A GPU-Based Grid Traverse Algorithm for Accelerating Lightning Geolocation Process

Most lightning location networks are based on real-time analytical solutions of certain simplified models, while the reality is much more complicated. In this paper, we introduce a graphics processing unit (GPU)-based parallel computing algorithm that can extensively benefit lightning geolocation networks. For a network running this GPU-based algorithm, one can build up a geolocation database based on numerical solutions of certain complete models in advance, and lightning geolocations can then be easily determined with a grid-searching technique in real time. One such grid-searching technique, is the grid traverse algorithm (GTA) for the traditional time of arrival technique. By running GPU-based GTA in a six-station two-dimensional (2-D) and a five-station 3-D networks, we show that extremely high network performance can be achieved, with a processing speed of about 2700 times faster than CPU-based GTA. The location accuracy of GPU-GTA is examined with Monte Carlo simulations, showing that GPU-GTA can locate a lightning source in real time with high accuracy. We also find that when the grid step is comparable with the inherent time uncertainty of a network, the location accuracy cannot be improved further with a finer grid step.