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185 results for “wind speed;”
Dataset - Downscaling ERA5 Wind Speed Data: A Machine Learning approach considering Topographic Influences
<p>This dataset provides three products:</p> <p><strong>1. The topographic data. </strong></p> <p>These data are provided as GeoTIFF files for Europe with 1km x 1km spatial resolution. These maps include:</p> <ul> <li>Digital Elevation Model (DEM) map: Europe_DEM.tif</li> <li>Slope map: Europe_slope.tif</li> <li>Aspect map: Europe_aspect.tif</li> <li>Topographic Position Index (TPI) with a 5 km radius map: Europe_TPI_5.tif</li> <li>Topographic Position Index (TPI) with a 75 km radius map: Europe_TPI_75.tif</li> <li>Terrain Diversity Index (TDI) map: Europe_TDI.tif</li> </ul> <p>These data can be used as input maps for the preprocessing step. In addition, the two TPI maps can also be used in the regression process.</p> <p><strong>2. The resulting map of the preprocessing step.</strong> </p> <p>This map offers predictions on the quality of ERA5 data across Europe and is also provided as a GeoTIFF file with 1km x 1km spatial resolution under the name:</p> <ul> <li> Europe_classification.tif</li> </ul> <p>In this map, Class1 represents a good ERA5 quality with an RMSE of less than 1.5 m/s, Class2 represents a moderate ERA5 quality with an RMSE bigger than 1.5 m/s but less than 3 m/s, while Class 3 indicates a poor ERA5 quality with an RMSE greater than 3 m/s.</p> <p><strong>3. The downscaled wind speed time series data. </strong></p> <p>Europe has been divided into 64 equal area blocks to accommodate the large data size. Each downscaled dataset is provided as a NetCDF file, offering hourly wind speed time series for a year (8760 hours) at approximately 1km x 1km spatial resolution. Each NetCDF file has three dimensions: 'lon' representing longitude, 'lat' representing latitude, and 'time' representing the hour. The variable name for wind speed in the NetCDF file is 'WindSpeed'. The 'WindSpeed' variable is stored as an Int32 data type in the NetCDF file, with values multiplied by 10000 in order to significantly reduce the data size. To utilize this variable, please divide it by 10000.</p> <p>For regions identified as Class1 and Class2, the downscaled wind speed is obtained through a simple nearest neighbour spatial interpolation of ERA5 due to the good quality of ERA5 in these regions. However, for the regions identified as Class3, the downscaled wind speed is derived using the machine learning-based regression approach described in the relevant publication. The geographic extent and the visual representation for each block are provided in 'Readme.pdf' document.</p> <p> </p> <p>To cite this dataset, please cite our published paper in Environmental Research Letters (<strong>DOI:</strong> 10.1088/1748-9326/aceb0a)</p>
High-frequency water temperature, chlorophyll fluorescence, wind speed, and photosynthetically active radiation data for 18 globally-distributed lakes 2008 - 2013
Abstract: This dataset was used in the analysis described in the manuscript by Rusak, J. A.J. Tanentzap, J.L. Klug, K. Rose, L.A. Winslow R. Smyth, E. Jennings, D. Pierson, S. Hendricks, A. Laas, E. Ryder, D. White, R. Adrian, L. Arvola, E. de Eyto, H. Feuchtmayr, M. Honti, V. Istanovics, I. Jones, C. McBride, S. Schmidt, G. Zhu. Wind and trophic status explain the temporal and spatial variability of chlorophyll in lakes. In review: Limnology and Oceanography Letters. The variation in chlorophyll fluorescence from 18 globally distributed lakes, was tested at monthly, daily and hourly scales in related to high-frequency measurements of wind, water temperature and radiation within lakes as well as lake productivity and morphometry among lakes. Overall, monthly variation in algal biomass was greater than that expressed at either daily or hourly scales but, combined, these latter time scales were equivalent to seasonal variation. Among lakes, algal biomass variation increased with trophic status while, within-lake variation increased with increasing wind speed variation. Together, our results suggest that predicted changes associated with a changing climate, as well as widespread ongoing cultural eutrophication, have the potential to substantially alter the variability of algal biomass and thus the predictability of the services it provides. This dataset includes the data used in the analysis described above.
