We introduce the empirical mode decomposition algorithm and applied low‐frequency filtering and high‐frequency noise reduction to the waveform of the electric field changes recorded in 1‐ms segments. This algorithm greatly improved the accuracy of the peak time extraction and the number of pulses in the low and very low frequency band, which enhanced the accuracy in positioning the pulse of the electric field change. Compared with the previous algorithm, the algorithm can significantly reduce the time error, giving a better positioning result. With a time error estimate of 100 ns and a limit of goodness of fit less than 5, the number of pulse locations is increased by nearly 7 times. The goodness‐of‐fit distribution of the pulse location results had a normal distribution, and the 95% confidence interval of goodness of fit was 0–4; the corresponding positioning space error was <60 m. The continuity of the lightning channel was significantly improved, and the development characteristics and fine structure of the lightning channel were clearly distinguished. The low‐frequency electric field detection array system gave detailed positioning results for a bolt from the blue lightning strike. By comparing the results from the low‐frequency electric field detection array with the actual lightning strike point, we objectively demonstrated the positioning performance of the new algorithm. The system gave positioning results for the lightning for all seven return strokes. The maximum horizontal distance between the locating point and the real lightning strike point was 57 m, the minimum horizontal distance between them was 3 m, and the mean distance was 27 m.

Accurate lightning forecast is significant for disaster prevention and reduction. However, the mainstream lightning forecast methods, which mainly rely on numerical simulations and parameterizations, can hardly cope with the spatiotemporal deviations. Meanwhile, the rapid and complex evolution of lightning regions go beyond the traditional extrapolation-based forecast methods. In this work, we propose a data-driven neural network model for hourly lightning forecast, which exploits both the numerical simulations and the recent historical lightning observations. The two kinds of data complement each other and play different roles at different stages of the forecast. The use of dual-source data greatly increases the amount of information available to improve the forecasting performance. To handle the variability of deviation patterns in numerical simulations, we introduce a channel-wise attention mechanism, which adaptively adjusts the proportion of each simulated parameter to maximize the useful information. The attention mechanism also enables the model to reveal the contribution of each simulated parameter for the forecast. Experimental results on a real-world dataset show that the proposed method outperforms several baseline methods. Ablation studies further demonstrate the effectiveness of our data fusion approach and attention module.

Monitoring lightning and its location is important for understanding thunderstorm activity and revealing lightning discharge mechanisms. This is often realized based on very low-frequency/low-frequency (VLF/LF) signals, very high-frequency (VHF) signals, and optical radiation signals generated during the lightning discharge process. The development of lightning monitoring and location technology worldwide has largely evolved from a single station to multiple stations, from the return strokes (RSs) of cloud-to-ground (CG) lightning flashes to total lightning flashes, from total lightning flashes to lightning discharge channels, and from ground-based lightning observations to satellite-based lightning observations, all of which have aided our understanding of atmospheric electricity. Lightning monitoring and positioning technology in China has kept up with international advances. In terms of lightning monitoring based on VLF/LF signals, single-station positioning technology has been developed, and a nationwide CG lightning detection network has been built since the end of the twentieth century. Research on total lightning flash positioning technology began at the beginning of the 21st century, and precision total lightning flash positioning technology has improved significantly over the last 10 years. In terms of positioning technology based on VHF signals, narrowband interferometers and wideband interferometers have been developed, and long-baseline radiation source positioning technology and continuous interferometers have been developed over the last ten years, significantly improving the channel characterization ability of lightning locations. In terms of lightning monitoring based on optical signals, China has for the first time developed lightning mapping imagers loaded by geosynchronous satellites, providing an important means for large-scale and all-weather lightning monitoring.

The application of empirical mode decomposition (EMD) in the analysis and processing of lightning electric field waveforms acquired by the low-frequency e-field detection array (LFEDA) in China has significantly improved the capabilities of the low-frequency/very-low-frequency (LF/VLF) time-of-arrival technique for studying the lightning discharge processes. However, the inherent mode mixing and the endpoint effect of EMD lead to certain problems, such as an inadequate noise reduction capability, the incorrect matching of multistation waveforms, and the inaccurate extraction of pulse information, which limit the further development of the LFEDA's positioning ability. To solve these problems, the advanced ensemble EMD (EEMD) technique is introduced into the analysis of LF/VLF lightning measurements, and a double-sided bidirectional mirror (DBM) extension method is proposed to overcome the endpoint effect of EMD. EEMD can effectively suppress mode mixing, and the DBM extension method proposed in this article can effectively suppress the endpoint effect, thus greatly improving the accuracy of a simulated signal after a 25-500-kHz bandpass filter. The resulting DBM_EEMD algorithm can be used in the LFEDA system to process and analyze the detected electric field signals to improve the system's lightning location capabilities, especially in terms of accurate extraction and location of weak signals from lightning discharges. In this article, a 3-D image of artificially triggered lightning obtained from an LF/VLF location system is reported for the first time, and methods for further improving the location capabilities of the LF/VLF lightning detection systems are discussed.

This study investigates the initial stage (IS) and return stroke (RS) currents of 50 triggered lightning flashes (TLFs) that were conducted in southern China. The IS of the negative TLFs has a longer duration and larger average current, charge transfer, and action integral than those reported elsewhere, with geometric means (GMs) of 347.9 ms, 132.5 A, 45.1 C, and 10.0 × 10³ A² s, respectively. Two positive TLFs containing no RS have much greater average currents, charge transfers, and action integrals in the IS when compared with the negative TLFs. The RS has a greater peak current (17.2 kA; GM, same to below), charge transfer within 1 ms (1.3 C), and action integral within 1 ms (5.8 × 10³ A² s), and shorter 10% to 90% rise time (0.4 μs) than elsewhere. The peak current is prominently correlated with the rate of rise, charge transfer within 1 ms, and action integral within 1 ms. Furthermore, when the total duration of the RS and any following continuing currents is longer than 40 ms, the peak current, charge transfer within 1 ms, and action integral within 1 ms of the RS are seldom greater than 25 kA, 2.6 C, and 15 × 10³ A² s, respectively. It is indicated that TLFs containing RSs tend to have a longer duration but a smaller charge transfer during the IS than those without RS. The peak current of the RS is weakly correlated with its preceding silence period when there was no channel base current.