An Artificially Intelligent System for the Automated Issuance of Tornado Warnings in Simulated Convective Storms
Open Access
Author:
Steinkruger, Dylan
Graduate Program:
Meteorology
Degree:
Master of Science
Document Type:
Master Thesis
Date of Defense:
March 19, 2020
Committee Members:
Paul Markowski, Thesis Advisor/Co-Advisor George Spencer Young, Thesis Advisor/Co-Advisor David Jonathan Stensrud, Committee Member David Jonathan Stensrud, Program Head/Chair
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. The probabilities are used to produce tornado warning decisions for each grid point. 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 performance of multiple AI systems is assessed. Overall, performance is encouraging and suggests that automated tornado warning guidance is worth exploring with real-time data.
