WIND Toolkit - Long-Term Ensemble Dataset
WIND Toolkit Long-term Ensemble Dataset (WTK-LED), an updated version of the meteorological WIND Toolkit, is a meteorological dataset providing high-resolution time series, including interannual variability and model uncertainty of wind speed at every modeling grid point to indicate ranges of possible wind speeds. The data were produced using the Weather Research and Forecasting Model (WRF). The vertical grid used in WTK-LED includes many vertical layers in the atmospheric boundary layer to provide information of atmospheric quantities across the rotor layer of utility scale and distributed wind turbines. The WTK-LED includes:
(1) Numerical simulations of wind speed and other meteorological variables covering the contiguous United States (CONUS) and Alaska, with high-resolution (5-minute [min], 2-kilometer [km]) data for 3 years (2018-2020): WTK-LED CONUS, WTK-LED Alaska.
(2) Climate simulations from Argonne National Laboratory covering North America, including Alaska, Canada, and most of Mexico and the Caribbean islands. These simulations complement the new WTK-LED to offer a 4-km, hourly dataset covering 20 years (2001-2020): WTK-LED Climate.
(3) Specific long-term, high-resolution offshore simulations have been conducted separately for the U.S. coasts, Hawaii, and the Great Lakes, leading to the 2023 National Offshore Wind dataset: NOW-23. The data for Hawaii include land-based data and are part of WTK-LED Hawaii.
Because the accuracy of simulations from a mesoscale model, such as WRF, varies depending on the location and weather situation, and can reach up to several m/s for wind speed, we provide simulated wind speed uncertainty estimates to the community to be used in conjunction with the deterministic model simulations.
This dataset was developed to satisfy a wide group of stakeholders across various wind energy disciplines, including but not limited to stakeholders in the distributed and utility scale wind industry, the new emerging airborne wind energy field, grid integration, power systems modeling, environmental modeling, and researchers in academia, and to close some of the gaps that current public datasets have.
Based on our validation results to date, we suggest use cases and applications for each dataset of the WTK-LED as shown in "WTK-LED Use Cases" resource below.
Citation Formats
National Renewable Energy Laboratory (NREL). (2024). WIND Toolkit - Long-Term Ensemble Dataset [data set]. Retrieved from https://data.openei.org/submissions/5987.
Wang, Jiali, Bodini, Nicola, Purkayastha, Avi, and Young, Ethan. WIND Toolkit - Long-Term Ensemble Dataset. United States: N.p., 24 Jan, 2024. Web. https://data.openei.org/submissions/5987.
Wang, Jiali, Bodini, Nicola, Purkayastha, Avi, & Young, Ethan. WIND Toolkit - Long-Term Ensemble Dataset. United States. https://data.openei.org/submissions/5987
Wang, Jiali, Bodini, Nicola, Purkayastha, Avi, and Young, Ethan. 2024. "WIND Toolkit - Long-Term Ensemble Dataset". United States. https://data.openei.org/submissions/5987.
@div{oedi_5987, title = {WIND Toolkit - Long-Term Ensemble Dataset}, author = {Wang, Jiali, Bodini, Nicola, Purkayastha, Avi, and Young, Ethan.}, abstractNote = {WIND Toolkit Long-term Ensemble Dataset (WTK-LED), an updated version of the meteorological WIND Toolkit, is a meteorological dataset providing high-resolution time series, including interannual variability and model uncertainty of wind speed at every modeling grid point to indicate ranges of possible wind speeds. The data were produced using the Weather Research and Forecasting Model (WRF). The vertical grid used in WTK-LED includes many vertical layers in the atmospheric boundary layer to provide information of atmospheric quantities across the rotor layer of utility scale and distributed wind turbines. The WTK-LED includes:
(1) Numerical simulations of wind speed and other meteorological variables covering the contiguous United States (CONUS) and Alaska, with high-resolution (5-minute [min], 2-kilometer [km]) data for 3 years (2018-2020): WTK-LED CONUS, WTK-LED Alaska.
(2) Climate simulations from Argonne National Laboratory covering North America, including Alaska, Canada, and most of Mexico and the Caribbean islands. These simulations complement the new WTK-LED to offer a 4-km, hourly dataset covering 20 years (2001-2020): WTK-LED Climate.
(3) Specific long-term, high-resolution offshore simulations have been conducted separately for the U.S. coasts, Hawaii, and the Great Lakes, leading to the 2023 National Offshore Wind dataset: NOW-23. The data for Hawaii include land-based data and are part of WTK-LED Hawaii.
Because the accuracy of simulations from a mesoscale model, such as WRF, varies depending on the location and weather situation, and can reach up to several m/s for wind speed, we provide simulated wind speed uncertainty estimates to the community to be used in conjunction with the deterministic model simulations.
This dataset was developed to satisfy a wide group of stakeholders across various wind energy disciplines, including but not limited to stakeholders in the distributed and utility scale wind industry, the new emerging airborne wind energy field, grid integration, power systems modeling, environmental modeling, and researchers in academia, and to close some of the gaps that current public datasets have.
Based on our validation results to date, we suggest use cases and applications for each dataset of the WTK-LED as shown in "WTK-LED Use Cases" resource below. }, doi = {}, url = {https://data.openei.org/submissions/5987}, journal = {}, number = , volume = , place = {United States}, year = {2024}, month = {01}}
Details
Data from Jan 24, 2024
Last updated Sep 25, 2024
Submitted Jan 24, 2024
Organization
National Renewable Energy Laboratory (NREL)
Contact
Caroline Draxl
Authors
Research Areas
Keywords
WIND Toolkit, wind resource assessment, resource uncertainty, WTK-LED, meteorological, data, processed data, WRF, simulation, weather, wind, energy, Long-term ensembleDOE Project Details
Project Name National Wind Resource Databaase
Project Number FY23 AOP 4.1.0.410