Improving tornado forecasts is an overarching aim of the VORTEX-SE/USA program. Research focused on improving forecast skill occurred among three main themes: 1) Assessment of weather prediction model skill, 2) Improvements to model design and data assimilation methods, and 3) Techniques to identify tornado likelihood from observations and model output. VORTEX-SE/USA research supported development of operational systems, like the high-resolution rapid refresh (HRRR) model and the Warm-on-Forecast System (WoFS). Studies using HRRR and WoFS focused on how future changes in model resolution and data assimilation methods may benefit tornado forecasts and how the skill of these systems in predicting storm rotation. Much research focused on identifying precursors to tornadoes in radiosonde observations and single or multi-radar blended analyses, like MRMS, finding signals in polarimetric variables and developing new algorithms to improve operational methods. VORTEX-SE/USA supported machine learning research to help forecasters identify tornado probabilities from these radar data that are proving effective as potential replacements for the current tornado detection algorithms. The reach of these results into operations has been supported at the Warning Decision Training Division (WDTD) through development of formal webinars and other R2O activities. Ongoing activities are focusing on use of WoFS to highlight regions of QLCSs favorable for tornadoes, using operational radars and profiler data to define the evolution of shear profiles ahead of QLCSs, and expanding the application of machine learning into other data sources to improve probabilistic guidance on tornado likelihood.
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