@Article{WeigangValFerLihSa:2000:RaEsMe,
               author = "Weigang, Li and Valverde Ramirez, Maria Cleofe and Ferreira, 
                         Nelson de Jesus and Lihua, Shi and Sa, Leonardo Deane de Abreu",
                title = "Rainfall estimation from meteorological satellite and radar data 
                         using multiresolution wavelet transform and neural networks 
                         methods",
              journal = "Journal of Nanjing Institute of Meteorology",
                 year = "2000",
               volume = "23",
               number = "2",
                pages = "277--282",
                month = "jun.",
             keywords = "METEOROLOGIA.",
             abstract = "Rainfall estimation from satellite data have many applications in 
                         climatology and meteorology but calculation associated requires a 
                         rapid processing to large amount of data in order to achieve 
                         significant result. The neural networks (NN)method is one of the 
                         several techniques employed to extract meteorologically-useful 
                         information from remotely sensed data. However this method is 
                         hardly used independently to yield quasi-real time rainfall 
                         estimates since a large amount os satellite data are needed to 
                         generate the input/output data for the NN training. In order to 
                         overcome this shortage, multiresolution wavelet transform 
                         (WT)technique is proposed to decompose the images to obtain the 
                         key information for further analysis. As a result, the NN training 
                         becomes easier and faster. In the paper a case study to estimate 
                         rainfall over the central part of S{\~a}o Paulo state, Brazil 
                         using both the NN and WT techniques is given. The analyses were 
                         performed using GOES-8 brightness temperature data and 
                         meteorological radar data from Bauru, SP. It is concluded that NN 
                         can be successfully used to estimate rainfall from remotely sensed 
                         imagery.",
                 issn = "1000-2022",
                label = "10204",
           targetfile = "9279.pdf",
        urlaccessdate = "01 maio 2024"
}