Pure and Applied Geophysics

, Volume 172, Issue 1, pp 109–120 | Cite as

Wind Forecasting Based on the HARMONIE Model and Adaptive Finite Elements

  • Albert Oliver
  • Eduardo Rodríguez
  • José María Escobar
  • Gustavo Montero
  • Mariano Hortal
  • Javier Calvo
  • José Manuel Cascón
  • Rafael Montenegro


In this paper, we introduce a new method for wind field forecasting over complex terrain. The main idea is to use the predictions of the HARMONIE meso-scale model as the input data for an adaptive finite element mass-consistent wind model. The HARMONIE results (obtained with a maximum resolution of about 1 km) are refined in a local scale (about a few metres). An interface between both models is implemented in such a way that the initial wind field is obtained by a suitable interpolation of the HARMONIE results. Genetic algorithms are used to calibrate some parameters of the local wind field model in accordance to the HARMONIE data. In addition, measured data are considered to improve the reliability of the simulations. An automatic tetrahedral mesh generator, based on the meccano method, is applied to adapt the discretization to complex terrains. The main characteristic of the framework is a minimal user intervention. The final goal is to validate our model in several realistic applications on Gran Canaria island, Spain, with some experimental data obtained by the AEMET in their meteorological stations. The source code of the mass-consistent wind model is available online at


Adaptive finite element method HARMONIE model Local wind field forecasting Mass-consistent model Complex topography Genetic algorithms 



This work has been supported by the Spanish Government, “Ministerio de Ciencia e Innovación”, Grant Contracts: CGL2011-29396-C03-01 and CGL2011-29396-C03-02, and by “Junta de Castilla León”, “Consejería de Educación”, Grant Contract SA266A12-2.


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Copyright information

© Springer Basel 2014

Authors and Affiliations

  • Albert Oliver
    • 1
  • Eduardo Rodríguez
    • 1
  • José María Escobar
    • 1
  • Gustavo Montero
    • 1
  • Mariano Hortal
    • 2
  • Javier Calvo
    • 2
  • José Manuel Cascón
    • 3
  • Rafael Montenegro
    • 1
  1. 1.University Institute for Intelligent Systems and Numerical Applications in Engineering (SIANI)University of Las Palmas de Gran Canaria (ULPGC)Las Palmas de Gran CanariaSpain
  2. 2.Agencia Estatal de Meteorología (AEMET)MadridSpain
  3. 3.Department of Economics and Economic History, Faculty of Economics and BusinessUniversity of SalamancaSalamancaSpain

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