Bias Correction and Bayesian Model Averaging for Ensemble Forecasts of Surface Wind Direction

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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 post-processing methods become ineffective, or do not apply at all. We propose 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. We apply these techniques to 48-hour forecasts of surface wind direction over the Pacific Northwest, using the University of Washington Mesoscale Ensemble, where they yield consistent improvements in forecast performance


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