@Article{CaseriSantStep:2022:CoReNe,
               author = "Caseri, Ang{\'e}lica N. and Santos, Leonardo Bacelar Lima and 
                         Stephany, Stephan",
          affiliation = "{Centro Nacional de Monitoramento e Alerta de Desastres Naturais 
                         (CEMADEN)} and {Centro Nacional de Monitoramento e Alerta de 
                         Desastres Naturais (CEMADEN)} and {Instituto Nacional de Pesquisas 
                         Espaciais (INPE)}",
                title = "A convolutional recurrent neural network for strong convective 
                         rainfall nowcasting using weather radar data in Southeastern 
                         Brazil",
              journal = "Artificial Intelligence in Geosciences",
                 year = "2022",
               volume = "3",
                pages = "8--13",
                month = "Dec.",
             keywords = "Deep learning, Extreme events, Nowcasting, Rainfall, Weather 
                         radar.",
             abstract = "Strong convective systems and the associated heavy rainfall events 
                         can trig-ger floods and landslides with severe detrimental 
                         consequences. These events have a high spatio-temporal 
                         variability, being difficult to predict by standard meteorological 
                         numerical models. This work proposes the M5Images method for 
                         performing the very short-term prediction (nowcasting) of heavy 
                         convective rainfall using weather radar data by means of a 
                         convolutional recurrent neural network. The recurrent part of it 
                         is a Long Short-Term Memory (LSTM) neural network. Prediction 
                         tests were performed for the city and surroundings of Campinas, 
                         located in the Southeastern Brazil. The convolutional recurrent 
                         neural network was trained using time series of rainfall rate 
                         images derived from weather radar data for a selected set of heavy 
                         rainfall events. The attained pre-diction performance was better 
                         than that given by the persistence forecasting method for 
                         different prediction times.",
                  doi = "10.1016/j.aiig.2022.06.001",
                  url = "http://dx.doi.org/10.1016/j.aiig.2022.06.001",
                 issn = "2666-5441",
             language = "en",
           targetfile = "A convolutional recurrent neural network for strong convective 
                         rainfall nowcasting using weather radar data in Southeastern 
                         Brazil.pdf",
        urlaccessdate = "03 maio 2024"
}