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Metadados

@InCollection{StephanyStCaLiGaPe:2019:DaMiAp,
               author = "Stephany, Stephan and Strauss, Cesar and Calheiros, Alan James 
                         Peixoto and Lima, Glauston Roberto Teixeira de and Garcia, 
                         Jo{\~a}o Victor Cal and Pessoa, Alex Sandro Aguiar",
                title = "Data mining approaches to the real-time monitoring and early 
                         warning of convective weather using lightning data",
            booktitle = "Towards mathematics, computers and environment: a disasters 
                         perspective",
            publisher = "Springer Nature",
                 year = "2019",
               editor = "Santos, Leonardo Bacelar Lima and Negri, Rog{\'e}rio Galante and 
                         Carvalho, Tiago Jos{\'e} de",
                pages = "83--101",
              address = "Cham, Switzerland",
             keywords = "data mining, real time monitoring, convective weather.",
             abstract = "Tracking and monitoring of convective events may require the 
                         analysis of a huge amount of data from sensors on the ground, such 
                         as weather radars, or on board of satellites, in addition to the 
                         forecasts of numerical models. Thunderstorms associated with 
                         severe convective events have a potential to cause strong winds, 
                         floods, and landslides, with serious environmental and 
                         socio-economic impacts. New approaches based on data mining have 
                         been proposed for countries like Brazil that lack a complete 
                         weather radar coverage, but have some ground-based lightning 
                         detector networks. Lightning data may help to visualize the 
                         current state of convective systems in near real-time or to 
                         estimate the amount of convective precipitation in a given area 
                         and period of time. Data mining algorithms can be trained using 
                         numerical model data and lightning data yielding specific data 
                         mining models, which can be used to predict the occurrence of 
                         convective activity from numerical model forecasts. These data 
                         mining models may help meteorologists to improve the accuracy of 
                         early warnings and forecastings, mainly in countries that lack a 
                         complete weather radar coverage.",
          affiliation = "{Instituto Nacional de Pesquisas Espaciais (INPE)} and {Instituto 
                         Nacional de Pesquisas Espaciais (INPE)} and {Instituto Nacional de 
                         Pesquisas Espaciais (INPE)} and {Centro Nacional de Monitoramento 
                         e Alertas de Desastres Naturais (CEMADEN)} and {Centro Nacional de 
                         Monitoramento e Alertas de Desastres Naturais (CEMADEN)} and 
                         {Climatempo Meteorologia}",
                  doi = "10.1007/978-3-030-21205-6_5",
                  url = "http://dx.doi.org/10.1007/978-3-030-21205-6_5",
                 isbn = "978-3-030-21204-9 and {978-3-030-21205-6 (eBook)}",
             language = "en",
           targetfile = "stephany_data.pdf",
        urlaccessdate = "10 abr. 2021"
}


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