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Bonanza Creek LTER: Hourly Wind Speed and Direction at 3m and 10 m from 1988 to Present in the Bonanza Creek Experimental Forest near Fairbanks, Alaska
This dataset contains the hourly output from Wind sensors for the Bonanza Creek Experimental Forest (BCEF). This includes Level 3 weather stations as well as smaller scale and temporal studies. This data can be sorted and viewed by site, year, hour, height of measurement, mean, min, and max value. Updates of each site are different since some are still on going while other had only a 2-3 year life cycle. Winter measurements may be inaccurate due to snow cover.
Hubbard Brook Experimental Forest: 15 Minute Wind Speed and Direction Measurements, 2012 – present
Wind speed and direction have been recorded at 15-minute resolution by an R.M. Young company wind sensor at three locations within Hubbard Brook Experimental Forest since 2012. These data were gathered at the Hubbard Brook Experimental Forest in Woodstock, NH, which is operated and maintained by the USDA Forest Service, Northern Research Station.
PIE LTER, Wind sensor data, 15 minute intervals, from the Ipswich Bay Yacht Club pier located in Ipswich, MA, year 2022.
Wind sensor measurements (wind speed and wind direction) for 2022 at the Ipswich Bay Yacht Club, Ipswich, MA, 15 minute average measurements.
PIE LTER 15-minute Wind speed and direction in the lower Plum Island Sound at the Ipswich Bay Yacht Club pier, Ipswich, MA, year 2023.
Wind speed and direction measurements for 2023 at Ipswich Bay Yacht Club, Ipswich, MA. Wind speed is measured every 5 seconds and reported as an average in 15 minute intervals. Maximum wind speed is also reported for each 15 minute interval with a timestamp. Wind direction is measured every 15 minutes.
SBC LTER: Wind data near Santa Barbara, from both land stations and ocean buoys
This data package includes wind data (e.g. wind speed, wind direction, wind velocity, air temperature, etc.) from 54 stations near Santa Barbara, CA since 1981. The wind data is collected from 3 agencies: Santa Barbara County Air Pollution Control District, California Department of Water Resources, and National Data Buoy Center. The data collected from the National Data Buoy Center also include the water temperature data. This data package is expected to update annually.
Winds on Hog Island Virginia 2009-2019
An RM Young wind monitor was installed at the Machipongo Station n the northern part of Hog Island, on the western side of the island upland. It was initially installed on top of the tower on the station at a height of approximately 20m. Subsequent to the demolition of the Machipongo Station in 2012, the monitor was moved to near the top of an abandoned Coast Guard flag tower (approx. 15 m high) located adjacent to the former site of the Machipongo Station. Measurements of wind speed and direction were taken each hour. The mean wind vector magnitude and direction are recorded based on 60 measurements made each hour. Standard deviation of the wind direction was calculated using the Campbell Scientifics formula. Starting in 2013 peak wind speeds were also recorded. Collection of this data ended in 2019. It was replaced by the dataset knb-lter-vcr.25 (Hourly Meteorological Data for the Virginia Coast Reserve LTER 1989-present) when a full-fledged meteological station was installed atop a 20-meter tall lookout tower on southern Hog Island at the site of the former town of Broadwater.
Wind tunnel distributed temperature sensing with actively heated fibers and microstructures for detecting wind direction
<p>Wind tunnel tests were performed using distributed temperature sensing with actively heated fibers that had microstructures attached in opposing directions on neighboring fibers. These microstructures created a temperature difference between fibers that depended on wind speed, providing a prototype for distributed sensing of wind direction. These data are connected to a publication detailing this work and method, <a href="https://www.atmos-meas-tech-discuss.net/amt-2019-188/">"Distributed observations of wind direction using microstructures attached to actively heated fiber-optic cables"</a>.</p> <p>Data are stored in a netcdf format and includes the instrument reported temperature ('instr_temp') and calibrated temperature ('cal_temp') with the various parameters tested in the linked paper available as coordinates, labeled along an 'expname' dimension.</p> <p>The included ipython notebooks provide examples and explanations for using these laboratory data.</p>
One-minute average horizontal wind velocity data (not corrected for air-flow distortion) from the Antarctic Circumnavigation Expedition (ACE) 2016/2017 legs 0 to 4.
<p><strong>Dataset abstract</strong></p> <p>This dataset contains the one-minute average horizontal wind velocity data from the Antarctic Circumnavigation Expedition (ACE) 2016/2017 legs 0 to 4. The data has been filtered for spurious observations and the true wind correction has been redone using the quality checked one-minute ship track velocity data. This data set has not been corrected for air-flow distortion, which was caused by the ship's super structure. The flow-distortion corrected data should be used for studies interested in the actual true wind speed near the ship's location.</p> <p><strong>Dataset contents</strong></p> <ul> <li>wind-observations-stbd-uncorrected-5min-legs0-4.csv, data file, comma-separated values</li> <li>wind-observations-port-uncorrected-5min-legs0-4.csv, data file, comma-separated values</li> <li>data_file_header, metadata, text format</li> <li>README.txt, metadata, text format</li> </ul> <p><strong>Dataset license</strong></p> <p>This one-minute averaged wind velocity dataset is made available under the Creative Commons Attribution 4.0 International License (CC BY 4.0) whose full text can be found at https://creativecommons.org/licenses/by/4.0/</p>
Na Wind-Temperature Lidar Data at Andes Lidar Observatory on 3/1/2016
<p>Measurement made by the Na Wind-Temperature Lidar at the Andes Lidar Observatory in Cerro Pachón, Chile. It includes Na density, temperature, zonal, meridional, and vertical wind, from 80 to 115 km altitude at 0.5 km interval and from 23.8 UT 2/29/2016 to 8.9 UT 3/1/2016 at 0.1 hour interval. Errors of these values are also included. -999 represents missing value. </p>
