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1,855 results for “winds”
Jornada Basin LTER wireless meteorological station at MNORT wind tower site: 5-minute summary data, 2006 - ongoing (provisional)
This dataset contains 5-minute summary data from the MNORT wind tower station. Average air temperature, wind speed and wind direction at multiple heights are measured and calculated based on 1-second scan rate of all sensors located at an automated meteorological station installed at Jornada LTER MNORT site (different than the M-NORT NPP site). Wind speed is measured at 135 cm, 230 cm, 345cm, 705cm, and 1515 cm, wind direction at 250cm and 850cm, and air temperature at 80cm and 1440cm. This climate station is operated by the Jornada LTER Program and this is an ongoing dataset. CAUTION: little to no QA/QC has been applied to this dataset and these data are therefore provisional.
Germination and early establishment of dryland grasses and shrubs on wind-eroded soils from the Jornada Basin LTER Scrape site, 2018-2019
In this dataset, we report germination and seedling growth of contrasting perennial grass (Bouteloua eriopoda, Sporobolus airoides, and Aristida purpurea) and shrub (Prosopis glandulosa, Atriplex canescens, and Larrea tridentata) functional groups grown on non-winnowed and winnowed soils collected from the Jornada Basin LTER Scrape Site at the Jornada Experimental Range (JER) in southern New Mexico, U.S.A. The soil physical and chemical properties of winnowed and non-winnowed soils were evaluated, and soil water retention curves were obtained for the two soil types. A controlled pot experiment was conducted under the well-watered greenhouse conditions at the University of Arizona campus, Tucson, AZ in 2018 and 2019 to test if topsoil "winnowing" by wind erosion would differentially affect grass and shrub seedling establishment to promote shrub recruitment over that of grass. Data include soil water retention curves, soil chemistry, nutrients, and texture, seedling germination, and seedling growth and biomass. This study is complete and the data are published in the article below. Niu, F., Pierce, N. A., Archer, S. R., & Okin, G. S. (2021). Germination and early establishment of dryland grasses and shrubs on intact and wind-eroded soils under greenhouse conditions. Plant and Soil, 1-16. DOI: 10.1007/s11104-021-05005-9
Year 2020, PIE LTER wind sensor data, 15 minute intervals, from the Ipswich Bay Yacht Club pier located in Ipswich, MA
Wind sensor measurements (wind speed and wind direction) for 2020 at the Ipswich Bay Yacht Club, Ipswich, MA, 15 minute average measurements.
Year 2021, PIE LTER wind sensor data, 15 minute intervals, from the Ipswich Bay Yacht Club pier located in Ipswich, MA
Wind sensor measurements (wind speed and wind direction) for 2021 at the Ipswich Bay Yacht Club, Ipswich, MA, 15 minute average measurements.
PIE LTER 15-minute Wind speed and direction in the lower Plum Island Sound at the Ipswich Bay Yacht Club pier, Ipswich, MA, year 2024.
Wind speed and direction measurements for 2024 at Ipswich Bay Yacht Club, Ipswich, MA. Wind speed is measured every 5 seconds and reported as an average in 15 minute intervals. Maximum wind speed is also reported for each 15 minute interval with a timestamp. Wind direction is measured every 15 minutes.
PIE LTER 15-minute Wind speed and direction in the lower Plum Island Sound at the Ipswich Bay Yacht Club pier, Ipswich, MA, year 2025.
Wind speed and direction measurements for 2025 at Ipswich Bay Yacht Club, Ipswich, MA. Wind speed is measured every 5 seconds and reported as an average in 15 minute intervals. Maximum wind speed is also reported for each 15 minute interval with a timestamp. Wind direction is measured every 15 minutes.
