Track: Data Analytics and Big Data

Abstract

Lightning is essentially an electromagnetic field phenomenon that can fundamentally be characterized using scientific methods. It would interest property designers and managers to have a facility of protection against lightning for property and life and one stage to effect the protection is an ability to predict the occurrence of lightning. In one type of approach, lightning alert regions can be configured around important assets such as buildings in different locations. Notifications about alerts in an area of interest can be sent via emails, SMS texts to phones and user interface (UI) platforms on personal computers. The meteorology industry can leverage real-time lightning information to improve forecasting, monitoring and safety of communities. This paper uses big data from 2016 to 2022 to show how much lightning Zambia experienced in the seven-year span. Big data was used to detect patterns and trends around the country. Big data is one common and contemporary factor that informs policy and decision in business sectors. The paper also shows how an alert region, Lusaka city, can be configured in order to send alerts and warnings to the users within the city about lightning threat developments. By employing AI-based analytics the results show that Zambia experienced a total of 89,826,330 lightning events from 2016 to 2022. By centering Lusaka city at coordinates 15.3875 oS and 28.3228 oE, 168 alerts were monitored using Thunderstorm Manager between 25 January 2023 and 24 February 2023. The longest lightning threat duration observed was 11 hours 20 minutes within Lusaka city.

Keywords
Buildings, Electromagnetic Fields, Forecasting, Lightning and Monitoring.