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1,855 results for “winds”
LiDAR Cluster Statistic of Wind Turbine Wakes
<p>Mean and standard deviation of the wake velocity field generated by utility-scale wind turbines for different turbulence intensity of the incoming wind and rotor thrust coefficient. Statistics are retrieved from wind LiDAR measurements. More details in this paper https://onlinelibrary.wiley.com/doi/full/10.1002/we.2430 </p>
Influence of He$^{++}$ and shock geometry on interplanetary shocks in the solar wind: 2D Hybrid simulations
<p>After protons, alpha particles (He$^{++}$) are the most important ion species in the solar wind, constituting typically about 5\% of the total ion number density. Due to their different charge-to-mass ratio protons and He$^{++}$ particles are accelerated differently when they cross the electrostatic potential in a collisionless shock. This behavior can produce changes in the velocity distribution function (VDF) for both species generating anisotropy in the temperature which is considered to be the energy source for various phenomena such as ion cyclotron and mirror mode waves. How these changes in temperature anisotropy and shock structure depend on the percentage of He$^{++}$ particles and the geometry of the shock is not completely understood. In this paper we have performed various 2D local hybrid simulations (particle ions, massless fluid electrons) with similar characteristics (e.g., Mach number) to interplanetary shocks for both quasi-parallel and quasi-perpendicular geometries self-consistently including different percentages of He$^{++}$ particles. We have found changes in the shock transition behavior as well as in the temperature anisotropy as functions of both the shock geometry and He$^{++}$ particle abundance: The change of the initial $\theta_{Bn}$ leads to variations of the efficiency with which particles can escape to the upstream region facilitating or not the formation of compressive structures in the magnetic field that will produce increments in perpendicular temperature. The regions where both temperature anisotropy and compressive fluctuations appear tend to be more extended and reach higher values as the He$^{++}$ content in the simulations increases.</p> <p> </p>
Dataset and R-script for simple mechanistic model of Heracleum sosnowskyi seed dispersal by wind
<p>The dataset contains:</p> <p>- primary data about Heracleum sosnowskyi seeds traits (terminal velocity, mass, area, wing loading) and release heights for <em>H. sosnowskyi</em> populations from two geographically distant Russia regions;</p> <p>- results of experiments of model seeds launches under different wind speeds;</p> <p>- R script for exploratory statistical analysis, linear regressions and mechanistc models testing.</p> <p>The anemochorous seed dispersal was generalized with a number of empirical and mechanistic models of varying complexity. The aim of this work was to develop the simplest possible mechanistic model of <em>Heracleum sosnowskyi</em> that allows to determine the distance of seed dispersal by wind with an accuracy comparable to that of empirical measurements. We measured and compared the characteristics of the seeds (terminal velocity, mass, area, wing loading) as well as the release height for <em>H. sosnowskyi</em> populations from two geographically distant Russia regions. We tested two simplest mechanistic models: a ballistic model and a wind gradient model using identical artificial seeds with characteristics similar to those of real <em>H. sosnowskyi</em> seeds. The wind gradient model gave the best results, despite the fact that uniform in shape, weight and size artificial <em>H. sosnowskyi</em> seeds, when dropped simultaneously from the same height, fly off at different distances. This model provides an estimate of dispersal distances with an accuracy comparable to that of empirical measurements. We plan to use the presented model to develop an individual-based model that will allow us to calculate the flight distances of <em>H. sosnowskyi</em> propagules, taking into account real weather conditions in different years and in different parts of its invasion range. All primary data and R-scripts used are freely available at the Zenodo repository (https://doi.org/10.5281/zenodo.3766035).</p> <p> </p>
