@InProceedings{PintoTcheLoviChou:2018:ReDePr,
               author = "Pinto, Leandro Ferreira Gentile and Tcheou, Michel Pompeu and 
                         Lovisolo, Lisandro and Chou, Sin Chan",
          affiliation = "{Universidade Estadual do Rio de Janeiro (UERJ)} and {Universidade 
                         Estadual do Rio de Janeiro (UERJ)} and {Universidade Estadual do 
                         Rio de Janeiro (UERJ)} and {Instituto Nacional de Pesquisas 
                         Espaciais (INPE)}",
                title = "Redu{\c{c}}{\~a}o de desvios de previs{\~a}o clim{\'a}tica 
                         usando filtragem adaptativa no dom{\'{\i}}nio da 
                         frequ{\^e}ncia",
            booktitle = "Anais...",
                 year = "2018",
                pages = "609--613",
         organization = "Simp{\'o}sio Brasileiro de Telecomunica{\c{c}}{\~o}es e 
                         Processamento de Sinais, 36. (SBrT)",
             keywords = "Filtragem adaptativa, algoritmo RLS, DCT2D, previs{\~a}o 
                         clim{\'a}tica, Adaptive filtering, RLS algorithm, DCT-2D, 
                         climatic forecast.",
             abstract = "Este trabalho aborda o uso de filtros adaptativos no 
                         dom{\'{\i}}nio da frequ{\^e}ncia, via algoritmo RLS (Recursive 
                         Least Squares) e DCT-2D (Two-dimensional Discrete Cosine 
                         Transform), com o objetivo de reduzir desvios de previs{\~a}o 
                         clim{\'a}tica produzidos por um modelo regional clim{\'a}tico. 
                         As previs{\~o}es do modelo regional (modelo Eta) s{\~a}o 
                         comparadas com os dados observados (rean{\'a}lises do NCEP). As 
                         vari{\'a}veis clim{\'a}ticas consideradas s{\~a}o as 
                         componentes zonal e meridional do vento, altura geopotencial e 
                         umidade espec{\'{\i}}fica, produzidas pelo modelo sazonal Eta, 
                         na resolu{\c{c}}{\~a}o espacial de 40 km. Os resultados indicam 
                         que a aplica{\c{c}}{\~a}o proposta {\'e} capaz de reduzir 
                         m{\'e}tricas de desvio, como o erro quadr{\'a}tico m{\'e}dio e 
                         o erro m{\'a}ximo, contribuindo com melhores previs{\~o}es 
                         clim{\'a}ticas. ABSTRACT: This work aims at using adaptive 
                         filters in the frequency domain, through the RLS (Recursive Least 
                         Squares) algorithm and DCT-2D (Two-dimensional Discrete Cosine 
                         Transform), with the objective of reducing deviations of climatic 
                         forecasts produced by a regional climate model. The regional model 
                         predictions (Eta model) are compared with the observed data (NCEP 
                         reanalysis). The climatic variables considered are the zonal and 
                         meridional components of the wind, geopotential height and 
                         specific humidity, produced by the Eta seasonal model, in the 
                         spatial resolution of 40 km. The results indicate that the 
                         proposed application is capable to reduce deviation metrics, such 
                         as the mean square error and the maximum error, contributing to 
                         better climate predictions.",
  conference-location = "Campina Grande, PB",
      conference-year = "16-19 set.",
                label = "lattes: 7112277465715838 2 MaiaLuceSilv:2018:NoArDe",
             language = "pt",
           targetfile = "pinto_reducao.pdf",
        urlaccessdate = "27 abr. 2024"
}