Open AccessNeural networks in satellite rainfall estimation
Author(s) -
Tapiador F. J.,
Kidd C.,
Hsu K.L.,
Marzano F.
Publication year - 2004
Publication title -
meteorological applications
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.672
H-Index - 59
eISSN - 1469-8080
pISSN - 1350-4827
DOI - 10.1017/s1350482704001173
Subject(s) - computer science , estimation , artificial neural network , complement (music) , satellite , cover (algebra) , artificial intelligence , machine learning , data mining , biochemistry , chemistry , management , complementation , engineering , economics , gene , phenotype , aerospace engineering , mechanical engineering
Neural networks (NNs) have been successfully used in the environmental sciences over the last two decades. However, only a few review papers have been published, most of which cover image processing, classification, prediction and geophysical retrieval in general, while neglecting rainfall estimation issues. This paper reviews, without aiming to be exhaustive, NN approaches to satellite rainfall estimation (SRE) by providing an overview of some of the methodologies proposed. A basic introduction to NNs is provided and the advantages of using NNs in SRE are explained, illustrating how NNs can be used to complement more computational‐expensive methods to generate quick and accurate results in near real time. The role of the NNs in statistical‐empirical algorithms is also reviewed. The last section aims to generate some discussion through comparing the empirical and deterministic algorithmic approaches and contrasting some of the apparent drawbacks of using NNs with a statistically based view of the satellite geophysical parameter estimation. Copyright © 2004 Royal Meteorological Society.