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Nonlinear Processes in Geophysics An interactive open-access journal of the European Geosciences Union
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https://doi.org/10.5194/npg-2019-60
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/npg-2019-60
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.

Submitted as: research article 16 Dec 2019

Submitted as: research article | 16 Dec 2019

Review status
A revised version of this preprint was accepted for the journal NPG and is expected to appear here in due course.

Vertical profiles of wind gust statistics from a regional reanalysis using multivariate extreme value theory

Julian Steinheuer1,2 and Petra Friederichs3 Julian Steinheuer and Petra Friederichs
  • 1Institute for Geophysics and Meteorology, University of Cologne
  • 2Hans-Ertel Centre for Weather Research, Cologne/Bonn
  • 3Institute of Geoscience, University of Bonn

Abstract. Many applications require wind gust estimates at very different atmospheric height levels. For example, the renewable energy sector is interested in wind and gust predictions at the hub height of a wind power plant. However, numerical weather prediction models typically derive estimates for wind gusts at the standard measurement height of 10 m above the land surface only. Here, we present a statistical post-processing to derive a conditional distribution for hourly peak wind speed as a function of height. The conditioning variables are taken from the regional reanalysis COSMO-REA6. The post-processing is trained using peak wind speed observations at five vertical levels between 10 m and 250 m of the Hamburg Weather Mast. The statistical post-processing is based on a censored generalized extreme value (cGEV) distribution with non-stationary parameters. We use a least absolute shrinkage and selection operator to select the most informative variables. Vertical variations of the cGEV parameters are approximated using Legendre polynomials, such that predictions may be derived at any desired vertical height. Further, the Pickands dependence function is used to assess dependencies between gusts at different heights. The most important predictors are the 10 m gust diagnostic, the barotropic and the baroclinic mode of absolute horizontal wind speed, the mean absolute horizontal wind in 700 hPa, the surface pressure tendency, and the lifted index. Proper scores show improvements with respect to climatology of up to 60 % especially at higher vertical levels. The post-processing model with a Legendre approximation is able to provide reliable predictions of gusts statistics at non-observed intermediate levels. The strength of dependency between gusts at different levels is non-stationary and strongly modulated by the vertical stability of the atmosphere.

Julian Steinheuer and Petra Friederichs

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Julian Steinheuer and Petra Friederichs

Julian Steinheuer and Petra Friederichs

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