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37 results for “wind velocity”
Wind Stress, Wind Stress Curl, and Upwelling Velocities in the Northwest Atlantic (80-45W, 30-45N) during 1980-2019
<p>This dataset contains three netcdf files that pertain to monthly, seasonal, and annual fields of surface wind stress, wind stress curl, and curl-derived upwelling velocities over the Northwest Atlantic (80-45W, 30-45N) covering a forty year period from 1980 to 2019. Six-hourly surface (10 m) wind speed components from the Japanese 55-year reanalysis (JRA-55; Kobayashi et al., 2015) were processed from 1980 to 2019 over a larger North Atlantic domain of 100W to 10E and 10N to 80N. Wind stress was computed using a modified step-wise formulation, originally based on (Gill, 1982) and a non-linear drag coefficient (Large and Pond, 1981), and later modified for low speeds (Trenberth et al., 1989). See Gifford (2023) for more details. </p> <p>After the six-hourly zonal and meridional wind stresses were calculated, the zonal change in meridional stress (curlx) and the negative meridional change in zonal stress (curly) were found using NumPy’s gradient function in Python (Harris et al., 2020) over the larger North Atlantic domain (100W-10E, 10-80N). The curl (curlx + curly) over the study domain (80-45W, 10-80N) is then extracted, which maintain a constant order of computational accuracy in the interior and along the boundaries for the smaller domain in a centered-difference gradient calculation. </p> <p>The monthly averages of the 6-hour daily stresses and curls were then computed using the command line suite climate data operators (CDO, Schulzweida, 2022) monmean function. The seasonal (3-month average) and annual averages (12-month average) were calculated in Python using the monthly fields with NumPy (NumPy, Harris et al., 2020). </p> <p>Corresponding upwelling velocities at different time-scales were obtained from the respective curl fields and zonal wind stress by using the Ekman pumping equation of the study by Risien and Chelton (2008; page 2393). Please see Gifford (2023) for more details. </p> <p>The files each contain nine variables that include longitude, latitude, time, zonal wind stress, meridional wind stress, zonal change in meridional wind stress (curlx), the negative meridional change in zonal wind stress (curly), total curl, and upwelling. Units of time begin in 1980 and are months, seasons (JFM etc.), and years to 2019. The longitude variable extends from 80W to 45W and latitude is 30N to 45N with uniform 1.25 degree resolution. </p> <p>Units of stress are in Pascals, units of curl are in Pascals per meter, and upwelling velocity is described by centimeters per day. The spatial grid is a 29 x 13 longitude x latitude array. </p> <p>Filenames: </p> <p><strong>monthly_windstress_wsc_upwelling.nc</strong>: 480 time steps from 80W to 45W and 30N to 45N.</p> <p><strong>seasonal_windstress_wsc_upwelling.nc</strong>: 160 time steps from 80W to 45W and 30N to 45N.</p> <p><strong>annual_windstress_wsc_upwelling.nc</strong>: 40 time steps from 80W to 45W and 30N to 45N.</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>
TEAMx-PC22 (TEAMx pre-campaign 2022) - Radial velocity and coplanar-retrieved horizontal wind fields from KITcube Leosphere/Vaisala Windcube WLS200s-124 and WLS200s-159
<p><strong>Abstract</strong></p> <p>This data set was collected during the TEAMx pre-campaign in summer 2022 (TEAMx-PC22) in the Inn Valley Target Area, Austria.</p> <p><strong>Data description</strong></p> <p>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. </p> <p>The time period is 29 June 2022, 00:00 UTC - 31 August 2022, 23:58 UTC.</p> <p>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).</p>
Oscillations of Offshore Wind Turbines undergoing Installation II: Filtered and Integrated data - acceleration, velocity, displacement
<p>This is dataset is based on the raw measurement data from <a href="https://zenodo.org/record/5009061">https://zenodo.org/record/5009061</a></p> <p>The data included in the archives are the resampled and high-pass filtered accelerations as well as the velocity and displacement data.</p>
Air-sea gas transfer velocities measured at wind speeds up to 85m/s in fresh water and seawater
<p>This data set contains gas transfer velocities of 12 tracers (CF4, He, SF6, He, Kr, Pentafluoroethane, Xe, Acetylene, Hexafluorobenzene, Difluoromethane, 1,4-Difluorobenzene, Dimethyl Sulfide, Methyl Acetate) measured in the Kyoto High Speed Wind-Wave tank with fresh water and modeled seawater and the Miami SUSTAIN wind-wave tank with seawater at wind speeds up to 85m/s.</p> <p> </p>
Five-minute average horizontal wind velocity data combined from both sensors (which has been corrected for air-flow distortion) from the Antarctic Circumnavigation Expedition (ACE) 2016/2017 legs 0 to 4.
