WTK-LED: The WIND Toolkit Long-Term Ensemble Dataset

WTK-LED)-is a meteorological dataset that provides 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.WTK-LED aims to close gaps in current public datasets to better serve stakeholders in the distributed and utility-scale wind industries, the emerging airborne wind energy field, grid integration, power systems modeling, environmental modeling, national laboratories, and academia.The data were produced using the Weather Research and Forecasting (WRF) Model.The vertical grid used in WTK-LED includes many vertical layers in the atmospheric boundary layer to provide information on atmospheric quantities across the rotor layer of utility-scale and distributed wind turbines.WTK-LED includes: WTK-LED CONUS and WTK-LED Alaska: 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) from 10 to 1,000 m above ground level. WTK-LED Climate: 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) from 10 to 1,000 m above ground level. NOW-23: 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.NOW-23 is a 2-km, 5-min dataset from 10 -500 m above ground level.Note that the original WIND Toolkit was developed for grid integration studies; it therefore mimicked forecast errors and contained power forecasts alongside meteorological "actuals."The WTK-LED was developed as a meteorological reanalysis-type dataset that satisfies the needs of many stakeholders, such as those in the distributed and utility-scale wind industry, the emerging airborne wind energy field, grid integration, power systems modeling, environmental modeling, and academia.As such, it was not tailored specifically to grid integration studies.Users should be aware of this difference between the WIND Toolkit and WTK-LED and are encouraged to follow the authors' recommendations for use documented in this report (Table ES-1).Because the accuracy of simulations from a mesoscale model such as WRF varies depending on location and weather situation, and because the model bias or errors can reach up to several meters per second for wind speed, we provide simulated wind speed uncertainty estimates to use in conjunction with the deterministic model simulations.Sixteen ensembles were run over CONUS, Alaska, Hawaii, and other areas in North America to estimate both the model structural uncertainty and uncertainty due to internal model variability.Structural uncertainty results from unknowns in the physics parameterizations used in the model.Internal variability results from the nonlinearity in the equations that underpin the weather forecasting models.Thus, when using different physics parameterizations or different initial conditions, models can generate different vi This report is available at no cost from the National Renewable Energy Laboratory at www.nrel.gov/publications.solutions.The estimates of the simulated wind speed uncertainty can be valuable for assessing distributions of simulated wind speeds per model grid point.In summary, we found that summer generally exhibits lower wind speeds than other seasons, while winter shows higher wind speeds than other seasons.However, summer has higher model internal variability, whereas winter has lower model internal variability but larger structural uncertainty.We also found that the larger model domain (i.e., North America Climate domain) shows larger internal variabilities and structural uncertainties (especially in the summer) than the smaller model domain (i.e., CONUS or Alaska).Comparing the two sources of uncertainty over the same domain and same season, the physics uncertainty is larger than the uncertainty from model internal variability in general but depends on specific locations.The uncertainty range due to internal variability does not change significantly when using different physics schemes, when using different forcing data, or in a different year.In general, the model uncertainty is much larger for shorter timescales, such as days or hours, and is smaller on a weekly, monthly, or seasonal scale.

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