@Article{RamosTaCuSiGoDi:2022:CaMoSe,
               author = "Ramos, Marcelo Paiva and Tasinaffo, P. M. and Cunha, A. M. and 
                         Silva, D. A. and Gon{\c{c}}alves, G. S. and Dias, L. A. V.",
          affiliation = "{Instituto Nacional de Pesquisas Espaciais (INPE)} and {Instituto 
                         Tecnol{\'o}gico de Aeron{\'a}utica (ITA)} and {Instituto 
                         Tecnol{\'o}gico de Aeron{\'a}utica (ITA)} and {Instituto 
                         Tecnol{\'o}gico de Aeron{\'a}utica (ITA)} and {Instituto 
                         Tecnol{\'o}gico de Aeron{\'a}utica (ITA)} and {Instituto 
                         Tecnol{\'o}gico de Aeron{\'a}utica (ITA)}",
                title = "A canonical model for seasonal climate prediction using Big Data",
              journal = "Journal of Big Data",
                 year = "2022",
               volume = "9",
               number = "1",
                pages = "e27",
                month = "Dec.",
             keywords = "Atmospheric numerical model, Big Data, Hadoop, Hive, MapReduce, 
                         Seasonal climate prediction.",
             abstract = "This article addresses the elaboration of a canonical model, 
                         involving methods, techniques, metrics, tools, and Big Data, 
                         applied to the knowledge of seasonal climate prediction, aiming at 
                         greater dynamics, speed, conciseness, and scalability. The 
                         proposed model was hosted in an environment capable of integrating 
                         different types of meteorological data and centralizing data 
                         stores. The seasonal climate prediction method called M-PRECLIS 
                         was designed and developed for practical application. The 
                         usability and efficiency of the proposed model was tested through 
                         a case study that made use of operational data generated by an 
                         atmospheric numerical model of the climate area found in the 
                         supercomputing environment of the Center for Weather Forecasting 
                         and Climate Studies linked to the Brazilian Institute for Space 
                         Research. The seasonal climate prediction uses ensemble members 
                         method to work and the main Big Data technologies used for data 
                         processing were: Python language, Apache Hadoop, Apache Hive, and 
                         the Optimized Row Columnar (ORC) file format. The main 
                         contributions of this research are the canonical model, its 
                         modules and internal components, the proposed method M-PRECLIS, 
                         and its use in a case study. After applying the model to a 
                         practical and real experiment, it was possible to analyze the 
                         results obtained and verify: the consistency of the model by the 
                         output images, the code complexity, the performance, and also to 
                         perform the comparison with related works. Thus, it was found that 
                         the proposed canonical model, based on the best practices of Big 
                         Data, is a viable alternative that can guide new paths to be 
                         followed.",
                  doi = "10.1186/s40537-022-00580-9",
                  url = "http://dx.doi.org/10.1186/s40537-022-00580-9",
                 issn = "2196-1115",
             language = "en",
           targetfile = "ramos_2022_canonical.pdf",
        urlaccessdate = "15 maio 2024"
}