Artificial Neural Network using for climate extreme in La-Plata Basin: Preliminary results and objectives

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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
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• OBJECTIVEThe 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.

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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) No-supervised auto-organization.
In this work, input Nodes is data base surface
station or Data interpolated
e.g. by Mendes and Marengo (2009).
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Multilayer Perceptons
O Multilayer Perceptons - The following diagram
illustrates a perceptron network with three
layers
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.

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