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@Article{RuivoSampRamo:2014:Ap2020,
               author = "Ruivo, Heloisa Musetti and Sampaio, Gilvan and Ramos, Fernando 
                         Manuel",
          affiliation = "{Instituto Nacional de Pesquisas Espaciais (INPE)} and {Instituto 
                         Nacional de Pesquisas Espaciais (INPE)} and {Instituto Nacional de 
                         Pesquisas Espaciais (INPE)}",
                title = "Knowledge extraction from large climatological data sets using a 
                         genome-wide analysis approach: application to the 2005 and 2010 
                         Amazon droughts",
              journal = "Climatic Change",
                 year = "2014",
               volume = "123",
                pages = "1",
             abstract = "Today, the volume of data generated in almost all disciplines, 
                         particularly in meteorology and climate science, is dramatically 
                         increasing. Among the challenges generated by this data deluge is 
                         the development of efficient knowledge discovery strategies. Here, 
                         we show that statistical and computational tools used to analyze 
                         large data sets of genome-wide studies can be fruitfully applied 
                         to a climatic context. Although not as powerful as some techniques 
                         already in use by climatologists, these tools are simple and 
                         robust, and can easily be adapted to detect early warning signals 
                         for extreme events like droughts or be used to filter large data 
                         sets before applying other more advanced and computationally 
                         expensive methods. We test this approach in our investigation of 
                         the causes of the Amazon droughts of 2005 and 2010. Our results 
                         highlight the major role played in these extreme events by the 
                         warming of the seas surface temperature, mainly in the tropical 
                         North Atlantic. Our findings are in agreement with several 
                         analyses published in the literature. The main message we convey 
                         is that free and open-source data mining and visualization 
                         techniques routinely used in genetic studies can be useful in 
                         helping scientists to extract knowledge from large climatic data 
                         sets, particularly in regions of the world that are vulnerable to 
                         climate change but where the availability of technical expertise 
                         is critically scarce.",
                  doi = "10.1007/s10584-014-1066-7",
                  url = "http://dx.doi.org/10.1007/s10584-014-1066-7",
                 issn = "0165-0009",
                label = "lattes: 0236607123089481 2 RuivoSampMRam:2014:Ap2020",
             language = "en",
        urlaccessdate = "05 dez. 2020"
}


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