PREDICTION OF OCCURRENCE OF CLOUD-TO-GROUND ELECTRICAL DISCHARGES USING FORECASTS OF THE BRAMS NUMERICAL MODEL

Alex de Almeida Fernandes, Glauston Roberto Teixeira de Lima, Stephan Stephany, Alan J. P. Calheiros

Revista Mundi Engenharia Tecnologia e Gestão · 30 May 2019

In one sentence. Neural networks trained on past BRAMS forecasts and lightning density fields predict cloud-to-ground discharge occurrence as an ancillary convective-alert tool.

Abstract

The prediction of occurrence of severe convective events allows to issue meteorological alerts in order to reduce potential catastrophic events. In many cases, numerical models for weather forecast are not able to efficiently simulate such events. Alternatively, considering the large amount and diversity of meteorological data, the employment of Data Mining techniques has become widespread. In the case of convective activity, the use of past data allows to identify characteristic patterns in the model forecasts by associating them to the corresponding field of density of occurrence of cloud-to-ground atmospheric electrical discharges. Such task is performed by a machine learning algorithm, in this case, a set of neural networks. As a consequence, these patterns can be detected in future forecasts generated by the numerical model, allowing to predict the occurrence of discharges, which are associated to convective activity. In the current work, the proposed approach was applied to BRAMS, a numerical model developed in the country for operational and research use in Meteorology. Consequently, the ability to predict the occurrence of discharges by the set of neural networks was analysed for some selected events as well as its usefulness as an ancillary tool in operational weather forecast.

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