Development and Initial Performance Assessment of The ULAT VLF Lightning Detection System in The Philippines

Abstract

An existing methodology for locating lightning events involves measuring the differences between the times when the signals reach the remote receivers located in different locations. This method is commonly known as the time-of-arrival (ToA) method. However, such approach commonly requires the entire signal source to be present and requires a lot of data and consumes processing power. To address this, the lightning geolocation algorithm used under the Understanding Lightning and Thunderstorm Project (ULAT) only uses specific lightning signatures to determine the location of cloud-to-ground (CG) lightning events. One challenge with this approach, however, is that it reduces the precision of the localization algorithm being used. This is also prone to the false selection of lightning events.In this paper, we investigate the performance of the existing implementation of the ULAT lightning geolocation algorithm and introduce improvements to the algorithm by (1) experimenting with different time-of-arrival algorithms using the same datasets, (2) adding a refining step using L-BFGS-B minimization, (3) using the sferic signal start time from the lightning data for sferic matching, (4) estimating location errors using Monte Carlo simulations. The geolocation results are then overlaid with the HIMAWARI satellite images to evaluate its relative performance in tracking the typhoon Noru (Karding) last September 25, 2022, and on a PAGASA thunderstorm advisory last November 15, 2022.