LAUSR

The utility of employing artificial intelligence (AI) to issue tornado warnings is explored using an ensemble of 128 idealized simulations. Over 700 tornadoes develop within the ensemble of simulations, varying in duration, length, and associated storm mode. Machine-learning models are trained to forecast the temporal and spatial probabilities of tornado formation for a specific lead time. The machine-learning probabilities are used to produce tornado warning decisions for each grid point and lead time. An optimization function is defined, such that warning thresholds are modified to optimize the performance of the AI system on a specified metric (e.g., increased lead time, minimized false alarms, etc.). Using genetic algorithms, multiple AI systems are developed with different optimization functions. The different AI systems yield unique warning output depending on the desired attributes of the optimization function. The effects of the different optimization functions on warning performance are explored. Overall, performance is encouraging and suggests that automated tornado warning guidance is worth exploring with real-time data.