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Abstract Ensembles provide an opportunity to greatly improve short-term prediction of local weather hazards, yet generating reliable predictions remain a significant challenge. In particular, convection-permitting ensemble forecast systems (CPEFSs) have persistent problems with underdispersion. Representing initial and or lateral boundary condition uncertainty along with forecast model error provides a foundation for building a more dependable CPEFS, but the best practice for ensemble system design is not well established. Several configurations of CPEFSs are examined where ensemble forecasts are nested within a larger domain, drawing initial conditions from a downscaled, continuously cycled, ensemble data assimilation system that provides state-dependent initial condition uncertainty. The control ensemble forecast, with initial condition uncertainty only, is skillful but underdispersive. To improve the reliability of the ensemble forecasts, the control ensemble is supplemented with 1) perturbed lateral boundary conditions; or, model error representation using either 2) stochastic kinetic energy backscatter or 3) stochastically perturbed parameterization tendencies. Forecasts are evaluated against stage IV accumulated precipitation analyses and radiosonde observations. Perturbed ensemble forecasts are also compared to the control forecast to assess the relative impact from adding forecast perturbations. For precipitation forecasts, all perturbation approaches improve ensemble reliability relative to the control CPEFS. Deterministic ensemble member forecast skill, verified against radiosonde observations, decreases when forecast perturbations are added, while ensemble mean forecasts remain similarly skillful to the control.
Romine et al. (Tue,) studied this question.