Short-Range Numerical Weather Prediction Using Time-Lagged Ensembles

Abstract

A time-lagged ensemble forecast system is developed using a set of hourly initialized Rapid Update Cycle model deterministic forecasts. Both the ensemble-mean and probabilistic forecasts from this time-lagged ensemble system present a promising improvement in the very short-range weather forecasting of 1?3 h, which may be useful for aviation weather prediction and nowcasting applications. Two approaches have been studied to combine deterministic forecasts with different initialization cycles as the ensemble members. The first method uses a set of equally weighted time-lagged forecasts and produces a forecast by taking the ensemble mean. The second method adopts a multilinear regression approach to select a set of weights for different time-lagged forecasts. It is shown that although both methods improve short-range forecasts, the unequally weighted method provides the best results for all forecast variables at all levels. The time-lagged ensembles also provide a sample of statistics, which can be used to construct probabilistic forecasts.