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.

中文翻译：

---

用于加速闪电地理定位过程的基于 GPU 的网格遍历算法

大多数闪电定位网络都是基于某些简化模型的实时分析解决方案，而实际情况要复杂得多。在本文中，我们介绍了一种基于图形处理单元 (GPU) 的并行计算算法，该算法可以广泛有益于闪电地理定位网络。对于运行这种基于 GPU 的算法的网络，可以预先根据某些完整模型的数值解建立一个地理定位数据库，然后可以通过网格搜索技术轻松地实时确定闪电地理定位。一种这样的网格搜索技术，是用于传统到达时间技术的网格遍历算法 (GTA)。通过在六站二维 (2-D) 和五站 3-D 网络中运行基于 GPU 的 GTA，我们表明可以实现极高的网络性能，处理速度比基于 CPU 的 GTA 快约 2700 倍。GPU-GTA 的定位精度通过 Monte Carlo 模拟检验，表明 GPU-GTA 可以高精度实时定位闪电源。我们还发现，当网格步长与网络固有的时间不确定性相当时，位置精度无法通过更精细的网格步长进一步提高。