A statistical scheme to forecast the daily lightning threat over southern Africa using the Unified Model
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Date
Authors
Gijben, Morne
Dyson, Liesl L.
Loots, Mattheus Theodor
Journal Title
Journal ISSN
Volume Title
Publisher
Elsevier
Abstract
Cloud-to-ground lightning data from the Southern Africa Lightning Detection Network and numerical weather prediction model parameters from the Unified Model are used to develop a lightning threat index (LTI) for South Africa. The aim is to predict lightning for austral summer days (September to February) by means of a statistical approach. The austral summer months are divided into spring and summer seasons and analysed separately. Stepwise logistic regression techniques are used to select the most appropriate model parameters to predict lightning. These parameters are then utilized in a rare-event logistic regression analysis to produce equations for the LTI that predicts the probability of the occurrence of lightning. Results show that LTI forecasts have a high sensitivity and specificity for spring and summer. The LTI is less reliable during spring, since it over-forecasts the occurrence of lightning. However, during summer, the LTI forecast is reliable, only slightly over-forecasting lightning activity. The LTI produces sharp forecasts during spring and summer. These results show that the LTI will be useful early in the morning in areas where lightning can be expected during the day.
Description
Keywords
Lightning, Numerical weather prediction, Rare-event logistic regression, Clouds, Forecasting, Lightning, Regression analysis, Cloud-to-ground lightning, Lightning detection, Logistic regression analysis, Numerical weather prediction, Numerical weather prediction models, Statistical approach, Statistical scheme, Weather forecasting, Numerical model, Prediction, Statistical analysis, Weather forecasting, South Africa (SA)
Sustainable Development Goals
Citation
Gijben, M., Dyson, L.L. & Loots, M.T. 2017, 'A statistical scheme to forecast the daily lightning threat over southern Africa using the Unified Model', Atmospheric Research, vol. 194, pp. 78-88.