An ensemble strategy for high-resolution regional model forecasts

An ensemble methodology is proposed for very high-resolution regional model Quantitative Precipitation Forecasts. To facilitate a systematic study, th e model and the boundary conditions are assumed to be perfect. The generati on of perturbations is derived on the basis that the largest errors in prec ipitation forecasts at very high resolutions arise from miss-specified diab atic heat sources and sinks which feedback erroneously to the grid scale va riables in the initial state. This methodology is tested in a Proxy Observe d System Simulation Experiment (POSSE) involving an intense cyclone over ea stern Canada. The perturbations of wind and temperature in this ensemble st rategy are obtained as normalized coefficients of a Combined Empirical Orth ogonal Function analysis of the difference fields between the control and t he diabatically initialized model runs. These perturbations are added to an d subtracted from the control initial state to obtain a set of two perturbe d initial states. Several such perturbed initial states are obtained from i nitializing observed rain rates at different times close to the time of the analysis. The results from the POSSE reveal that the Quantitative Precipit ation Forecast of the ensemble mean outperforms the control model run.