Tropical cyclone low-level wind speed, shear, and veer: sensitivity to the boundary layer parameterization in WRF
<p>This repository contains namelists needed to reproduce the WRF(V4.4) simulations analyzed in "Tropical cyclone low-level wind speed, shear, and veer: sensitivity to the boundary layer parameterization in WRF"</p>
Vertical profiles of air temperature, relative humidity, wind speed and direction observed using UAV over the Mukhrino peatland in June 2022
<p>Vertical profiles of air temperature and relative humidity were measured using the iMetXQ2 sensor onboard DJI Phantom 4 quad-copter; vertical profiles of wind speed and direction were obtained from the Phantom 4 flight logs as produced by the DJI proprietary algorithm. </p>
Assessment of future wind speed and wind power changes over South Greenland using the MAR regional climate model : MAR ouptuts and KATABATA weather stations timeseries
<p>Daliy MARv3.12 outputs and KATABATA weather stations timeseries used in :</p> <p>Lambin, C., Fettweis, X., Kittel, C., Fonder, M., & Ernst, D. (2022).Assessment of future wind speed and wind power changes over South Greenland using the Modèle Atmosphérique Régional regional climate model. <em>International Journal of Climatology</em>, 43(1),558–574. https://doi.org/10.1002/joc.7795574 </p> <p> </p>
Data from : Damage to tropical forests caused by tropical cyclones is driven by wind speed but mediated by topographical exposure and tree characteristics
<p>These datasets have been used in the following paper:</p> <p>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 <em>Global Change Biology</em>.</p> <p>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.</p> <p>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).</p> <p>MSW: Maximum sustained wind speed (m.s-1)</p> <p>EXP: Topographical exposure to wind</p> <p>DBH: Diameter at breast height (cm)</p> <p>WD: Wood density (g.cm-3)</p>
Figure 3 in Validation of Wind Speed Calculated on Satellite Altimetry Data by Measurements on Weather Stations Located Along the White Sea Coast
Figure 3. The dependence of the speed wind at a height Figure 4. In-situ MS data breakdown scheme for a
Figure 5 in Validation of Wind Speed Calculated on Satellite Altimetry Data by Measurements on Weather Stations Located Along the White Sea Coast
Figure 5. The dependence of the correlation coefficient between in-situ wind speed at the WS and remote sensing data on the orientation angle of the main quadrants (a) and their position relative to the White Sea coastline (b).
Offshore wind turbine damage probability maps and hub height TC wind speeds for U.S. Atlantic and Gulf Coasts exposed to historical and future tropical cyclones
<p>Damage probability maps for offshore wind turbines exposed to tropical cyclones (TCs) under both historical and future climate scenarios along the U.S. Atlantic and Gulf Coasts are presented in this dataset. TCs are generated using <a href="../records/10392725" target="_blank" rel="noopener">The Risk Analysis Framework for Tropical Cyclones (RAFT)</a>, forced by <a href="https://pcmdi.llnl.gov/CMIP6/" target="_blank" rel="noopener">CMIP6</a> historical and future global climate simulations. Maximum wind speeds for 20- and 50-year TCs are processed through a <a href="https://www.sciencedirect.com/science/article/pii/S0960148120311423">fragility function</a> specific to offshore wind (OSW) turbines in order to estimate the probability of damage – specifically yielding and buckling – based on wind speed intensity. </p> <p><strong>Included data:</strong></p> <ul> <li><strong>TC wind speeds:</strong> Peak 10-min mean hub height (90m) TC wind speed maps</li> <li><strong>Damage states:</strong> Yielding and Buckling probability maps for OSW turbines</li> <li><strong>Geographic coverage:</strong> U.S. Atlantic and Gulf Coasts (up to 200km from the shoreline)</li> <li><strong>Time periods:</strong> Historic (1980-2014) and Future (2066-2100)</li> </ul> <p><strong>Methodology:</strong></p> <ul> <li><strong>Tropical cyclone simulation:</strong> The RAFT TC model is used to simulate storms for historical and future climates using CMIP6 environmental conditions.