Observational study demonstrates 2D spatiotemporal mapping of positive narrow bipolar lightning events using VHF interferometry, indicating a viable framework for regional equatorial tracking.
Accurate lightning mapping is crucial for improving lightning characterisation studies in Indonesia. However, the limitations of existing technology and mapping methods hinder a deeper understanding of this phenomenon. This study presents the preliminary implementation of a very high frequency (VHF) interferometry (ITF) system at the Universitas Sriwijaya (UNSRI) Lightning Station, representing the first regional implementation of its kind in Indonesia. The interferometry system utilises three VHF antennas, designated as antennas B, C, and D, along with one fast field antenna, to capture radio frequency emissions from lightning events. The system is located at the Graha Bukit Asam, UNSRI, with the antennas arranged as two orthogonal 10 m baselines. A MATLAB-based processing chain computes azimuth and elevation using a cross-correlation technique by measuring the time differences between antennas B and C, and antennas B and D. The upsampled VHF signals were then segmented using a 131,072-sample window and a 1024-sample shift between consecutive windows. A total of eleven positive Narrow Bipolar Events (PNBEs) were observed between April and June 2024; of these, two events satisfied a 70% radiation-capture threshold and were successfully mapped in two-dimensional azimuth–elevation space, illustrating the spatiotemporal evolution of the PNBE radiation sources. The primary contribution of this study is a preliminary high-temporal-resolution two-dimensional azimuth–elevation reconstruction of two selected PNBE cases at an equatorial site (Palembang), providing a methodological foundation for further regional implementation. The mapped result presented here is preliminary and based on two PNBE cases; the site-specific calibration of the UNSRI station is currently in progress, and broader cross-validation against established lightning detection networks remains future work.
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Sidik et al. (2026) studied this question.
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