Improvements on Lightning Density Estimation Based on Analysis of Lightning Location System Performance Parameters: Brazilian Case

摘要

Density maps are one of the most common and powerful lightning data applications, and they are more efficient the more detailed they are. When working with CG lightning data from lightning location systems, some aspects must be included in the analysis to overcome network performance variations. Two parameters are typically used to evaluate system performance: detection efficiency (DE) and location accuracy (LA).For the Brazilian National Integrated Lightning Detection Network, DE is typically evaluated by models, and LA is analyzed through confidence ellipses. This paper presents climatological analysis of lightning activity, including the most recent relative DE model developed in Brazil, as well as an adapted kernel smoothing method based on confidence ellipses [called Gaussian kernel based on confidence ellipses (GKBCE)] as approaches to minimize and/or include the spatial variation of the system's performance in the analysis. The maps are produced over the central-south portion of Brazil (mainly along ITAIPU power transmission lines), using 11 years of data available from the network (from January 1999 to December 2009). The model increased density by ${sim}{20%}$ over the entire region, without making considerable changes to the spatial pattern. The GKBCE seems to work well in smoothing, obtaining better results than the standard cell count (quadrat) method, by working independently of the grid size (allowing the creation of high-resolution maps), and by including location errors in the analysis. The use of these procedures might result in more detailed maps and more suitable results when analyzing lightning data.