**Overview** Scaled Wind Farm Technology (SWiFT) Facility meteorological tower (MET), turbine, and Technical University of Denmark (DTU) SpinnerLidar data acquired on 20161216 UTC during a neutral atmospheric boundary layer inflow at a single focus distance of 2.5 D (D=27 m). **Data Quality** Data information is provided in uploaded documentation: *SWiFT Wake Steering Instrumentation and Data Processing* (PDF).

**Overview** The SUMR-D CART2 turbine data are recorded by the CART2 wind turbine's supervisory control and data acquisition (SCADA) system for the Advanced Research Projects Agency–Energy (ARPA-E) SUMR-D project located at the National Renewable Energy Laboratory (NREL) Flatirons Campus. For the project, the CART2 wind turbine was outfitted with a highly flexible rotor specifically designed and constructed for the project. More details about the project can be found here: https://sumrwind.com/. The data include power, loads, and meteorological information from the turbine during startup, operation, and shutdown, and when it was parked and idle. **Data Details** Additional files are attached: sumr_d_5-Min_Database.mat - a database file in MATLAB format of this dataset, which can be used to search for desired data files; sumr_d_5-Min_Database.xlsx - a database file in Microsoft Excel format of this dataset, which can be used to search for desired data files; loadcartU.m - this script loads in a CART data file and puts it in your workspace as a Matlab matrix (you can call this script from your own Matlab scripts to do your own analysis); charts.mat - this is a dependency file needed for the other scripts (it allows you to make custom preselections for cartPlotU.m); cartLoadHdrU.m - this script loads in the header file information for the data file (the header is embedded in each data file at the beginning); cartPlotU.m - this is a graphic user interface (GUI) that allows you to interactively look at different channels (to use it, run the script in Matlab, and load in the data file(s) of interest; from there, you can select different channels and plot things against each other; note that this script has issues with later versions of MATLAB; the preferred version to use is R2011b). **Data Quality** Wind turbine blade loading data were calibrated using blade gravity calibrations prior to data collection and throughout the data collection period. Blade loading was also checked for data quality following data collection as strain gauge measurements drifted throughout the data collection. These drifts in the strain gauge measurements were removed in post processing.

**Overview** Data submission for WFIP2 at Vansycle Ridge (Oregon). Vertical-profiling lidar data from a Wind Cube v2, included 10-minute averages of wind speed and wind direction up to 200 m. Data collected during WFIP2. **Data Details** Z-wind has the following notation: negative = upward positive = downward probe volume is +/- 20 m at all heights **Data Quality** Other than the Quality Assurance (QA) procedures implemented by the lidar during data processing, no other quality procedures are applied. These data include a carrier-to-noise ratio (CNR) threshold of -23. CNR below this threshold result in -9999 for all data points

**Overview** The dataset includes 15-minute average wind speed and direction records from 30 m to 330 m above ground level (AGL) in 10-m range gates. Data were collected by a Scintec MFAS wind profiler installed at the Decker Ranch in Oregon, about 4.4 km southeast of Kent, Ore., and are intended for validating WFIP2 model improvements. **Data Details** Instrument location: * N 45°09'54.42" (N 45.165117) * W120°39'20.87" (W 120.655799) Instrument clock and computer system time set to UTC. **Data Quality** The Scintec MFAS wind profiler instrument installed at the Decker Ranch is capable of measuring at heights up to 1000 m. For this study, the maximum height was set to 330 m. The instrument was oriented to true north, so no corrections to the wind direction should be made. Scintec wind profilers come with the APRun software package, which performs data collection and quality control (QC), among other functions. Version 1.46 of APRun was used in this study. The APRun manual states: *The primary results are checked against local signal quality criteria, combined signal quality criteria and two-dimensional spatial/temporal consistency tests. Any data that does not pass all quality control tests is devalidated and removed*. Devalidation means replacing the value with an error value, usually a series of ‘9’s, such as 99.99 or 999.99. Not all devalidated data are actually removed from the *.mnd files, so the user must filter them out. There are some error flags that indicate the type of error, but these are not included in the *.mnd files, and we have no access to them. Because QC already has been performed by APRun, our QC procedures consisted of removing samples with error values and performing a visual inspection of the data to see if larger patterns indicated any kind of problem. There are 623 gaps of two hours or less and 61 gaps of more than two hours. The longest gap is 15.31 days, from 2016-12-07 03:00Z to 2016-12-22 10:30Z. All gaps that exceed two hours are listed in file: Decker_Ranch_gaps.txt.

