Lightning Distance Estimation Using LF Lightning Radio Signals via Analytical and Machine-Learned Models
Lightning Distance Estimation Using LF Lightning Radio Signals via Analytical and Machine-Learned Models
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DOI：
10.1109/tgrs.2020.2972153
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发表时间：
2020-02
影响因子：
8.2
通讯作者：
A. L. Antunes de Sa;R. Marshall
中科院分区：
文献类型：
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作者：
A. L. Antunes de Sa;R. Marshall
闪电地理定位在各种应用中是有用的，从天气临近预报到更好地了解雷暴演变过程和电离层遥感。闪电产生的无线电信号可用于闪电回击的距离估计，其中最常用的技术是闪电探测网络中的到达时间差。虽然这些仪器网络提供了最可靠和最佳的准确性，但无法访问它们的用户可以使用独立仪器从闪电地理定位中受益。在这篇文章中，我们提出了训练快速模型的框架，该模型能够从单台仪器观测的甚低频/低频（VLF/LF，3-300 kHz）无线电脉冲或“sferics”中估计负云地闪电位置，而无需了解电离层的D-区域状态。该模型使用分析方法生成，基于地面和天波之间的延迟，以及机器学习方法。训练框架应用于三个不同的数据集以评估模型的准确性。这些数据集的机器学习模型，其中包括模拟和观察到的sferics的验证，证实了这种技术作为一个有前途的解决方案，使用单个接收器的闪电距离估计。使用机器学习模型对堪萨斯观测到的天球进行距离估计，得出的RMSE为53 km，其中68%在9.8 km以内。使用分析方法的估计被发现有一个RMSE为54公里，其中68%是在32公里。我们的方法和潜在的改进进行调查的局限性也进行了讨论。
Lightning geolocation is useful in a variety of applications, ranging from weather nowcasting to a better understanding of thunderstorm evolution processes and remote sensing of the ionosphere. Lightning-generated radio signals can be used in range estimation of lightning return strokes, for which the most commonly employed technique is the time difference of arrival in lightning detection networks. Though these instrument networks provide the most reliability and best accuracy, users without access to them can instead benefit from lightning geolocation using a standalone instrument. In this article, we present the framework for training fast models capable of estimating negative cloud-to-ground lightning location from single-instrument observations of very low frequency/low frequency (VLF/LF, 3–300 kHz) radio pulses or “sferics,” without knowledge of the ionosphere’s D-region state. The models are generated using an analytical method, based on the delay between ground and skywave, and a machine learning method. The training framework is applied to three different data sets to assess model accuracy. Validation of the machine-learned models for these data sets, which include both simulated and observed sferics, confirms this technique as a promising solution for lightning distance estimation using a single receiver. Distance estimates using a machine-learned model for observed sferics in Kansas yield an RMSE of 53 km with 68% of them being within 9.8 km. Estimates using the analytical method are found to have an RMSE of 54 km with 68% of them being within 32 km. Limitations of our methodology and potential improvements to be investigated are also discussed.