DeepOWT v2.25.1: An updated and improved global offshore wind turbine dataset until 2025Q1
<p>DeepOWT (deep learning derived global offshore wind turbines) is an independent and openly accessible data set of offshore wind energy infrastructure locations and their temporal deployment dynamics on a global scale. It is derived by applying deep learning based object detection on ESA's spaceborne Sentinel-1 synthetic aperture radar (SAR) archive. DeepOWT provides OWT locations along with their quarterly deployment stages from 2016Q1 until 2025Q1. It differentiates between platforms under construction, OWTs which are readily deployed and offshore wind farm substations, such as transformer stations.<br><br>The dataset continues the work of <a href="https://essd.copernicus.org/articles/14/4251/2022/">10.5194/essd-14-4251-2022</a>.</p> <p>File metadata</p> <table> <tbody> <tr> <th>File</th> <th>Time</th> <th>Geometry</th> <th>Spatial extent</th> </tr> <tr> <td>DeepOWT.geojson (Dataset)</td> <td>2016Q1-2025Q1</td> <td>points</td> <td>Global</td> </tr> <tr> <td>gt_2021Q2_nsb.geojson (Ground Truth Location)</td> <td>2021Q2</td> <td>polygons</td> <td>North Sea Basin</td> </tr> <tr> <td>gt_2021Q2_ecs.geojson (Ground Truth Location)</td> <td>2021Q2</td> <td>polygons</td> <td>East China Sea</td> </tr> <tr> <td>gt_2021Q2_vtn.geojson (Ground Truth Location)</td> <td>2021Q2</td> <td>polygons</td> <td>Southeast Vietnamese Coast</td> </tr> <tr> <td>gt_nsb_gridded.geojson (Ground Truth Region)</td> <td>-</td> <td>polygon</td> <td>North Sea Basin</td> </tr> <tr> <td>gt_ecs_gridded.geojson (Ground Truth Region)</td> <td>-</td> <td>polygon</td> <td>East China Sea</td> </tr> <tr> <td>gt_ecs_gridded.geojson (Ground Truth Region)</td> <td>-</td> <td>polygon</td> <td>Southeast Vietnamese Coast</td> </tr> </tbody> </table> <p> </p> <table> <thead> <tr> <th>Used semantic label</th> </tr> </thead> <tbody> <tr> <td>open sea</td> </tr> <tr> <td>under construction</td> </tr> <tr> <td>offshore wind turbine</td> </tr> <tr> <td>offshore wind farm substation</td> </tr> </tbody> </table> <p> </p>
Wind Value Literature Selection for End-of-Life Valuation Excel File WP 5.1
<p>Authors from Wind Value, the Re-Wind Network and IEA Wind Task 45 carried out research on methods of processing end-of-life wind turbine blades. This included a structured literature review which selected the literature in the Excel file. Part of this work helps to estimate the value of an end-of-life wind farm contributing to Work Package 5.1 of the Wind Value project. The paper was pubished as Deeney et al. (2025) <a href="https://www.sciencedirect.com/science/article/pii/S1364032125000917?via%3Dihub">End-of-life wind turbine blades and paths to a circular economy</a>, <em>Renewable and Sustainable Energy Reviews. </em></p>
Wind WAVES TDSF Dataset
<p><strong><em>Wind</em> Spacecraft:</strong></p> <p>The <em>Wind</em> spacecraft (<a href="https://wind.nasa.gov">https://wind.nasa.gov</a>) was launched on November 1, 1994 and currently orbits the first Lagrange point between the Earth and sun. A comprehensive review can be found in <a href="https://ui.adsabs.harvard.edu/abs/2021RvGeo..5900714W/abstract"><em>Wilson et al.</em> [2021]</a>. It holds a suite of instruments from gamma ray detectors to quasi-static magnetic field instruments, Bo. The instruments used for this data product are the fluxgate magnetometer (MFI) [<a href="https://ui.adsabs.harvard.edu/abs/1995SSRv...71..207L/abstract"><em>Lepping et al.</em>, 1995</a>] and the radio receivers (WAVES) [<a href="https://ui.adsabs.harvard.edu/abs/1995SSRv...71..231B/abstract"><em>Bougeret et al.</em>, 1995</a>]. The MFI measures 3-vector <strong>B</strong><sub>o</sub> at ~11 samples per second (sps); WAVES observes electromagnetic radiation from ~4 kHz to >12 MHz which provides an observation of the upper hybrid line (also called the plasma line) used to define the total electron density and also takes time series snapshot/waveform captures of electric and magnetic field fluctuations, called TDS bursts herein.</p> <p><strong>WAVES Instrument:</strong></p> <p>The WAVES experiment [<a href="https://ui.adsabs.harvard.edu/abs/1995SSRv...71..231B/abstract"><em>Bougeret et al.</em>, 1995</a>] on the <em>Wind</em> spacecraft is composed of three orthogonal electric field antenna and three orthogonal search coil magnetometers. The electric fields are measured through five different receivers: Low Frequency FFT receiver called FFT (0.3 Hz to 11 kHz), Thermal Noise Receiver called TNR (4-256 kHz), Radio receiver band 1 called RAD1 (20-1040 kHz), Radio receiver band 2 called RAD2 (1.075-13.825 MHz), and the Time Domain Sampler (TDS). The electric field antenna are dipole antennas with two orthogonal antennas in the spin plane and one spin axis stacer antenna.</p> <p>The TDS receiver allows one to examine the electromagnetic waves observed by <em>Wind</em> as time series waveform captures. There are two modes of operation, TDS Fast (TDSF) and TDS Slow (TDSS). TDSF returns 2048 data points for two channels of the electric field, typically E<sub>x</sub> and E<sub>y</sub> (i.e. spin plane components), with little to no gain below ~120 Hz (the data herein has been high pass filtered above ~150 Hz for this reason). TDSS returns four channels with three electric(magnetic) field components and one magnetic(electric) component. The search coils show a gain roll off ~3.3 Hz [e.g., see <a href="https://ui.adsabs.harvard.edu/abs/2010JGRA..11512104W/abstract"><em>Wilson et al.</em>, 2010</a>; <a href="https://ui.adsabs.harvard.edu/abs/2012GeoRL..39.8109W/abstract"><em>Wilson et al.</em>, 2012</a>; <a href="https://ui.adsabs.harvard.edu/abs/2013JGRA..118....5W/abstract"><em>Wilson et al.</em>, 2013</a> and references therein for more details].