TrackingStorm: Visualization Tool for a Storm Detector Network (SDN) in the LF Spectrum

• Autor(a) principal: Adams,Augusto Mathias
• Data de Publicação: 2021
• Outros Autores: Heilmann,Armando, Adams,Anselmo Daniel, Dartora,César Augusto, Odake Junior,Edson Masao, Tertuliano Filho,Horácio, Santos,Luis Augusto Cordeiro dos
• Tipo de documento: Artigo
• Idioma: eng
• Título da fonte: Brazilian Archives of Biology and Technology
• Texto Completo: http://old.scielo.br/scielo.php?script=sci_arttext&pid=S1516-89132021000200213
• topic: lightning; detection efficiency; location accuracy; short range detector; lightning risk alert system

Abstract During the last year the Group of Atmospheric Electricity Phenomena (FEA/UFPR) developed a short range lightning location network based on a sensor device called Storm Detector Network (SDN), along with a set of algorithms that enables to track storms, determining the Wide Area Probability (WAP) of lightning occurrence, risk level of lightning and Density Extension of the Flashes (DEF), using the geo-located lightning information as input data. These algorithms compose a Dashboard called Tracking Storm Interface (TSI), which is the visualization tool for an experimental short range Storm Detector network prototype in use on the region of Curitiba-Paraná, Brazil. The algorithms make use of Geopandas and clustering algorithms to locate storms, estimate centroids, determine dynamic storm displacement and compute parameters of thunderstorms like velocity, head edge of electrified cloud, Mean Stroke Rate, and tracking information, which are important parameters to improve the alert system which is subject of this research. To validate these algorithms we made use of a simple storm simulation, which enabled to test the system with huge amounts of data. We found that, for long duration storms, the tracking results, velocity and directions of the storms are coherent with the values of simulation and can be used to improve an alert system for the Storm Detector network. WAP can reach at least 75% of prediction efficiency when used 6 past WAP data, but can reach 98.86% efficiency when more data is available. We use storm dynamics to make improved alert predictions, reaching an efficiency of ~87%.