The weather is a chaotic system. Small errors in the initial conditions of a forecast grow rapidly and predictability is limited by model errors due to the approximate simulation of atmospheric processes of the state-of-the-art numerical models. These two sources of uncertainties limit the skill of single, deterministic
forecasts in an unpredictable way, with days of high/poor quality forecasts randomly followed by days of high/poor quality forecasts. Two recent advances in numerical weather prediction, the operational implementation of ensemble prediction systems and the development of objective procedures to target adaptive observations are discussed. These advances have been thought and designed to reduce forecast errors and to provide forecasters with more complete weather predictions. Ensemble prediction is a feasible method to estimate the probability distribution function of forecast states. Ensemble systems can provide forecasters with an objective way to predict the skill of single deterministic forecasts. Adaptive observations targeted in sensitive regions can reduce the initial conditions’ uncertainties, and thus decrease forecast errors. Singular vectors that identify unstable regions of the atmospheric flow can be used to identify optimal ways to adapt the atmospheric observing system. The European Centre for Medium-Range Weather Forecasts Ensemble Prediction System is described, and targeting experiments are discussed