</p> <p>The original calibration of the electric field antenna found that the effective antenna lengths are roughly 41.1 m, 3.79 m, and 2.17 m for the X, Y, and Z antenna, respectively. The +E<sub>x</sub> antenna was broken twice during the mission as of June 26, 2020. The first break occurred on August 3, 2000 around ~21:00 UTC and the second on September 24, 2002 around ~23:00 UTC. These breaks reduced the effective antenna length of Ex from ~41 m to 27 m after the first break and ~25 m after the second break [e.g., see <a href="https://ui.adsabs.harvard.edu/abs/2014GeoRL..41..266M/abstract"><em>Malaspina et al.</em>, 2014</a>; <a href="https://ui.adsabs.harvard.edu/abs/2016JGRA..121.9369M/abstract"><em>Malaspina & Wilson</em>, 2016</a>].</p> <p><strong>TDS Bursts:</strong></p> <p>TDS bursts are waveform captures/snapshots of electric and magnetic field data. The data is triggered by the largest amplitude waves which exceed a specific threshold and are then stored in a memory buffer. The bursts are ranked according to a quality filter which mostly depends upon amplitude. Due to the age of the spacecraft and ubiquity of large amplitude electromagnetic and electrostatic waves, the memory buffer often fills up before dumping onto the magnetic tape drive. If the memory buffer is full, then the bottom ranked TDS burst is erased every time a new TDS burst is sampled. That is, the newest TDS burst sampled by the instrument is always stored and if it ranks higher than any other in the list, it will be kept. This results in the bottom ranked burst always being erased. Earlier in the mission, there were also so called honesty bursts, which were taken periodically to test whether the triggers were working properly. It was found that the TDSF triggered properly, but not the TDSS. So the TDSS was set to trigger off of the Ex signals.</p> <p>A TDS burst from the <em>Wind</em>/WAVES instrument is always 2048 time steps for each channel. The sample rate for TDSF bursts ranges from 1875 samples/second (sps) to 120,000 sps. Every TDS burst is marked a unique set of numbers (unique on any given date) to help distinguish it from others and to ensure any set of channels are appropriately connected to each other. For instance, during one spacecraft downlink interval there may be 95% of the TDS bursts with a complete set of channels (i.e., TDSF has two channels, TDSS has four) while the remaining 5% can be missing channels (just example numbers, not quantitatively accurate). During another downlink interval, those missing channels may be returned if they are not overwritten. During every downlink, the flight operations team at NASA Goddard Space Fligth Center (GSFC) generate level zero binary files from the raw telemetry data. Those files are filled with data received on that date and the file name is labeled with that date. There is no attempt to sort chronologically the data within so any given level zero file can have data from multiple dates within. Thus, it is often necessary to load upwards of five days of level zero files to find as many full channel sets as possible. The remaining unmatched channel sets comprise a much smaller fraction of the total.</p> <p>All data provided here are from TDSF, so only two channels. Most of the time channel 1 will be associated with the E<sub>x</sub> antenna and channel 2 with the E<sub>y</sub> antenna. The data are provided in the spinning instrument coordinate basis with associated angles necessary to rotate into a physically meaningful basis (e.g., GSE).</p> <p><strong>TDS Time Stamps:</strong></p> <p>Each TDS burst is tagged with a time stamp called a spacecraft event time or SCET. The TDS datation time is sampled after the burst is acquired which requires a delay buffer. The datation time requires two corrections. The first correction arises from tagging the TDS datation with an associated spacecraft major frame in house keeping (HK) data. The second correction removes the delay buffer duration. Both inaccuracies are essentially artifacts of on ground derived values in the archives created by the WINDlib software (<em>K. Goetz, Personal Communication</em>, 2008) found at <a href="https://github.com/lynnbwilsoniii/Wind_Decom_Code">https://github.com/lynnbwilsoniii/Wind_Decom_Code</a>.</p> <p>The WAVES instrument's HK mode sends relevant low rate science back to ground once every spacecraft major frame. If multiple TDS bursts occur in the same major frame, it is possible for the WINDlib software to assign them the same SCETs. The reason being that this top-level SCET is only accurate to within +300 ms (in 120,000 sps mode) due to the issues described above (at lower sample rates, the error can be slightly larger). The time stamp uncertainty is a positive definite value because it results from digitization rounding errors. One can correct these issues to within +10 ms if using the proper HK data.</p> <p><strong>*** The data stored here have not corrected the SCETs! ***</strong></p> <p>The 300 ms uncertainty, due to the HK corrections mentioned above, results from WINDlib trying to recreate the time stamp after it has been telemetered back to ground. If a burst stays in the TDS buffer for extended periods of time (i.e., >2 days), the interpolation done by WINDlib can make mistakes in the 11<sup>th</sup> significant digit. The positive definite nature of this uncertainty is due to rounding errors associated with the onboard DPU (digital processing unit) clock rollover. The DPU clock is a 24 bit integer clock sampling at ∼50,018.8 Hz. The clock rolls over at ∼5366.691244092221 seconds, i.e., (16*2<sup>24</sup>)/50,018.8. The sample rate is a temperature sensitive issue and thus subject to change over time. From a sample of 384 different points on 14 different days, a statistical estimate of the rollover time is 5366.691124061162 ± 0.000478370049 seconds (<em>calculated by Lynn B. Wilson III</em>, 2008). Note that the WAVES instrument team used <em>UR8</em> times, which are the number of 86,400 second days from 1982-01-01/00:00:00.000 UTC.</p> <p>The method to correct the SCETs to within +10 ms, were one to do so, is given as follows:</p> <ol> <li>Retrieve the DPU clock times, SCETs, UR8 times, and DPU Major Frame Numbers from the WINDlib libraries on the VAX/ALPHA systems for the TDSS(F) data of interest.</li> <li>Retrieve the same quantities from the HK data.</li> <li>Match the HK event number with the same DPU Major Frame Number as the TDSS(F) burst of interest.