Wind turbine blade simulations under changing environment for benchmarking SHM algorithms
<p>This data set contains the flapwise vibration response simulation of a wind turbine blade under <em>Environmental and Operational Variability</em> (EOV) as well as increasing damage. The blade’s dynamics are represented by means of a 4 element FEM of a cantilever beam, while dynamic loading corresponds to a discretized turbulent wind field calculated with the help of the software <em>TurbSim</em> for prescribed 10-minute average wind speed and turbulence. Rotation effects are ignored. The wind loading is coupled with the structural dynamics considering aeroelastic interactions, based on lift and drag forces calculated from a NACA 64-618 airfoil. Ambient temperature (10-minute average) is used to set the elasticity (Young’s) modulus of the blade material.</p> <p>While on the healthy state, the vibration response of the blade is simulated over a year of temperature and wind speed variations according to the average values measured in an area of north-central Switzerland. In addition, a week of extreme weather (abnormally high temperature in summer) and a month where the blade is subject to increasing damage are also simulated. Damage is represented as a decrement of the stiffness on a single FEM element located on the blade’s root. Damage increments linearly from 0 to 25% decrease of the total stiffness during a period of two weeks, while on the remaining two weeks a 25% stiffness decrement is sustained.</p> <p>The main aim of this data set is to be used as a benchmark of vibration based SHM methods, particularly on damage detection and localization under EOV. To this end, both the blade’s vibration response and the environmental and operational parameters (temperature and wind) used to simulate each response are provided. Further details can be found in the publication attached.</p>
Multi-thousand-year simulations of December-February precipitation and zonal upper-level wind
<p>This dataset contains multi-thousand-year ensemble simulations of wintertime (December-February) precipitation total and average zonal winds at 250 hPa and 850 hPa. It includes data in a present-day scenario (2006-2015) and two future scenarios within which the world would be 1.5°C and 2.0°C warmer than pre-industrial conditions in 1850-1900. The simulations were run through the global model of the atmosphere and land surface HadAM4 (Williams et al., 2003) with a horizontal resolution of 5/6°x5/9° (approximately 60km in middle latitudes) and 38 vertical levels and a large ensemble. Following the HAPPI experiment design described by Mitchell et al. (2017), simulations were driven by prescribed fields of sea ice concentration, sea surface temperature, and atmospheric gas concentrations. The prescribed fields are observations for the present-day scenario. For future simulations, the prescribed fields were modified based on changes derived from CMIP5 multi-model means. The different realisations of the large ensemble were obtained through perturbing the initial conditions of each ensemble member on November 1st. For more details, see the description in Watson et al. (2020), who present the dataset, and in Bevacqua et al. (2021) where the dataset was used to study the spatial footprint of wintertime precipitation extremes.</p> <p><strong>IMPORTANT</strong>: Note that a small fraction of the ensemble members is repeated in the dataset. Duplicates should be identified (for example, via the function duplicated() in the R software) and removed prior to any analysis. </p>
Wind field and cloud layer data for Stuttgart, Germany
<p>This dataset contains computed wind field, cloud base and mixing layer height time series for locations in the City Centre of Stuttgart, Germany. The observations were made using a network of Doppler lidar and ceilometer instrumentation. The measurements were conducted within the framework of the Urban Climate under Change [UC]2 program. </p> <p>Up-to-date information about the program can be found at: <a href="http://uc2-program.org">http://uc2-program.org</a></p> <p>Versions:</p> <ul> <li>1.x.y: Data are in compliance with the [UC]2 Data Standard (<a href="http://uc2-program.org/uc2_data_standard.pdf">http://uc2-program.org/uc2_data_standard.pdf</a>). Included are computed variables for days during the two Intensive Observation Periods (IOPs) in winter and summer of 2017.</li> <li>2.x.y: Data include computed variables for most of the available observations during 2017. The data stores conform to an approach developed for ScaleX.</li> </ul>
