Today, there is a growing interest in developing short-term wind power forecasting tools able to provide reliable information about particular, so-called ‘extreme’ situations. One of them is the large and sharp variation of the production a wind farm can experience within a few hours called ramp event. Developing forecast information specially dedicated to ramps is of primary interest because of both the difficulties that usual models have to predict and the potential risk they represent in the management of a power system. First, we propose a methodology to characterize ramps of wind power production with a derivative filtering approach derived from the edge detection literature. Then we investigate the skill of numerical weather prediction ensembles to make probabilistic forecasts of ramp occurrence. Through conditioning probability forecasts of ramp occurrence to the number of ensemble members forecasting a ramp in time intervals, we show how ensembles can be used to provide reliable forecasts of ramps with sharpness. Our study relies on 18 months of wind power measures from an 8 MW wind farm located in France and forecasts ensemble of 51 members from the Ensemble Prediction System of the European Center for Medium-Range Weather Forecasts. Copyright © 2012 John Wiley & Sons, Ltd.