</li> <li>Find the difference in DPU clock times between the TDSS(F) burst of interest and the HK event with matching major frame number (<strong>Note:</strong> The TDSS(F) DPU clock time will always be greater than the HK DPU clock if they are the same DPU Major Frame Number and the DPU clock has not rolled over).</li> <li>Convert the difference to a UR8 time and add this to the HK UR8 time. The new UR8 time is the corrected UR8 time to within +10 ms.</li> <li>Find the difference between the new UR8 time and the UR8 time WINDlib associates with the TDSS(F) burst. Add the difference to the DPU clock time assigned by WINDlib to get the corrected DPU clock time (Note: watch for the DPU clock rollover).</li> <li>Convert the new UR8 time to a SCET using either the IDL WINDlib libraries or TMLib (STEREO S/WAVES software) libraries of available functions. This new SCET is accurate to within +10 ms.</li> </ol> <p>One can find a UR8 to UTC conversion routine at <a href="https://github.com/lynnbwilsoniii/wind_3dp_pros">https://github.com/lynnbwilsoniii/wind_3dp_pros</a> in the <strong>~/LYNN_PRO/Wind_WAVES_routines/</strong> folder.</p> <p>Examples of good waveforms can be found in the notes PDF at <a href="https://wind.nasa.gov/docs/wind_waves.pdf">https://wind.nasa.gov/docs/wind_waves.pdf</a>.</p> <p><strong>Data Set Description</strong></p> <p>Each Zip file contains 300+ IDL save files; one for each day of the year with available data. This data set is not complete as the software used to retrieve and calibrate these TDS bursts did not have sufficient error handling to handle some of the more nuanced bit errors or major frame errors in some of the level zero files. There is currently (as of June 27, 2020) an effort (by <em>Keith Goetz et al.</em>) to generate the entire TDSF and TDSS data set in one repository to be put on SPDF/CDAWeb as CDF files. Once that data set is available, it will supercede and replace this one as it will be more complete and all SCETs will be corrected.</p> <p>When one restores any given IDL save file, they will find an IDL structure named struc. Inside are several tags and for each date there are T number of TDSF bursts, each of which has K (= 2048) time stamps. The structure contains the following tags:</p> <ul> <li><strong>SCETS</strong>: [T]-Element [string] array of SCETs at start of TDS burst with format 'YYYY-MM-DD/hh:mm:ss.xxx'</li> <li><strong>UNIX</strong>: [T,K]-Element [double] array of quasi-Unix times (i.e., converted from UTC times without removing leap seconds)</li> <li><strong>CH1EXDA_CH2EXYZAC_WAVES</strong>: [T,K,5]-Element [float] array of electric fields [mV/m] in spinning WAVES coordinates, where the components are: <ul> <li>[*,*,0] = E<sub>x</sub> in DC-coupled mode from channel 1</li> <li>[*,*,1] = E<sub>x</sub> in AC-coupled mode from channel 1</li> <li>[*,*,2] = E<sub>x</sub> in AC-coupled mode from channel 2</li> <li>[*,*,3] = E<sub>y</sub> in AC-coupled mode from channel 2</li> <li>[*,*,4] = E<sub>z</sub> in AC-coupled mode from channel 2</li> </ul> </li> <li><strong>SRATE</strong>: [T]-Element [float] array of sample rates for each TDSF burst [Hz]</li> <li><strong>FILTER_FREQ</strong>: [T]-Element [float] array of soft low pass filter frequencies [Hz]</li> <li><strong>EVENT_NUM</strong>: [T]-Element [long] array of TDS event numbers [N/A]</li> <li><strong>UR8_START</strong>: [T]-Element [double] array of UR8 times at start of TDS burst [# of 86,400 second days from 1982-01-01/00:00:00.000 UTC]</li> <li><strong>EX_START_ANG</strong>: [T]-Element [float] array of counter-clockwise angles between the +Ex antenna and the spacecraft-to-sun line at the start of each TDS burst [e.g., see <a href="https://ui.adsabs.harvard.edu/abs/2016JGRA..121.9369M/abstract"><em>Malaspina & Wilson</em>, 2016</a> for definitions]</li> <li><strong>EX___END_ANG</strong>: [T]-Element [float] array of counter-clockwise angles between the +Ex antenna and the spacecraft-to-sun line at the end of each TDS burst [e.g., see <a href="https://ui.adsabs.harvard.edu/abs/2016JGRA..121.9369M/abstract"><em>Malaspina & Wilson</em>, 2016</a> for definitions]</li> <li><strong>THETA_AX</strong>: [T]-Element [float] array of average counter-clockwise angles between the +Ex antenna and the Earth-to-sun line [e.g., see <a href="https://ui.adsabs.harvard.edu/abs/2016JGRA..121.9369M/abstract"><em>Malaspina & Wilson</em>, 2016</a> for definitions]</li> <li><strong>BO_GSE</strong>: [T,3]-Element [float] array of <strong>B</strong><sub>o</sub> 3-vectors [nT] in GSE coordinate basis at start of TDS bursts</li> <li><strong>SC_GSE_POS</strong>: [T,3]-Element [float] array of spacecraft position 3-vectors [R<sub>E</sub>] in GSE coordinate basis at start of TDS bursts</li> <li><strong>SC_SPIN_RATE</strong>: [T]-Element [float] array of spacecraft spin rates [deg/s] at start of TDS bursts</li> <li><strong>JULIAN</strong>: [T]-Element [double] array of Julian day numbers [N/A]</li> <li><strong>EARTH_WAKE</strong>: [T]-Element [byte] array of logical values defining whether spacecraft is in Earth's optical wake [1 = TRUE, 0 = FALSE]</li> <li><strong>LUNAR_WAKE</strong>: [T]-Element [byte] array of logical values defining whether spacecraft is in the lunar optical wake [1 = TRUE, 0 = FALSE]</li> <li><strong>CHANNEL_1_LABS</strong>: [T]-Element [string] array of channel 1 labels defining the source type shown in the <strong>CH1EXDA_CH2EXYZAC_WAVES</strong> tag</li> <li><strong>CHANNEL_2_LABS</strong>: [T]-Element [string] array of channel 2 labels defining the source type shown in the <strong>CH1EXDA_CH2EXYZAC_WAVES</strong> tag</li> <li><strong>CHANNEL_1_INT</strong>: [T]-Element [integer] array of channel 1 labels [<strong>obsolete</strong>]</li> <li><strong>CHANNEL_2_INT</strong>: [T]-Element [integer] array of channel 2 labels [<strong>obsolete</strong>]</li> <li><strong>UNITS</strong>: [19]-Element [string] array defining the units of each structure tag</li> <li><strong>NOTES</strong>: [11]-Element [string] array defining some useful things about the data set</li> </ul> <p>Note that the angles have all been shifted by +360 degrees to avoid rollover issues. The reason being that the spacecraft rotates such that <strong>EX_START_ANG</strong> is always larger than <strong>EX___END_ANG</strong>. All angles herein are counter-clockwise angles in the GSE basis.