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Advances in Science and Research Contributions in Applied Meteorology and Climatology
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Volume 8, issue 1
Adv. Sci. Res., 8, 115–120, 2012
https://doi.org/10.5194/asr-8-115-2012
© Author(s) 2012. This work is distributed under
the Creative Commons Attribution 3.0 License.
Adv. Sci. Res., 8, 115–120, 2012
https://doi.org/10.5194/asr-8-115-2012
© Author(s) 2012. This work is distributed under
the Creative Commons Attribution 3.0 License.

  15 May 2012

15 May 2012

On the skill of various ensemble spread estimators for probabilistic short range wind forecasting

A. Kann A. Kann
  • Central Institute for Meteorology and Geodynamics, Vienna, Austria

Abstract. A variety of applications ranging from civil protection associated with severe weather to economical interests are heavily dependent on meteorological information. For example, a precise planning of the energy supply with a high share of renewables requires detailed meteorological information on high temporal and spatial resolution. With respect to wind power, detailed analyses and forecasts of wind speed are of crucial interest for the energy management. Although the applicability and the current skill of state-of-the-art probabilistic short range forecasts has increased during the last years, ensemble systems still show systematic deficiencies which limit its practical use. This paper presents methods to improve the ensemble skill of 10-m wind speed forecasts by combining deterministic information from a nowcasting system on very high horizontal resolution with uncertainty estimates from a limited area ensemble system. It is shown for a one month validation period that a statistical post-processing procedure (a modified non-homogeneous Gaussian regression) adds further skill to the probabilistic forecasts, especially beyond the nowcasting range after +6 h.

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