On Pyramidal Classification in the Delimitation of Climatic Regions in Catalonia

  • J. Conde
  • M. A. Colomer
  • C. Capdevila
  • A. Gil
Conference paper
Part of the Studies in Classification, Data Analysis, and Knowledge Organization book series (STUDIES CLASS)

Abstract

In this paper we define a new probabilistic model (which we call p-Weibull), considering the Weibull distribution function with an additional parameter, related to the probability of a wet day. We show that from this model and a classification method (for instance the pyramidal one) several climatic regions can be delimited. Each region can be characterized in terms of the same p-Weibull model in order to simulate daily precipitation in each region and any period of time. As an example we get several climatic regions in Catalonia.

Keywords

Climatic Region Weibull Distribution Daily Precipitation Precipitation Amount Rainfall Simulation 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Bertrand, P. and Diday, E. (1985): A Visual Representation of the Compatibility between an Order and a Dissimilarity Index: the Pyramids. Computational Statistics Quarterly, 2(Issue 1):31–41.Google Scholar
  2. Capdevila, C., Gil, A.J., and Arcas, A. (1995): Algunas Propiedades de un Algoritmo de Clasificación Piramidal. Estadística Española 37, (138), 101–126.Google Scholar
  3. CAPDEVILA C., Gil, A.J., and Arcas, A. (1996): Efficiency and Sensitivity of Three Pyramidal Classification Methods. In: A. Prat and E. Ripoll (ed.): Proceedings in Computational Statistics COMPSTAT’96, Universitat Politècnica de Catalunya, 19–20.Google Scholar
  4. Colomer, M.A. (1996): Modelización Numérico-Estocástica para Simular Series de Precipitación y Temperatura Diarias. Aplicación a la Provincia de Lleida. Ph.D. Thesis, Universitat Politècnica de Catalunya.Google Scholar
  5. Diday, E. (1986): Orders and Overlapping Clusters by Pyramids. In: J. de Leeuw, W. Heiser, J. Meulman, and F. Critchley (ed.): Multidimensional Data Analysis. Leiden: DSWO, 201–234.Google Scholar
  6. Durand, C. and Fichet, B. (1988): One-to-one Correspondences in Pyramidal Representation: A Unified Approach. In: H.H. Bock (ed.): Classification and Related Methods of Data Analysis. North-Holland, Amsterdam.Google Scholar
  7. Gaul, W. and Schader, M. (1994): Pyramidal Classification Based on Incomplete Dissimilarity Data. Journal of Classification, 11, 171–193.CrossRefGoogle Scholar
  8. Gil, A.J. and Capdevila, C. (1996): A New Program of Pyramidal Classification. In: A. Prat and E. Ripoll (ed.): Proceedings in Computational Statistics COMPSTAT’96, Universitat Politècnica de Catalunya, 189–190.Google Scholar
  9. Roldan, J., Garcia-Guzman, A., and Losada, A. (1982): A Stochastic Model for Wind Occurrence. Journal of Applied Meterology, 21(5), 740–744.CrossRefGoogle Scholar
  10. Selker J.S. and Haith D.A. (1990): Development and Testing of Single-Parameter Precipitation Distributions. Water Resources Reserch, 26,(11), 2733–2740.CrossRefGoogle Scholar
  11. Van Cutsem. B. (ed.) (1994): Classification and Dissimilarity Analysis. Springer, New York.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2000

Authors and Affiliations

  • J. Conde
    • 1
  • M. A. Colomer
    • 1
  • C. Capdevila
    • 1
  • A. Gil
    • 2
  1. 1.Departament de MatemàticaUniversitat de LleidaLleidaSpain
  2. 2.Departament d’Economia i EmpresaUniversitat Pompeu FabraBarcelonaSpain

Personalised recommendations