</p> <p><strong>Rotating B<sub>o</sub> from GSE to WAVES coordinates</strong></p> <p>Since the quasi-static magnetic field is given in GSE coordinates, one may want to examine the wave fields relative to the <strong>B</strong><sub>o</sub> direction. If you examine the attached figure labeled GSE-Basis_to_WAVES_rotation_2.jpg, you will see how to rotated from GSE into WAVES coordinates. The angle <span class="math-tex">\(\phi\)</span> in the image corresponds to the <strong>THETA_AX</strong> structure tag values. The angle <span class="math-tex">\(\pi\)</span> in the image is the actual value of pi in radians. This is is necessary to flip the GSE vectors to match the WAVES +z-axis, which is pointed toward the south ecliptic pole, not the north like Z-GSE.</p> <p>It is important to rotate <strong>B</strong><sub>o</sub> into WAVES rather than the electric fields into magnetic field-aligned coordinates since each antenna has different noise and response functions.</p>
Historical Weather, Load, Wind, and Solar Data for the Salt River Project
<p>We created and curated a dataset of historical (1980-2019) hourly meteorology, load, wind, and solar data for the Salt River Project (SRP) region. The data was created by PNNL's <a href="https://godeeep.pnnl.gov/">GODEEEP</a> project. Each row in the dataset is a single hour and each column is a variable. All meteorological variables are spatially-averaged over the SRP service territory. The variables and their units are as follows:</p><ol><li>"Time_UTC"; Coordinated Universal Time (UTC); Time of day.</li><li>"T2"; Fahrenheit; 2-m air temperature.</li><li>"Q2"; kg/kg; 2-m water vapor mixing ratio.</li><li>"SWDOWN"; W/m^2; Downwelling shortwave radiative flux at the surface.</li><li>"GLW"; W/m^2; Downwelling longwave radiative flux at the surface.</li><li>"WSPD"; m/s; 10-m wind speed.</li><li>"Scaled_2019_Load"; MWh; Simulated hourly demand for electricity that is scaled to 2019 levels of annual energy. This load estimate does not account for historical changes in population and economics within the SRP service territory. It is included to make it easier to isolate weather impacts on load without having to consider long-term changes.</li><li>"Load"; MWh; Simulated hourly demand for electricity.</li><li>"Agua_Fria_Solar_Capacity"; N/A; Solar capacity factor for the SRP Agua Fria project with plant configurations taken from the EIA-860 database.</li><li>"Phoenix_Solar_Capacity"; N/A; Solar capacity factor for hypothetical solar plants derived using the grid cell nearest to Phoenix, AZ.</li><li>"Flagstaff_Solar_Capacity"; N/A; Solar capacity factor for hypothetical solar plants derived using the grid cell nearest to Flagstaff, AZ.</li><li>"Phoenix_Wind_Capacity"; N/A; Wind capacity factor for hypothetical 80-m plants derived using the grid cell nearest to Phoenix, AZ.</li><li>"Flagstaff_Wind_Capacity"; N/A; Wind capacity factor for hypothetical 80-m plants derived using the grid cell nearest to Flagstaff, AZ.</li></ol>
Wind measurement data from the publication: "Development of a load model validation framework applied to synthetic turbulent wind field evaluation"
<h3>Dataset description:</h3> <p>This datasat represents supplementary material used in the contribution "Development of a load model validation framework applied to<br>synthetic turbulent wind field evaluation" by Meyer, Huhn and Gottschall.</p> <p>Wind measurements from the Testfeld BHV are made available. For installation details, see the mentioned reference.</p> <p> </p> <h3>File description:</h3> <ul> <li>Lidar_HWS.nc - Horizontal wind speed measurements (10 min averages) from a WindCube V2 vertical profiler for one day with a low-level jet occurrence ( <div> <div>2021-04-20)</div> </div> </li> <li>Cups_HWS.nc - Horizontal wind speed measurements (10 min averages) from cup anemometer installed on a met mast for the same day</li> <li>Ensemble_averaged_Spectra.nc - Ensemble averaged spectra for neutral and near neutral situations from a Gill Windmaster at 110m above ground level, used to fit the Mann and KSEC model parameters</li> </ul> <h3> </h3> <h3>Referencing:</h3> <p>When used, please cite like the following:</p> <p>Meyer, Paul J., Matthias L. Huhn, and Julia Gottschall. 2024. "Development of a Load Model Validation Framework Applied to Synthetic Turbulent Wind Field Evaluation" <em>Energies</em> 17, no. 4: 797. https://doi.org/10.3390/en17040797</p> <p> </p> <p> </p>
Simulated severe convective wind events and environments from the Bureau of Meteorology Atmospheric Regional Projections for Australia (BARPA)
<p>Contacts for further details:</p> <ul> <li>This data record and associated research: Andrew Brown (andrewb1@student.unimelb.edu.au)</li> <li>BARPA data: Chun Hsu Su (chunhsu.su@bom.gov.au), Christian Stassen (Christian.Stassen@bom.gov.au), Harvey Ye (harvey.ye@bom.gov.au)</li> </ul> <h1>Introduction</h1> <p>This record contains data in support of Brown et al. (2024), including post-processed regional climate model data, automatic weather station observations, and post-processed reanalysis data over southeastern Australia for various time periods <strong>over December-Febrary months only</strong> (see descriptions below). This data relates to analysis of severe convective wind gusts in historical and future climate, with analysis scripts in <a href="https://github.com/andrewbrown31/BARPA/tree/main/wind_gust_analysis">this repository</a>. The data are described here according to the directory structure of this record (noting the files and directories have been compressed into <code>barpa_data.tgz</code>), as well as the relevant data sources. </p> <h1>Data sources</h1> <ul> <li><strong>The Bureau of Meteorology Atmospheric Regional Projections for Australia (BARPA)</strong>. A regional climate model containing a regional (BARPA-R) and convection-permitting (BARPAC-M) configuration, with large-scale forcing from ERA-Interim (1990-2015) and ACCESS1-0 using a historical (1985-2005) and RCP8.5 (2039-2059) forcing. See Brown et al. (2024) and Su et al. (2021) for more details. Note that any reuse of this data should properly acknowledge the Australian Bureau of Meteorology. Note also that the BARPA data used here was produced as part of the <a href="https://www.climatechangeinaustralia.gov.au/en/projects/esci/">Electricity Sector Climate Information project</a> (with licence and disclaimers <a href="https://www.climatechangeinaustralia.gov.au/en/projects/esci/risk-assessment/#Disclaimer">here</a>)<em>,</em> with more current BARPA versions (not used here) available at <a href="https://dx.doi.org/10.25914/z1x6-dq28" target="_blank" rel="noopener">https://dx.doi.org/10.25914/z1x6-dq28</a>. </li> <li><strong>Measured wind gusts from automatic weather stations (AWS)</strong>. Gust data is provided by the <a href="http://www.bom.gov.au/climate/data/stations/">Australian Bureau of Meteorology</a>, from 272 AWS locations over 2005-2015. Note that any reuse of this data should properly acknowledge the Australian Bureau of Meteorology.