Fiber-optic Distributed Temperature Sensing and Wind Profiler Data during the Shallow Cold Pool Experiment
<p>The <a href="https://www.eol.ucar.edu/field_projects/scp">Shallow Cold Pool (SCP) experiment</a> was an <a href="https://www.eol.ucar.edu/observing_facilities/isfs">Integrated Surface Flux System (ISFS)</a> deployment conducted by the <a href="https://ncar.ucar.edu/">National Center for Atmospheric Research (NCAR)</a>, the <a href="https://ceoas.oregonstate.edu/">College of Earth, Ocean and Atmospheres (CEOAS)</a>, the <a href="https://bee.oregonstate.edu/">Department of Biological & Ecological Engineering (BEE)</a>, and the <a href="https://ctemps.org/">Center for Transformative Environmental Monitoring Programs (CTEMPS)</a> of <a href="https://oregonstate.edu/">Oregon State University</a>, in a shallow gully within the Pawnee Grasslands, Coloradp, USA. The primary goal of SCP was to examine the formation and maintenance of common shallow cold pools. These cold pools had not been previously examined with turbulence measurements and very little was known about their dynamics and interaction with gravity waves and other submesoscale motions.</p> <p>SCP consisted of a dense network of ultrasonic anemometers with 19 units being installed at 1m above ground level (agl) and 8 being mounted at different heights on a 20m high tower. In addition, air temperature, humidity, and carbon dioxide concentrations measurements were taken. This data can be found on <a href="https://data.eol.ucar.edu/project/SCP">https://data.eol.ucar.edu/project/SCP</a>.</p> <p>The unique observational technique featured in SCP was a cross-valley transect of the innovative active and passive fiber-optic distributed sensing technique (FODS) using a Distributed Temperature Sensing (DTS) unit (Model Ultima SR, Silixa, London, UK) as well as a ground-based acoustic wind profiler (SODAR, PCS2000-24, Metek GmbH, Elmshorn, Germany) in addition to the classical sonic anemometer network. The data archived in this submission publishes the FODS data and contains data for nine (9) nights between 16th November until 27th November between the hours of 19:00 and 05:00 MST (Local time). Details of the FODS setup are contained in <a href="https://rmets.onlinelibrary.wiley.com/doi/abs/10.1002/qj.3508?af=R">Pfister et al. (2019)</a> and <a href="https://agupubs.onlinelibrary.wiley.com/doi/full/10.1002/2015GL066729">Sayde et al. (2015</a>).<br> The fiber-optic cross-valley transect was 240m long and stretched from the North to the South shoulder of the gully and contained FODS observations at three heights (0.5m, 1m, 2m agl). By combining passive and active FODS, air temperatures and wind speeds were measured spatially continuously with a temporal and spatial resolution of 5s and 25cm, respectively. Air temperatures were measured with an unheated white-PVC jacketed optical glass fiber cable with an outer diameter of 0.9mm, while for the wind speed measurements an additional actively heated stainless-steel uncoated fiber-optic cable (1.3mm outer diameter) was deployed. Wind speeds were derived from the difference between the heated and unheated fiber-optic pair similar to a hotwire anemometer (<a href="https://agupubs.onlinelibrary.wiley.com/doi/full/10.1002/2015GL066729">Sayde et al. 2015</a>).