@InProceedings{WeigangRamFerSaOli:1998:MuWaTr,
               author = "Weigang, Li and Ramirez, Maria Cleofe Valverde and Ferreira, 
                         Nelson Jesus and Sa, Leonardo Deane de Abreu and Oliveira, 
                         Jos{\'e} Lu{\'{\i}}s de",
          affiliation = "{CPTEC-INPE-Cachoeira Paulista-12630-000-SP-Brasil}",
                title = "Multiresolution wavelet transform and neural networks methods for 
                         rainfall estimation from meteorological satellite and radar data",
            booktitle = "Anais...",
                 year = "1998",
         organization = "Congresso Brasileiro de Meteorologia, 10.",
             keywords = "estimativa da precipitacao, satelite, radar, redes, neuronais, 
                         wavelets.",
             abstract = "Rainfall estimation from satellite data have many applications in 
                         climatological and meteorological studies. Their calculation 
                         requires a rapid processing of large amounts of data in order to 
                         achieve the desired result. The Neural Networks (NN) method is one 
                         of the several techniques employed to extract meteorologically 
                         useful information from remote sensing data. However this method 
                         is hardly used by itself to yield quasi-real time rainfall 
                         estimates once this demands a large amount of satellite data to 
                         generate the input/output data for the NN training. In order to 
                         overcome this, we propose to use Multiresolution Wavelet Transform 
                         (WT) technique to decompose the images retaining only the key 
                         information for the current problem. As a result, the NN training 
                         becomes easier and faster. We propose in this study to estimate 
                         rainfall over the central part of the S{\~a}o Paulo state, Brazil 
                         using both the NN and WT techniques. The analyses were performed 
                         using GOES-8 brightness temperature and meteorological radar data 
                         from Bauru, SP. The results suggest that NN can successfully 
                         estimate rainfall from remote sensing imagery.",
  conference-location = "Brasilia",
      conference-year = "26-30 out. 1998",
           copyholder = "SID/SCD",
             language = "en",
         organisation = "SBMET",
           targetfile = "Weigang_Multiresolution wavelet transform .pdf",
        urlaccessdate = "01 maio 2024"
}