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Revista Brasileira de Meteorologia
Print version ISSN 0102-7786
PESSOA, Alex Sandro Aguiar et al. Meteorological data mining for the prediction of severe convective events. Rev. bras. meteorol. [online]. 2012, vol.27, n.1, pp. 61-74. ISSN 0102-7786. http://dx.doi.org/10.1590/S0102-77862012000100007.
This work aims the early detection of possible occurrences of severe convective events in Central and Southeast Brazil by means of monitoring the output of the Eta numerical weather prediction model for each forecasted time interval and for a selected set of variables. The studied period ranges from January to February 2007. Classifiers were developed by two approaches, vector similarity and rough sets, in order to identify Eta outputs that can be associated to such events. It was assumed that severe convective events can be correlated to a large number of atmospheric electric discharges. The classifiers grouped the Eta meteorological model outputs for these selected variables based on the density of occurrences of cloud-to-ground atmospheric electrical discharges. Both classifiers show good performance for the chosen 2-month period at the three selected mini-regions of the Brazilian territory.
Keywords : data mining; weather forecast; convective events.