Offshore wind competitiveness in mature markets without subsidy - Supplementary Data
<p>This is the data set named "Supplementary Data 1" for the research paper "Offshore wind competitiveness in mature markets without subsidy". This data set also contains the raw data for reproducing Figure 1 through to Figure 4. The paper is currently under review and access is for peer-review purposes only.</p>
Some Similarities and Differences between the Observed Alfvénic Fluctuations in the Fast Solar Wind and Navier-Stokes Turbulence
<p>Three text data sets are uploaded: wind tunnel data (modane1.txt), solar-wind magnetic-field data (Flat1maginterp.txt) and solar-wind velocity data (Flat15interp3DP.txt)</p>
A two-year intercomparison of CW focusing wind lidar and tall mast wind measurements at Cabauw
<p>Dataset (.csv files) and software (python scripts) for generating figures, including data analysis, in our manuscript "A two-year intercomparison of CW focusing wind lidar and tall mast wind measurements at Cabauw", submitted to Atmos. Meas. Tech.</p>
Wind observations in a 22 m tower at El Palmar state reserve, Yucatan, Mexico
<p>This database contains wind measurements, at the ecohydrological monitoring site of “El Palmar” state reserve (21.0293 ° N, 90.0637 ° W; 1.86 mamsl), Yucatan, Mexico. The acquisition of these data was performed from December 7th 2016 to August 21st 2019, in a 22 m Eddy-Covariance tower, using a 3D sonic anemometer (WindMaster Pro, Gill Instruments, UK) connected to a LI-7550 Interface Unit. Data was processed using the EddyPro 6.2.2 software, and are presented in averages of every half hour. A quality control was performed on the data.</p> <p>For more information visit: http://ocse.mx/en/experimento/torre-de-flujos-palmar</p>
Capacity factors for wind turbines
<p>Simulated capacity factors in Finland for six wind turbine models, Vestas V90-3.0 MW, V90-2.0 MW, V112-3.3 MW, V126-3.3 MW, V117-3.45 MW and V136-3.45 MW at four turbine hub heights 75, 100, 125, 150 m. Wind speed data are from Finnish Wind Atlas [1, 2], from which the Weibull distribution shape and scale parameters (labelled ‘Weibull all data k’ and ‘Weibull all data A’, respectively) and the frequencies of the wind sectors (‘Frequency all data’) were used.</p> <p>File <em>FWA_coordinates_2500m.csv</em> holds the geographical coordinates (WGS 84) of the Wind Atlas in 2.5×2.5 km<sup>2</sup> resolution.</p> <p>To simulate a wind farm where each turbine experiences a slightly different wind speed, we used a normal distribution with variance <span class="math-tex">\(\sigma^2(v) = 0.2v + 0.6\,\mathrm{m/s}\)</span>, (where <em>v</em> is wind speed) to smooth (convolute) the original power curves [3, 4].</p> <p>The calculation of capacity factor cf at wind atlas grid point k is described by the formula<br> <span class="math-tex">\(\mathit{CF}_k = \mathop{\mathbb{E}}_{i, s} g(v_i) \approx \sum_{s=1}^{12} f_{k,s} \sum_{i=1}^N p_{k,s}(v_i) g(v_i) \Delta v\)</span>,<br> where g(v) is the power curve function for current wind turbine model, vi the mean wind speed of bin i, fk,s the frequency of occurrence of wind direction s at point k, N the number of wind speed bins, pk,s(v) the Weibull probability density function for sector s at point k at the hub height and Δv the width of the wind speed bin.</p> <p><strong>References</strong></p> <ol> <li>Finnish Meteorological Institute, “Finnish Wind Atlas,” 2008. [Online]. Available: <a href="http://www.windatlas.fi">http://www.windatlas.fi</a>. [Accessed: 28-Jun-2016]</li> <li>B. Tammelin, T. Vihma, E. Atlaskin, J. Badger, C. Fortelius, H. Gregow, M. Horttanainen, R. Hyvönen, J. Kilpinen, J. Latikka, K. Ljungberg, N. G. Mortensen, S. Niemelä, K. Ruosteenoja, K. Salonen, I. Suomi, and A. Venäläinen, “Production of the Finnish Wind Atlas,” Wind Energy, vol. 16, no. 1, pp. 19–35, Jan. 2013.