| Abstract | This dataset contains the numerical simulation output created and analyzed by Markowski (2020) in his study of supercell intrinsic predictability. A 25-member ensemble of relatively high-resolution (75-m horizontal grid spacing) numerical simulations of tornadic supercell storms was used to obtain insight on their intrinsic predictability. Small random temperature perturbations present in the initial conditions trigger turbulence within the boundary layers. The turbulent boundary layers are given 12 h to evolve to a quasi-steady state before storms are initiated via the introduction of a warm bubble. The spatially averaged environments are identical within the ensemble; only the random number seed and/or warm bubble location is varied. All of the simulated storms in the highly unstable, strongly sheared environment are long-lived supercells with intense updrafts and strong mesocyclones extending to the lowest model level. Even the storms with the weakest near-surface rotation probably could be regarded as being weakly tornadic. However, despite the identical mean environments, there is considerable divergence in the fine-scale details of the simulated storms, even within just 15 min of storm initiation. The timing and intensity of the tornado-like vortices that develop are largely unpredictable, with the intensities ranging from EF0 to EF3. The simulation differences only can be explained by differences in how the initial warm bubbles and/or storms interact with turbulent boundary-layer structures. The results suggest very limited intrinsic predictability with respect to predicting the formation time, duration, and intensity of tornadoes. The dataset also includes all code used to perform the analyses and create the figures shown in the above-referenced journal publication. |