This dataset provides locations and technical specifications of wind turbines in the United States, almost all of which are utility-scale. Utility-scale turbines are ones that generate power and feed it into the grid, supplying a utility with energy. They are usually much larger than turbines that would feed a house or business. The regularly updated database contains wind turbine records that have been collected, digitized, and locationally verified. Turbine data were gathered from the Federal Aviation Administration's (FAA) Digital Obstacle File (DOF) and Obstruction Evaluation Airport Airspace Analysis (OE-AAA), American Clean Power (ACP) Association (formerly American Wind Energy Association (AWEA)), Lawrence Berkeley National Laboratory (LBNL), and the United States Geological Survey (USGS), and were merged and collapsed into a single dataset. Verification of the turbine positions was done by visual interpretation using high-resolution aerial imagery in ESRI ArcGIS Desktop. A locational error of plus or minus 10 meters for turbine locations was tolerated. Technical specifications for turbines were assigned based on the wind turbine make and models as provided by manufacturers and project developers directly, and via FAA datasets, information on the wind project developer or turbine manufacturer websites, or other online sources. Some facility and turbine information on make and model did not exist or was difficult to obtain. Thus, uncertainty may exist for certain turbine specifications. Similarly, some turbines were not yet built, not built at all, or for other reasons cannot be verified visually. Location and turbine specifications data quality are rated, and confidence is recorded for both. None of the data are field verified.

**Overview** This dataset contains measurements from eight different Vaisala Triton Wind Profiler instruments. The Triton Wind Profiler is a sodar wind profiler that measures wind speed, direction, and turbulence intensity at heights from 30 m to 200 m above ground every 10 minutes. The eight Tritons are located at various sites around the WFIP2 study area. **Data Details** Regarding the minimum requirements for the site description, a Keyhole Markup Language (KML) file is attached with all of the AON Triton locations. Unfortunately, there are no photos of the sites. The layout of each site is simple. At all locations, the Triton Wind Profiler is placed on the ground with the solar panel facing due south. Each unit is solar powered and communicates its data via satellite, so there are no cables of any kind. Also, the specified start and end dates are for the entire AON network. Some individual units start later or end earlier. All start/end dates for the individual units are given as follows: AON1 (z17): 10/1/2015 -- 7/31/2017 AON2 (z14): 10/1/2015 -- 7/31/2017 AON3 (z18): 10/1/2015 -- 7/31/2017 AON4 (z12): 12/5/2015 -- 7/31/2017 AON5 (z06): 10/1/2015 -- 7/31/2017 AON6 (z05): 10/1/2015 -- 7/31/2017 (w/gap 2016-08-01 -- 2016-09-28) AON7 (z02): 10/1/2015 -- 7/31/2017 (w/gap 2016-07-01 -- 2016-11-18) AON8 (z01): 12/7/2015 -- 4/9/2016 AON9 (z20): 11/19/2016 -- 7/31/2017 **Data Quality** The Triton firmware has a quality assessment algorithm that assigns a quality factor (“quality”) to each time/height measurement of wind, expressed as a percent value in the range 0-100. In addition, the upward Doppler velocity (“vert”) is measured and can be used as an indicator of falling precipitation, which negatively affects data quality. Two automated procedures are applied in real time as these data (level 00) are sent to the DAP. Data are set to a missing value (null in the CSV file) when either “quality” < 90% or “vert” < -1.5 m/s. Because the data level is from a real-time feed, no manual quality control (QC) has been performed. Periods of missing data are likely due to real-time glitches, many of which will be filled in once the final data retrieval and QC process are performed (level b0). Finally, for the first few months of the study, the variables provided in the real-time (level "00") files differ slightly than what is specified on this metadata web page. The header line in the earlier CSV files is fairly self-explanatory in defining the variables and units. **Uncertainty** When compared to nearby towers instrumented with cup anemometers and wind vanes, the root mean square (RMS) difference in 10-minute wind speed between the Triton and met tower typically is around 0.5 m s-1. When tested at 30 different sites in a recent validation study, the RMS difference in long-term mean wind speed between the Triton and met tower is 1.3%. **Constraints** Various meteorological and environmental conditions can lead to either weaker returns or enhanced noise, resulting in a poor measurement. The higher the target point, the more difficult it is to retrieve a strong signal. Hence, a common situation is that good data will be obtained up to some height yet not above it. The percentage of time that good data are recovered at a particular height is the data recovery rate. In a recent validation study, data recovery rates were around 98% at lower heights, slowly dropping off to 96% at 100 m, 83% at 160 m, and 70% at 200 m.

