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607 results for “wind data”
Data Supplement for 'Curled Wake Development of a Yawed Wind Turbine at Turbulent and Sheared Inflow' - Wind Energy Science Journal
<p>Data Supplement for 'Curled Wake Development of a Yawed Wind Turbine at Turbulent and Sheared Inflow' - Wind Energy Science Journal</p> <p>This database contains the measurement using a model wind turbine with 0.6m diameter(D) in a wind tunnel. A short-range Lidar WindScanner facilitated mapping the wake with a high spatial and temporal resolution in vertical, cross-stream planes at different downstream locations and in a horizontal plane at hub height.</p> <p>The measurement campaign was conducted in the large wind tunnel at ForWind-University of Oldenburg. The wind tunnel has a test section cross-section with the dimensions of 3m x3m. For this study three movable test section elements of 6m length were attached for a total enclosed length of 18m. The roof of the test section was adjusted to compensate for boundary layer growth to achieve a zero pressure gradient for the target wind speed of the experiments, nominally 7.5m/s, with an empty tunnel with no grid or turbine installed. The three-bladed MoWiTO 0.6 wind turbine model(Schottler et al.(2016)), with a hub height (h) of 0.77m and a diameter of 0.58m was placed at a distance of 2.4D downstream of the test section inlet, where the distance was measured to the centre of the rotor. In addition, the distance between the rotor center and the tower center is 110mm.<br> The flow blockage, based on rotor swept area and tower flow-facing area, was 2.7%. The wind turbine controller is based on the torque of the generator (Petrovi ́c et al. (2018)) leading to a tip speed ratio of 5.7 at the operational point during non-misaligned cases with no grid. More information can be found in the paper.</p> <p>The folder contains 12 unique .mat files each containing a matlab structure. The matlab structure conatins the vertical and horizontal scan for each inflow and operational condition:<br> With the upstream turbine installed:<br> - Yaw0_Uniform_NoGrid<br> - 1D, 2D, 3D, 5D, 13D, 16D, Horizontal<br> - Yaw30_Uniform_NoGrid<br> - 1D, 2D, 3D, 5D, 13D, 16D, Horizontal<br> - Yawneg30_Uniform_NoGrid<br> - 1D, 2D, 3D, 5D, 13D, 16D, Horizontal</p> <p> - Yaw0_Uniform_PassiveGrid<br> - 1D, 2D, 3D, 5D, 7D, 10D, Horizontal<br> - Yaw30_Uniform_PassiveGrid<br> - 1D, 2D, 3D, 5D, 7D, 10D, Horizontal<br> - Yawneg30_Uniform_PassiveGrid<br> - 1D, 2D, 3D, 5D, 7D, 10D, Horizontal </p> <p> - Yaw0_BoundaryLayer_PassiveGrid<br> - 1D, 2D, 3D, 5D, 7D, 10D, Horizontal<br> - Yaw30_BoundaryLayer_PassiveGrid<br> - 1D, 2D, 3D, 5D, 7D, 10D, Horizontal<br> - Yawneg30_BoundaryLayer_PassiveGrid<br> - 1D, 2D, 3D, 5D, 7D, 10D, Horizontal </p> <p>Without the upstream turbine installed:<br> - NoTurbine_Uniform_NoGrid<br> - 1D, 2D, 3D, 5D, 13D, 16D<br> - NoTurbine_Uniform_PassiveGrid<br> - 0D, 1D, 2D, 3D, 5D, 7D, 10D<br> - NoTurbine_BoundaryLayer_PassiveGrid<br> - 0D, 1D, 2D, 3D, 5D, 7D, 10D </p> <p>Within each substructure the following parameters are provided:<br> - v_los [m/s] ----------> Line of sight velocity<br> - sigma [m/s] ----------> Spectrum width<br> - x_Global_frame [m] ---> x-coordinate referenced at the lower grid midpoint<br> - y_Global_frame [m] ---> y-coordinate referenced at the lower grid midpoint<br> - z_Global_frame [m] ---> z-coordinate referenced at the lower grid midpoint<br> - xx [m] ---------------> Grid of the x-coordinate referenced at the lower grid midpoint<br> - yy [m] ---------------> Grid of the y-coordinate referenced at the lower grid midpoint<br> - zz [m] ---------------> Grid of the z-coordinate referenced at the lower grid midpoint<br> - uu [m/s] -------------> Horizontal wind speed at the position of the gridded coordinates, these data have been interpolated onto the grid<br> </p> <p>When using this database please reference to the journal paper.</p> <p>All data has been included without warranty, express or implied.</p> <p>For further questions, please contact the corresponding author.<br> </p>
Data for : "Three months of combined high resolution rainfall and wind data collected on a wind farm"
<p>The data set corresponds the data presented in the data paper : “Three months of combined high resolution rainfall and wind data collected on a wind farm “ Earth System Science Data” (https://www.earth-system-science-data.net/).</p> <p>More details can be found in the Read_me_v1.txt file and in the paper.</p>
Data generated for study of simultaneous design of wind turbines and cable layout in offshore wind
