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Bias Corrected NOAA HRRR Wind Resource Data for Grid Integration Applications

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To address the need for regularly updated wind resource data, NREL has processed the High-Resolution Rapid Refresh (HRRR) outputs for use in grid integration modeling. The HRRR is an hourly-updated operational forecast product produced by the National Oceanic and Atmospheric Administration (NOAA) (Dowell et al., 2022).

Several barriers have prevented the HRRR's widespread proliferation in the wind energy industry: missing timesteps (prior to 2019), challenging file format for wind energy analysis, limited vertical height resolution, and negative bias versus legacy WIND Toolkit data (2007-2013). NREL has applied re-gridding, interpolation, and bias-correction to the native HRRR data to overcome these limitations. This results in the now-publicly-available bias corrected and interpolated HRRR (BC-HRRR) dataset for weather years 2015 to 2023.

Bias correction is necessary for wind resource consistency across weather years to be used simultaneously in planning-focused grid integration studies alongside the original WIND Toolkit data. We show that quantile mapping with the WIND Toolkit as a historical baseline is an effective method for bias correcting the interpolated HRRR data: the BC-HRRR has reduced mean bias versus comparable gridded wind resource datasets (+0.12 m/s versus Vortex) and has very low mean bias versus ground measurement stations (+0.01 m/s) (Buster et al., 2024).

BC-HRRR's consistency with the legacy WIND Toolkit allows NREL to extend grid integration analysis to 15+ weather years of wind data with low-overhead extensibility to future years as they are made available by NOAA. As with historical datasets like the WIND Toolkit, BC-HRRR is intended for use in grid integration modeling (e.g., capacity expansion, production cost, and resource adequacy modeling) both independently and alongside the legacy WIND Toolkit.

Citation Formats

TY - DATA AB - To address the need for regularly updated wind resource data, NREL has processed the High-Resolution Rapid Refresh (HRRR) outputs for use in grid integration modeling. The HRRR is an hourly-updated operational forecast product produced by the National Oceanic and Atmospheric Administration (NOAA) (Dowell et al., 2022). Several barriers have prevented the HRRR's widespread proliferation in the wind energy industry: missing timesteps (prior to 2019), challenging file format for wind energy analysis, limited vertical height resolution, and negative bias versus legacy WIND Toolkit data (2007-2013). NREL has applied re-gridding, interpolation, and bias-correction to the native HRRR data to overcome these limitations. This results in the now-publicly-available bias corrected and interpolated HRRR (BC-HRRR) dataset for weather years 2015 to 2023. Bias correction is necessary for wind resource consistency across weather years to be used simultaneously in planning-focused grid integration studies alongside the original WIND Toolkit data. We show that quantile mapping with the WIND Toolkit as a historical baseline is an effective method for bias correcting the interpolated HRRR data: the BC-HRRR has reduced mean bias versus comparable gridded wind resource datasets (+0.12 m/s versus Vortex) and has very low mean bias versus ground measurement stations (+0.01 m/s) (Buster et al., 2024). BC-HRRR's consistency with the legacy WIND Toolkit allows NREL to extend grid integration analysis to 15+ weather years of wind data with low-overhead extensibility to future years as they are made available by NOAA. As with historical datasets like the WIND Toolkit, BC-HRRR is intended for use in grid integration modeling (e.g., capacity expansion, production cost, and resource adequacy modeling) both independently and alongside the legacy WIND Toolkit. AU - Buster, Grant A2 - Pinchuk, Pavlo A3 - Lavin, Luke A4 - Benton, Brandon A5 - Bodini, Nicola DB - Open Energy Data Initiative (OEDI) DP - Open EI | National Renewable Energy Laboratory DO - 10.25984/2480349 KW - energy KW - power KW - HRRR KW - weather KW - meteorology KW - wind KW - wind resource KW - spatiotemporal KW - data KW - wind toolkit KW - BCHRRR KW - wind power KW - weather years KW - operational forecast KW - NOAA LA - English DA - 2024/10/15 PY - 2024 PB - National Renewable Energy Lab (NREL) T1 - Bias Corrected NOAA HRRR Wind Resource Data for Grid Integration Applications UR - https://doi.org/10.25984/2480349 ER -
Export Citation to RIS
Buster, Grant, et al. Bias Corrected NOAA HRRR Wind Resource Data for Grid Integration Applications. National Renewable Energy Lab (NREL), 15 October, 2024, Open Energy Data Initiative (OEDI). https://doi.org/10.25984/2480349.
Buster, G., Pinchuk, P., Lavin, L., Benton, B., & Bodini, N. (2024). Bias Corrected NOAA HRRR Wind Resource Data for Grid Integration Applications. [Data set]. Open Energy Data Initiative (OEDI). National Renewable Energy Lab (NREL). https://doi.org/10.25984/2480349
Buster, Grant, Pavlo Pinchuk, Luke Lavin, Brandon Benton, and Nicola Bodini. Bias Corrected NOAA HRRR Wind Resource Data for Grid Integration Applications. National Renewable Energy Lab (NREL), October, 15, 2024. Distributed by Open Energy Data Initiative (OEDI). https://doi.org/10.25984/2480349
@misc{OEDI_Dataset_6218, title = {Bias Corrected NOAA HRRR Wind Resource Data for Grid Integration Applications}, author = {Buster, Grant and Pinchuk, Pavlo and Lavin, Luke and Benton, Brandon and Bodini, Nicola}, abstractNote = {To address the need for regularly updated wind resource data, NREL has processed the High-Resolution Rapid Refresh (HRRR) outputs for use in grid integration modeling. The HRRR is an hourly-updated operational forecast product produced by the National Oceanic and Atmospheric Administration (NOAA) (Dowell et al., 2022).

