The HRRR Meteorology, Energy, and Transmission (MET) Toolkit: Advancing high-resolution atmospheric data for contiguous U.S. energy applications

Abstract. High-quality, multiyear atmospheric data are foundational for power system planning and grid integration. While the legacy Wind Integration National Dataset (WIND) Toolkit has long served as the industry standard, its historical record ends in 2013, leaving a critical gap in current modeling capabilities. Modern alternatives, such as the WIND Toolkit Long-term Ensemble Dataset (WTK-LED) and its Climate variant, offer extended coverage but exhibit higher wind speed biases and are computationally intensive to produce. This study introduces the High-Resolution Rapid Refresh Meteorology, Energy, and Transmission (HRRR MET) Toolkit, a repackaged version of the National Oceanic and Atmospheric Administration's native HRRR data. The HRRR MET Toolkit is designed to overcome the significant technical barriers associated with accessing native HRRR formats by providing a streamlined, user-friendly dataset with high vertical resolution at power generation-relevant heights. To ensure seamless continuity for long-term studies, the HRRR MET Toolkit is provided on the same uniform 2 km horizontal grid as the legacy WIND Toolkit, offering both modern accessibility and spatial consistency with the established historical record. To evaluate potential performance gains, we also assessed an experimental bias-corrected version using quantile mapping against the WIND Toolkit as a climatological reference. We provide a comprehensive validation of both HRRR variants alongside the WTK-LED, its Climate variant, and the 2023 National Offshore Wind (NOW-23) dataset against long-term observations across the contiguous United States. Results indicate that the HRRR MET Toolkit significantly outperforms the WTK-LED suite; for instance, it reduces hub-height average wind speed bias to 0.10 m s^-1 (compared to 0.82 m s^-1 for the WTK-LED) and achieves an hourly wind speed correlation of 0.82. Critically, the comparison between the native and bias-corrected HRRR variants reveals that the statistical correction offers marginal benefit and in some cases exacerbates positive wind speed biases in complex terrain. We conclude that the native HRRR physics are sufficiently robust for energy applications and therefore recommend the HRRR MET Toolkit as a highly accessible, accurate, and less complex standard for modern power system studies in the United States.