<p>This set of files contains the results of the models proposed in the manuscript: "Pérez-Rúa, J.-A. and Cutululis, N. A.: A Framework for Simultaneous Design of Wind Turbines and Cable Layout in Offshore Wind, Wind Energ. Sci. Discuss. [preprint], https://doi.org/10.5194/wes-2021-47, in review, 2021."</p>
Atmospheric visibility inferred from continuous-wave Doppler wind lidar, data set
<p>Visibility data from Pershore, UK, between 2018 and 2020</p>
Data from: Applicability of artificial neural networks to integrate socio-technical drivers of buildings recovery following extreme wind events
<p>The data provided and the associated MATLAB code were used to build an Artificial Neural Network Model to capture the reconstruction (recovery) of various buildings subjected to tornado events in the State of Missouri. The ANN model utilizes relevant tornado, societal demographic, and structural data to determine a building's resulting damage state from an extreme wind event and the subsequent recovery time. Abstract for the publication is as follows:</p> <p>In a companion article, previously published in Royal Society Open Science, the authors used Graph Theory to evaluate artificial neural network models for potential social and building variables interactions contributing to building wind damage. The results promisingly highlighted the importance of social variables in modeling damage as opposed to the traditional approach of solely considering physical characteristics of a building. Within this update article, the same methods are used to evaluate two different artificial neural networks for modelling building repair and/or rebuild (recovery) time. In contrast to the damage models, the recovery models consider (A) primarily social variables and then (B) introduce structural variables. These two models are then evaluated using centrality and shortest path concepts of Graph Theory as well as validated against data from the 2011 Joplin Tornado. The results of this analysis do not show the same distinctions as were found in the analysis of the damage models from the companion article. The overarching lack of discernible and consistent differences in the recovery models suggests that social variables that drive damage are not necessarily contributions to recovery. The differences also serve to reinforce that machine learning methods are best used when the contributing variables are already well understood.</p>
Bioacoustic data for evaluating wind denoising and detection methods
<p>Acoustic survey data accompanying publication "Wind-robust sound event detection and denoising for bioacoustics", by Julius Juodakis and Stephen Marsland.</p> <p>Contents (see the publication for details):</p> <ul> <li>pilotdata/: short audio files used for selecting the best-fit wind model</li> <li>surveys/LSK_additional_wavs/: remaining recordings from the little spotted kiwi survey that have not been deposited previously</li> <li>surveys/rawannots/: annotations produced by the unadjusted, OLS-adjusted and QR-adjusted detectors, for two bird surveys</li> <li>surveys/reviewed/: annotations passing human review for the unadjusted and OLS-adjusted detectors, for two bird surveys</li> <li>denoising/: examples of noise and signal which were mixed to evaluate the denoising.</li> </ul> <p>Audio files are provided in WAV PCM format. Annotations and filters are intended for use with AviaNZ software (https://www.avianz.net/), and provided in AviaNZ-compatible JSON format.</p>
Wind Data for Station-wise assessment of wind speed and direction under future climates across the United States
<p>This study employs statistical techniques to evaluate climate model performance in wind speed and direction and their projected future changes under the representative concentration pathway (RCP) 8.5 scenario over inland and offshore across the Continental United States (CONUS). It extends the scope of existing studies by characterizing the changes of the full range of the joint wind speed and direction distribution via a conditional approach. Projected uncertainties associated with different climate models and model internal variability are investigated and compared with the climate change signal to quantify the statistical significance of the future projections. The proposed conditional approach provides a better way to characterize the directional wind speed distributions that offers additional insights for the joint assessment of speed and direction. </p> <p>WRF data: We focus on seasonal (December-January-February (winter hereafter) and June-July-August (summer hereafter) statistics computed from the 3-hourly RCM outputs on both wind speed and direction over ten locations with different local topological features. We use three WRF simulations driven by Community Climate System Model 4 (CCSM4), the Geophysical Fluid Dynamics