Several barriers have prevented the HRRR's widespread proliferation in the wind energy industry: missing timesteps (prior to 2019), challenging file format for wind energy analysis, limited vertical height resolution, and negative bias versus legacy WIND Toolkit data (2007-2013). NREL has applied re-gridding, interpolation, and bias-correction to the native HRRR data to overcome these limitations. This results in the now-publicly-available bias corrected and interpolated HRRR (BC-HRRR) dataset for weather years 2015 to 2023.

Bias correction is necessary for wind resource consistency across weather years to be used simultaneously in planning-focused grid integration studies alongside the original WIND Toolkit data. We show that quantile mapping with the WIND Toolkit as a historical baseline is an effective method for bias correcting the interpolated HRRR data: the BC-HRRR has reduced mean bias versus comparable gridded wind resource datasets (+0.12 m/s versus Vortex) and has very low mean bias versus ground measurement stations (+0.01 m/s) (Buster et al., 2024).

BC-HRRR's consistency with the legacy WIND Toolkit allows NREL to extend grid integration analysis to 15+ weather years of wind data with low-overhead extensibility to future years as they are made available by NOAA. As with historical datasets like the WIND Toolkit, BC-HRRR is intended for use in grid integration modeling (e.g., capacity expansion, production cost, and resource adequacy modeling) both independently and alongside the legacy WIND Toolkit.}, url = {https://data.openei.org/submissions/6218}, year = {2024}, howpublished = {Open Energy Data Initiative (OEDI), National Renewable Energy Lab (NREL), https://doi.org/10.25984/2480349}, note = {Accessed: 2025-04-25}, doi = {10.25984/2480349} }
https://dx.doi.org/10.25984/2480349

Details

Data from Oct 15, 2024

Last updated Feb 17, 2025

Submitted Nov 22, 2024

Organization

National Renewable Energy Lab (NREL)

Contact

Grant Buster

720.495.6245

Authors

Grant Buster

National Renewable Energy Lab

Pavlo Pinchuk

National Renewable Energy Lab

Luke Lavin

National Renewable Energy Lab

Brandon Benton

National Renewable Energy Lab

Nicola Bodini

National Renewable Energy Lab

DOE Project Details

Project Name Energy Sector Modeling and Impacts (WETO Modeling & Analysis)

Project Number 32298

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