<p><strong>Dataset abstract</strong></p> <p>The horizontal wind velocity data from the Antarctic Circumnavigation Expedition (ACE) 2016/2017 legs 0 to 4 has been corrected for air-flow distortion. The measurements from both the port and starboad side anemometer were averaged to five-minute resolution and have been combined via vector averaging of the data. The ten meter neutral wind speed (U10N) has been estimated using ERA-5 surface heat fluxes, which were interpolated onto the ship's track, and the COARE 3.5 drag coefficient. This data set provides a continous and high-resolution record of the wind speed and direction near to the ship's location.</p> <p><strong>Dataset contents</strong></p> <ul> <li>wind-observations-port-stbd-corrected-combined-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 five-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>
One-minute average horizontal wind velocity data (which has been corrected for air-flow distortion) from the Antarctic Circumnavigation Expedition (ACE) 2016/2017 legs 0 to 4.
<p><strong>Dataset abstract</strong></p> <p>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. The data has been corrected for air-flow distortion, which was caused by the ship's super structure.</p> <p><strong>Dataset contents</strong></p> <ul> <li>wind-observations-stbd-corrected-1min-legs0-4.csv, data file, comma-separated values</li> <li>wind-observations-port-corrected-1min-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>
Wind tunnel measurements of concentration and velocity in urban geometries with trees
<p><span>This dataset contains concentration and velocity mesurements performed in the aerodynamic wind tunnel of the Ecole Centrale de Lyon (France). <br>An idealized urban district was simulated by an array of blocks, and two rows of model trees were arranged inside a street. <br>Reduced scale trees were chosen to mimic a realistic shape and aerodynamic behaviour.<br>Three different spacings between the trees were considered: "zero" (no trees), "half" (14 cm distance between the tree trunks) and "full" (7 cm distance between the tree trunks).</span></p> <p><span>The dataset includes: <strong> </strong></span></p> <ul> <li><span>concentration and velocity measurements performed within a street canyon, under various geometries and wind directions; </span></li> <li><span>the characterization of the boundary layer above the buildings;</span></li> <li><span>the characterization of the tree drag.</span></li> </ul> <p><span>The detailed description of the dataset is contained in the document <code>Info_dataset.pdf</code></span></p> <p> </p> <p> </p>
Velocity field of "Toward ultra-efficient high fidelity predictions of wind turbine wakes"
<p>This data collection contains the velocity field obtained from VFS-Wind LES simulations, FLORIS v3.4 GCH-model and the new ML model.</p>
Figures data from papers "Breakdown of the velocity and turbulence in the wake of a wind turbine", parts 1 and 2
<p>This python file makeFigure.py along with the .json files in folder Data/ allows to draw pictures corresponding to the articles "Breakdown of the velocity and turbulence in the wake of a wind turbine" - Part 1 and Part 2 published in Wind Energy Science. The .sh file makeFolders selects and separate the figures needed for the two parts.</p> <p>Slightly more informations are available in the data compared to the article, due to lack of space. In particular, one can found all the planes data from 1 to 8D downstream (instead of only 1D, 5D and 8D) and some data for the unstable and stable cases that were only shown for the neutral case in the paper.</p> <p><br> Do not hesitate to contact me if more informations are needed.</p>
TEAMx-PC22 (TEAMx pre-campaign 2022) - Animations of radial velocity and coplanar-retrieved horizontal wind fields from KITcube Leosphere/Vaisala Windcube WLS200s-124 and WLS200s-159
