Grid operators responsible for making decisions on what kind of power generation to use to keep the grid in balance (conventional versus weather dependent, such as wind or solar) need a reliable numerical weather prediction (NWP) model to ensure grid stability. A statistically significant improvement of the ramp event forecast skill is found through the assimilation of the special WFIP data in two different study areas, and variations in model skill between up‐ramp versus down‐ramp events are found.
Researchers leveraged a multi-scale dataset of observations from the second Wind Forecast Improvement Project field campaign in the northwest U.S. to diagnose and quantify systematic forecast errors in the operational High-Resolution Rapid Refresh (HRRR) model during weather events of particular concern to wind energy forecasting. Examples of such events are cold pools, gap flows, thermal troughs/marine pushes, mountain waves, and topographic wakes. This study describes the model development and testing undertaken during WFIP2, and demonstrates forecast improvements. Specifically, WFIP2 found that mean absolute errors in rotor-layer wind speed forecasts could be reduced by 5-20% in winter by improving the turbulent mixing lengths, horizontal diffusion, and gravity wave drag. The model improvements made in WFIP2 are also shown to be applicable to regions outside of complex terrain. Ongoing and future challenges in model development will also be discussed.