</li> <li><strong>The ERA5 reanalysis </strong>from the European Center for Medium Range Weather Forecasting (Hersbach 2020).</li> <li><strong>The ERA-Interim reanalysis</strong> from the European Center for Medium Range Weather Forecasting (Dee 2011).</li> </ul> <h1>/10min_points</h1> <p>This directory contains .csv files, with wind gust and related environmental data at 10-minute intervals at point locations, corresponding to automatic weather station locations. Data is available over 2005-2015. Files are named in the form <code>barpac_m_aws_<state>.csv</code> and <code>barpac_m_aws_<state>_barpa_r_interp.csv</code>. Here, <state> represents different administrative regions in southeast Australia, including New South Wales (nsw), Victoria (vic), South Australia (sa) and Tasmania (tas). See Figure 1 in Brown et al. (2024) for a map of station locations, that is also included in the /meta directory. The <code>barpa_r_interp</code> suffix indicates that the BARPAC-M wind gusts have been interpolated to the BARPA-R grid for comparison.</p> <p>This data is used in Brown et al. (2024) for evaluation and analysis of BARPA wind gusts in the historical climate (forced by ERA-Interim). The user is directed to that paper for more information on data processing. For the .csv files here, column descriptions are provided in Table 1, below.</p> <h1>/daily_points</h1> <p>This directory contains .csv files, with data associated with daily maximum wind gusts at point locations. This data is derived from the 10min_points data described above, with the same column descriptions in Table 1, below. The different files in this directory are as follows:</p> <ul> <li> <p><code>barpac_m_aws_dmax_obs.csv</code><br>Daily maximum observed wind gust from AWS measurements, with associated wind gust ratio and environmental conditions from ERA5.</p> </li> <li> <p><code>barpac_m_aws_dmax_2p2km.csv</code><br>Daily maximum simulated wind gust from BARPAC-M at closest grid point to AWS location, with associated wind gust ratio, lighting flash count, and environmental conditions from BARPA-R.</p> </li> <li> <p><code>barpac_m_aws_dmax_12km.csv</code><br>Daily maximum simulated wind gust from BARPA-R at closest grid point to AWS location, with associated wind gust ratio, lightning flash count, and environmental conditions.</p> </li> <li> <p><code>barpac_m_aws_dmax_erai.csv</code><br>Daily maximum simulated wind gust from ERA-Interim at closest grid point to AWS location, with associated wind gust ratio and environmental conditions from ERA5.</p> </li> <li> <p><code>barpac_m_aws_dmax_2p2km_barpa_r_interp.csv</code><br>As in <code>barpac_m_aws_dmax_2p2km.csv</code>, but wind gusts are interpolated to the BARPA-R grid prior to calculating the daily maximum.</p> </li> </ul> <h1>/monthly_nc</h1> <p>This directory contains monthly netcdf files at 12 km horizontal grid spacing, with post-processed BARPA data, relating to simulated severe convective wind gusts (from BARPAC-M), and their associated large-scale environments (from BARPA-R). This includes BARPAC-M and BARPA-R data that has been forced by the ACCESS1-0 global climate model, that is intended for analysis of future changes in severe convective wind events and environments. For further information, the user can refer to the internal file metadata, as well as Brown et al. (2024). The files in this directory are as follows (where <experiment> is either <code>hist</code> for historical climate forcing (1985-2005) or <code>rcp</code> for RCP8.5 climate forcing (2039-2059)):</p> <ul> <li> <p><code>barpac_scws_<experiment>_monthly.nc</code><br>Monthly counts of simulated severe convective wind events from BARPAC-M, for each type of environment (see <code>cluster</code> in Table 1).</p> </li> <li> <p><code>barpar_<experiment>_monthly.nc</code><br>Monthly counts of favouable severe convective wind environments from BARPA-R (using <code>bdsd</code>, see Table 1), for each type of environment (see <code>cluster</code> in Table 1).</p> </li> <li> <p><code>barpac_scws_bdsd_<experiment>.nc</code><br>Monthly counts of simulated severe convective wind events from BARPAC-M, that occur under favourable environmental conditions from BARPA-R. For each type of environment (see <code>cluster</code> in Table 1).</p> </li> <li> <p><code>barpac_max_<experiment>_monthly.nc</code><br>Monthly maximum simulated severe convective wind gust, from BARPAC-M, for each type of environment (see <code>cluster</code> in Table 1).</p> </li> </ul> <h1>/daily_nc</h1> <p>This directory contains monthly netcdf files at 12 km horizontal grid spacing, with daily maximum wind gusts from BARPAC-M, as well as the wind gust ratio (see <code>wgr_4</code> in Table 1) and the type of convective environment (from BARPA-R, see <code>cluster</code> in Table 1). This includes BARPA data that has been forced by ERA-Interim and by ACCESS1-0. For further information, the user can refer to the file metadata, as well as Brown et al. (2024). The files in this directory are as follows (where <code><experiment></code> is either <code>historical</code> for historical climate forcing or <code>rcp85</code> for RCP8.5 climate forcing, <code><forcing_model></code> is either <code>erai</code> for ERA-Interim or <code>ACCESS1-0</code>, <<code>date1></code> is the file start date and <code><date2></code> is the file end date):</p> <ul> <li><code>barpa_scw_<forcing_model>_<experiment>_0_<date1>_<date2>.nc</code></li> </ul> <h1>/meta</h1> <p>Lists of AWS stations, for each administrative state, with a file containing column descriptions. Note that not all of the stations listed in these files are used for analysis. Fig1.jpeg is from Brown et al. (2024), showing the BARPAC-M domain (also defines the netcdf file spatial extents), and the location of AWS.