Ensemble Forecast: A New Approach to Uncertainty and Predictability

Ensemble techniques have been used to generate daily numerical weather forecasts since the 1990s innumerical centers around the world due to the increase in computation ability. One of the main purposesof numerical ensemble forecasts is to try to assimilate the initial uncertainty (initial error) and the forecastuncertainty (forecast error) by applying either the initial perturbation method or the multi-model/multi-physics method. In fact, the mean of an ensemble forecast offers a better forecast than a deterministic(or control) forecast after a short lead time (3-5 days) for global modelling applications. There is abouta 1-2-day improvement in the forecast skill when using an ensemble mean instead of a single forecastfor longer lead-time. The skillful forecast (65% and above of an anomaly correlation) could be extendedto 8 days (or longer) by present-day ensemble forecast systems. Furthermore, ensemble forecasts candeliver a probabilistic forecast to the users, which is based on the probability density function (PDF)instead of a single-value forecast from a traditional deterministic system. It has long been recognized thatthe ensemble forecast not only improves our weather forecast predictability but also offers a remarkableforecast for the future uncertainty, such as the relative measure of predictability (RMOP) and probabilisticquantitative precipitation forecast (PQPP). Not surprisingly, the success of the ensemble forecast and itswide application greatly increase the confidence of model developers and research communities.