Abstract Using a local atmospheric electric field sensor network (Field Mill), an electromagnetic link model was proposed considering a free space electric field and a theoretical electric field calculated from an atmospheric discharge. With this, it was possible to compare the theoretical value of this electric field with the experimental value of the local atmospheric electric field (estimated by the Field Mill sensor network). The results are initially analyzed considering wavelengths in the EHF spectrum (λ ~ 10-5m) and the VLF spectrum (λ ~ 104m). As assumed before, admitting that the lightning channel acts as an isotropic irradiator and a Field Mill sensor acts as a receiving antenna for the spectrum emitted by the lightning, a irradiated power model of the electromagnetic waves reaching the Field Mill sensor, according to the ITU-R P.873.4 standard, was calculated, thus allowing a correlation with a satellite radio link, in which the Field Mill sensor acts as a ground receiving antenna and the main channel of the lightning strikes acts as a transmitting antenna. Also, a model of the power irradiated by each lightning discharge, recorded by the Field Mill sensor network, was proposed, and the Equivalent Isotropically Radiated Power - EIRP was calculated for different values of effective electric field (Eef) and then compared with the model of the power irradiated by the antenna (lightning). This makes it possible to do an analysis and determine if the lightning discharge can be approximated to an isotropically irradiated antenna model.

Abstract: The global atmospheric electric circuit is based on a model of electrical connection between the earth and the ionosphere (waveguide), capable of representing the flow of electric current in this waveguide. In the proposed model, a storm acts as a generator, allowing the ionosphere to maintain its highest electrical potential (approximately 300kV) in relation to Earth. When a storm forms, the bottom of the cloud becomes negatively charged. This study is focused on modeling this specific part of the global atmospheric electric circuit, which is renamed local atmospheric electric circuit. In the methodology, we use an RLC circuit to calculate the effects of electrified clouds in a 375kV transmission line considering an electrical coupling between them (an RLC circuit is an electrical circuit consisting of a resistor (R), an inductor (L), and to capacitor (C). The mathematical formulation was developed using transmission line theory considering a connection with the top of the storm cloud. Then, a model simulation using GNU Octave was performed, and the results demonstrated how this coupling affects voltage drop and phase shift in a 375kV transmission line. Thus, a local atmospheric electric circuit model, considering the particularities of the environment immersed in a real transmission line model, configures an important model in the perspective of project management of electric energy transmission networks.

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%.