorm Probability, and NOAA's Localized Aviation MOS Product (LAMP). This presentation will discuss the machine learning techniques used combine output from these different types of forecast models to provide a measure of weather impact on airspace and its uncertainty, and the benefits to the aviation airspace decision process.