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Automatic and Probabilistic Foehn Diagnosis with a Statistical Mixture Model


  • David Plavcan


  • Georg J. Mayr


  • Achim Zeileis



Diagnosing foehn winds from weather station data downwind of topographic obstacles requires distinguishing them from other downslope winds, particularly nocturnal ones driven by radiative cooling. We present an automatic classification scheme to obtain reproducible results that include information about the (un)certainty of the diagnosis. A statistical mixture model separates foehn and no-foehn winds in a measured time series of wind. In addition to wind speed and direction, it accommodates other physically meaningful classifiers such as relative humidity or the (potential) temperature difference to an upwind station (e.g., near the crest). The algorithm was tested for the central Alpine Wipp Valley against human expert classification and a previous objective method (Drechsel and Mayr 2008), which the new method outperforms. Climatologically, using only wind information gives nearly identical foehn frequencies as when using additional covariables, making the method suitable for comparable foehn climatologies all over the world where station data are available for at least one year.

Suggested Citation

  • David Plavcan & Georg J. Mayr & Achim Zeileis, 2013. "Automatic and Probabilistic Foehn Diagnosis with a Statistical Mixture Model," Working Papers 2013-22, Faculty of Economics and Statistics, University of Innsbruck.
  • Handle: RePEc:inn:wpaper:2013-22

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    References listed on IDEAS

    1. Leisch, Friedrich, 2004. "FlexMix: A General Framework for Finite Mixture Models and Latent Class Regression in R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 11(i08).
    2. GrĂ¼n, Bettina & Leisch, Friedrich, 2008. "FlexMix Version 2: Finite Mixtures with Concomitant Variables and Varying and Constant Parameters," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 28(i04).
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    More about this item


    foehn wind; foehn diagnosis; finite mixture model; model-based clustering;

    JEL classification:

    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • C29 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Other
    • Q54 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Environmental Economics - - - Climate; Natural Disasters and their Management; Global Warming

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