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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.
Complete Metadata
| @type | dcat:Dataset |
|---|---|
| accessLevel | public |
| bureauCode |
[
"019:20"
]
|
| contactPoint |
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"fn": "Wind Toolkit Group",
"@type": "vcard:Contact",
"hasEmail": "mailto:windtoolkit@nrel.gov"
}
|
| dataQuality |
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| description | 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. |
| distribution |
[
{
"@type": "dcat:Distribution",
"title": "WTK-LED Data on AWS",
"format": "HTML",
"accessURL": "https://data.openei.org/s3_viewer?bucket=nrel-pds-wtk&prefix=wtk-led%2F",
"mediaType": "text/html",
"description": "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. Publicly available in AWS S3. "
},
{
"@type": "dcat:Distribution",
"title": "NOW-23 OEDI Submission",
"format": "HTML",
"accessURL": "https://data.openei.org/submissions/4500",
"mediaType": "text/html",
"description": "The 2023 National Offshore Wind data set (NOW-23) data submission on OEDI including documentation and links to the dataset in AWS S3. See "WTK-LED Use Cases" resource for more information."
},
{
"@type": "dcat:Distribution",
"title": "WIND Toolkit HSDS Usage Examples",
"format": "html#data-access-examples",
"accessURL": "https://nrel.github.io/rex/misc/examples.wind.html#data-access-examples",
"mediaType": "application/octet-stream",
"description": "Examples of using the HSDS service to access WIND Toolkit data. "
},
{
"@type": "dcat:Distribution",
"title": "Wind Integration National Dataset Registry on AWS",
"format": "HTML",
"accessURL": "https://registry.opendata.aws/nrel-pds-wtk/",
"mediaType": "text/html",
"description": "AWS public dataset program registry page for data released under the Wind Integration National Dataset. The registry page contains information about dataset documentation, access, and contact, for each of the WIND Toolkit datasets."
},
{
"@type": "dcat:Distribution",
"title": "Wind Resource Database",
"format": "gov",
"accessURL": "https://wrdb.nrel.gov/",
"mediaType": "application/octet-stream",
"description": "The Wind Resource Database (WRDB) is a collection of modeled wind resource estimates for the United States and various countries around the globe of various lengths up to over 20 years. Most of the data are available in 5 min intervals at 2 km spatial resolution. For the United States, uncertainty estimates are also provided."
},
{
"@type": "dcat:Distribution",
"title": "WTK-LED Publication",
"format": "HTML",
"accessURL": "https://www.osti.gov/servlets/purl/2473210/",
"mediaType": "text/html",
"description": "WTK-LED: The WIND Toolkit Long-Term Ensemble Dataset publication detailing the full project. "
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"downloadURL": "https://data.openei.org/files/5987/WTK-LED%20Use%20Cases.pdf"
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|
| DOI | 10.25984/2504176 |
| identifier | https://data.openei.org/submissions/5987 |
| issued | 2024-01-24T07:00:00Z |
| keyword |
[
"Long-term ensemble",
"WIND Toolkit",
"WRF",
"WTK-LED",
"data",
"energy",
"meteorological",
"processed data",
"resource uncertainty",
"simulation",
"weather",
"wind",
"wind resource assessment"
]
|
| landingPage | https://data.openei.org/submissions/5987 |
| license | https://creativecommons.org/licenses/by/4.0/ |
| modified | 2025-01-22T17:46:35Z |
| programCode |
[
"019:000",
"019:010",
"019:023"
]
|
| projectNumber | FY23 AOP 4.1.0.410 |
| projectTitle | National Wind Resource Databaase |
| publisher |
{
"name": "National Renewable Energy Laboratory (NREL)",
"@type": "org:Organization"
}
|
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|
| title | WIND Toolkit Long-Term Ensemble Dataset |
