Bias correction and bayesian model averaging for ensemble forecasts of surface wind direction

Le Bao, Tilmann Gneiting, Eric P. Grimit, Peter Guttorp, Adrian E. Raftery

Research output: Contribution to journalArticlepeer-review

60 Scopus citations


Wind direction is an angular variable, as opposed to weather quantities such as temperature, quantitative precipitation, or wind speed, which are linear variables. Consequently, traditional model output statistics and ensemble postprocessing methods become ineffective, or do not apply at all. This paper proposes an effective bias correction technique for wind direction forecasts from numerical weather prediction models, which is based on a state-of-the-art circular-circular regression approach. To calibrate forecast ensembles, a Bayesian model averaging scheme for directional variables is introduced, where the component distributions are von Mises densities centered at the individually bias-corrected ensemble member forecasts. These techniques are applied to 48-h forecasts of surface wind direction over the Pacific Northwest, using the University of Washington mesoscale ensemble, where they yield consistent improvements in forecast performance.

Original languageEnglish (US)
Pages (from-to)1811-1821
Number of pages11
JournalMonthly Weather Review
Issue number5
StatePublished - May 2010

All Science Journal Classification (ASJC) codes

  • Atmospheric Science


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