@InProceedings{HamadaMaGhThGoLaAl:2011:AvPrPr,
               author = "Hamada, Em{\'{\i}}lia and Maia, Aline de Holanda Nunes and 
                         Ghini, Raquel and Thomaz, Mar{\'{\i}}lia Campos and 
                         Gon{\c{c}}alves, Renata Ribeiro do Valle and Lana, Jos{\'e} 
                         Tadeu de Oliveira and Almeida, Elias Gomes de",
          affiliation = "{Embrapa Meio Ambiente} and {Embrapa Meio Ambiente} and {Embrapa 
                         Meio Ambiente} and {Universidade Estadual de Campinas - UNICAMP} 
                         and {Universidade Estadual de Campinas - UNICAMP} and {Embrapa 
                         Meio Ambiente} and {Embrapa Meio Ambiente}",
                title = "Avalia{\c{c}}{\~a}o da precipita{\c{c}}{\~a}o projetada pelos 
                         modelos clim{\'a}ticos globaispara o Sudeste do Brasil utilizando 
                         SIG",
            booktitle = "Anais...",
                 year = "2011",
               editor = "Epiphanio, Jos{\'e} Carlos Neves and Galv{\~a}o, L{\^e}nio 
                         Soares",
                pages = "4047--4054",
         organization = "Simp{\'o}sio Brasileiro de Sensoriamento Remoto, 15. (SBSR).",
            publisher = "Instituto Nacional de Pesquisas Espaciais (INPE)",
              address = "S{\~a}o Jos{\'e} dos Campos",
             keywords = "climate change, GIS, cluster analysis, mudan{\c{c}}a 
                         clim{\'a}tica, SIG, an{\'a}lise de agrupamento 
                         hier{\'a}rquico.",
             abstract = "Global climatic models (GCM) are considered the best tool to 
                         project climate change scenarios, despite their uncertainties. 
                         Projections for each region are different and vary among seasons. 
                         The aim of this study was to evaluate projections of precipitation 
                         from 15 GCMs provided by the IPCC-AR4 at Southeastern Brazil for 
                         2071-2100 period, scenario A2. A geographic database has been 
                         structured with projected climate data and the observed data 
                         obtained from the Climate Research Unit. Principal component and 
                         cluster analysis were applied for grouping models with similar 
                         performance, based on bias rainfall projections, after bias 
                         correction. Models were clusterd accordingly to spatial similarity 
                         of projections, for each season. Spatial similarities varied among 
                         seasons.",
  conference-location = "Curitiba",
      conference-year = "30 abr. - 5 maio 2011",
                 isbn = "{978-85-17-00056-0 (Internet)} and {978-85-17-00057-7 (DVD)}",
             language = "pt",
         organisation = "Instituto Nacional de Pesquisas Espaciais (INPE)",
                  ibi = "3ERPFQRTRW/39ULRBE",
                  url = "http://urlib.net/ibi/3ERPFQRTRW/39ULRBE",
           targetfile = "p0394.pdf",
                 type = "Meteorologia, Atmosfera e Agrometeorologia",
        urlaccessdate = "19 maio 2024"
}