Lightning discharges in the atmosphere owe their existence to the combination of complex dynamic and microphysical processes. Knowledge discovery and data mining methods can be used for seeking characteristics of data and their teleconnections in complex data clusters. We have used machine learning techniques to successfully hindcast nearby and distant lightning hazards by looking at single-site observations of meteorological parameters. We developed a four-parameter model based on four commonly available surface weather variables (air pressure at station level (QFE), air temperature, relative humidity, and wind speed). The produced warnings are validated using the data from lightning location systems. Evaluation results show that the model has statistically considerable predictive skill for lead times up to 30 min. Furthermore, the importance of the input parameters fits with the broad physical understanding of surface processes driving thunderstorms (e.g., the surface temperature and the relative humidity will be important factors for the instability and moisture availability of the thunderstorm environment). The model also improves upon three competitive baselines for generating lightning warnings: (i) a simple but objective baseline forecast, based on the persistence method, (ii) the widely-used method based on a threshold of the vertical electrostatic field magnitude at ground level, and, finally (iii) a scheme based on CAPE threshold. Apart from discussing the prediction skill of the model, data mining techniques are also used to compare the patterns of data distribution, both spatially and temporally among the stations. The results encourage further analysis on how mining techniques could contribute to further our understanding of lightning dependencies on atmospheric parameters.

大气中的闪电放电是由于复杂的动态过程和微物理过程的结合而存在的。知识发现和数据挖掘方法可用于寻找复杂数据集群中的数据及其远程连接的特征。通过查看气象参数的单点观测结果，我们已经使用机器学习技术成功地将附近和远处的雷电灾害进行了预报。我们基于四个常用的地面天气变量（站级气压（QFE），气温，相对湿度和风速）开发了一个四参数模型。产生的警告使用闪电定位系统的数据进行验证。评估结果表明，该模型在长达30分钟的交货时间方面具有统计学上可观的预测能力。此外，输入参数的重要性与驱动雷暴的地表过程的广泛物理理解相符（例如，地表温度和相对湿度将是雷暴环境的不稳定性和水分可用性的重要因素）。该模型还改进了用于生成雷电警告的三个竞争性基线：（i）基于持续性方法的简单但客观的基线预测，（ii）基于地面垂直静电场强度阈值的广泛使用的方法，最后（iii）一种基于CAPE阈值的方案。除了讨论模型的预测技巧外，数据挖掘技术还用于比较站点之间在空间和时间上的数据分布模式。