Prediction of Cloud-to-Ground Lightning Through Gaussian Process Regression with Satellite Thermal Infrared Imagery and Numerical Weather Prediction Modeling Data

Among the effects of the climate change we are experiencing, the increase in the frequency of extreme event occurrences is evident. In this context, numerous studies confirmed the link between extreme meteorological events and the lightning activity. The possibility of having short-term predictions of the intensity of lightning phenomena would allow the near-real time monitoring of the evolution of such events. This paper proposes a multidisciplinary approach aiming at developing a regression algorithm to nowcast the density of cloud-to-ground lightning strokes one hour in advance. The proposed algorithm is developed thanks to the possibility to operate jointly with remote sensing imagery, numerical weather prediction model outcomes, and historical information on lightning. The possible dependence between meteorological data and lightning is seeked using Gaussian process regression models. The results obtained suggest that the proposed model can estimate low numbers of strokes accurately, whereas larger numbers of strokes are underestimated. Nevertheless, their presence is correctly detected. This suggests the potential of the proposed method as a processing tool to support the management of weather-related hazards.