</li> <li>Staffell, Iain, and Richard Green. 2014. “How Does Wind Farm Performance Decline with Age?” Renewable Energy 66. Elsevier Ltd: 775–86. doi:10.1016/j.renene.2013.10.041.</li> <li>Staffell, Iain, and Stefan Pfenninger. 2016. “Using Bias-Corrected Reanalysis to Simulate Current and Future Wind Power Output.” Energy 114 (November): 1224–39. doi:10.1016/j.energy.2016.08.068.</li> </ol> <p> </p>
Conjunctions between ICON-MIGHTI and 4 meteor radars, used in "Validation of ICON-MIGHTI thermospheric wind observations: 2. Greenline comparisons to meteor radars" by Harding et al. (2020, Submitted)
<pre>This dataset was used to generate the figures in the paper mentioned above and is being made available for the sake of reproducibility and future analysis. The primary variables are los_wind (the line of sight wind profiles observed by ICON-MIGHTI) and los_wind_r (the wind profiles observed by the meteor radar, interpolated in time and altitude to the MIGHTI sample, and projected onto the MIGHTI line of sight). Dimensions are "time" and "row" (which refers to the row of the MIGHTI CCD, roughly equivalent to altitude. Velocity units are m/s, distances are km, and lat/lon are in degrees. More information can be found in the paper.</pre>
Three-component modelling of C-rich AGB-star winds V. – dataset
<p>The provided data include all parameter files, binary output files, and log<br> files that are the basis for the publication in MNRAS.</p> <p>The file 'file_listing.txt' contains a complete list of files and<br> directories in all gzipped tar files. Each individual gzipped tar file is<br> formatted as follows:</p> <p> Mm.m_Ll.ll_Ttttt_CtOc.cc.tar.gz</p> <p>where<br> m.m :: the assumed mass of the model, in solar masses<br> l.ll :: The assumed luminosity, in log10(solar luminosities)<br> tttt :: The effective temperature of the star, in Kelvin.<br> c.cc :: The carbon-to-oxygen excess, in log10(n_C/n_H-n_O/n_H)+12</p> <p><br> The contents vary according to the model, but here is the general directory<br> structure:</p> <p> nodr/ :: non-drift / PC models<br> drift/ :: drift models</p> <p> nodr/init<br> drift/init :: Initial model files created using John Connor.</p> <p><br> File suffixes are the following:</p> <p> .par :: Plain-text parameter file that contains all parameters that are<br> different from the respective default value in the model.<br> Consequently, to see all used parameters it is necessary to look in<br> the log file (see below).</p> <p> .bin :: Binary file that contains converged models. Each model is stored in<br> two versions, first the previous time step and then the current time<br> step (both are needed to restart model calculations at that time<br> step).</p> <p> The initial model file only contains one model; where the previous<br> time step data are the same as the current time step data.</p> <p> The format of this file is explained below.</p> <p> Note! These files can get pretty large and are therefore only<br> available for a smaller number of the models here. Please ask the<br> corresponding author for the missing files should the need appear.</p> <p> .log :: Plain-text log file that shows the used model parameters and a number<br> of key properties for each converged model. The encoding of this file<br> is UTF-8.</p> <p> .inf :: Plain-text secondary log file that contains the header of the<br> [primary] log file as well as timing information.</p> <p> .tpb :: Secondary binary file that contains a number of properties specified<br> at the outer boundary, typically for each consecutive time step.</p> <p> .lis :: Plain-text file with the iteration history. Available for some files.</p> <p> .liv :: Plain-text file with values specified for a number of properties at<br> each gridpoint. Available for a smaller number of files.