@Article{AlmeidaSaKoSiGuSu:2020:InMaSe,
               author = "Almeida, Eug{\^e}nio Sper de and Santana, M{\'a}rcio 
                         Ant{\^o}nio Aparecido and Koga, Ivo Kenji and Silva, Marcos Paulo 
                         da and Guimar{\~a}es, Patr{\'{\i}}cia L{\'u}cia de Oliveira 
                         and Sugawara, Luciana Miura",
          affiliation = "{Instituto Nacional de Pesquisas Espaciais (INPE)} and {Instituto 
                         Nacional de Pesquisas Espaciais (INPE)} and {Instituto Nacional de 
                         Pesquisas Espaciais (INPE)} and {Instituto Nacional de Pesquisas 
                         Espaciais (INPE)} and {Instituto Nacional de Pesquisas Espaciais 
                         (INPE)} and {Instituto Nacional de Pesquisas Espaciais (INPE)}",
                title = "Integration and management of sensor data for rainfall 
                         monitoring",
              journal = "SN Applied Sciences",
                 year = "2020",
               volume = "2",
               number = "2",
                pages = "e238",
                month = "feb",
             keywords = "Rainfall monitoring, Metrological metadata, Meteorological sensor, 
                         Data processing, Tipping Bucket Rain Gauge (TBRG).",
             abstract = "Meteorological observation systems are extremely data-driven. 
                         However, several factors affect measurements, which require the 
                         use of environmental metrology techniques to increase the quality 
                         of measurements, decrease errors and evaluate measurements 
                         uncertainty. In this paper, we propose and develop a framework 
                         that integrates, process and visualizes sensor data and its 
                         associated metadata (for rainfall monitoring). This task is 
                         accomplished with a workflow designed to correct raw sensor data, 
                         which uses an elastic stack based infrastructure to collect, 
                         transform, and store sensor data and metadata. We validated our 
                         framework using real precipitation data from a Tipping Bucket Rain 
                         Gauge.",
                  doi = "10.1007/s42452-020-2037-4",
                  url = "http://dx.doi.org/10.1007/s42452-020-2037-4",
                 issn = "2523-3963",
             language = "en",
           targetfile = "almeida_integration.pdf",
        urlaccessdate = "28 abr. 2024"
}