Reply on AC1

作者：Nicola Bodini, Emina Maric, Ulrike Egerer, Grant Buster, Luke Lavin, Pavlo Pinchuk, Brandon Benton, David D. Turner · 年份：2026 · DOI：10.5194/wes-2026-67-rc2 · 研究领域：Meteorology、Computer science、Environmental science、Data science

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, i...