Statistical Modeling for Improvement of Numerical-Model-Based Solar Radiation Forecasts

  • Marek Brabec
  • Krystof Eben
  • Emil Pelikan
  • Pavel Krc
  • Jaroslav Resler
  • Pavel Jurus
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 565)


We first analyze some features of numerical weather predictions (NWP) for global solar radiation and notice that they are undersmooth. This finding opens a way to improvements via various smoothing strategies. Then we introduce a statistical modeling framework based on modern semiparametric regression. We use a numerical weather prediction (NWP) model output as one of the inputs for our statistical model. The statistical model is build on the modern regression formalism, utilizing nonparametric B-splines for nonlinear parts whose exact shape is unknown a priori (apart from physically motivated smoothness). Then we illustrate its abilities for systematic development of strategies for NWP calibration and further development. The results are useful both for practical forecasting and as a source of feedback for NWP modelers.


Generalized additive model Numerical weather forecast model Global solar radiation Calibration Semiparametric modeling 



The work on this article was partly supported by the long-term strategic development financing of the Institute of Computer Science (RVO:67985807) and 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 2018

Authors and Affiliations

  • Marek Brabec
    • 1
  • Krystof Eben
    • 1
  • Emil Pelikan
    • 1
  • Pavel Krc
    • 1
  • Jaroslav Resler
    • 1
  • Pavel Jurus
    • 1
  1. 1.Institute of Computer SciencePraha 8Czech Republic

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