Using NSSL’s Experimental Warn-on-Forecast System to Message Uncertainty in High-Impact Convective Events

In the last two to three years, a renewed emphasis has been placed on the importance of skillful mesoanalysis during National Weather Service (NWS) warning operations. This is likely due, in part, to the number of high-resolution datasets (e.g., GOES-16 satellite imagery and Multi-Radar/Multi-Sensor System data) available to the operational forecaster in near real time. One dataset, in the next few years, that may provide an unprecedented opportunity to improve forecaster situational awareness and probabilistic threat communication before and during convective events is probabilistic forecast guidance from the National Severe Storms Laboratory (NSSL) experimental Warn-on-Forecast System (WOFS). The WOFS is a frequently updated, on-demand convection-allowing ensemble analysis and prediction system that assimilates radar, satellite, and surface data every 15 minutes and generates 0-3 h probabilistic short-term forecasts every 30 minutes or 0-6 h forecasts every 60 minutes using 3-km grid spacing. In this retrospective applied research, three events that led to significant tornadoes from May 2019 were analyzed to explore how the mesoanalyst could have used WOFS probabilistic forecast guidance to improve and enhance impact-based decision support services (IDSS) in the preceding 0-2 hours. For example, on the evening of 20 May 2019 in the Tulsa, Oklahoma NWS County Warning Area, the WOFS guidance may have provided enough confidence to message the threat of tornadoes in a one-to-two county area 90 minutes before a supercell and quasi-linear convective system merged to develop a significant tornado. Currently, the WOFS guidance is experimental and is only available at certain times of the year. However, this applied research shows how a prototype WOFS can assist the operational meteorologist with conveying a continuum of information aimed at assisting core partners in preparing for high-impact events.