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Supplementary audio files: Propagation effects in the synthesis of wind turbine noise

The audio files are supplementary files required for the audio article titled: "Propagation effects in the synthesis of wind turbine noise". Each audio file refers to a signal of a test obtained from the wind turbine noise model which is described in the article.  The audio files can be read as:      NNx-x-TETIN-eeeeeee.mp3 NNx-x refers to the Test case and case number for the syntheisized trailing edge and turbulent inflow noise, eeeeeee is the description of the specific case. (tauTT- angle of the receiver, ff - for free field, GPE for ground and propagation effects included, GPE_Turb for ground and propagation effects included with turbulence scattering) eg: C2-2-TETIN_tau80_GPE_Turb.mp3 is the synthesized sound for Test case C2-2 in the article which includes the ground and propagation effects and also scattering due to turbulence. The receiver is at an angle of 80° with respect to the wind direction.

"A physics-based model for wind turbine wake expansion in the atmospheric boundary layer"

Vahidi, Dara, and Fernando Porté-Agel. "A physics-based model for wind turbine wake expansion in the atmospheric boundary layer." Journal of Fluid Mechanics 943 (2022).

"Wind turbine wakes on escarpments: A wind-tunnel study"

Dar, Arslan Salim, and Fernando Porté-Agel. "Wind turbine wakes on escarpments: A wind-tunnel study." Renewable Energy 181 (2022): 1258-1275.

"An experimental investigation of a roof-mounted horizontal-axis wind turbine in an idealized urban environment"

Dar, Arslan Salim, Guillem Armengol Barcos, and Fernando Porté-Agel. "An experimental investigation of a roof-mounted horizontal-axis wind turbine in an idealized urban environment." Renewable Energy (2022).

Dataset of "Vertical-Wind-Induced Cloud Opacity Variation in Low Latitudes Simulated by a Venus GCM"

This dataset contains the GrADS data of Venus GCM results used for figures in the paper "Vertical-Wind-Induced Cloud Opacity Variation in Low Latitudes Simulated by a Venus GCM" by H. Karyu et al. (2022).  The file 'dataset_day1' contains the three-dimensional (X: longitude, Y: latitude, Z:altitude (km)) data of temperature (unit: K), zonal wind velocity (unit: m/s), meridional wind velocity (unit: m/s), vertical wind velocity (unit: m/s), geopotential height (unit: m), cloud mass mixing ratio of mode 1, 2, 2', 3 particles, mass mixing ratio of sulfuric acid, air density (unit: kg/m3), cloud mass mixing ratio changing rate of mode 1, 2, 2', 3 particles (unit: 1/s), in snapshots of every 3 hours for the periods of the first Venusian days (117 Earth days). The file 'dataset_day2' contains the same for the second Venusian days. The file 'cloudtau-wc' contains three-dimensional (X: longitude, Y: latitude, Z:altitude (km)) data of column-integrated optical depth (COD) of mode 1, 2, 2' 3 particles and column mass abundance of mode 1, 2, 2' 3 particles (unit: kg/m2), in snapshots of every 3 hours for the periods of 2 Venusian days (234 Earth days). The COD at each altitude corresponds to the integrated value from the top of the atmosphere, and the column mass abundance of each altitude corresponds to the integrated value from the bottom of the atmosphere.  The COD is calculated with the cloud mass mixing ratio stored in the file ‘dataset’ and extinction efficiency shown in the paper. The file 'stf-wc' contains two-dimensional (Y: latitude, Z:altitude (km)) data of mass stream function (unit: kg/s) and residual mass stream function (unit: kg/s), in snapshots of every 3 hours for the periods of 2 Venusian days (234 Earth days). One should refer to Holton (2004) for the definition of the (residual) mass stream function. The files 'dataset_comp' and 'taudataset_comp' are composite mean data of 'dataset' and 'cloudtau-wc', respectively, in snapshots of every 3 hours for the period of 30 days starting from day 86 of the simulation (Earth day). The composite mean is calculated by averaging atmospheric parameters with respect to the frame moving at the rotation period of 7.1-day. The file ‘scripts’ contains FORTRAN scripts and some additional files to derive the atmospheric parameters stored in 'cloudtau-wc’, 'stf-wc’, 'dataset_comp' and ‘taudataset_comp' from the GCM output file ‘dataset’. Please refer to the ‘README.txt’ contained in ‘scripts’ for how to use FORTRAN scripts, required input files and their output. The .tar.xz files can be extracted in Linux with 'tar Jxvf' command, and .grd and .ctl files with the same stem are generated.

