Thesis (Master's)--University of Washington, 2017-03Recent work has suggested that large-scale (O(100 km)) initial errors in a numerical weather forecast may exert more control on the propagation of errors than small-scale errors potentially as small as butterflies. The intrinsic predictability of the atmosphere at the mesoscales (5–400 km) is studied using idealized simulations of organized deep convection under several different profiles of environmental vertical wind shear. Initial errors of equal amplitude are introduced in the moisture field either at small scales (8 km) or large scales (128 km). It is found that small- and large-scale initial errors have virtually identical impacts on predictability at lead times of 4–5 h for all wind shear profiles. Reducing the amplitude of the initial errors produces diminishing returns in predictability lead time and reveals no difference be- tween small- and large-scale initial errors. Additionally, the idealized simulations all produce a k^−5/3 spectrum of kinetic energy, in agreement with observations of the atmosphere at the mesoscales. The simulations provide evidence of the importance of the unbalanced, divergent gravity-wave component of the flow produced by thunderstorms in generating the observed kinetic energy spectrum
