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Observational and model resolution limitations currently preclude analysis of the smallest scales important to numerical prediction of convective storms. These missing scales can be recovered if the forecast model is integrated on a sufficiently fine grid, but not before errors are introduced that subsequently grow in scale and magnitude. This study is the first to systematically evaluate the impact of these initial-condition (IC) resolution errors on high-resolution forecasts of organized convection. This is done by comparing high-resolution supercell simulations generated using identical model settings but successively coarsened ICs. Consistent with the Warn-on-Forecast paradigm, the simulations are initialized with ongoing storms and integrated for 2 h. Both idealized and full-physics experiments are performed in order to examine how more realistic model settings modulate the error evolution.

In all experiments, scales removed from the IC (wavelengths, 2, 4, 8, or 16 km) regenerate within 10-20 min of model integration. While the forecast errors arising from the initial absence of these scales become quantitatively large in many instances, the qualitative storm evolution is relatively insensitive to the IC resolution. It therefore appears that adopting much finer forecast (e.g., 250 m) than analysis (e.g., 3 km) grids for data assimilation and prediction would improve supercell forecasts given limited computational resources. This motivates continued development of mixed-resolution systems. The relative insensitivity to IC resolution further suggests that convective forecasting can be more readily advanced by improving model physics and numerics and expanding extrastorm observational coverage than by increasing intrastorm observational density.