Probing NWP model deficiencies by statistical postprocessing

Martin Haubjerg Rosgaard, Henrik Aalborg Nielsen, Torben S. Nielsen, Andrea N. Hahmann

Research output: Contribution to journalJournal articleResearchpeer-review

331 Downloads (Pure)


The objective in this article is twofold. On one hand, a Model Output Statistics (MOS) framework for improved wind speed forecast accuracy is described and evaluated. On the other hand, the approach explored identifies unintuitive explanatory value from a diagnostic variable in an operational numerical weather prediction (NWP) model generating global weather forecasts four times daily, with numerous users worldwide. The analysis is based on two years of hourly wind speed time series measured at three locations; offshore, in coastal and flat terrain, and inland in complex topography, respectively. Based on the statistical model candidates inferred from the data, the lifted index NWP model diagnostic is consistently found among the NWP model predictors of the best performing statistical models across sites.
Original languageEnglish
JournalQuarterly Journal of the Royal Meteorological Society
Issue number695 Part B
Pages (from-to)1017–1028
Publication statusPublished - 2016


  • NWP model development
  • Statistical forecasting
  • General linear modelling
  • Model Output Statistics
  • Backward elimination
  • Backward stepwise selection
  • Bayesian Information Criterion
  • Wind energy scheduling

Fingerprint Dive into the research topics of 'Probing NWP model deficiencies by statistical postprocessing'. Together they form a unique fingerprint.

Cite this