Analysis of lightning occurence in Zambia

Nyambe, Silumelume H; Gomes, Chandima; Lubasi, Foster Chileshe; Gomes, Ashen

doi:10.1109/iclp.2014.6973442

Cited by 5 publications

(2 citation statements)

References 9 publications

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“…Determination of local environmental lightning flash density, Ng values, is important because lightning activities are not uniformly distributed around the globe (Christian et al 2003, King 2023and Nyambe et al 2014). These N g values are a necessity for lightning risk assessment (LRA) of any infrastructure and the subsequent appropriate lightning protection system (LPS) design.…”

Section: Literature Reviewmentioning

confidence: 99%

Towards a Lightning Detection and Warning System in Zambia

Mpanga¹,

Mwanza²,

Zulu³

2023

Proceedings of the International Conference on Industrial Engineering and Operations Management

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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 o S and 28.3228 o E, 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.

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Section: Literature Reviewmentioning

confidence: 99%

Towards a Lightning Detection and Warning System in Zambia

Mpanga¹,

Mwanza²,

Zulu³

2023

Proceedings of the International Conference on Industrial Engineering and Operations Management

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“…Zambia's climate is influenced by the equatorial low and the Inter-Tropical Convergence Zone (ITCZ) with its southward and northward movements over Africa in different months of the year [34]. The rains brought by the ITCZ are characterized by thunderstorms, and are occasionally severe, with much lightning [35] and sometimes hail.…”

Section: Study Areamentioning

confidence: 99%

Sensitivity of Simulations of Zambian Heavy Rainfall Events to the Atmospheric Boundary Layer Schemes

Bopape

Waitolo

Plant

et al. 2021

Climate

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Weather forecasting relies on the use of numerical weather prediction (NWP) models, whose resolution is informed by the available computational resources. The models resolve large scale processes, while subgrid processes are parametrized. One of the processes that is parametrized is turbulence which is represented in planetary boundary layer (PBL) schemes. In this study, we evaluate the sensitivity of heavy rainfall events over Zambia to four different PBL schemes in the Weather Research and Forecasting (WRF) model using a parent domain with a 9 km grid length and a 3 km grid spacing child domain. The four PBL schemes are the Yonsei University (YSU), nonlocal first-order medium-range forecasting (MRF), University of Washington (UW) and Mellor–Yamada–Nakanishi–Niino (MYNN) schemes. Simulations were done for three case studies of extreme rainfall on 17 December 2016, 21 January 2017 and 17 April 2019. The use of YSU produced the highest rainfall peaks across all three cases; however, it produced performance statistics similar to UW that are higher than those of the two other schemes. These statistics are not maintained when adjusted for random hits, indicating that the extra events are mainly random rather than being skillfully placed. UW simulated the lowest PBL height, while MRF produced the highest PBL height, but this was not matched by the temperature simulation. The YSU and MYNN PBL heights were intermediate at the time of the peak; however, MYNN is associated with a slower decay and higher PBL heights at night. WRF underestimated the maximum temperature during all cases and for all PBL schemes, with a larger bias in the MYNN scheme. We support further use of the YSU scheme, which is the scheme selected for the tropical suite in WRF. The different simulations were in some respects more similar to one another than to the available observations. Satellite rainfall estimates and the ERA5 reanalysis showed different rainfall distributions, which indicates a need for more ground observations to assist with studies like this one.

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