<br> The acoustic wind profiler (Sound Detection and Ranging, SODAR) was installed at the gully bottom about 200m down the gully from the fiber-optic transect (between station A18 and A19) and measured with a 5-min resolution, a 10-m gate range, and 17000 Hz, see map in <a href="https://rmets.onlinelibrary.wiley.com/doi/abs/10.1002/qj.3508?af=R">Pfister et al. (2019)</a>. The observational range was between 10m to 320m agl. The data provided is the cluster data output of the wind profiler, which is quality-controlled by the internal data processing software. The published data include horizontal wind speed (speed), wind direction (direction), unrotated along-wind component (u_unrot), unrotated cross-wind component (v_unrot), and unrotated vertical-wind component (w_unrot).</p> <p>By combining the fiber-optic distributed sensing, the sensor network, and the wind profiler, we were able to investigate specific class of submeso-scale motions in detail. The submeso-scale motion occurred frequently during SCP, significantly impacted air temperature, wind speed and direction, as well as the near-surface turbulence within less than a few minutes. These motions are not described or categorized by existing boundary layer regimes or concepts. Consequently, further research on submeso-scale motions using continuous FODS measurements is necessary to better understand the stable boundary layer.</p> <p> </p> <p>Pfister, L., Sayde, C., Selker, J., Mahrt, L., & Thomas, C. K. (2019). Classifying the Nocturnal Atmospheric Boundary Layer into Temperature and Flow Regimes. <em>Quart. J. Roy. Meteorol. Soc.</em>, <em>145</em>(721), 1515–1534. <a href="https://doi.org/10.1002/qj.3508">https://doi.org/10.1002/qj.3508</a></p> <p> </p> <p>Sayde, C., Thomas, C. K., Wagner, J., & Selker, J. S. (2015). High-resolution wind speed measurements using actively heated fiber optics. <em>Geophys. Res. Lett.</em>, <em>42</em>(22), 10,064–10,073. <a href="https://doi.org/10.1002/2015GL066729">https://doi.org/10.1002/2015GL066729</a></p>
A global atlas of extreme wind speeds for wind energy applications
<p>Here we present a global, homogenized and geospatially explicit digital atlas of the sustained fifty-year return period wind speed (<em>U<sub>50</sub></em>)<em><sub> </sub></em>and associated confidence intervals based on ERA5 reanalysis output at 100 m a.g.l.. Four different approaches are used to derive <em>U<sub>50</sub></em> estimates using 40 years of hourly disjunct 20-minute sustained wind speeds. All rely on use of the Gumbel distribution to fit extreme wind speeds but differ in how the distribution parameters are derived. Resulting values of <em>U<sub>50</sub></em> are compared to reference wind speeds <em>U<sub>ref</sub></em> derived using five times the mean wind speed as specified in the International Electrotechnical Commission (IEC) wind turbine design standards. An observationally derived dataset used in evaluation of the atlas is also included, along with a MATLAB script used in deriving the <em>U<sub>50</sub></em> estimates.</p> <p>Associated publication is: Pryor S.C. and Barthelmie R.J. (2021): A global assessment of extreme wind speeds for wind energy applications. <em>Nature Energy</em> DOI: 10.1038/s41560-020-00773-7</p>
Oscillations of Offshore Wind Turbines undergoing Installation I: Raw Measurements
<p><strong>Overview</strong></p> <p>This repository contains data from an offshore measurement campaign conducted during the installation of the offshore wind farm trianel wind farm borkum II (https://www.trianel-borkumzwei.de/). The wind farm consists of 32 Senvion 6XM152 turbines. The installation took place between August 2019 and May 2020.</p> <p>An offshore wind turbine undergoing installation is interesting from a research point of view for several reasons:</p> <ul> <li>Simple geometry: turbine foundation and tower are both rotationally symmetric steel tubes. Rotational symmetry also leads to (approximate) isotropical structural characteristics in the plane normal to tower and foundation.</li> <li>High Reynolds number flow: Assuming a tower diameter of 6 m, and average wind speeds ranging from 5 m/s to 12 m/s under installation conditions, Reynolds numbers range from 4.5 million to 10.5 million.</li> <li>Wave loading under full-scale conditions.</li> <li>Practical relevance to improving the competetivity of offshore wind.