<p><strong>Abstract</strong></p> <p>This data set was collected during the TEAMx pre-campaign in summer 2022 (TEAMx-PC22) in the Inn Valley Target Area, Austria.</p> <p><strong>Data Description</strong></p> <p>This data set is comprised of 64 daily .mp4 files showing:</p> <ul> <li>post-processed radial velocity fields sampled by KITcube Leosphere/Vaisala Windcube WLS200s-124 and WLS200s-159</li> <li>their coplanar-retrieved horizontal wind field output</li> <li>additionally, horizontal wind speed and direction sampled by the KITcube Vaisala Windcube v2.1 (WLS7-1489) at 60-m above ground level is added as an independent measurement for subjective validation of the coplanar-retrieved wind</li> </ul> <p>The wind fields shown in the animations originate from an accompanying Zenodo data set (DOI: 10.5281/zenodo.7212801), where complete information concerning lidar locations, scan parameters, and post-processing may be found.</p>
Setup of a 3D printed wind tunnel: application for calibrating bi-directional velocity probes used in Fire Engineering Applications
<p>The research presented here focuses on the development of a 3D printed wind tunnel and the relevant equipment to be used for calibrating bi-directional velocity probes (BDVP). BDVP are equipment to be used for measuring velocity flow by determining the pressure difference of hot gases generated during fires. The manufactured probes require calibration to determine the calibration factor to achieve precise measurement. The calibration is usually performed in wind tunnels which can be difficult to access due to costs, complexity and the various pieces of equipment required. The aim of the current study is to develop and assemble an inexpensive and easy-to-build bench-scale wind tunnel, with a data-logging system and fan control functionalities for fast and effective calibration of BDVP. A 3D printer with a PET-G filament is used, able to produce parts for the wind tunnel system which are durable and easy to handle and assemble. The system additionally includes an Arduino-based measuring unit with a hot-wire anemometer and temperature correction: Rev. P. This takes precise measurements; continuously logging data on a computer through a USB interface and capable of saving data on an SD card. This design provides users with parameters of velocity flow up to 4 m/s with standard deviation of 1.2 % and turbulence intensity of 1 %. The main advantages of this wind tunnel are its simplicity to build and portability.</p> <p>The dataset contains design files for 3D printing of the wind tunnel, BOM and wiring of electronic components.</p> <p>The project is prototype under development and authors are not responsible for any damages or injuries caused by inappropriate construction or operation. This source is distributed WITHOUT ANY EXPRESS OR IMPLIED WARRANTY, INCLUDING OF MERCHANTABILITY, SATISFACTORY QUALITY AND FITNESS FOR A PARTICULAR PURPOSE.</p>
Turbulent dissipation rate and velocity spectrum at two radar wind profiler sites in North China
<p>This dataset includes the monthly mean velocity spectrum width and turbulence dissipation rate from 0900 local standard time (LST) to 1700 LST in 2021, which are retrieved from the radar wind profiler measurements at Baoding (urban) and Zhangbei (plateau) stations. Each data file is stored in xlsx format and contains two sheets, and each sheet is a two-dimensional data, including velocity spectrum width and turbulence dissipation rate. These two variables are stored in a matrix of 30 rows and 9 columns. The row refers to the height at an interval of 120 meters, and the columns refers to time which corresponds to 0900 to 1700 LST. </p>
Wind velocity vertical profile in a 50 m wind mast at Sisal, Yucatán, Mexico
<p>A meteorological mast, instrumented with sonic anemometers was implemented during the years 2010-2014, in order to study high frequency wind data at five different heights above the ground. The mast is 50 m height, located about 100 m from the shoreline at the Sisal campus of UNAM. An automated acquisition system recorded raw data in a database (server) directly through a RF link.</p> <p>For more information visit: http://ocse.mx/en/experimento/torre-sisal</p> <p> </p>
Representation model of wind velocity fluctuations and saltation sand transport in aeolian sand flow
<p>The data of the figures in the article.</p>
Data from: Covariation in abscission force and terminal velocity of wind-borne sibling seeds alters long distance dispersal projections
