@InProceedings{VerdelhoBeCaCaOlZa:2022:QuPrEs,
               author = "Verdelho, F. and Beneti, C. and Calvetti, L. and Calheiros, R. and 
                         Oliveira, L. and Zanata, M.",
          affiliation = "SIMEPAR and SIMEPAR and {Universidade Federal do Paran{\'a} 
                         (UFPR)} and SIMEPAR and {Universidade Federal do Paran{\'a} 
                         (UFPR)} and {Universidade Federal do Paran{\'a} (UFPR)}",
                title = "Quantitative precipitation estimation using weather radar and rain 
                         gauge data fusion with machine learning",
            booktitle = "Anais...",
                 year = "2022",
         organization = "Encontro dos Alunos de P{\'o}s Gradua{\c{c}}{\~a}o em 
                         Meteorologia (EPGMET), 21.",
                 note = "{Resumo simples}",
             keywords = "precipitation, radar, machine learning, nowcasting.",
             abstract = "Quality data in quantitative precipitation estimation (QPE) is an 
                         important tool for many applications such as flash flood 
                         forecasting and hydropower generation management. Precipitation 
                         estimates have benn generated using differente radar Z-R and 
                         polarimetric relationships, both from the literature and locally 
                         adjusted,with reasonable adjustments with rain gauges and 
                         distrometers, considering data filtering, range from radar, 
                         orography, signal propagations among other factos that may affect 
                         the estimates. We have developed and used operationally a QPE 
                         multi-sensor fusion approach with the usage of weather radar, 
                         satellite and rain gauge data which does not require frequent 
                         processing to update the weights of the data sources, as in other 
                         schemes.",
  conference-location = "Cachoeira Paulista",
      conference-year = "24-27 out. 2022",
             language = "en",
           targetfile = "EPGMET_Simples_Fernanda_Verdelho - Fernanda Verdelho.pdf",
        urlaccessdate = "16 jun. 2024"
}