Received: 24 Aug 2017 – Accepted for review: 20 Sep 2017 – Discussion started: 25 Sep 2017
Abstract. This paper puts forward an algorithm for estimating the detection efficiency (DE) of a lightning location system (LLS). The algorithm can be applied to lightning flash/stroke density correction (e.g., cloud-to-ground (CG) lightning flash/stroke density) and LLS performance evaluation. A lightning strike density correction for DE promotes the applicability of the LLS data. Fundamentally, the generalized extreme value (GEV) distribution was found to best fit the probability distribution of the signal strengths of the lightning observed by the ADTD detectors in Beijing, China. With respect to this probability distribution, we estimated the single-station acceptance damped by the uneven underlying land surface conductivity. Accounting for the multi-detector location modes supported by single-station acceptance, the iterative algorithm was applied for deducing the DE of a LLS. In this case study, the DE estimates of the ADTD network were lower in the mountainous areas than in the plains. These lower estimates can be due to the low underlying conductivity of the mountainous areas, which creates a high attenuation effect on the lightning electromagnetic signals, and the greater distances from the lightning detectors. Subsequently, the cloud-to-ground (CG) lightning flash/stroke density derived from the ADTD data was corrected for the DEs. The results indicated that the CG lightning flash/stroke densities in the northern and northwestern mountainous areas are lower than that in the highly urbanized plains. This anomaly is due to the effects of the increased roughness of the underlying land surfaces, enhanced aerosols, urban heat island (UHI), and intensifying thunderstorm activities in urban areas, but this anomaly is not likely related to the DE discrepancy.
How to cite. Hu, H. and Zhang, X.: An algorithm for estimating the detection efficiency of a lightning location system, Nat. Hazards Earth Syst. Sci. Discuss. [preprint], https://doi.org/10.5194/nhess-2017-307, in review, 2017.