Wednesday, 26 January 2011
Handout (899.2 kB)
The inherent variability of wind requires accurate forecasts to optimize wind power generation. The weather research and forecasting (WRF) model with 10-km horizontal resolution was used to explore improvements in wind speed forecasts at hub height (80m). Results were validated using wind speed measurements at 80 m from a meteorological tower at the Pomeroy wind farm in northwestern Iowa. An ensemble consisting of different planetary boundary layer (PBL) schemes showed little spread between the individual PBL members. A second configuration using three random perturbations of the Global Forecast System (GFS) model produced more spread in the wind speed forecasts, but less model skill. A third ensemble with members having different initialization times showed model spread comparable to that from the perturbation results, but model skill was not compromised. In addition, we examined post-processing techniques such as bias correction of the diurnal cycle, training of the model for the day 2 forecast based on day 1 results, and bias correction based on observed wind direction. Early evaluation suggests that the ensemble mean of the first and third ensembles provides a more skillful wind forecast than any particular member, and further improvements occur when a bias correction of the diurnal cycle is applied to these forecasts.
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