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Advances in Geosciences An open-access journal for refereed proceedings and special publications
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Volume 10
Adv. Geosci., 10, 67–76, 2007
https://doi.org/10.5194/adgeo-10-67-2007
© Author(s) 2007. This work is licensed under
the Creative Commons Attribution-NonCommercial-ShareAlike 2.5 License.
Adv. Geosci., 10, 67–76, 2007
https://doi.org/10.5194/adgeo-10-67-2007
© Author(s) 2007. This work is licensed under
the Creative Commons Attribution-NonCommercial-ShareAlike 2.5 License.

  26 Apr 2007

26 Apr 2007

Spatiotemporal monthly rainfall reconstruction via artificial neural network – case study: south of Brazil

P. S. Lucio1,2, F. C. Conde1,3, I. F. A. Cavalcanti4, A. I. Serrano1, A. M. Ramos1, and A. O. Cardoso4 P. S. Lucio et al.
  • 1Centro de Geofísica de Évora (CGE), Universidade de Évora, Apartado 94, 7002-554 Évora, Portugal
  • 2Departamento de Estatística (DEST), Universidade Federal do Rio Grande do Norte (UFRN), Brazil
  • 3Instituto Nacional de Meteorologia (INMET), Brasília DF, Brazil
  • 4Centro de Previsão do Tempo e Estudos Climáticos (CPTEC/INPE), Cachoeira Paulista SP, Brazil

Abstract. Climatological records users, frequently, request time series for geographical locations where there is no observed meteorological attributes. Climatological conditions of the areas or points of interest have to be calculated interpolating observations in the time of neighboring stations and climate proxy. The aim of the present work is the application of reliable and robust procedures for monthly reconstruction of precipitation time series. Time series is a special case of symbolic regression and we can use Artificial Neural Network (ANN) to explore the spatiotemporal dependence of meteorological attributes. The ANN seems to be an important tool for the propagation of the related weather information to provide practical solution of uncertainties associated with interpolation, capturing the spatiotemporal structure of the data. In practice, one determines the embedding dimension of the time series attractor (delay time that determine how data are processed) and uses these numbers to define the network's architecture. Meteorological attributes can be accurately predicted by the ANN model architecture: designing, training, validation and testing; the best generalization of new data is obtained when the mapping represents the systematic aspects of the data, rather capturing the specific details of the particular training set. As illustration one takes monthly total rainfall series recorded in the period 1961–2005 in the Rio Grande do Sul – Brazil. This reliable and robust reconstruction method has good performance and in particular, they were able to capture the intrinsic dynamic of atmospheric activities. The regional rainfall has been related to high-frequency atmospheric phenomena, such as El Niño and La Niña events, and low frequency phenomena, such as the Pacific Decadal Oscillation.

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