</p> <p> .inp :: Plain-text file that is used to launch a model; some are still there.</p> <p> .eps :: Encapsulated PostScript files created by John Connor when calculating<br> the initial model.</p> <p><br> Model evolution structure - file endings before the suffix:</p> <p> _rlx :: Files related to relaxing the T-800 calculations on the initial model<br> created by John Connor.</p> <p> _exp :: Files related to expanding the initially compact model to using the<br> full radial domain.</p> <p> _fix :: Files related to the intermediate stage where calculations are changed<br> from expansion to outflow.<br> <br> _out :: Files related to the outflow stage of the calculations; this is what<br> you want to look at to see the wind evolution. Results in the paper<br> are calculated using these data.</p> <p> <br> Note! Some outflow stage calculations continue the evolution of the previous<br> set of files. The underlying reason for continued calculations is typically<br> that the calculated time interval is too short. Such files are typically<br> given the extension '_cont.lin_out', '_cont2.lin_out', etc.</p> <p><br> Load files:</p> <p> Two tools are provided here that can load the binary data files using the<br> Interactive Data Language (IDL):</p> <p> sc_load_bin (for files with the suffix '.bin'):</p> <p> Loads the full content of a T-800 binary file and returns a structure<br> with the data.</p> <p><br> sc_load_tpb (for files with the suffix '.tpb'):</p> <p> Loads the full content of a T-800 'tpb' binary file and returns a<br> structure with the data.</p> <p> Note! Due to the way models run on clusters, this file is sometimes<br> incomplete; this happens when the model code T-800 is stopped as the<br> cluster-specific walltime is reached. If this is the case, it is<br> necessary to use the binary file instead, where data are saved<br> typically every 20:th time step.</p> <p> Alternative tools for use with Python and Julia could be considered for<br> writing, but where not yet available when this dataset was made public.<br> Please contact the corresponding author for a current status on this issue.</p>
Precipitating Solar Wind Hydrogen at Mars: Improved Calculations of the Backscatter and Albedo with MAVEN Observations
<p>These files contain the derived data products used in the paper, including the penetrating and backscatter energy spectra and directional fluxes. See Readme.txt for a description of the data that is stored in each file.</p>
SKiYMET Meteor Radar Horizontal Wind at Andes Lidar Observatory 2009-2014
<p>This is horizontal wind measured by a SKiYMET Meteor Radar near Andes Lidar Observatory in Cerro Pachón, Chile (30.05 S, 70.82 W) from Sep 2009 to Aug 2014. The radar was previously installed at Maui, Hawaii and is described in the paper</p> <p>Franke, S. J., X. Chu, A. Z. Liu, W. K. Hocking (2005), Comparison of meteor radar and Na Doppler lidar measurements of winds in the mesopause region above Maui, Hawaii, <em>J. Geophys. Res.</em>, <em>110</em>, D09S02, doi:10.1029/2003JD004486.</p> <p>The data is in NetCDF format, at 1 hr and 2 km resolution from 80 to 100 km altitude. Time is in UT. Both time and altitude refer to the center of the 1 hr bin. Wind rms errors and numbers of meteor detections used for wind retrieval are also inicluded.</p> <p> </p>
Onshore & offshore WRF generated wind data
