PRESENT SITUATION AND PROSPECTS OF ENSEMBLE NUMERICAL PREDICTION
Jun Du (National Centers for Environmental Prediction/NOAA, Washington DC, USA )
Over the past few years ensemble prediction has come to the fore as a major element in defining the future of numerical weather prediction (NWP) and operational weather forecasting. This stems basically from convergence of increasing recognition of the importance of explicitly addressing the intrinsic uncertainties in forecasts (originated from both initial conditions and model physics) with rapid advance in expanding capability to provide quantitative estimates of those uncertainties. It is widely agreed that ensemble based probabilities and measures of confidence hold the best potential for enhancing the ability to make user dependent informed decisions. Indeed, the U.S. National Weather Service is requiring that many forecast products evolve to become probabilistic in nature, especially for quantitative precipitation forecasting. In this paper, the basic concepts, outstanding issues and recent development of ensemble technique are briefly described, which include (1) how to establish and validate an ensemble forecasting system; (2) how to correctly represent intrinsic uncertainties in both initial conditions and model physics; and (3) how to extract useful information out of an ensemble of forecasts and how to interpret and evaluate ensemble products especially probabilistic forecasts. Besides its application to direct weather forecasting, application of ensemble technique to adaptive observation and data assimilation are also mentioned.
【CateGory Index】： P456.7