University of Washington - Department of Statistics
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. In my talk, I will present a method 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.
This is joint work with Tilmann Gneiting.