<p>These data sets provide the WRF [1] calculated wind data for Pritzwalk (onshore) and FINO3 (offshore) as Python dictionaries. Additionally, the files contain k-means cluster objects derived from these profiles. These data sets were used for power assessment and design exploration of Airborne Wind Energy Systems using the awebox [2] optimization toolbox.</p> <p> </p> <p>WRF setups are described in detail and used in publication [3,4,5].</p> <p>Wind data are interpolated to fixed heights of: [10, 28, 50, 70, 90, 100, 150, 200, 250, 300, 350, 400, 450, 500, 550, 600, 700, 800, 1000, 1200] meters above ground.</p> <p> </p> <p>Onshore wind data: </p> <ul> <li> <p>Location lat: 53° 10.78' N; long: 12° 11.35' E</p> </li> <li> <p>Time: 1 September 2015 - 31 August 2016</p> </li> <li> <p>Timestep: 10 min</p> </li> </ul> <p>Offshore wind data: </p> <ul> <li> <p>Location lat: 55° 11.7' N, long: 7° 9.5' E</p> </li> <li> <p>Time: 1 September 2013 - 31 August 2014</p> </li> <li> <p>Timestep: 10 min</p> </li> </ul> <p> </p> <p>The clusters are derived from both horizontal wind velocity components using the scikit-learn’s k-means clustering algorithm [6]. For our purposes, wind vectors were rotated such that the main wind speed always points in the same direction (u_main,u_deviation).</p> <p>[1]: <a href="https://www.mmm.ucar.edu/weather-research-and-forecasting-model"> Weather Research and Forecasting Model </a></p> <p>[2]: <a href="https://github.com/awebox/awebox">awebox</a></p> <p>[3]: <a href="https://doi.org/10.5194/wes-4-563-2019">Improving mesoscale wind speed forecasts using lidar-based observation nudging for airborne wind energy systems</a></p> <p>[4]: <a href="https://doi.org/10.5194/wes-2020-120">Offshore and onshore ground-generation airborne wind energy power curve characterization </a></p> <p>[5]:<a href="https://doi.org/10.5194/wes-2020-123">Ground-generation airborne wind energy design space exploration </a></p> <p>[6]: <a href="https://scikit-learn.org/stable/modules/generated/sklearn.cluster.KMeans.html">sklearn.cluster.KMeans</a></p>
Vertical Wind and Temperature Gravity Wave Perturbations Derived from Na Lidar Observations
<p>The gravity wave perturbations associated with vertical wind and temperature in the mesopause region for heat flux calculations. </p>
The Complex Geometry and Dynamical Role of Stellar Wind Bubbles in Turbulent Molecular Clouds
<p>Research Data Management Package for paper in Monthly Notices of the Royal Astronomical Society with same title and author list</p>
Effects of wind on honeybee and bumblebee foraging behaviour on multiple plant species
<p>Dataset of results used for two publications. It shows the foraging behaviours of honeybees and bumblebees on multiple plant species in different wind speeds,</p>
Low-level updraft intensification in response to environmental wind profiles
<p>Supercell storms can develop a "dynamical response" whereby upward accelerations in the lower troposphere amplify as a result of rotationally induced pressure falls aloft. These upward accelerations likely modulate a supercell's ability to stretch near-surface vertical vorticity to achieve tornadogenesis. This study quantifies such a dynamical response as a function of environmental wind profiles commonly found near supercells. Self-organizing maps (SOMs) were used to identify recurring low-level wind profile patterns from 20,194 model-analyzed, near-supercell soundings. The SOM nodes with larger 0–500 m storm-relative helicity (SRH) and streamwise vorticity (ω<sub>s</sub>) corresponded to higher observed tornado probabilities. The distilled wind profiles from the SOMs were used to initialize idealized numerical simulations of updrafts. In environments with large 0–500 m SRH and large ω<sub>s</sub>, a rotationally induced pressure deficit, increased dynamic lifting, and a strengthened updraft resulted. The resulting upward-directed accelerations were an order of magnitude stronger than typical buoyant accelerations. At 500 m AGL, this dynamical response increased the vertical velocity by up to 25 m s<sup>–1</sup>, vertical vorticity by up to 0.2 s<sup>–1</sup>, and pressure deficit by up to 5 hPa. This response specifically augments the near-ground updraft (the midlevel updraft properties are almost identical across the simulations). However, dynamical responses only occurred in environments where 0–500 m SRH and ω<sub>s</sub> exceeded 110 m<sup>2</sup> s<sup>–2</sup> and 0.015 s<sup>–1</sup>, respectively. The presence vs. absence of this dynamical response may explain why environments with higher 0–500 m SRH and ω<sub>s</sub> correspond to greater tornado probabilities.</p>