Laboratory Earth System Model 2 (GFDL-ESM2G), and the Hadley Centre Global Environment Model version 2 (HadGEM2-ES). These three GCMs represent a range of climate sensitivities that encompasses most of the coupled model intercomparison project phase 5 (CMIP5) GCMs when projecting future temperature changes. In this work, we focus on RCP 8.5 scenario for future projections. A 16-member ensemble of one-year of RCM simulation using bias corrected CCSM-driven WRF is also generated for analyzing the uncertainty due to the RCM's internal variability (IV). </p> <p>Benchmark data: Reanalysis data are used as a verification dataset in order to evaluate the RCMs' wind conditions under study for the historical time period. For the seven inland locations, we use the second phase of the multi-institution North American Land Data Assimilation System project, phase 2, at a spatial resolution of 12 km and hourly resolution. NLDAS-2 is an offline data assimilation system featuring uncoupled land surface models driven by observation-based atmospheric forcing. The non-precipitation land surface forcing fields for NLDAS-2 are derived from the analysis fields of the NCEP North American Regional Reanalysis (NARR). NARR analysis fields are at a 32-km spatial resolution and 3-hourly temporal frequency.</p> <p>In-situ measurement: Since reanalysis data can present errors and uncertainties, ground measurements and offshore buoy measurements are used to consolidate the evaluation of RCMs' wind conditions for inland and offshore locations in historical climates. Observational data are extracted from the Automated Surface Observing System (ASOS) network that consists stations covers the U.S. territory, available at ftp://ftp.ncdc.noaa.gov/pub/data/asos-onemin. The offshore downscaled wind speeds from the historical decade are compared with National Data Buoy Center (NDBC) buoy observations of near-surface wind velocities available at https://www.ndbc.noaa.gov. The observed winds at the NBDC anemometers are adjusted to 10-m above ground height and at 3-hourly rate. </p> <p> </p> <p> </p>
Data accompanying "Saildrone direct covariance wind stress in various wind and current regimes of the tropical Pacific"
<p>Data used for creating figures in the draft article "Saildrone direct covariance wind stress in various wind and current regimes of the tropical Pacific". This includes:</p> <ul> <li>1-minute average fields as broadcast in real time by Saildrone;</li> <li>10-minute average direct covariance fluxes (calculated by Jack Reeves Eyre);</li> <li>10-minute average bulk fluxes (calculated by Dongxiao Zhang using the COARE 3.5 algorithm).</li> </ul> <p>Code used to calculate the direct covariance fluxes, and to create the plots, is in a Github repository (https://github.com/NOAA-PMEL/SaildroneCovarianceFlux). The repository is also archived on Zenodo (https://doi.org/10.5281/zenodo.6484795).</p> <p>The data are archived in a directory structure that should work with the code - only a simple edit of "src/config.py" should be required. </p> <p> </p> <p>Version history:</p> <ul> <li>v0.1.0 -- submission to PMEL internal review (goes with code v0.1.0, https://doi.org/10.5281/zenodo.6484796).</li> </ul>
GAIA model simulate data of doubled CO2 (Forces, advections, and winds)
<p>This dataset contains forces, advections, and winds output from the GAIA model, that are related to the Figures in the paper. The forces and advections are divided by the Coriolis parameter or a zonal mean absolute vorticity. </p>
Supporting Data for Figures in "Wind Effects on Near and Midfield Mixing in Tidally Pulsed River Plumes"
<p>Supporting data for figures in "Wind Effects on Near and Midfield Mixing in Tidally Pulsed River Plumes" by Preston S. Spicer, Kelly L. Cole, Kimberly D. Huguenard, Daniel G. MacDonald, and Michael M. Whitney. The scientific journal article is published in the Journal of Geophysical Research: Oceans (2022). The main objectives of this study on the tidal Merrimack River plume are (1) quantify the net influence of straining, advection, and mixing on tidal plume stratification under realistic winds and (2) evaluate the mechanisms responsible for variability in mixing within the near and midfield plume regions over multiple tidal pulses under differing winds. A numerical modeling approach is taken. Data are from the Regional Ocean Modeling System (ROMS) results for the study area. Files are in MATLAB data format and are named FigXX_data. mat. Variable names and units correspond to graphed data of each figure in the journal article.</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>
Policy choices and outcomes for offshore wind auctions globally - Supplementary Data
