SUMMARY
Tornado outbreaks (TOs) are a major hazard to life and property for locations east of the Rocky Mountains. Improving tornado outbreak (TO) forecasts will help minimize risks associated with these major events. In this study, we present a methodology for quantifying TO forecasts of varying quality, based on Storm Prediction Center convective outlook forecasts, and provide synoptic and mesoscale composite analyses to identify important features characterizing these events. Synoptic-scale composites from the North American Regional Reanalysis (NARR) are presented for TO forecasts at three forecast quality levels, H-class (high quality), M-class (medium quality), and L-class (low quality), as well as false alarm TO forecasts. H-class and false alarm TO forecasts share many meteorological similarities, particularly in the synoptic-scale, though false alarm events show less well-defined low-level synoptic-scale features. M- and L-class TOs present environments dominated by mesoscale thermodynamic processes (particularly dryline structures), contrasting H-class TOs which are clearly synoptically driven. Simulations of these composites reveal higher instability in M- and L-class TOs that lack key kinematic structures that characterize H-class TOs. The results presented offer important forecast feedback that can help inform future TO predictions and ultimately produce improved TO forecast quality.