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Gerstengarbe, Friedrich-Wilhelm; Kücken, Martin; Werner, Peter C. (2005)
Publisher: Co-Action Publishing
Journal: Tellus A
Languages: English
Types: Article
On the basis of an extended cluster analysis algorithm, we present a new validation method for the evaluation of simulation experiments characterized by more than one parameter. This method allows the assessment of any parameter combination in space and time. As an example for the effectiveness of the algorithm, the results of two regional climate model runs and observational data have been tested and interpreted.
  • The results below are discovered through our pilot algorithms. Let us know how we are doing!

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    • Forgy, E. W. 1965. Cluster analysis of multivariate data: efficiency versus interpretability of classifications. Biometrics 21, 768.
    • Gerstengarbe, F.-W. and Werner, P. C. 1997. A method to estimate the statistical confidence of cluster separation. Theor. Appl. Climatology 57, 103-110.
    • Gerstengarbe, F.-W., Werner P. C. and Fraedrich, K. 1999. Applying nonhierarchical cluster analysis algorithms to climate classification: Some problems and their solution. Theor. Appl. Climatology 64, 143-150.
    • IPCC 2001. Climate Change 2001-The Scientific Basis. Cambridge University Press, Cambridge, 881 pp.
    • Ku¨cken, M., Gerstengarbe, F.-W. and Werner, P.C. 2002. Cluster analysis results of regional climate model simulations in the PIDCAP period. Boreal Env. Research 7, 219-223.
    • Pettitt, A. N. 1979. A non-parametric approach to the change-point problem. Appl. Statistics 28, 126-135.
    • Scha¨ttler, U. and Doms, G. 2000. The non-hydrostatic limited-area model LM (LokalModell) of DWD - Part III: user guide. German Weather Service, Offenbach/M.
    • Steinhausen, D. and Langer, K. 1977. Clusteranalyse-Einfu¨hrung in Methoden und Verfahren der Automatischen Klassifikation. Walter de Gruyter, Berlin, 411 pp.
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