</li> <li><strong>TC impact metric:</strong> Wind speeds associated with 20- and 50-year return period TCs are used to estimate the aerodynamic and sea wave loading on OSW turbines.</li> <li><strong>Fragility functions:</strong> Wind speeds are input into a fragility function developed for OSW turbines, estimating the probability of yielding and buckling damage.</li> <li><strong>Damage probability maps:</strong> The results consist of eight (8) gridded damage probability maps representing the likelihoods of yielding and buckling to OSW turbines from 20- and 50-year TCs under historical and future climatic conditions.</li> </ul> <p><strong>Potential Uses:</strong></p> <ul> <li>Assessing the spatial vulnerability of OSW infrastructure to TCs</li> <li>Supporting decision-making for the design and siting of turbines</li> <li>Evaluating the impact of climate change on the risk of damage to OSW infrastructure</li> </ul> <p>For further insights into this dataset, users are encouraged to refer to the associated paper: <a href="https://www.nature.com/articles/s43247-024-01887-6">https://www.nature.com/articles/s43247-024-01887-6</a></p> <p>This dataset offers valuable insights into the potential impact of TCs on offshore wind infrastructure, aiding in risk assessment and resilience planning for the renewable energy sector.</p> <p> </p>
Datasets with weather forecasts (Temperature, Wind Direction, Humidity, Pressure, Wind Speed, GHI)
<p>Datasets with weather forecasts for HLU7 (Temperature, Wind Direction, Humidity, Pressure, Wind Speed, GHI) of the CROSSBOW project.</p>
Average monthly wind speed in Nile Basin
<p>JPEG and GIF versions.</p> <p>Source: <a href="http://atlas.nilebasin.org/treatise/wind-speed/">http://atlas.nilebasin.org/treatise/wind-speed/</a> [archived], superimposed on the map on <a href="http://atlas.nilebasin.org/treatise/nile-basin-water-resources-atlas/">http://atlas.nilebasin.org/treatise/nile-basin-water-resources-atlas/</a> [<a href="https://web.archive.org/web/20221014051215/http:/atlas.nilebasin.org/treatise/nile-basin-water-resources-atlas/">archived</a>].</p>
Diagrams of zonal mean temperature variations along 80º N and zonal wind speed along 60 N from 1979 to 2021
<p>Diagrams of zonal mean temperature variations along 80º N and zonal wind speed along 60 N at a height of 1 hPa and 10 hPa, x-axis - years, y-axis - days of the month.</p>
Datasets and Codesets for "Wavelet Decomposition and Neural Networks: A Potent Combination for Short Term Wind Speed and Power Forecasting"
<p>This is the datasets and codesets used in the paper:</p> <p>A. E. Kio, J. Xu, N. Gautam, and Y. Ding, 2024, “Wavelet decomposition and neural networks: A potent combination for short term wind speed and power forecasting,” Frontiers in Energy Research, section of Wind Energy, Vol. 12, pp. 1277464. </p> <p>The PDF file, "Reproducibility Report," explains how to reproduce the results in the tables and figures.</p>
Multisensor Advanced Climatology (MAC) Daily Ocean-Only Surface Wind Speed
<p>Multisensor Advanced Climatology (MAC) of Over-Ocean Surface Wind produced using the MAC-LWP algorithm (Elsaesser et al., 2017, J. Climate), but with Remote Sensing Systems surface wind products swapped in for liquid water path products, and with the product being provided at daily resolution instead of at monthly resolution. Available for 1 deg grid boxes, from 1998 - 2016. Data in grid-boxes over land are set to missing. Uncertainty estimates also provided, following Elsaesser et al. 2017. </p> <p>Reference:</p> <p>Elsaesser, G.S., C.W. O'Dell, M.D. Lebsock, R. Bennartz, and T.J. Greenwald, 2017: The Multi-Sensor Advanced Climatology of Liquid Water Path (MAC-LWP). J. Climate, <strong>30</strong>, no. 24, 10193-10210, doi:10.1175/JCLI-D-16-0902.1.</p>
McMurdo Dry Valleys Glacier melt modeling: Wind Speed 1996-2011
