Cloud-to-Ground lightning nowcasting using Machine Learning
Alice La Fata, Federico Amato, M. Bernardi, Mirko D’Andrea, Renato Procopio, Elisabetta Fiori
Abstract
This paper discusses the use of Random Forest (RF), a popular Machine Learning (ML) algorithm, to perform spatially explicit nowcasting of cloud-to-ground lightning occurrence. An application to the Italian territory and the surrounding seas is then presented. Specifically, a dataset including eighteen geo-environmental features has been used to forecast 1-hour ahead lightning occurrence over a three-months period (August- October 2018). The features' importance resulting from the best RF model showed how data-driven models are able to identify relationships between variables, in agreement with previous physically-based knowledge of the phenomenon. The encouraging results obtained in terms of forecasting accuracy suggest how, after proper improvements, ML-based algorithms could find their place in wider early-warning systems to support disaster risk management procedures.