The intent during WFIP was to assimilate the special WFIP observations into the research ESRL_RAP and HRRR models, but not into the operational NCEP_RUC and NCEP_RAP, and then to compare the skill of these models.
The latency of the NCEP_RUC, NCEP_RAP, and ESRL_RAP models was approximately 1 h during WFIP, while the latency of ESRL_HRRR was 1.5-2 h.
NCEP_RUC was used as a baseline forecast against which to compare the upgraded WFIP forecasts until RUC ceased operations on 1 May 2012.
Thus, the regional improvement from the combination of RAP and assimilation of the WFIP observations in the NSA and SSA represents close to a decade's worth of improvement typically found in the operational models over North America, marking a significant advancement for the wind energy industry.
One of the primary goals of WFIP was to determine the impact of the special WFIP observations on the model forecast skill of turbine hub-height winds.
For all hours in both the NSA and SSA, the experimental simulations (that assimilate the WFIP observations) have smaller or equal RMSEs than the control.
To demonstrate that the WFIP observations also have a positive impact on a deeper layer of the atmosphere than only at turbine heights, we show the improvement in vector-wind 0-2-km layer-averaged RMSE, using data from the radar wind profilers as verification (Fig.
7 (right panel), with the solid curves for the average of the 55 days of the data-denial control simulations and the dashed curves for the experimental simulations assimilating the new WFIP observations, using all 133 tall towers for verification.
The difference between the dashed lines and solid lines shows the improvement from assimilating the new WFIP observations at the various degrees of spatial averaging.
Although evaluation of postprocessing techniques commonly used in the wind energy forecasting sector was not the focus of WFIP, it is important to assess whether the fundamental wind speed forecast improvements achieved for the raw forecasts remain after typical postprocessing is applied to them.
Since several months of data are required for the training process, the trained forecasts were generated starting in January 2012 and continued through the WFIP field campaign ending in August 2012.
While the training process reduces the forecast error differences somewhat between the various model-based forecasts, the ESRL_RAP trained forecasts (which assimilate in the WFIP observations) are still the best of the individual models, with RMSEs lower by 0.62% of rated capacity compared with the NCEP_RAP trained forecasts.