<p>This is the dataset to the academic paper with the title:</p> <p>"Policy choices and outcomes for offshore wind auctions globally"</p> <p>Please cite the dataset as follows:</p> <p>Jansen,M.; Beiter, P.;Riepin, I. Müsgens, Felix; Juarez Guajardo-Fajardo, Victor, Staffell, I.; Bulder, B.; Kitzing, L. (2022) Policy choices and outcomes for offshore wind auctions globally. Energy Policy. DOI: https://doi.org/10.1016/j.enpol.2022.113000</p>
Hillslope Roughness Reveals Forest Sensitivity to Extreme Winds (Codes and Data)
<p>These are data are python scripts for creating figures in Doane et al., 2022 (Hillslope Roughness Reveals Forest Sensitivity to Extreme Winds). </p>
Global offshore wind turbine analysis with Sentinel-1 - supplementary data
<p>Gloabl offshore wind turbine analysis with Sentinel-1 - supplementary data</p> <p>The files are supplementary data of the publication:</p> <p>Global dynamics of the offshore wind energy sector monitored with Sentinel-1: Turbine count, installed capacity and site specifications</p> <p>which is currently under review in the International Journal of Applied Earth Observation and Geoinformation</p> <p>supplementary_data_B_OWT_height_capacity.csv holds 50 pairs of offshore wind turbine hub heights and the corresponding installed capacities along with the offshore wind farm project name, the number of turbines of this wind farm, and the source the information originates from.</p> <p>supplementary_data_B_DeepOWT_1_21_2_plus.geojson is the extended version of the DeepOWT data set (https://zenodo.org/record/5933967) with all of the derived attributes in the respective publication e.g. OWT hub height and installed capacity.</p>
EOOffshore: ERA5 Wind Data for the Irish Continental Shelf Region
<p><a href="https://eooffshore.github.io">EOOffshore</a> is a <a href="https://www.seai.ie/">Sustainable Energy Authority of Ireland (SEAI)</a> funded <a href="https://www.seai.ie/data-and-insights/seai-research/research-projects/details/building-upon-copernicus-earth-observation-services-to-augment-wind-measurement-coverage-of-the-oredp-offshore-renewable-energy-assessment-areas">project</a>, which commenced in June 2020 in the <a href="https://www.ucd.ie/physics/">School of Physics</a> in <a href="https://www.ucd.ie/">University College Dublin (UCD)</a>. It presents a case study that demonstrates the utility of the <a href="https://pangeo.io/">Pangeo</a> software ecosystem in the development of offshore wind speed and power density estimates, increasing wind measurement coverage of offshore renewable energy assessment areas in the <a href="https://www.marine.ie/Home/site-area/irelands-marine-resource/real-map-ireland">Irish Continental Shelf (ICS)</a> region. It has involved the creation of a new <a href="https://eooffshore.github.io/datasets.html">wind data catalog</a> for this region, consisting of a collection of analysis-ready, cloud-optimized (ARCO) datasets featuring up to 21 years of available in situ, reanalysis, and satellite observation wind data products.</p> <p><a href="https://www.ecmwf.int/en/forecasts/datasets/reanalysis-datasets/era5">ERA5</a> is the fifth generation global reanalysis data set produced by the <a href="https://www.ecmwf.int/">European Centre for Medium-Range Weather Forecasts (ECMWF)</a>. It is a component of the <a href="https://climate.copernicus.eu/">Copernicus Climate Change Service (C3S)</a>, where data products are publicly available in the <a href="http://As requested by the ECMWF - Licence to Use Copernicus Products, this Zarr store was: Generated using Copernicus Climate Change Service information [2001 - 2021]">C3S Climate Data Store</a>. This particular catalog data set (<em>eooffshore_ics_era5_single_level_hourly_wind.zarr.tar.gz</em>) contains 2001-2021 products for the ICS region from the <a href="https://cds.climate.copernicus.eu/cdsapp#!/dataset/reanalysis-era5-single-levels?tab=overview"><em>ERA5 hourly data on single levels from 1979 to present</em></a> data set, which provides hourly data from 1979 to the present day, at single levels (atmospheric, ocean-wave and land surface quantities). Wind speed and direction have been calculated from the <em>uX</em> and <em>vX</em> variables, where <em>X = 10 m</em> and <em>100 m</em> above sea level. This ERA5 data set was used in the EOOffshore project outputs presented (<em><a href="https://meetingorganizer.copernicus.org/EGU22/EGU22-2746.html">Scalable Offshore Wind Analysis With Pangeo</a></em>) at the <em><a href="https://meetingorganizer.copernicus.org/EGU22/session/42046">Meeting Exascale Computing Challenges with Compression and Pangeo</a></em> <a href="https://www.egu22.eu/">2022 EGU General Assembly</a> session.</p> <p>Description and example usage of the ERA5 data set in EOOffshore:</p> <ul> <li><a href="https://eooffshore.github.io/ERA5_ICS_Wind_Data.html">ERA5 Wind Data for Irish Continental Shelf region</a></li> <li><a href="https://eooffshore.github.io/Offshore_Wind_AOI.html">Offshore Wind in Irish Areas Of Interest</a></li> <li><a href="https://eooffshore.github.io/Comparison_Wind_Power.html">Comparison of Offshore Wind Speed Extrapolation and Power Density Estimation</a></li> </ul> <p>As requested by the <a href="https://apps.ecmwf.int/datasets/licences/copernicus/">ECMWF - Licence to Use Copernicus Products</a>, this Zarr store was:</p> <ul> <li> <p>Generated using Copernicus Climate Change Service information [2001 - 2021]</p> </li> </ul>
