@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 = "15 jun. 2024"
}