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Advances in Science and Research Contributions in Applied Meteorology and Climatology
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Volume 6, issue 1
Adv. Sci. Res., 6, 129–135, 2011
https://doi.org/10.5194/asr-6-129-2011
© Author(s) 2011. This work is distributed under
the Creative Commons Attribution 3.0 License.
Adv. Sci. Res., 6, 129–135, 2011
https://doi.org/10.5194/asr-6-129-2011
© Author(s) 2011. This work is distributed under
the Creative Commons Attribution 3.0 License.

  17 May 2011

17 May 2011

Data-driven exploration of orographic enhancement of precipitation

L. Foresti1, M. Kanevski1, and A. Pozdnoukhov2 L. Foresti et al.
  • 1Institute of Geomatics and Analysis of Risk, University of Lausanne, Switzerland
  • 2National Centre for Geocomputation, National University of Ireland Maynooth, Ireland

Abstract. This study presents a methodology to analyse orographic enhancement of precipitation using sequences of radar images and a digital elevation model. Image processing techniques are applied to extract precipitation cells from radar imagery. DEM is used to derive the topographic indices potentially relevant to orographic precipitation enhancement at different spatial scales, e.g. terrain convexity and slope exposure to mesoscale flows. Two recently developed machine learning algorithms are then used to analyse the relationship between the repeatability of precipitation patterns and the underlying topography. Spectral clustering is first used to characterize stratification of the precipitation cells according to different mesoscale flows and exposure to the crest of the Alps. At a second step, support vector machine classifiers are applied to build a computational model which discriminates persistent precipitation cells from all the others (not showing a relationship to topography) in the space of topographic conditioning factors. Upwind slopes and hill tops were found to be the topographic features leading to precipitation repeatability and persistence. Maps of orographic enhancement susceptibility can be computed for a given flow, topography and forecasted smooth precipitation fields and used to improve nowcasting models or correct windward and leeward biases in numerical weather prediction models.

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