Quality Control Technique for Ground-Based Lightning Detection Data Based on Multi-Source Data over China

Lightning is one of the most severe natural disasters, characterized by its sudden onset, short duration, and significant damage. Existing quality control (QC) schemes for millisecond-level lightning observation data from a single source are primarily limited by the instrument and equipment, leading to inadequate monitoring, forecasting, and early warning accuracy in severe convective weather. This study proposes a comprehensive QC scheme for lightning location data from the China Meteorological Administration ground-based National Lightning Detection Network (CMA-LDN). The scheme integrates radar composite reflectivity (CREF) and FY-4A cloud-top brightness temperature (TBB), exploring the coupled relationship between lightning activity and severe weather processes. Through experimental analysis of convective processes over different time periods, QC thresholds are established based on the CREF, TBB, and area ratio. In this research, CREF ≥ 10 dBZ, TBB ≤ 270 K, and an 80% area ratio are tuned to filter false signals. Based on the regional threshold and area ratio results, gross error elimination and spatiotemporal clustering are combined to achieve an overall QC rate of 28.7%. The most effective quality control (QC) method is spatial-temporal clustering, achieving a QC efficiency of 20.9%. The processed lightning data are further merged with CREF and generated a 1 km and 6 min resolution lightning location dataset, which significantly improves the accuracy of ground-based lightning detection and supports operational forecasting of severe convective weather.