Effective Mesoscale, Short-Range Ensemble Forecasting

This study developed and evaluated a short-range ensemble forecasting (SREF) system with the goal of producing useful, mesoscale forecast probability (FP). Real-time, 0 to 48-h SREF predictions were produced and analyzed for 129 cases over the Pacific Northwest. Eight analyses from different operational forecast centers were used as initial conditions (ICs) for running the Fifth-Generation Pennsylvania State University−National Center of Atmospheric Research Mesoscale Model (MM5). Model error is a large source of forecast uncertainty and must be accounted for to maximize SREF utility, particularly for mesoscale, sensible weather phenomena. Although inclusion of model perturbations (in addition to boundary condition perturbations) improved FP skill (both reliability and resolution) and increased dispersion toward statistical consistency, dispersion remained inadequate. Furthermore, systematic model errors (i.e., biases) must be removed from a SREF since they contribute to forecast error but not to forecast uncertainty. A grid-based, two-week, running-mean bias correction was shown to improve FP skill through: 1) better reliability by adjusting the ensemble mean toward the verification's mean, and 2) better resolution by removing unrepresentative ensemble variance. Comparison of the multimodel (each member uses a unique model) and perturbed-model (each member uses a unique version of MM5) approaches indicated that the multimodel SREF exhibited greater dispersion and superior performance. It was also found that an ensemble of unequally likely members can be skillful as long as each member occasionally performs well. Lastly, smaller grid spacing led to greater ensemble spread as smaller scales of motion were modeled. This study indicates substantial utility in current SREF systems and suggests several avenues for further improvement.