**Overview** This dataset contains measurements from eight different Vaisala Triton Wind Profiler instruments. The Triton Wind Profiler is a sodar wind profiler that measures wind speed, direction, and turbulence intensity at heights from 30 m to 200 m above ground every 10 minutes. The eight Tritons are located at various sites around the WFIP2 study area. **Data Details** Regarding the minimum requirements for the site description, a Keyhole Markup Language (KML) file is attached with all of the AON Triton locations. Unfortunately, there are no photos of the sites. The layout of each site is simple. At all locations, the Triton Wind Profiler is placed on the ground with the solar panel facing due south. Each unit is solar powered and communicates its data via satellite, so there are no cables of any kind. Also, the specified start and end dates are for the entire AON network. Some individual units start later or end earlier. All start/end dates for the individual units are given as follows: AON1 (z17): 10/1/2015 -- 7/31/2017 AON2 (z14): 10/1/2015 -- 7/31/2017 AON3 (z18): 10/1/2015 -- 7/31/2017 AON4 (z12): 12/5/2015 -- 7/31/2017 AON5 (z06): 10/1/2015 -- 7/31/2017 AON6 (z05): 10/1/2015 -- 7/31/2017 (w/gap 2016-08-01 -- 2016-09-28) AON7 (z02): 10/1/2015 -- 7/31/2017 (w/gap 2016-07-01 -- 2016-11-18) AON8 (z01): 12/7/2015 -- 4/9/2016 AON9 (z20): 11/19/2016 -- 7/31/2017 **Data Quality** The Triton firmware has a quality assessment algorithm that assigns a quality factor (“quality”) to each time/height measurement of wind, expressed as a percent value in the range 0-100. In addition, the upward Doppler velocity (“vert”) is measured and can be used as an indicator of falling precipitation, which negatively affects data quality. Two automated procedures are applied in real time as these data (level 00) are sent to the DAP. Data are set to a missing value (null in the CSV file) when either “quality” < 90% or “vert” < -1.5 m/s. Because the data level is from a real-time feed, no manual quality control (QC) has been performed. Periods of missing data are likely due to real-time glitches, many of which will be filled in once the final data retrieval and QC process are performed (level b0). Finally, for the first few months of the study, the variables provided in the real-time (level "00") files differ slightly than what is specified on this metadata web page. The header line in the earlier CSV files is fairly self-explanatory in defining the variables and units. **Uncertainty** When compared to nearby towers instrumented with cup anemometers and wind vanes, the root mean square (RMS) difference in 10-minute wind speed between the Triton and met tower typically is around 0.5 m s-1. When tested at 30 different sites in a recent validation study, the RMS difference in long-term mean wind speed between the Triton and met tower is 1.3%. **Constraints** Various meteorological and environmental conditions can lead to either weaker returns or enhanced noise, resulting in a poor measurement. The higher the target point, the more difficult it is to retrieve a strong signal. Hence, a common situation is that good data will be obtained up to some height yet not above it. The percentage of time that good data are recovered at a particular height is the data recovery rate. In a recent validation study, data recovery rates were around 98% at lower heights, slowly dropping off to 96% at 100 m, 83% at 160 m, and 70% at 200 m.