1. Despite the fact that seeds are unlikely to be identical—even among siblings within a maternal individual—dispersal models typically use one mean trait value to represent the ability of an entire species to disperse. Previous work has shown that the environmental conditions under which individuals leave the maternal site strongly affect how far seeds will travel. However, less is known about how trait variation within individuals contributes to dispersal or how such variation might interact with abiotic factors. 2. Here, we develop the use of an ergometer in a novel application to investigate variation in seed traits, specifically the force required for seed abscission and seed terminal velocity, exhibited by seeds from different locations within maternal capitula of the invasive species Carduus nutans (Asteraceae). 3. We find that seeds from the center of capitula are significantly easier to liberate and have slower falling velocities than siblings found near the edge of capitula. When abscission force is positively correlated with terminal velocity, slowly falling, easily abscised seeds are projected to travel much further than the average seed on slow winds. 4. Our experimental and theoretical results, which together show that within-individual variation can strongly affect model projections of species dispersal, have important implications for our broader understanding of population spread rates, the spatial structure of populations, metapopulation connectivity, and gene flow in the landscape.
Data in Effects of wind velocity and nebkha geometry on shadow dune formation
<p>File in this dataset include all the data in this manuscript, such as the flow data numerical simulation, flow and sand deposition data in wind tunnel simulation, and some other remote sense data.</p>
Data in "Effects of Wind Velocity and Nebkha Geometry on Shadow Dune Formation"
<p>This file includes the data for wind flow, sand deposition and relevant materials in manuscript "<strong>Effects of Wind Velocity and Nebkha Geometry on Shadow Dune Formation" </strong></p>
Data from: Covariation in abscission force and terminal velocity of wind-borne sibling seeds alters long distance dispersal projections
Open the record for dataset details and reuse information.
Improving measurements of the falling trajectory and terminal velocity of wind-dispersed seeds
<p>1. Seed dispersal by wind is one of the most important dispersal mechanisms in plants. The key seed trait affecting seed dispersal by wind is the effective terminal velocity (hereafter "terminal velocity", Vt), the maximum falling speed of a seed in still air. Accurate estimates of Vt are crucial for predicting intra- and interspecific variation in seed dispersal ability. However, existing methods produce biased estimates of Vt for slow- or fast-falling seeds, fragile seeds, and seeds with complex falling trajectories.</p> <p>2. We present a new video-based method that estimates the falling trajectory and Vt of wind-dispersed seeds. The design involves a mirror that enables a camera to simultaneously record a falling seed from two perspectives. Automated image analysis then determines three-dimensional seed trajectories at high temporal resolution. To these trajectories, we fit a physical model of free fall with air resistance to estimate Vt. We validated this method by comparing the estimated Vt of spheres of different diameters and materials to theoretical expectations, and by comparing the estimated Vt of seeds to measurements in a vertical wind tunnel.</p> <p>3. Vt estimates closely match theoretical expectations for spheres and vertical wind tunnel measurements for seeds. However, our Vt estimates for fast-falling seeds are markedly higher than those in an existing trait database. This discrepancy seems to arise because previous estimates inadequately accounted for seed acceleration. </p> <p>4. The presented method yields accurate, efficient and affordable estimates of the three-dimensional falling trajectory and terminal velocity for a wide range of seed types. The method should thus advance the understanding and prediction of wind-driven seed dispersal.</p>
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