@Article{AlmeidaFranCamp:2020:ShFoSy,
               author = "Almeida, Vin{\'{\i}}cius Albuquerque de and Franca, Gutembert 
                         Borges and Campos Velho, Haroldo Fraga de",
          affiliation = "{Universidade Federal do Rio de Janeiro (UFRJ)} and {Universidade 
                         Federal do Rio de Janeiro (UFRJ)} and {Instituto Nacional de 
                         Pesquisas Espaciais (INPE)}",
                title = "Short-range forecasting system for meteorological convective 
                         events in Rio de Janeiro using remote sensing of atmospheric 
                         discharges",
              journal = "International Journal of Remote Sensing",
                 year = "2020",
               volume = "41",
               number = "11",
                pages = "4372--4388",
                month = "jun.",
             abstract = "In this study, a method is presented for meteorological convective 
                         event forecasting at the terminal control area of the Galeao 
                         International Airport, Rio de Janeiro, Brazil, using machine 
                         learning, sounding and remotely sensed atmospheric discharge data 
                         from 2001 to 2016. A monthly and daily climatology were computed 
                         for the atmospheric discharge temporal distribution in the study 
                         area. Six machine learning models were trained and cross-validated 
                         for 10 years (2001-2010), and a test was produced for 6 years 
                         (2011-2016). The results showed that the deep learning 
                         fully-connected (dense) algorithm achieved the best results for 
                         storm forecast and severity based on the following statistics: 
                         probability of detection (0.91 and 0.85), BIAS (1.03 and 1.07), 
                         false-alarm ratio (0.12 and 0.20) and CSI (0.81 and 0.69), 
                         respectively. The 6-year test analysis showed that the model has 
                         increasing performance for high-impact events, and this 
                         performance decreases gradually as the events become weaker and 
                         more frequent. The models presented here could be useful tools for 
                         air traffic management purposes.",
                  doi = "10.1080/01431161.2020.1717669",
                  url = "http://dx.doi.org/10.1080/01431161.2020.1717669",
                 issn = "0143-1161",
             language = "en",
           targetfile = "Short range forecasting system for meteorological convective 
                         events in Rio de Janeiro using remote sensing of atmospheric 
                         discharges.pdf",
        urlaccessdate = "28 abr. 2024"
}