Expressing weather forecasts as probabilities has been a regular (though small) part of operational meteorological forecasting in the U.S. since at least 1965, when the Weather Bureau produced its first probability of precipitation forecasts. In fact, the concept that weather forecasts are uncertain has been understood since the early days of weather forecasting (e.g., in the late 1800s, Cleveland Abbe, the “Father” of weather forecasting in the U.S., called his forecasts “probabilities”). However, soon after Abbe’s time, the meteorological community generally turned away from probabilistic forecasting, and even today most weather forecasts are expressed categorically. In recent years, there has been a movement toward embracing the inherent uncertainty in weather forecasts and developing ways to both estimate and communicate the uncertainty. Currently, uncertainty in weather forecasts is estimated in a number of ways, including subjectively and statistically. In recent years, new methods have been developed to derive probabilistic weather forecast information from multiple (ensemble) runs of dynamical weather prediction models. Many issues still remain, including how to capture all sources of forecast uncertainty and how to express the uncertainty so it is meaningful for end users and decision makers. The development of probabilistic forecasting in meteorology and current approaches for estimating and expressing forecast uncertainty will be explored.