The Asia-Pacific Lightning Location Network (APLLN) is a lightning location system consisting of a series of very low-frequency signal detection sites. Since 2018, 16 detection sites have been deployed with an average baseline longer than 1000 km. The detection site used a trigger sampling method to record the lightning signal with a duration of 2 ms and calculates the lightning arrival time based on digital filtering and the Hilbert envelope method. APLLN used a time difference location algorithm and improved Levenberg–Marquardt non-linear least squares iterative algorithm to calculate and optimize the lightning location results. The analysis results of a strong thunderstorm process show that the average detection efficiency of APLLN was 55.34% for intracloud (IC) strokes, 63.55% for cloud-to-ground (CG) strokes and 61.83% for all strokes (IC + CG). The average location error of APLLN for this thunderstorm is 5–10 km.

This study investigates the latency of lightning-caused fires in the boreal coniferous forests of the Greater Khingan Mountains, employing advanced machine learning techniques to analyze the relationship between meteorological factors, lightning characteristics, and fire ignition and smoldering processes. Using the Random Forest Model (RFM) combined with Recursive Feature Elimination with Cross-Validation (RFECV) and SHapley Additive exPlanations (SHAP), the study identifies key factors influencing fire latency. Two methods, Min distance and Min latency, were used to determine ignition lightning, with the Min distance method proving more reliable. The results show that lightning-caused fires cluster spatially and peak temporally between May and July, aligning with lightning activity. The Fine Fuel Moisture Code (FFMC) and precipitation were identified as the most influential factors. This study underscores the importance of fuel moisture and weather conditions in determining latency of lightning-caused fire, offering valuable insights for enhancing early warning systems. Despite limitations in data resolution and the exclusion of topographic factors, this study advances our understanding of lightning-fire latency mechanisms and provides a foundation for more effective wildfire management strategies under climate change.

Given the increasing threat of lightning to modern electronic systems and human activities, the accurate identification and classification of lightning electromagnetic pulses has become a critical research focus, prompting the present study. A dataset was established by collecting lightning electromagnetic signals generated by various types of lightning under diverse environmental conditions via the lightning location system of the Institute of Electrical Engineering, Chinese Academy of Sciences. Subsequently, A deep learning model integrating a convolutional neural network was developed for feature extraction and pattern recognition using the multi-station data. Experimental results demonstrate that the proposed model significantly improves LEMP identification accuracy (exceeding 97%) compared to existing single-station methods. Moreover, it effectively uncovers complex hidden features within the data, outperforming conventional approaches in both accuracy and robustness. In conclusion, the proposed deep learning model offers a reliable technical foundation for lightning monitoring and localization based on LEMP signals.

Accurate lightning data are critical for disaster warning and climate research. This study systematically compares the Fengyun-4A Lightning Mapping Imager (FY-4A LMI) satellite and the Advanced Time-of-arrival and Direction (ADTD) lightning location network in the Beijing-Tianjin-Hebei (BTH) region (April–August, 2020–2023) using coefficient of variation (CV) analysis, Welch’s independent samples t-test, Pearson correlation analysis, and inverse distance weighting (IDW) interpolation. Key results: (1) A significant systematic discrepancy exists between the two datasets, with an annual mean ratio of 0.0636 (t = −5.1758, p < 0.01); FY-4A LMI shows higher observational stability (CV = 5.46%), while ADTD excels in capturing intense lightning events (CV = 28.01%). (2) Both datasets exhibit a consistent unimodal monthly pattern peaking in July (moderately strong positive correlation, r = 0.7354, p < 0.01) but differ distinctly in diurnal distribution. (3) High-density lightning areas of both datasets concentrate south of the Yanshan Mountains and east of the Taihang Mountains, shaped by topography and water vapor transport. This study reveals the three-factor (climatic background, topographic forcing, technical characteristics) coupled regulatory mechanism of data discrepancies and highlights the complementarity of the two datasets, providing a solid scientific basis for satellite-ground data fusion and regional lightning disaster defense.