Evaluating the ability of the operational High Resolution Rapid Refresh model version 3 (HRRRv3) and version 4 (HRRRv4) to forecast wind ramp events in the US Great Plains

Abstract. Incorporating more renewable energy into the electric grid is an important part of the strategy to mitigate climate change. To make the incorporation of renewable energy into the grid more efficient and reliable, numerical weather prediction models need to be able to predict the intrinsic nature of weather-dependent renewable energy resources. This allows grid operators to plan accurately the amount of energy they will need from each source (e.g., wind, solar, fossil fuel, etc.). For this reason, wind ramp events (rapid changes in wind speed over short periods of time) are important to forecast accurately. This is because one of their consequences is that wind energy could quickly be available in abundance or temporarily cease to exist. In this study, the ability of the operational High Resolution Rapid Refresh numerical weather prediction model to forecast wind ramp events is assessed in its two most recent versions: version 3 (HRRRv3, operational from August 2018 to December 2020) and version 4 (HRRRv4, operational from December 2020 onward). The datasets used in this analysis were collected in the United States Great Plains, an area with a large amount of installed electricity generation from wind. The results are investigated from both annual and seasonal perspectives and show that the HRRRv4 is more accurate at forecasting wind ramp events compared to HRRRv3. Specifically, the HRRRv4 shows increased correlation coefficient and reduced root mean square error relative to the change in wind power capacity factor found in the observations, and in the skill of forecasting both up and down wind ramp events, with a marked increase in the HRRRv4’s skill at detecting up ramps during the summer (the HRRRv4 is nearly 50 % more skillful than the HRRRv3). This demonstrates that the HRRR’s continuing evolution will better support the integration of wind energy into the electric grid.