Wind Speed Forecasting for a Large-Scale Measurement Network and Numerical Weather Modeling

  • Marek Brabec
  • Pavel Krc
  • Krystof Eben
  • Emil Pelikan
Conference paper
Part of the Contributions to Statistics book series (CONTRIB.STAT.)


We investigate various problems encountered when forecasting wind speeds for a network of measurements stations using outputs of numerical weather prediction (NWP) model as one of the predictors in a statistical forecasting model. First, it is interesting to analyze prediction error properties for different station types (professional and amateur). Secondly, the statistical model can be viewed as a calibration of the original NWP model. Hence, careful semi-parametric smoothing of NWP input can discover various weak points of the NWP, and at the same time, it improves forecasting performance. It turns out that useful information is contained not only in the latest prediction available. It is beneficial to combine different horizon NWP predictions to one target time. GARCH sub-model for the residuals then shows complicated structure usable for short-term forecasts.


Semiparametric modeling GAM Wind speed forecasting Numerical weather prediction model Measurement network 



The work on this article was partly supported by the CVUT (Czech Technical University in Prague, Czech Republic) institutional resources for research and by the long-term strategic development financing of the Institute of Computer Science (RVO:67985807) and also by the Czech Science Foundation grant GA13-34856S, Advanced random field methods in data assimilation for short-term weather prediction.


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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Marek Brabec
    • 1
    • 2
  • Pavel Krc
    • 1
    • 2
  • Krystof Eben
    • 1
    • 2
  • Emil Pelikan
    • 1
    • 2
  1. 1.Institute of Computer SciencePrague 8Czech Republic
  2. 2.Institute of Informatics, Robotics, and Cybernetics, Czech Technical University in PraguePrague 6Czech Republic

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