</p> <h3>Table 1</h3> <table> <tbody> <tr> <td><strong>Column</strong></td> <td><strong>Name</strong></td> <td><strong>Notes</strong></td> </tr> <tr> <td>stn_id</td> <td>Automatic weather station (AWS) identifier</td> <td> </td> </tr> <tr> <td>time</td> <td>Wind gust time (UTC)</td> <td> </td> </tr> <tr> <td>gust</td> <td>Observed wind gust speed from AWS (m/s)</td> <td>Observed wind gusts are measured at a height of 10 m, and represent a 3-second average. Data is provided as a one-minute maximum, and is resampled to a 10-minute maximum here for comparison with BARPA</td> </tr> <tr> <td>wgr_4</td> <td>Wind gust ratio</td> <td>The observed wind gust ratio, defined as the ratio between <code>gust</code>, and the 4-hour mean from the 10-minute data here.</td> </tr> <tr> <td>time_6hr</td> <td>6-hourly time (UTC)</td> <td>The most recent 6-hourly time step prior to <code>time</code>, associated with environmental diagnostics.</td> </tr> <tr> <td>mu_cape</td> <td>Most unstable convective available potential energy (J/kg)</td> <td>Environmental diagnostic derived from BARPA-R. See Brown et al. (2024) for more information.</td> </tr> <tr> <td>s06</td> <td>Bulk vertical wind shear from the surface to 6 km above ground level (m/s)</td> <td>Environmental diagnostic derived from BARPA-R. See Brown et al. (2024) for more information.</td> </tr> <tr> <td>dcape</td> <td>Downdraft convective available potential energy (J/kg)</td> <td>Environmental diagnostic derived from BARPA-R. See Brown et al. (2024) for more information.</td> </tr> <tr> <td>bdsd</td> <td>Brown and Dowdy (2021) Statistical Diagnostic (BDSD) for identifying favourable severe convective wind environments</td> <td>Environmental diagnostic derived from BARPA-R. See Brown et al. (2024) for more information.</td> </tr> <tr> <td>qmean01</td> <td>Mass-weighted mean mixing ratio from the surface to 1 km (g/kg)</td> <td>Environmental diagnostic derived from BARPA-R. See Brown et al. (2024) for more information.</td> </tr> <tr> <td>Umean06</td> <td>Mass-weighted mean wind speed from the surface to 6 km above ground level (m/s)</td> <td>Environmental diagnostic derived from BARPA-R. See Brown et al. (2024) for more information.</td> </tr> <tr> <td>lr13</td> <td>Temperature lapse rate from 1 km above ground level to 3 km above ground level (◦C/km)</td> <td>Environmental diagnostic derived from BARPA-R. See Brown et al. (2024) for more information.</td> </tr> <tr> <td>cluster</td> <td>Environment type</td> <td> <p>Based on statistical clustering of severe convective wind environments by Brown et al. 2023<br>0: Strong background wind cluster<br>1: Steep lapse rate cluster<br>2: High moisture cluster</p> <p>Derived from BARPA-R</p> </td> </tr> <tr> <td>s06_era5</td> <td>See s06</td> <td>Environmental diagnostic derived from ERA5. See Brown et al. (2024) for more information.</td> </tr> <tr> <td>qmean01_era5</td> <td>See qmean01</td> <td>Environmental diagnostic derived from ERA5. See Brown et al. (2024) for more information.</td> </tr> <tr> <td>Umean06_era5</td> <td>See Umea06</td> <td>Environmental diagnostic derived from ERA5. See Brown et al. (2024) for more information.</td> </tr> <tr> <td>lr13_era5</td> <td>See lr13</td> <td>Environmental diagnostic derived from ERA5. See Brown et al. (2024) for more information.</td> </tr> <tr> <td>bdsd_era5</td> <td>See bdsd</td> <td>Environmental diagnostic derived from ERA5. See Brown et al. (2024) for more information.</td> </tr> <tr> <td>dcape_era5</td> <td>See dcape</td> <td>Environmental diagnostic derived from ERA5. See Brown et al. (2024) for more information.</td> </tr> <tr> <td>mu_cape_era5</td> <td>See mu_cape</td> <td>Environmental diagnostic derived from ERA5. See Brown et al. (2024) for more information.</td> </tr> <tr> <td>cluster_era5</td> <td>See cluster</td> <td> <p>Based on statistical clustering of severe convective wind environments by Brown et al. 2023<br>0: Strong background wind cluster<br>1: Steep lapse rate cluster<br>2: High moisture cluster</p> <p>Derived from ERA5</p> </td> </tr> <tr> <td>wg10_12km_point</td> <td>Simulated wind gust from BARPA-R (m/s)</td> <td>Intended to represent a 10 meter wind gust. See Ma et al. (2018) for gust parameterisation details.</td> </tr> <tr> <td>wgr_12km_point</td> <td>Wind gust ratio from BARPA-R </td> <td>See wgr_4</td> </tr> <tr> <td>wg10_2p2km_point</td> <td>Simulated wind gust from BARPC-M (m/s)</td> <td>Intended to represent a 10 meter wind gust. See Ma et al. (2018) for gust parameterisation details.</td> </tr> <tr> <td>wgr_2p2km_point</td> <td>Wind gust ratio from BARPAC-M (see wgr_4)</td> <td>See wgr_4</td> </tr> <tr> <td>n_lightning_fl</td> <td>Number of daily lightning flashes from BARPAC-M</td> <td>See Brown et al. (2024) for more information.</td> </tr> <tr> <td>erai_wg10</td> <td>Simulated wind gust from ERA-Interim (m/s).</td> <td>Intended to represent a 10 meter wind gust. Note that ERA-Interim is provided in 3-hourly intervals, rather than 10-minute intervals for BARPA.</td> </tr> </tbody> </table> <p> </p>
Wind power plants layouts according to arbitrary reference points, Thanet, West of Duddon Sands, Ormonde, Westermost Rough, Horns Rev 1 & 2, Anholt, and London Array