This is the data and metatada for modeled Wind Speed - part of six modeled parameters that comprise the Taylor Valley Galcier Melt modeling Data contained and described in this document correspond to the physically-based surface energy balance model for the glaciers of Taylor Valley developed by the dataset owners. The spatial variability in ablation (ice melt and sublimation), runoff, and climate sensitivity of the glaciers was modeled using 16 years of meteorological and surface mass balance (the net mass gain or loss of ice on the surface of the glacier) observations collected in Taylor Valley (see figure).  An unusual aspect of the model is the inclusion of transmission of solar radiation into the ice and subsequent drainage of some subsurface melt .  Melt model was applied to the ablation zones of the glaciers of Taylor Valley, identified by colored areas. Mass balance stakes, meteorological stations, and stream gages shown for reference. This dataset package is part of a 6-pack multi-set, which you can find at http://mcmlter.org The input files, parameters and examples are found in this package: http://mcmlter.org/content/glacier-melt-modeling-inputs-and-example-m-file-reader
PIE LTER, Year 2013-2018, remote sensing derived sediment concentration maps, movies, transect averaged sediment concentation, water level, dh/dt, wind direction and speed, river discharges at Plum Island Sound, Massachusetts.
PIE LTER, Year 2013-2018, remote sensing (Landsat8 OLI sensors and Sentinel-2A/2B) derived sediment concentration maps, transect averaged sediment concentation, water level, dh/dt, wind direction and speed, river discharges for Plum Island Sound estuary, Massachusetts.
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>
High resolution wind speed measurements with multicopters of the SWUF-3D UAS fleet - calibration and verification in a wind tunnel with active grid
<p>This dataset contains aggregated measurements from multicopter UAS. The data were measured during the period from October 5, 2022 to October 12, 2022 in the ForWind wind tunnel at the University of Oldenburg with UAS of the SWUF-3D fleet against Constant Temperature Anemometer (CTA). </p><p>Recorded data are provided for measurement flights in different generated wind profiles, i.e. staircase profiles, gusts, velocity steps and statistical turbulence. The measurement data consist of the accelerations measured by the UAS in its longitudinal and lateral axes, as well as the wind speeds measured by the CTA. The latter data were sampled down to the sampling rate of the UAS wind measurement. For the measurements in statistical turbulence, additional files are provided which contain the wind speeds measured by the CTA in its original sampling rate. Each file contains the data for a single measurement flight, as well as information in the header about the ambient conditions in the wind tunnel. The file names contain the following metadata:</p><p>for "gust" files:</p><ul><li>V0 : inertial velocity [m/s]</li><li>V_g : gust velocity amplitude [m/s]</li></ul><p>for "staircase" files:</p><ul><li>uas : the ID of the UAS used [-]</li><li>heading : yaw angle of UAS in relation to longitudinal axes of wind tunnel</li></ul><p>for "turbulence" files:</p><ul><li>V0 : fan wind speed [m/s]</li><li>I : turbulence intensity [%]</li><li>f_cta : sampling frequency of reference sensor [Hz] (for files with original sampling rate)</li></ul><p>for "velocity step" files:</p><ul><li>V0 : lower wind speed</li><li>V_du : wind speed aimed for of upward and downward velocity step</li></ul><p>All filenames end with the test date in YYYY-MM-DD format.</p>
Forest bat activity declines with increasing wind speed in the proximity of operating wind turbines