</li> </ul> <p>For fluid mechanics, closely monitoring offshore wind turbines under wind and wave loading thus compares to a full-scale experiment. Monitoring 32 turbines undergoing installation thus enables the measurement a broad spectrum of different states.</p> <p>The investigation into the data is ongoing, questions and contributions are welcome. The current data release still does not include all data. The dataset will thus be updated again in the future with more data to come. First analytic results can be found here:</p> <p>Sander, A, Haselsteiner, AF, Barat, K, Janssen, M, Oelker, S, Ohlendorf, J, & Thoben, K. "Relative Motion During Single Blade Installation: Measurements From the North Sea." Proceedings of the ASME 2020 39th International Conference on Ocean, Offshore and Arctic Engineering. Volume 9: Ocean Renewable Energy. Virtual, Online. August 3–7, 2020. V009T09A069. ASME. <https://doi.org/10.1115/OMAE2020-18935></p> <p>Sander, A, Meinhardt, C & Thoben, KD. "Monitoring of Offshore Wind Turbines under Wind and Wave Loading during Installation" Proceedings of the EuroDyn 2020 XI International Conference on Structural Dynamics. Volume 1. Virtual, Online. November 23-26, 2020. <https://generalconferencefiles.s3-eu-west-1.amazonaws.com/eurodyn_2020_ebook_procedings_vol1.pdf></p> <p>Recordings of the conference presentations are available on youtube:</p> <ul> <li>OMAE20: https://www.youtube.com/watch?v=QcAwdv6Z4e4</li> <li>EURODYN20: https://www.youtube.com/watch?v=iL-jAe0luTw</li> </ul> <p><strong>Physical Background</strong></p> <ol> <li>An offshore wind turbine under installation conditions can be simplified as a cantilevered beam (circular cross-section, rotationally symmetric wall thickness) with an eccentric mass (nacelle with generator) vibrating transversally (fore-aft and side-side in the reference system of the nacelle) under wind and wave loads.</li> <li>Both wind and wave loads are stochastic and are described using statistical models.</li> <li>Wave loads are a function of the sea state. For a sea state, the most important parameters are significant wave heigh H_m0 and Wave peak period T_P. To a lesser extend, wave direction, zero upcrossing period and maximum wave height are also important. Different statistical models can be used to describe the sea state and the relationship between significant wave heigh H_m0 and wave peak period. Most prominent in the North Sea is the JONSWAP spectrum.</li> <li>Wind loads are depending on wind speed, wind direction, shear factor and turbulence intensity. Different statistical models are available to describe the wind spectrum.</li> <li>Wind and wave loads trigger a structural response of the turbine. The structural response depends on the loading spectrum as well as the transfer function. Furthermore, the structural response is strongly depending on the damping and elasticity of the structure. In turbines, damping is typically very low (~ 0.5 - 1.5 %).</li> <li>The response is dominated by the first Eigenfrequency of the turbine. As the turbine is assumed to be rotationally symmetric, the fore-aft and side-side mode are extremely close together if not indistinguishable [1].</li> <li>The response has the characteristics of a narrow-band random vibration. A narrow-band random vibration is characterized by being dominated by a single, narrow frequency peak (here: first Eigenfrequency). The amplitude envelope follows a Rayleigh distribution and the phase angle is equally distributed between 0 and 2 pi.</li> <li>If viewed from above, the structural response describes a closed curve (orbit) which can be characterized by it shape (eccentricity), mean amplitude and direction. Mathematically speaking, this is a lissajous-figure, where the time series from one response direction is plotted as a function of the time series of the second response direction.</li> </ol> <p>[1]: under installation condition</p> <p><strong>Experimental Setup</strong></p> <p>Several locations were used to record data during the installation of the wind farm. They are listed in the following table:</p> <ul> <li>helihoist-{1,2}: data recorded from the helicopter hoisting platform atop the turbine nacelle. For most installations, two sensor boxes were deployed to ensure data availability.