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Automatic and Probabilistic Foehn Diagnosis with a Statistical Mixture Model

David Plavcan (), Georg J. Mayr () and Achim Zeileis ()

Working Papers from Faculty of Economics and Statistics, University of Innsbruck

Abstract: 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.

Keywords: foehn wind; foehn diagnosis; finite mixture model; model-based clustering (search for similar items in EconPapers)
JEL-codes: C53 C29 Q54 (search for similar items in EconPapers)
Date: 2013-09
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