CESM2 MDM data for "Historical changes in wind driven ocean circulation can accelerate global warming" - submitted to GRL

CESM2 Experiment names: <ul> <li>MD = mechanically decoupled model (referred to as MDM in paper), CESM2</li> <li>FC = fully coupled model (referred to as FCM in paper), CESM2</li> </ul> Decoding file names: Variables that are a single value per time step (e.g. global means and globally integrated values) are given in dimensions of time by ensemble member. Variables that include values at every grid point at each point in time are provided with an ensemble mean trend and an ensemble standard deviation of the trend.  <ul> <li>ensmean refers to ensemble mean</li> <li>ensstd refers to ensemble standard deviation</li> <li>trend refers to linear trend over 1979-2014</li> <li>annual refers to annual mean anomalies, relative to reference period of 1941-1970</li> </ul> Variables: <ul> <li>aice = ice area</li> <li>AMOC = Atlantic meridional overturning circulation</li> <li>N_HEAT = northward heat transport </li> <li>BSF = barotropic streamfunction </li> <li>TREFHT = reference level air temperature </li> <li>Qnet = net surface heat flux (defined as FSNS - FLNS - LHFLX - SHFLX)</li> <li>TOA = top of atmosphere radiation </li> <li>TOAC = top of atmosphere radiation, clearsky </li> <li>FLNT = net longwave flux at top of model</li> <li>FLNTC = net longwave flux at top of model, clearsky</li> <li>FSUTOA = upwelling solar flux at top of atmosphere</li> <li>FSNTOA = net solar flux at top of atmosphere</li> <li>FSNTOAC = net solar flux at top of atmosphere, clearsky</li> </ul>

opencc-by-4.0Dec 2021View details →

Text-fig. 13. Zoophycos isp. a: BK 17, Layer No. 6; b: BK 27, Layer No. 8; c: lateral tunnel continuing from spreite side to the surrounding rock, BK 28, Layer No. 23; d: BK 22, Layer No. 1; e: "juvenile" stage of the structure on a horizontal winding tunnel, BK 21, Layer No. 26; f: BK 24, Layer No. 17; g: broad winding tunnel adjacent to spreite, BK 26, Layer No. 6; h: BK 15, Layer No. 18; i: BK 23, Layer No. 2. Scale bar = 1 cm. in Early Complex Tiering Pattern: Upper Ordovician, Barrandian Area, The Czech Republic

Text-fig. 13. Zoophycos isp. a: BK 17, Layer No. 6; b: BK 27, Layer No. 8; c: lateral tunnel continuing from spreite side to the surrounding rock, BK 28, Layer No. 23; d: BK 22, Layer No. 1; e: "juvenile" stage of the structure on a horizontal winding tunnel, BK 21, Layer No. 26; f: BK 24, Layer No. 17; g: broad winding tunnel adjacent to spreite, BK 26, Layer No. 6; h: BK 15, Layer No. 18; i: BK 23, Layer No. 2. Scale bar = 1 cm.

D7.3 Dataset of noise produced by Hog-Jaeren wind park (simulation)

This dataset was used in Deliverable 7.3 of Upwards. For further explanations refer to Work Package 4. Contained are results of the noise simulation for the Hog-Jaeren wind park with south-east wind direction. The yaw misalignment of the turbines in the freestream was varied as indicated by the folder names. Here 270 corresponds to a yaw misalignment of 0° while 260 corresponds to -10°. There are several types of 2D plots available: <ul> <li>OASPL: Shows the OASPL in dB(A) in the area of the park</li> <li>noise_regulation: Contains the OASPL in dB(A) in the neghborhood of the park as well as lines indicating distance and noise based regulations by the Norwegian government.</li> <li>annoyance_level: Based on the OASPL the percentage of people being annoyed by the noise is presented.</li> </ul> The file 'OASPL_at_observer_in_dBA.txt' contains the coordinates of an observer as well as the OASPL at that point.

D6.6 Dataset of turbine properties of Lillgrund wind park

This dataset was used in Deliverable 6.6 of Upwards. It contains turbine properties of turbines in a simulation of the Lillgrund wind park. The simulation was conducted for different yaw misalignments of the turbines in the freestream. Thus this dataset contains 4 folders which each correspond to one yaw misalignment. The folder name indicates the yaw misalignment where 270 corresponds to a yaw misalignment of 0° while 260 corresponds to -10°. The files in each folder contain the following properties: <ul> <li>time</li> <li>torqueGen</li> <li>powerRotor</li> <li>rotSpeed</li> <li>thrust</li> <li>torqueRotor</li> </ul> These properties are available for all turbines in the park. Further the accumulated properties are available for each row of turbines and the whole park.

