Location accuracy evaluation of lightning location systems using natural lightning flashes recorded by a network of high-speed cameras

2014 
This work presents a preliminary method for the evaluation of location accuracy of all Lightning Location System (LLS) in operation in southeastern Brazil (e.g. BrasilDAT, RINDAT, StarNet, WWLLN, GLD360), using natural cloud-to-ground (CG) lightning flashes. This can be done through a multiple high-speed cameras network (RAMMER network) installed in the Paraiba Valley region - SP - Brazil. The RAMMER network (Automated Multi-camera Network for Monitoring and Study of Lightning) is composed by four high-speed cameras operating at 2,500 frames per second. Three stationary black-and-white (B&W) cameras were situated in the cities of Sao Jose dos Campos and Cacapava. A fourth color camera was mobile (installed in a car), but operated in a fixed location during the observation period, within the city of Sao Jose dos Campos. The average distance among cameras was 13 kilometers. Each RAMMER sensor position was determined so that the network can observe the same lightning flash from different angles and all recorded videos were GPS (Global Position System) time stamped, allowing comparisons of events between cameras and the LLS. The RAMMER sensor is basically composed by a computer, a Phantom high-speed camera version 9.1 and a GPS unit. The lightning cases analyzed in the present work were observed by at least two cameras, their position was visually triangulated and the results compared with BrasilDAT network, during the summer seasons of 2011/2012 and 2012/2013. The visual triangulation method is presented in details. The calibration procedure showed an accuracy of 9 meters between the accurate GPS position of the object triangulated and the result from the visual triangulation method. Lightning return stroke positions, estimated with the visual triangulation method, were compared with LLS locations. Differences between solutions were not greater than 1.8 km.
    • Correction
    • Source
    • Cite
    • Save
    • Machine Reading By IdeaReader
    23
    References
    0
    Citations
    NaN
    KQI
    []