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@PhDThesis{Viana:2015:SiRePa,
               author = "Viana, Denilson Ribeiro",
                title = "Sistema de reconhecimento de padr{\~o}es estat{\'{\i}}sticos 
                         aplicado {\`a} previs{\~a}o clim{\'a}tica de temperatura e 
                         precipita{\c{c}}{\~a}o no Centro-Sul do Brasil",
               school = "Instituto Nacional de Pesquisas Espaciais (INPE)",
                 year = "2015",
              address = "S{\~a}o Jos{\'e} dos Campos",
                month = "2015-09-15",
             keywords = "precipita{\c{c}}{\~a}o, temperatura, previs{\~a}o 
                         clim{\'a}tica, reconhecimento de padr{\~o}es, precipitation, 
                         temperature, climate forecasting, pattern recognition.",
             abstract = "Este trabalho tem por objetivo o desenvolvimento, a 
                         aplica{\c{c}}{\~a}o e a avalia{\c{c}}{\~a}o de um modelo de 
                         previs{\~a}o clim{\'a}tica mensal e sazonal de 
                         precipita{\c{c}}{\~a}o e temperatura para o Centro-Sul do 
                         Brasil, atrav{\'e}s da abordagem estat{\'{\i}}stica de 
                         Reconhecimento de Padr{\~o}es. A metodologia consiste em um 
                         conjunto de etapas que envolvem: 1) aquisi{\c{c}}{\~a}o dos 
                         dados, 2) pr{\'e}-processamento, 3) extra{\c{c}}{\~a}o de 
                         atributos, 4) sele{\c{c}}{\~a}o de atributos, 5) 
                         classifica{\c{c}}{\~a}o e, 6) avalia{\c{c}}{\~a}o. Os 
                         preditandos, precipita{\c{c}}{\~a}o e temperatura, foram obtidos 
                         atrav{\'e}s das respectivas bases, GPCC (\emph{Global 
                         Precipitation Climatology Centre}) e CRU (\emph{Climatic Research 
                         Unit}). Os preditores atmosf{\'e}ricos [Altura Geopotencial (Z) 
                         em 850, 700, 500 e 250 mb, Press{\~a}o ao N{\'{\i}}vel do Mar 
                         (PNM), Temperatura do ar em 850 mb (T850), Conte{\'u}do de 
                         {\'A}gua Precipit{\'a}vel (CAP)] e de superf{\'{\i}}cie 
                         [Albedo (ALB) e Umidade do Solo (UMS)] foram obtidos da 
                         Rean{\'a}lises \${'}\$S{\'e}culo 20\${'}\$ (\emph{20th 
                         Century Reanalysis, V2 NCEP/NCAR}). Os preditores oce{\^a}nicos 
                         [Temperatura da Superf{\'{\i}}cie do Mar (TSM) e 
                         Concentra{\c{c}}{\~a}o de Gelo Marinho (CGM)] s{\~a}o oriundos 
                         da base \emph{HadISST1 do Met Office Hadley Centre}. Os dados 
                         compreendem o per{\'{\i}}odo entre 1951 e 2010, totalizando 60 
                         anos de registros mensais. A extra{\c{c}}{\~a}o de atributos foi 
                         realizada atrav{\'e}s de tr{\^e}s procedimentos distintos: 1) 
                         defini{\c{c}}{\~a}o das Regi{\~o}es Homog{\^e}neas dos 
                         preditandos (RH) utilizando An{\'a}lise de Agrupamentos 
                         Hier{\'a}rquicos (AAH), 2) defini{\c{c}}{\~a}o das 
                         Regi{\~o}es-Chave (RC) dos preditores atmosf{\'e}ricos e 
                         oce{\^a}nicos por meio de An{\'a}lise de Componentes Principais 
                         (ACP) em modo S e, 3) c{\'a}lculo das anomalias dos preditores de 
                         superf{\'{\i}}cie para as RH identificadas. Com base nas 
                         anomalias mensais dos preditandos, foram identificadas quatro 
                         Regi{\~o}es Homog{\^e}neas de precipita{\c{c}}{\~a}o acumulada 
                         e tr{\^e}s de temperatura m{\'e}dia. As an{\'a}lises, 
                         conduzidas por ACP, mostraram que, em m{\'e}dia, 22 componentes 
                         captam em torno de 70\% da vari{\^a}ncia acumulada para os 
                         campos mais est{\'a}veis, relacionados {\`a} press{\~a}o 
                         atmosf{\'e}rica (Z e PNM) e a TSM. Ao todo, foram analisadas 594 
                         vari{\'a}veis, das quais 259 foram selecionadas para a 
                         previs{\~a}o clim{\'a}tica. Nas fases de sele{\c{c}}{\~a}o de 
                         atributos e de classifica{\c{c}}{\~a}o, as s{\'e}ries temporais 
                         dos preditores atmosf{\'e}ricos, oce{\^a}nicos e de 
                         superf{\'{\i}}cie foram correlacionadas com as s{\'e}ries dos 
                         preditandos, por meio de tercis, utilizando An{\'a}lise 
                         Discriminante Linear (ADL). Os resultados mostraram que, tanto 
                         para precipita{\c{c}}{\~a}o, quanto para a temperatura, houve um 
                         ganho m{\'e}dio de 29\% em rela{\c{c}}{\~a}o {\`a} 
                         climatologia. Para a precipita{\c{c}}{\~a}o, destacam-se as 
                         vari{\'a}veis relacionadas {\`a} press{\~a}o atmosf{\'e}rica 
                         (Z e PNM), bem como o CAP, a TSM e os campos de 
                         superf{\'{\i}}cie (ALB e UMS), e ainda, o papel da 
                         circula{\c{c}}{\~a}o atmosf{\'e}rica na Ant{\'a}rtica e 
                         adjac{\^e}ncias. Para a temperatura m{\'e}dia, destacam-se 
                         novamente as vari{\'a}veis relacionadas {\`a} press{\~a}o, 
                         juntamente com a TSM, T850, CAP e CGM. O campo de T850 nas 
                         regi{\~o}es do Pac{\'{\i}}fico Equatorial, costa leste do 