</li> <li>tp: Measurements from the transition piece</li> <li>sbitroot: Measurements from the blade lifting yoke's blade root side. The Z-axis is aligned to the blade main axis, X-Axis is perpendicular.</li> <li>sbittip: Measurements from the tip side of the blade lifting yoke. Z-axis aligned with the blade main axis, X-axis perpendicular</li> <li>damper: Measurements from the tuned mass damper used during single blade installation</li> <li>towertop: measurements from inside the turbine tower at the upper lift plattform</li> <li>towertransfer: measurements from atop the towers during sail out from the base harbour to the installation site </li> </ul> <p><strong>Organization of data</strong></p> <p>For each turbine installation, a separate folder can be found, e.g. turbine-01 for the first and turbine-16 for the 16th turbine. Turbine numbering follows the order of installation.</p> <p>Different data sources are organized in subfolders for each turbine dataset. Unfortunately, not every data source is available for each turbine. Data sources are roughly sorted into categories. The following table lists these categories:</p> <ul> <li>location / tom : data from custom build sensor boxes. Data includes acceleration, angular acceleration, magnetic field, gnss recording and rough estimates of the eulerian angles.</li> <li>waves / wmb-sued : Sea state statistics for the installatin period of the turbine.</li> <li>waves / fino : Sea state statistics from the german research platform FINO1 located approx. 6 km from the installation site.</li> <li>waves / waveradar : Sea state statstics, recorded by a wave rider wave laser. </li> <li>wind / lidar : high fidelity wind data recorded on the installation vessel during the installation of the wind farm.</li> <li>wind / scada : 10 min. mean wind statistisc recorded on wind turbines in the vicinity of the installation site. This data is used in case no LIDAR data is available.</li> <li>wind / anemometer : During some of the installations, anemometers were present on the installation vessel. These recordings are sorted into this sub-subfolder.</li> <li>wind / fino : Additonal wind statistics recorded by the FINO research station. Least recommended for investigations, as these recordings were taken approx. 6 km from the installation site.</li> </ul> <p>The zenodo data set includes 16 zip archives (for 16 turbines) as well as one zip archive including environmental data. The following lists the folder structure of the turbine-04.zip archive (with most of the data files removed for clarity).</p> <pre><code class="language-bash">└── turbines ├── turbine-04 │ ├── helihoist-1 │ │ └── tom │ │ └── clean │ │ ├── turbine-04_helihoist-1_tom_clean_2019-09-01-11-27-17_2019-09-01-11-54-00.csv │ │ ├── turbine-04_helihoist-1_tom_clean_2019-09-01-11-54-00_2019-09-01-12-20-44.csv │ ├── sbitroot │ │ └── tom │ │ └── clean │ │ ├── turbine-04_sbitroot_tom_clean_2019-09-07-06-48-53_2019-09-07-07-17-16.csv │ │ ├── turbine-04_sbitroot_tom_clean_2019-09-07-07-17-16_2019-09-07-07-45-59.csv │ ├── towertop │ │ └── tom │ │ └── clean │ │ ├── turbine-04_towertop_tom_clean_2000-01-06-18-55-52_2000-01-06-20-30-10.csv │ │ ├── turbine-04_towertop_tom_clean_2000-01-06-20-30-11_2000-01-06-22-04-31.csv │ ├── towertransfer │ │ └── tom │ │ └── clean │ │ ├── turbine-04_towertransfer_tom_clean_2019-08-31-03-11-53_2019-08-31-04-00-09.csv │ │ ├── turbine-04_towertransfer_tom_clean_2019-08-31-04-00-10_2019-08-31-04-48-19.csv │ └── tp │ └── tom │ └── clean │ ├── turbine-04_tp_tom_clean_2019-08-31-18-34-45_2019-08-31-19-07-52.csv │ ├── turbine-04_tp_tom_clean_2019-08-31-19-08-00_2019-08-31-19-40-43.csv </code></pre> <p>The following list the contents of the environment.zip archive. Note that again most of the data files have been removed for clarity. </p> <pre><code class="language-bash">└── environment ├── waves │ └── wmb-sued │ ├── wmb-sued_2019-08-15.csv │ ├── wmb-sued_2019-08-16.csv │ ├── wmb-sued_2019-08-17.csv └── wind └── lidar ├── lidar_2019-08-03.csv ├── lidar_2019-08-04.csv ├── lidar_2019-08-05.csv </code></pre> <p><strong>TOM data description</strong></p> <p>The abbreviation <strong>TOM</strong> referes to <em>Tower Oscillation Measurement</em> and the data that was acquired using a specific set of sensor boxes built by university of Bremen for this specific purpose. These Sensor Boxes were initially designed to measure accelerations and GPS tracks of offshore wind turbine towers undergoing installation. During the installation of the wind farm Trianel Windpark Borkum II they were subsequentially used to track the complete installation with a focus on single blade installation</p> <p>Data from the TOM devices comes as CSV files. The firmware of the boxes was designed, such that data was written into 10 MB sized txt files instead of one large txt file in order to circumvent data corruption due to power loss. However, this leads to a few milliseconds of missing data between log files. Additionally, jitter due to IO-Operations is present in the data as well and data should under all circumstance be resampled befor further analysis.</p> <p>Parameters provided by the TOM devices are listed in the following:</p> <ul> <li>epoch : machine readable time stamp based on the unix epoch in UTC [s] </li> <li>runtime : time since last boot of the tom device [ms] </li> <li>latitude : Degrees latitude in decimal writing. For Trianel: [degree due North] </li> <li>longitude : Degrees longitude in decimal writing. For Trianel: [degree due East] </li> <li>elevation : elevation above mean sea level [m] </li> <li>rot_{x,y,z} : Rotational acceleration around the three cartesian axis {x,y,z} of the TOM box. [degree / s^2] </li> <li>acc_{x,y,z} : linear acceleration in each of the three cartesian axis {x,y,z} in the reference system of the TOM box [m/s^2] </li> <li>mag_{x,y,z} : magnetic field strength in each of the local cartesian TOM box axis {x,y,z} [micro-Tesla] </li> <li>roll : eulerian roll angle (angle around the x Axis of the tom box) [degree] </li> <li>pitch : eulerian pitch angle (angle around the y Axis of the tom box) [degree] </li> <li>yaw : eulerian yaw angle (angle around the z Axis of the tom box) [degree] </li> </ul> <p><strong>LIDAR wind data description</strong></p> <p>A Leosphere WindCube LIDAR was mounted on the installation vessel <em>Taillevent</em> to record the atmospheric boundary layer during installation. The data recorded by the lidar was exported as csvs and provided by the vessel operator. The raw data includes the following parameters</p> <ul> <li>epoch : time stamp as a unix epoch in UTC [s]</li> <li>wind_speed_N : The wind speed at the N'th return level [m/s]</li> <li>wind_dir_N : Wind direction at the N'th return level in the vessels reference frame [degree]</li> <li>wind_dir_N_corr : Wind direction at the N'th return level due North [degree due North]</li> <li>heigh_N : The height of the lidar return level [m] </li> </ul> <p>Data is resampled to a 1 s return interval. </p> <p><strong>wave data description</strong></p> <p>Wave data was recorded using a waverider wave buoy (DWR-G). The wave rider buoy was located at 54 00' 238'' degree North and 6 26' 553'' degree East. In decimals: 54.0031096 North, 6.4425532 East. Based on the raw data, sea state statistics were derived with a return period of 30 minutes.</p> <p>The data comes as csv, column oriented text files. The first line entails parameter names and units and is commented out by a hashbang (unix commentary). The data is a combination of two different data files as provided by the buoy: \*.HIS Data Text File (History of Spectrum parameters) and \*.HIW Data Text File (History of Wave Statistics). This results in the two timestamps in the data file because history of wave statistics data is available immediately after each measurement time period, whereas history of spectrum parameters take approx. 