The performance of the warning system suggests warning automation is possible in simulated convective storms and the framework presented may be feasible to explore using real-time, real-life data.Īll simulations are performed on a 156 km × 192 km × 18 km grid. The warning system is summarized schematically in Fig. This paper develops a framework for the automated issuance of tornado warnings in simulated convective storms. 2018) already exist, and so it seems reasonable to explore the feasibility of automated decision making in the tornado warning decision process. However, systems such as the German Weather Service’s automated severe convection warning proposal system ( James et al. 2018) in the forecasting of severe weather. 2012) has provided an opportunity to study the “human–machine mix” ( Karstens et al. How will human forecasters interpret the output? How will human forecasters handle increased amounts of data from the ML products? The hazardous weather test bed ( Kain et al. Several questions arise as the role and skill of ML model guidance expands in the warning process. 2014, 2018), provide probabilistic guidance that a storm will produce severe weather based on observations and model output. Real-time operational products, such as the Bayesian classifier ProbSevere ( Cintineo et al. Whereas data mining approaches assist in research, the predictions made by ML models can also be used to aid operational severe storm hazard forecasting. Because of their ability to interpret data, ML models provide a means for identifying data features that are critical to the tornadogenesis process, but hidden in massive amounts of data ( McGovern et al. The advantage of well-trained ML models over human developed algorithms is their ability to self-identify important characteristics in large amounts of data without human interpretation. 2017) and tornadoes ( Marzban and Stumpf 1996 Adrianto et al. 2017), severe hail ( Marzban and Witt 2001 Gagne et al. 2017a, 2019a), severe wind ( Marzban and Stumpf 1998 Lagerquist et al. ML models have been used in the explicit prediction of severe weather hazards including storm longevity ( McGovern et al. More recently, machine learning (ML) has been introduced as a tool to aid in data processing and assist in the forecasting of severe storms. 1998) used human developed rule-based algorithms to inform forecasters of severe weather hazards. 1998), and tornado detection algorithm ( Mitchell et al. 1998), hail detection algorithm ( Witt et al. Products such as the mesocyclone detection algorithm ( Stumpf et al. With more data available to forecasters, automated data processing algorithms were developed to rapidly identify convective threats. Part of the increase in skill was due to the implementation of a national network of Doppler radars throughout the 1990s that provided more information about the current state of potentially tornadic storms. With increasing observational capabilities and theoretical understanding of tornadoes, warning skill increased over time ( Brooks 2004 Brooks and Correia 2018). Over the next seven decades, tornado forecasts evolved and transformed into the modern tornado warnings currently issued by the National Weather Service ( Coleman et al. Following the successful tornado forecast by Fawbush and Miller in 1948 ( Miller and Crisp 1999), the first tornado forecasts were issued to the public throughout the 1950s ( Galway 1989 Doswell et al. As early as the 1880s, an automated tornado warning system was envisioned using telegraph wires to trigger public alerts when winds exceeded 70 miles per hour ( Holden 1883 Coleman and Pence 2009). The concept of a tornado warning has evolved over the past two centuries. Overall, performance is encouraging and suggests that automated tornado warning guidance is worth exploring with real-time data. The effects of the different optimization functions on warning performance are explored. The different AI systems yield unique warning output depending on the desired attributes of the optimization function. Using genetic algorithms, multiple AI systems are developed with different optimization functions. 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.). The machine-learning probabilities are used to produce tornado warning decisions for each grid point and lead time. Machine-learning models are trained to forecast the temporal and spatial probabilities of tornado formation for a specific lead time. Over 700 tornadoes develop within the ensemble of simulations, varying in duration, length, and associated storm mode. The utility of employing artificial intelligence (AI) to issue tornado warnings is explored using an ensemble of 128 idealized simulations.
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