                         Brasil e no continente Ant{\'a}rtico e adjac{\^e}ncias, foram 
                         relevantes para a temperatura. A avalia{\c{c}}{\~a}o das 
                         previs{\~o}es, realizada por meio de um conjunto de escores 
                         categ{\'o}ricos e probabil{\'{\i}}sticos, mostrou que os 
                         resultados obtidos foram superiores aos modelos atuais. Tanto para 
                         a precipita{\c{c}}{\~a}o, quanto para a temperatura, o melhor 
                         desempenho do modelo ocorreu nas categorias extremas (acima/abaixo 
                         da normal), sendo que uma determinada previs{\~a}o nessas 
                         categorias tem maior probabilidade de acerto. ABSTRACT: This study 
                         aims to develop, implement and evaluate a precipitation and 
                         temperature seasonal and monthly climate forecasting model for the 
                         Central-Southern regions of Brazil, using a Statistical Pattern 
                         Recognition system. The methodology consists of a set of steps 
                         involving: 1) data acquisition, 2) pre-processing, 3) attribute 
                         extraction, 4) attribute selection, 5) classification, and 6) 
                         validations of the results. The predictands, rainfall and 
                         temperature, were obtained from GPCC (Global Precipitation 
                         Climatology Centre) and CRU (Climatic Research Unit) data bases. 
                         Atmospheric predictors [Geopotential height (Z) at 850, 700, 500 
                         and 250 mb, Sea Level Pressure (PNM) Air temperature at 850 mb 
                         (T850), Precipitable Water Content (CAP)] and surface predictors 
                         [Albedo (ALB) and Soil Moisture (UMS)] were from 20th Century 
                         Reanalysis V2 - NCEP/NCAR. Oceanic predictors [Sea Surface 
                         Temperature (SST) and Sea Ice Concentration (CGM)] came from the 
                         HadISST1 Met Office Hadley Centre data base. The data covers a 
                         period between 1951 and 2010, totaling 60 years of monthly 
                         records. The attribute extraction was performed by three distinct 
                         procedures: 1) definition of Homogeneous Regions of predictands 
                         (RH), using Hierarchical Cluster Analysis (AAH), 2) definition of 
                         the Key Regions (RC) of atmospheric and oceanic predictors, 
                         through Principal Component Analysis (ACP) in S mode, and 3) 
                         calculation of surface anomalies of the RH identified predictors. 
                         Based on the monthly anomalies of predictands, we have identified 
                         four RH of accumulated rainfall and three RH for average 
                         temperature. The ACP analysis showed that, on average, 22 
                         components explain approximately 70\% of the accumulated 
                         variance, for the more stable fields related to the atmospheric 
                         pressure (Z and SLP) and TSM. In all, 594 variables were analyzed, 
                         of which 259 were selected for climate prediction. In the 
                         attribute selection and classification stages, the atmospheric, 
                         ocean and surface predictor time series were correlated with the 
                         terciles of forecasting series, using Linear discriminant analysis 
                         (ADL). Results showed that for both precipitation and temperature, 
                         there was an average gain of 29\% in relation to the climatology. 
                         As for precipitation, atmospheric pressure (Z and PNM), CAP, SST 
                         and surface fields (ALB and UMS) variables presented the best 
                         results. Also highlighted, is the role of atmospheric circulation 
                         in the Antarctic region and its surroundings. Considering the 
                         average temperature, again, the pressure related variables, along 
                         with TSM, T850, CAP and CGM presented the best results. The T850 
                         field of the Equatorial Pacific, the eastern coastal region of 
                         Brazil and the Antarctic continent and surrounding areas were 
                         relevant to the temperature. The forecast evaluation was achieved 
                         by a set of categorical and probabilistic scores, showing that 
                         these results were superior to current models. The best model 
                         performances were obtained for the extreme rainfall and 
                         temperature categories (above/below normal).",
            committee = "Kayano, Mary Toshie (presidente) and Sansigolo, Cl{\'o}vis Angeli 
                         (orientador) and Coelho, Caio Augusto dos Santos and Fortes, Lauro 
                         Tadeu Guimar{\~a}es and Lucio, Paulo S{\'e}rgio",
           copyholder = "SID/SCD",
         englishtitle = "Statistical pattern recognition system applied to climate 
                         forecasts of Central-Southern Brazil",
             language = "pt",
                pages = "246",
                  ibi = "8JMKD3MGP8W/3K3LAH2",
                  url = "http://urlib.net/rep/8JMKD3MGP8W/3K3LAH2",
           targetfile = "publicacao.pdf",
        urlaccessdate = "28 nov. 2020"
}


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