The success of ensemble forecasting in the medium range forecasts in the past decade has spurred considerable interest in the operational and research communities to consider adopting ensemble techniques for mesoscale predictions. While the medium range ensemble experiences are valuable, the degree to which those results are applicable to mesoscale ensemble predictions is unclear. First, as the scales of phenomena are decreased, our knowledge of analysis uncertainty decreases, because of the lack of observations. Second, the dynamics of the model atmosphere in the medium range in mid-latitudes is dictated largely by synoptic and planetary scale disturbances, which are mostly quasi-geostrophic in nature and grow due to baroclinic instability. Third, the global ensemble prediction systems are integrated for several days, so initial perturbations have considerable time to organize and grow largely due to baroclinic instability, leading to nonlinear error growth.

The design of a mesoscale ensemble prediction system (MEPS) poses many challenges. They include the development of optimal strategies for creating the initial perturbations and determining the appropriate roles of ensemble resulting from initial condition perturbations versus those generated using perturbations to model physics. In this study, we compare three well-established techniques for generating initial condition perturbations: a) the simple Monte Carlo method; b) the breeding of growing modes method, which is used at NCEP; and c) the perturbed observations method, which is used operationally at the Canadian Meteorological Center. In this paper, we will present not only the details of our MEPS, which is based on the MM5 modeling system, but also show results from the application of the MEPS to three mid-latitude cyclone cases. Specifically, ensemble characteristics such as spread and skill from initial condition perturbations, using the aforementioned three methods, will be compared against those due to changes in model physics. Other traditional measures of ensemble performance such as relative operating characteristics and reliability will presented for these different ensembles.