TEAMx-PC22 (TEAMx pre-campaign 2022) - Radial velocity and coplanar-retrieved horizontal wind fields from KITcube Leosphere/Vaisala Windcube WLS200s-124 and WLS200s-159

Abstract This data set was collected during the TEAMx pre-campaign in summer 2022 (TEAMx-PC22) in the Inn Valley Target Area, Austria. Data description This data set is comprised of a single TAR file containing 1536 hourly NetCDF files. Within these, radial velocities from KITcube Leosphere/Vaisala Windcube WLS200s-124 and WLS200s-159 Doppler wind lidars, as well as coplanar-retrieved horizontal wind speed components in their common scanning plane are stored.  The time period is 29 June 2022, 00:00 UTC - 31 August 2022, 23:58 UTC. More details about the variables, lidar locations, scan details, as well as post-processing can be found in the NetCDF metadata. The wind fields stored in the NetCDF files are also available in daily animation form under an accompanying Zenodo Video/Audio data set (DOI: 10.5281/zenodo.7212837).

opencc-zeroOct 2022View details →

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Supplementary data for: Comparison of optical flow derivation techniques for retrieving tropospheric winds from satellite image sequences

This study introduces a validation technique for quantitative comparison of algorithms which retrieve winds from passive detection of cloud- and water vapor-drift motions, also known as Atmospheric Motion Vectors (AMVs). The technique leverages airborne wind-profiling lidar data collected in tandem with 1-min refresh rate geostationary satellite imagery. AMVs derived with different approaches are used with accompanying numerical weather prediction model data to estimate the full profiles of lidar-sampled winds which enables ranking of feature tracking, quality control, and height-assignment accuracy and encourages meso-scale, multi-layer, multi-band wind retrieval solutions. The technique is used to compare the performance of two brightness motion, or "optical flow," retrieval algorithms used within AMVs, 1) Patch Matching (PM; used within operational AMVs) and 2) an advanced Variational Optical Flow (VOF) method enabled for most atmospheric motions by new-generation imagers. The VOF AMVs produce more accurate wind retrievals than the PM method within the benchmark in all imager bands explored. It is further shown that image regions with low texture and multi-layer-cloud scenes in visible and infrared bands are tracked significantly better with the VOF approach, implying VOF produces representative AMVs where PM typically breaks down. It is also demonstrated that VOF AMVs have reduced accuracy where the brightness texture does not advect with the mean wind (e.g. gravity waves), where the image temporal noise exceeds the natural variability, and when the height-assignment is poor. Finally, it is found that VOF AMVs have improved performance when using fine-temporal refresh rate imagery, such as 1-min versus 10-min data.

Supplementary audio files: Physics-based synthesis of wind turbine noise

The supplementary files required in the thesis "Physics-based synthesis of wind turbine noise" for the "Chapter 3: Synthesis tool".  A repository can also be found on: https://sites.google.com/view/david-mascarenhas For the test cases: Section 3.9 -------------------------------------------------- The audio files are supplementary files required for the audio article titled: "Propagation effects in the synthesis of wind turbine noise". Each audio file refers to a signal of a test obtained from the wind turbine noise model which is described in the article.  The audio files can be read as:      NNx-x-TETIN-eeeeeee.mp3 NNx-x refers to the Test case and case number for the syntheisized trailing edge and turbulent inflow noise, eeeeeee is the description of the specific case. (tauTT- angle of the receiver, ff - for free field, GPE for ground and propagation effects included, GPE_Turb for ground and propagation effects included with turbulence scattering) eg: C2-2-TETIN_tau80_GPE_Turb.mp3 is the synthesized sound for Test case C2-2 in the article which includes the ground and propagation effects and also scattering due to turbulence. The receiver is at an angle of 80° with respect to the wind direction.

opencc-by-4.0May 2019View details →

Simulation data used for publication "Seeding of equatorial plasma bubbles by vertical neutral wind" by Yokoyama et al.