<p>The increasing use of onshore wind energy is leading to an increased deployment of wind turbines in structurally rich habitats such as forests. Forest-affiliated bats, in turn, are at risk of colliding with the rotor blades. Due to the legal protection of bats in Europe, it is imperative to restrict the operation of wind turbines to periods of low bat activity to avoid collisions. However, bats have also been observed to avoid wind turbines over several hundred meters distance, indicating a displacement that cannot solely be explained by modifications to the habitat. This avoidance suggests a displacement of bats by indirect factors related to wind turbine operation, e.g., wake turbulences and noise emissions. Therefore, we investigated whether the activity of forest-affiliated bats is influenced by operation mode (on/off) under variable wind conditions along transects from 80 to 450 m distance to wind turbines. We divided recordings by foraging guild, i.e., either narrow-space (<em>Myotis</em>, <em>Plecotus)</em>, edge-space (<em>Pipistrellus, Barbastella</em>), or open-space foraging bats (<em>Nyctalus, Eptesicus, Vespertilio</em>), and analyzed the effects of wind turbine operation and wind speed on the recorded bat guild activity with mixed effects models. The acoustic activity of narrow-space foraging bats decreased by 91% with increasing wind speed when wind turbines were operating, while bat activity remained unaffected by wind speed when turbines were not operating. This was neither observed for open-space foraging bats nor for edge-space foraging bats, and neither wind turbine operation nor wind speed (ranging between 0 – 4 m/s at 10 m height above ground) were found to affect bat activity when considered alone. Wind turbine noise emissions are known to increase with rotor speed and consequently, wind speed, thus presenting a likely explanation for the interactive negative effect of turbine operation and wind speed specifically on noise-sensitive narrow-space foraging bats. To understand potential ecological long-term consequences for bat populations in forest areas with wind turbines and to design effective conservation measures, future research should focus on disentangling the effects of different disturbances related to turbine operation.</p>
WindSightNet: Catalogue of wind speed and direction data from NASA InSight lander on Mars using seismic data
<p>Dataset associated with the publication "WindSightNet: the inter-annual variability of Martian winds retrieved from InSight's seismic data with machine learning" submitted to JGR: Planets.</p> <p>Authors:</p> <p>A. E. Stott, R. F. Garcia, N. Murdoch, D. Mimoun, M. Drilleau, C. Newman, A. Spiga, D. Banfield, M. Lemmon, S. Navarro, L. Mora-Sotomayor, C. Charalambous, W. T. Pike, P. Lognonné, W. B .Banerdt</p> <p>Files containing catalogue of winds produced from the seismic data on the NASA InSight mission using machine learning algorithm produced in above publication. Please refer to this publication for technical details.</p> <p> </p> <p>Contents:</p> <p>WindSightNet.csv - file containing wind speed and direction produced from the WindSightNet neural network based on seismic data</p> <p>TWINS.csv - comparitive wind speed and direction from TWINS wind sensor when available. </p> <p>TWINS data originally available from:</p> <p>J A Manfredi, Insight Auxiliary Payload Sensor Subsystem (APSS) Temperatures and Wind Sensor for Insight (TWINS) Archive Bundle, (2019), https://doi.org/10.17189/1518950</p> <p> </p> <p>Each file contains values for:</p> <p>Wind Speed</p> <p>Wind dir.</p> <p>Sol - number of sol of InSight mission </p> <p>UTC - Coordinated Universal Time of sample</p> <p>LTST - Local True Solar Time of sample</p> <p>L_s - Solar longitude value of sample</p> <p>Time - seconds since UNIX epoch</p> <p>Data is considered to be sampled at a rate of 0.01 Hz when there are no gaps.</p> <p> </p> <p>Example code for plotting paper figures can be found:</p> <p>https://doi.org/10.5281/zenodo.14267939</p>
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
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International Brain Laboratory public data
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OpenNeuro
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