Kalman filter, analog and wavelet postprocessing in the NCAR-Xcel operational wind-energy forecasting system
The raw wind observations collected at the nacelle-height of each wind turbine is noisier than standard surface observations because of the wake effects of upwind turbines as well as turbulence induced by the turbine rotors. For this reason a wavelet procedure is implemented to de-noise the 15-min raw wind farm observations as well as the model predictions produced with the NCAR RTFDDA (Real Time Four-Dimensional Data Assimilation and forecasting system) run with three nested-grid domains with 3.3 km grid spacing for the finest mesh domain.
Results from tests with different configurations and combinations of the ANKF and AN methods with the wavelet de-noising procedure will be presented. The different postprocessing strategies will be tested over multiple wind farms with data from a time period of several months up to one year. Preliminary results show that the new methods improve raw wind predictions at most wind farms by 10 to 30%. The corrected predictions exhibit lower systematic and random errors as measured by bias and centered-root-mean-square-error, respectively. The latter, underline the ability of the new methods to improve the predictive skill of the raw prediction. The application of ANKF and AN combined with wavelet filtering results also in wind predictions with a stronger monotone association with the observations (as measured by rank correlation).