Artificial Neural Network using for climate extreme in La-Plata Basin: Preliminary results and objectives. David Mendes * José Antonio Marengo * Chou Sin Chan +. *Centro de Ciência do Sistema Terrestre – CCST/INPE +Centro de Previsão de Tempo e Estudos Climáticos – CPTEC/INPE.
Artificial Neural Network using for climate extreme in La-Plata Basin: Preliminary results and objectives
José Antonio Marengo*
Chou Sin Chan+
*Centro de Ciência do Sistema Terrestre – CCST/INPE
+Centro de Previsão de Tempo e Estudos Climáticos – CPTEC/INPE
CLARIS Annual Meeting -WP 5- Rome, 22nd to 26th of February 10
Artificial Neural Network (ANN) in Meteorology
Artificial Neural Network (RNA)
The ANN approach can be viewed as a computer system that is made up of several simple to the highly interconnected processing elements similar to the neuron architecture of human brain (McClelland et al., 1986).
Supervised: observed precipitation (CRU);
In this work, input Nodes is data base surface station or
e.g. by: Mendes and Marengo (2009).
O Multilayer Perceptons - The following diagram illustrates a perceptron network with three layers:
Training Multilayer Perceptron Networks
Extremes indices for La-PlataBasin have already been calculated from these daily station. Indices are calculated using standard software produced on behalf of the ETCCDMI by the Climate Research Branch of the Meteorological Service of Canada.
Preliminary results and objectives
Initial Results for exemple:
R25 (Number of very heavy precipitation day) – Annual count when prp > 25 mm day