An Artificially Intelligent System for the Automated Issuance of Tornado Warnings in Simulated Convective Storms

Abstract

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.

Approximation of a Convective-Event-Monitoring System Using GOES-R Data and Ensemble ML Models

“…In this study, eight different ML approaches are compared (Table 3), such as LR, RF, and Multi-layer perceptron (MLP), which are widely used in convective-hazard forecasting (e.g., [14,17,22,47,48]), along with ML approaches based on Ensemble Learning techniques (i.e., a group of predictors, called an ensemble, are trained together to improve predictive ability; Figure 3). All ML models, assembly strategies, metric estimation, and preprocessing methods were taken from scikit-learn, a free software ML library for the Python programming language (https://scikit-learn.org/stable/, accessed on 5 December 2023).…”

“…Along with LR, RF is one of the most widely used ML models in issues related to convective-hazard forecasting, mostly because of its ability to capture non-linear association patterns between predictor and predictand, such as a convective storm system or precipitation [69]. Examples of RF applications in the literature are the works of [2][3][4]17,22,28,47,48,[70][71][72].…”