The dataset includes two-dimensional simulation output used in the paper. "altitude.dat" and "zonal.dat" contains grid information. "read_n_phi_2D.pro" is an IDL file to read the dataset, with detailed description of each data. The original three-dimensional simulation output is too large to publish at the repository. Author (TY) is willing to share the original data.

opencc-by-4.0Apr 2024View details →

Data from : Damage to tropical forests caused by tropical cyclones is driven by wind speed but mediated by topographical exposure and tree characteristics

These datasets have been used in the following paper: Ibanez, T., Bauman, B., Aiba, S.-i., Arsouze, T. Bellingham, P.J., Birkinshaw, C., Birnbaum, P., Curran, T.J., DeWalt, S.J., Dwyer, J., Fourcaud, T., Franklin, J., Kohyama, T.S., Menkes, C. Metcalfe, D.J., Murphy, H., Muscarella, R., Plunkett, G.M., Sam, C., Tanner, E., Taylor, B.N., Thompson, J., Ticktin, T., Tuiwawa, M.V., Uriarte, U., Webb, E.L., Zimmerman, J.K., Keppel, G. Damage to tropical forests caused by tropical cyclones is driven by wind speed but mediated by topographical exposure and tree characteristics. Accepted for publication in Global Change Biology. Data users are invited to cite this paper and the original paper(s) corresponding to the data they use (see "Reference" column in each dataset). We also encourage potential users to contact the data owners for collaboration. These datasets are compiled empirical data on the damage caused by 11 cyclones occurring over the past 40 years, from 74 forest plots representing tropical regions worldwide. Damage are given at the tree (whether or not each tree has been uprooted or snapped) and at the plot level (number of uprooted or snapped trees in each plot). MSW: Maximum sustained wind speed (m.s-1) EXP: Topographical exposure to wind DBH: Diameter at breast height (cm) WD: Wood density (g.cm-3)

opencc-by-4.0Sep 2023View details →

Wind shadows from U.S. east coast offshore wind energy lease areas.

Georeferenced data layers describing whole wind farm wakes (wind shadows) for use in planning and development along the U.S. east coast based on WRF simulations performed using the accompanying namelist. Full details of the analysis are provided in: Pryor and Barthelmie: Wind shadows impact planning of large offshore wind farms   This work is supported by the U.S. Department of Energy (DoE) (DE-SC0016605). The research used computing resources from the National Science Foundation: Extreme Science and Engineering Discovery Environment (XSEDE) (allocation award to SCP is TG-ATM170024) and National Energy Research Scientific Computing Center, a DOE Office of Science User Facility supported by the Office of Science of the U.S. Department of Energy under Contract No. DE-AC02-05CH11231.

opencc-by-4.0May 2024View details →

Wind Tunnel Testing of Tethered Inflatable Wings

This dataset consists of all the data collected and presented in the AIAA Journal of Aircraft titled "Wind Tunnel Testing of Tethered Inflatable Wings". The attached zipped folder contains a README file that explains the dataset.

opencc-by-4.0Sep 2023View details →

Radar-based sensing of wind turbines blades based on 35 GHz FMCW sensors installed at operational wind turbine towers

The dataset contains radar-based measurements of rotor blades from three operational wind turbines as part of a structural health monitoring system. For this purpose, a sensor box with a 35 GHz radar sensor (about 1 000 measurements per second) and a camera system (about 100 images per second), is mounted on each wind turbine tower at approximately 100 m height. In order to distinguish individual rotor blades, a machine-readable marker printed on a self-adhesive film was applied on the blade’s surface. When a rotor blade passes the sensor, the camera captures an image of the marker while the radar records a measurement. The marker is then identified and the recorded data is assigned to a particular rotor blade. The measurements demonstrate that the damage detection methodology can be transferred to an image processing problem. The challenge is to manage the strong influence from variable environmental and operational conditions, e.g. wind speed, azimuth orientation, that modify the rotor blade appearance in the radargram significantly. The dataset contains measurements from the intact turbine blade conditions, because it was not possible to introduce structural damage.

opencc-by-4.0Feb 2024View details →

Supplementary to "Quantifying the wind-induced bias of rainfall measurements for the Thies optical disdrometer"

Supplementary material for the paper "Quantifying the wind-induced bias of rainfall measurements for the Thies optical disdrometer" submitted to the journal Water Resources Research

opencc-by-4.0Jan 2014View details →

Fig. 6 in Influence of Dardanelles outflow induced thermal fronts and winds on drifter trajectories in the Aegean Sea

Fig. 6: Same as Figure 4, but of the drifters during the 2009 experiment: A) drifter d1 and B) drifter c1 (red), drifter c2 (magenta) and drifter d3 (yellow).