Estimation of –CG lightning distances using single-station E-field measurements and machine learning techniques

Abstract

Machine learning (ML) techniques have been used around the world to solve different problems. In this work, we applied ML techniques to estimate lightning distance for return strokes (RS) in negative cloud-to-ground (-CG) flashes. The approach uses E-field records from a single-station system. A lightning electric field waveform dataset containing more than 1500 waveforms of negative RS recorded at LOG, Florida, US was used to train and validate the ML classifiers. The dataset was split into day time and night time records. For day-time records, the quadratic Support Vector Machine (SVM) classifier was the one with the best accuracy (80%) and for night-time, the best one was the linear SVM with an accuracy of 88%. The ML classifiers were applied to estimate lightning distance in thunderstorms in the Amazon region of Brazil, and the results were compared against GOES-16 images and STARNET lightning location data. The main application of such methodology is for regions with no lightning location systems or no communication links to obtain lightning location data.