As wind energy penetration continues to grow, there is a critical need for probabilistic forecasts of wind resources. In addition, there are many other societally relevant uses for forecasts of wind speed, ranging from aviation to ship routing and recreational boating. Over the past two decades, ensembles of numerical weather prediction (NWP) models have been developed, in which multiple estimates of the current state of the atmosphere are used to generate a collection of deterministic forecasts. However, even state-of-the-art ensemble systems are uncalibrated and biased. Here we propose a novel way of statistically post-processing NWP ensembles for wind speed using heteroskedastic censored (Tobit) regression, where location and spread derive from the ensemble forecast. The resulting ensemble model output statistics (EMOS) method is applied to 48-hour ahead forecasts of maximum wind speed over the North American Pacific Northwest in 2003 using the University of Washington Mesoscale Ensemble. The statistically post-processed EMOS density forecasts turn out to be calibrated and sharp, and result in substantial improvement over the unprocessed NWP ensemble or climatological reference forecasts.
Key words and phrases: Continuous ranked probability score; Density forecast; Ensemble system; Numerical weather prediction; Heteroskedastic censored regression; Tobit model; Wind energy.