Supplementary Material: A Large-Eddy Simulation Study of Vertical Axis Wind Turbine Wakes in the Atmospheric Boundary Layer
<p>Supplementary material for <em>Energies</em> <strong>2016</strong>, <em>9</em>, 366; doi:10.3390/en9050366:</p> <p><strong>Video S1:</strong> Normalized instantaneous streamwise velocity field both on a vertical plane (<em>x</em>-<em>z</em>) going through the center of the turbine and on a horizontal plane at the equator height of the turbine (Note: the physical time corresponding to this video is 1 minute and 17 seconds, and the size of the blades is magnified for illustration purposes).</p> <p><strong>Video S2:</strong> Normalized instantaneous streamwise velocity field on a horizontal plane at the equator height of the turbine for two cases: when the turbine starts to operate (top) and when the flow has reached statistically steady condition (bottom) (Note: the physical time corresponding to both videos is 1 minute and 17 seconds, and the size of the blades is magnified for illustration purposes).</p>
Modified pool system based on the IEEE RTS-96 system incl. 39 wind power producers
<p>This is the data-set associated with the numerical simulations in the paper "A. Papakonstantinou, P. Pinson, <em>Population Dynamics for Renewables in Electricity Markets: A Minority Game View</em>". The paper will be presented in 2016 International Conference on Probabilistic Methods Applied to Power Systems (PMAPS) in Oct. 16-20, 2016 in Beijing, China.</p> <p>We modify the original data-set [1] by adding the marginal costs for conventional generation introduced by [2] and flexible generators capable of providing up and down regulation following [3]. The cost of up-regulation is assumed to be 10% higher than the day-ahead cost and the cost of down-regulation 9% less than the day-ahead ahead costs.</p> <p>Furthermore, regarding stochastic generation, we assume zero marginal and cost free spilling action, while load shedding induces a cost of 1000 EUR/MWh. Finally, we assume that the total demand is at 80% of the conventional generation [3], while the total capacity of the 39 stochastic producers is at 30% of the demand.</p> <p>Within the data file the specific data used for the analysis in the paper are under pes_input().</p> <p>[1] IEEE RTS Task Force of APM Subcommittee, “The ieee reliability test system-1996.” IEEE Transactions on Power Systems, vol. 14, no. 3, pp. 1010–1020, 1999.</p> <p>[2] D. Kirschen. Unit commitment data for modernized ieee rts-96. Accessed: 10-03-2016. [Online]. Available: http://www.ee.washington.edu/research/real/library.html</p> <p>[3] A. J. Conejo, M. Carri ́on, and J. M. Morales, Decision Making Under Uncertainty in Electricity Markets. Springer, 2010.</p> <p> </p> <p> </p> <p> </p> <p> </p>
X-ray CT data: fatigue damage in glass fibre/polyester composite used for wind turbine blades
<p>These data are obtained using a Zeiss Xradia Versa 520 scanner to scan a uni-directional glass fibre reinforced polyester composite made from a non-crimp fabric used for wind turbine blades. The scans were performed to study the fatigue damage progression in this material. The data is published together with the below journal paper, in which more information can be found. The present videos of the data relate directly to the figures in this paper.</p> <p>Jespersen, K. M., Zangenberg Hansen, J., Lowe, T., Withers, P. J., & Mikkelsen, L. P. (2016). <em>Fatigue damage assessment of uni-directional non-crimp fabric reinforced polyester composite using X-ray computed tomography</em>. <em>Composites Science and Technology</em>, <em>136</em>, 94–103. DOI:10.1016/j.compscitech.2016.10.006</p> <p>For use of these data, please remember to cite the above mentioned paper.</p> <p>Corresponding author, K. M. Jespersen, e-mail kmun@dtu.dk</p>
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.