1. 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 CLARIS Annual Meeting -WP 5- Rome, 22nd to 26th of February 10

2.  OBJECTIVE  OBJECTIVE The objective of this study is to identify climate extreme (RClimDex), using Artificial Neural Network (ANN) that can capture the complex relationship between selected large-scale predictors and locally observed meteorological variables for temporal scale (predictands).  Using Artificial Neural Network to diagnose extremes in La-Plata Basin using Eta-HadCM3;  For Control Period (20c3m) and A1B scenarios;  20c3m (1961-2000) or (1978-2000)  A1B scenarios (2071-2100) Artificial Neural Network (ANN) in Meteorology  Recently, non-linear approaches have been developed (in particular, the Artificial Neural Network - ANN) and adopted as tools to downscale local and regional climate variables and extreme (climate) from large-scale atmospheric circulation variables (e.g. Crane and Hewitson, 1998; Trigo and Palutikof, 1999). Reference: Gardner and Dorling (1998) – Review of applications in the atmospheric sciences. Trigo and Palutikof (1999) – Simulation of Temperature for climate change over Portugal. Sailor et al., (2000) – ANN approach to local downscaling of GCMs outputs. Olsson et al., (2001) – Statistical atmospheric downscaling of short-term extreme rainfall. Boulanger et al., (2006/2007) – Projection of Future climate change in South America.

3. 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: Supervised: observed precipitation (CRU); No-supervised: No-supervised: auto-organization. e.g. by: Mendes and Marengo (2009). In this work, input Nodes is data base surface station or Data interpolated

4. Multilayer Perceptons O Multilayer Perceptons - The following diagram illustrates a perceptron network with three layers: Input nodes Hidden nodes output nodes Training Multilayer Perceptron Networks  Selecting how many hidden layers to use in the network.  Deciding how many neurons to use in each hidden layer.  Finding a globally optimal solution that avoids local minima.  Converging to an optimal solution in a reasonable period of time.  Validating the neural network to test for overfitting.

5. Model

6. Extreme Climate 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.

7. Preliminary results and objectives Initial Results for exemple: R25 (Number of very heavy precipitation day) – Annual count when prp > 25 mm day From 1978-1990 Control (20c3m)

9. Future