Data from: Active anemosensing hypothesis: How flying insects could estimate ambient wind direction
<p>Estimating the direction of ambient fluid flow is a crucial step during chemical plume tracking for flying and swimming animals. How animals accomplish this remains an open area of investigation. Recent calcium imaging with tethered flying <em>Drosophila</em> has shown that flies encode the angular direction of multiple sensory modalities in their central complex: orientation, apparent wind (or airspeed) direction, and direction of. Here we describe a general framework for how these three sensory modalities can be integrated over time to provide a continuous estimate of ambient wind direction. After validating our framework using a flying drone, we use simulations to show that ambient wind direction can be most accurately estimated with trajectories characterized by frequent, large magnitude turns. Furthermore, sensory measurements and estimates of their derivatives must be integrated over a period of time that incorporates at least one of these turns. Finally, we discuss approaches that insects might use to simplify the required computations and present a list of testable predictions. Together, our results suggest that ambient flow estimation may be an important driver underlying the zigzagging maneuvers characteristic of plume tracking animals' trajectories.</p>
Replication Data for: ``Impact of Parameterized Isopycnal Diffusivity on Shelf-Ocean Exchanges under Upwelling-Favorable Winds: Offline Tracer Simulations Augmented by Artificial Neural Network''
<p>This dataset contains the modified MAMEBUS source code, configuration files for the MITgcm and MAMEBUS simulations, model diagnostics used in the paper, and scripts to train the Artificial Neural Networks.</p>
Dynamics of the Young Solar Wind: Weakened Magnetization and Onset of Large Scale Turbulence (Simulation Data)
<p>Simulation data used in "Dynamics of the Young Solar Wind: Weakened Magnetization and Onset of Large Scale Turbulence", submitted to Geophysical Research Letters.</p>
MDM data for "Wind driven ocean circulation changes can amplify future cooling of the North Atlantic warming hole" - submitted to Journal of Climate
<p>Data files for MDM simulation used in Journal of Climate submission, "Wind driven ocean circulation changes can amplify future cooling of the North Atlantic warming hole"</p>
Considerations for high-resolution regional meteorological wind modelling over complex terrain: a typhoon case study for assessing forestry damage (data)
<p>This is the experiment data.</p> <p>The Weather Research and Forecasting (WRF) model is a popular and easily used as a numerical weather prediction (NWP) model, but configuring WRF to produce accurate results can be time-consuming. This is especially so when simulating extreme events, over complex terrain, or at high resolutions. In this study, a strong wind event from Tropical Cyclone (TC) Thad in year 1981 was simulated at 200 m resolution over an experiment forest in a mountainous region of Hokkaido island, Japan. The simulation configuration is challenging, in order to cover a larger area to produce a TC with appropriate track and intensity, and at the same time to resolve the smallest domain of sub-km grid spacing with computational stability. A mixed nesting method was applied with two-way nesting up for the first three domains, followed a separate simulation over the smallest domain. The mixed method could produce 10 min wind speed distributions similar to that of the full simulation with two-way nesting of all four domains, if a 30-minute boundary update interval was used for the separate simulation. Mixed nesting improves the efficiency of the simulation process, since the larger phenomenon scale and smaller human impact scale can be tuned separately. </p>
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International Brain Laboratory public data
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OpenNeuro
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