<p><strong>If this dataset helps your research, please cite it and the papers further below (according to which OWPP you study).</strong></p> <p>This dataset contains the layouts of the Thanet, West of Duddon Sands, Ormonde, Westermost Rough, Horns Rev 1 & 2, Anholt, and London Array offshore wind power plants (OWPPs), which can be utilized in a variety of studies. </p> <p>The X and Y coordinates, in kilometers, were written according to arbitrary reference points. The positions of wind turbine generators (WTGs) and substations (SS) for the OWPPs came from the sources below.</p> <p><strong>Thanet</strong>: WTGs from [1] (page 7), SS based on [3] (page 9).<br><strong>West of Duddon Sands</strong>: WTGs from [2] (page 5), SS based on [3] (page 9).<br><strong>Ormonde</strong>: WTGs and SS from [4] (page 2).<br><strong>Westermost Rough</strong>: WTGs and SS from [5] (page 4).<br><strong>Horns Rev 1</strong>: WTGs and SS from [6] (page 6).<br><strong>Horns Rev 2</strong>: WTGs and SS from [7] (page 5).<br><strong>Anholt</strong>: WTGs and SS from [8] (page 3).<br><strong>London Array</strong>: WTGs from [9] (page 15), SS based on [10] (page 2).</p> <p>From the OWPPs' layout figures [1-9], I used Graph Grabber 2.0.2* to extract the data points. Then, based on visual inspection of layouts in [1-9], I utilized 2D projections to align WTGs that should be aligned. References [1], [2], and [9] do not provide the SS positions. Thus, I carefully overlapped the layouts with other layouts from [3] and [10] to approximate the SS locations.</p> <p>Regarding the arbitrary reference points for the coordinates, although the values in the X and Y axes differ from [1-9], note that the distances among the WTGs are the same from [1-9]. Furthermore, there are no axes in [1,5]. Instead, distances are given, which are enough to obtain the layout. Values in meters were converted to kilometers.</p> <p>As seen in the attachments, the coordinates can be obtained via either "h5" or "csv" files, which can be easily read by Matlab, Julia, and Python, among others. <strong>The name of the datasets in the "h5" file are</strong>: "Thanet", "WDS", "Ormonde", "WMR", "HornsRev1", "HornsRev2", "Anholt", and "LondonArray".</p> <p><strong>Except for the London Array OWPP, the first row in all data matrices represents the SS coordinates, whereas the subsequent rows represent the WTGs. London Array has 2 substations, thus the first and second rows represent their coordinates. In all matrices, the first and second columns are the X and Y coordinates, respectively.</strong></p> <p>*<a href="https://www.quintessa.org/software/downloads-and-demos/graph-grabber-2.0.2">Graph Grabber 2.0.2 | Downloads And Demos | Software | Quintessa Limited | Scientific and Mathematical Consultancy</a></p>
Hydroelastic response of the scaled model of a floating offshore wind turbine platform in waves: HELOFOW Project Database
<p>This dataset contains the data measured during the <strong>HELOFOW </strong>model test campaign, performed at the Ocean and Hydrodynamic Engineering wave tank of Ecole Centrale Nantes (ECN): decay tests, regular wave tests and irregular waves tests. The preprocessed measured data is contained in MAT files.</p> <p>The model, the measurements and the tests are described in the appended Excel files. A Matlab(R) function is given as a short example to show how the MAT files are structured and how data may be handled for a plot. </p> <p>As stated in the reference paper (Leroy et al., <em>Ocean Engineering</em>, 2022):</p> <p>"As the size of floating wind turbines continues to increase, floating platforms reach dimensions that make their elastic and hydro-elastic behaviour significant. Several works in connection with the numerical modelling of the elastic behaviour of these wind turbines have been carried out but few validation data are available. This study focuses on the hydro-elastic response of a large floating wind turbine, in regular waves and severe sea-states. A new experimental wind turbine model has been designed to represent a 1:40 Froude-scaled spar platform carrying the DTU 10 MW turbine. The main challenge is here to reproduce a 1st bending mode frequency and hydrodynamic loads representative of a realistic large floating wind turbine. The platform model is made of a flexible backbone, reproducing the correct flexibility, and light floaters fixed on it provide the correctly scaled geometry. This experimental model is tested in various conditions including regular waves of several periods and steepness, and irregular waves of various intensity, including extreme 50-year return period conditions."</p> <p> </p> <p>This work was carried out within the framework of the WEAMEC, West Atlantic Marine Energy Community, and with funding from the Pays de la Loire Region and Europe (European Regional Development Fund). <br><br>HELOFOW project on <a href="https://www.weamec.fr/en/projects/helofow/">the WEAMEC website</a>. </p>
Reproduction package for the paper "Bottling the Champagne: Dynamics and Radiation Trapping of Wind-Driven Bubbles around Massive Stars"
<p>Research Data Management package for "Bottling the Champagne: Dynamics and Radiation Trapping of Wind-Driven Bubbles around Massive Stars"</p> <p>Authors: Sam Geen & Alex de Koter</p> <p>Status: Accepted by MNRAS<br> This package aims to provide a full data reproduction pipeline. Please see Readme.md for more information.</p>
CFD simulation and measurements of effect of wind on non-catching rain gauge
<p>Simulation_dataset file shows the results of a CFD simulation of the measurements of a Thies laser precipitation monitor under different conditions of wind.</p> <p>Wind_tunnel_dataset shows the results of the model validation using an actual wind tunnel.</p>
HomogWS-se: A century-long homogenized dataset of near-surface wind speed observations since 1925 rescued in Sweden
<p>Creating a century-long homogenized near-surface wind speed (WS) observation dataset is essential to improve our knowledge about the uncertainty and causes of WS stilling and recovery. We rescued paper-based WS records dating back to the 1920s at 13 stations in Sweden and established a four-step homogenization procedure to generate the first 10-member centennial homogenized WS dataset (HomogWS-se) for community uses among climatology, ecology, hydrology and energy industry. HomogWS-se can be used to study the WS variability and change, assess climate reanalysis, and constrain climate simulations for better future projection of changes in the WS and wind energy potential. HomogWS-se contains 13 individual text files with 10-member century-long homogenized monthly WS series, as well as the member-mean series.</p>
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