Improving WRF GHI forecasts with model output statistics

Burak Barutcu*, Tilev Tanriover Seyda Tilev Tanriover, Serim Sakarya, Selahattin Incecik, F. Mert Sayinta, Erhan Caliskan, Abdullah Kahraman, Bulent Aksoy, Ceyhan Kahya, Sema Topcu

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingChapterpeer-review

1 Citation (Scopus)


Solar energy applications need reliable forecasting of solar irradiance. In this study, we present an assessment of a short-term global horizontal irradiance forecasting system based on Advanced Research Weather Research and Forecasting (WRF-ARW) meteorological model and neural networks as a post-processing method to improve the skill of the system in a highly favorable location for the utilization of solar power in Turkey. The WRF model was used to produce 1 month of 3 days ahead solar irradiance forecasts covering Southeastern Anatolia of Turkey with a horizontal resolution of 4 km. Single-input single-output (SISO) and multi-input single-output (MISO) artificial neural networks (ANN) were used. Furthermore, the overall results of the forecasting system were evaluated by means of statistical indicators: mean bias error, relative mean bias error, root mean square error, and relative root mean square error. The MISO ANN gives better results than the SISO ANN in terms of improving the model predictions, provided by WRF-ARW simulations for August 2011.

Original languageEnglish
Title of host publicationAnalysis and Modeling
PublisherSpringer International Publishing
Number of pages9
ISBN (Electronic)9783319167091
ISBN (Print)9783319167084
Publication statusPublished - 27 Aug 2015

Bibliographical note

Publisher Copyright:
© Springer International Publishing Switzerland 2015. All rights reserved.


  • Artificial neural networks
  • Model output statistics
  • Turkey
  • Weather research and forecasting


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