5 minutes to calculate by the buoy and thus have a slightly later time stamp. For actual postprocessing purposes, a third timestamp rounded to full half hour is used. Parameters included in the data files are:</p> <ul> <li>epoch: number of seconds since 1st of January 1970 in UTC. Common time stamp format in computing. [s]</li> <li>Tp Tp := 1 / fp, peak period, the frequency at which S(f) is maximal [s]</li> <li> Dirp peak direction, the direction at f = fp [deg due North]</li> <li> Sprp peak spread, the directional spread at f = fp [s]</li> <li> Tz Tz := sqrt(m0 / m2), zero-upcross period [s]</li> <li> Hm0 Hm0 := 4*sqrt(m0), the significant waveheight [m]</li> <li> TI TI := sqrt(m[-2] / m0), integral period [s]</li> <li> T1 T1 := m0 / m1, mean period [s]</li> <li> Tc Tc := sqrt(m2 / m4), crest period [s]</li> <li> Tdw2 Tdw2 := sqrt(m[-1] / m1) [s]</li> <li> Tdw1 Tdw1 := sqrt(m[-1,2] / m0) [s]</li> <li> Tpc Tpc := m[-2] * m1 / m0 ^ 2, calculated peak period [s]</li> <li> nu nu := sqrt((T1 / Tz) ^ 2 - 1), band width parameter [-]</li> <li> eps eps := sqrt(1 - (Tc / Tz) ^ 2), bandwidth parameter [-]</li> <li> QP QP := 2 * m[1,2] / m0 ^ 2, Goda's peakedness parameter [-]</li> <li> Ss Ss :=2 * pi / g * Hs / Tz ^ 2, significant steepness [-]</li> <li> TRef TRef, reference temperature (25deg) [C]</li> <li> TSea TSea, sea surface temperature [C]</li> <li> Bat Bat, battery status (0..7) </li> <li> m[n] m[n] := Integral from f=0 to f=Inf over S(f) * f ^ n </li> <li> m[n,2] m[n,2] := Integral from f=0 to f=Inf over S(f) ^ 2 * f ^ n </li> <li> Percentage Percentage of data with no reception errors [%] </li> <li> Hmax Height of the highest wave [cm] </li> <li> Tmax Period of the highest wave) [s] </li> <li> H(1/10) Average height of 10% highest waves [cm] </li> <li> T(1/10) Average period of 10% highest waves [s] </li> <li> H(1/3) Average height of 33% highest waves [cm] </li> <li> T(1/3) Average period of 33% highest waves [s] </li> <li> Hav Average height of all waves [cm] </li> <li> Tav Average period of all waves) [s] </li> <li> Eps bandwidth parameter </li> <li> #Waves Number of waves</li> </ul> <p>Note: the m's are moments of the power spectral density S(f).</p> <p> </p> <p> </p>
Quantifying wind-driven dispersal of zooplankton in a Mediterranean pond
<p>Dispersal is an essential component in the life history of organisms and has strong ecological implications. Although it has been assumed that small organisms have very high dispersal rates, quantitative data supporting this claim remains scarce. In the context of zooplankton, wind stands out as a primary vector facilitating passive dispersal. We quantified short-distance wind-driven dispersal of propagules across various zooplankton taxa in a Spanish Mediterranean coastal temporary pond. We have also studied dispersal patterns in relation to the wind regime (intensity, steadiness and gust direction), source pond status (volume of water) and demographic dynamics (the abundance of individuals in water column populations). Further, we related propagules size merurements (surface, L2 and volume, L3) with dispersal distances. Additionally, we have performed measurements of the dispersed propagules and investigated the relationship between their dimensions and the dispersive distance on a local scale. Here, we present the raw data gathered during research.</p>
Dataset from: A quiet public? Procedural justice in Portuguese wind energy governance
<p>This dataset accompanies a journal article related with public participation in wind and solar energy in Portugal. It contains a database of web scraped public consultation processes related with wind power plants and decentralized solar power plants until 2023. It also contains the R Markdown files that were used to analyze the scraped data. The results of this analyzes, and their discussion, can be found in the associated article.</p>
Data set for paper "Ramparts around lakes on Titan impact winds and methane evaporation"
<p>Data and post-processing code used for the paper "Ramparts around lakes on Titan impact winds and methane evaporation", submitted to PSJ in 2024.</p> <p>Are made available:<br> - a list of the simulations (list_simulations_ramparts2D.pdf)<br> - the simulations' netCDF outputs (run-t##.nc.gz)<br> - the input files used to run the simulations (in input_files/)<br> - the post-processing python codes used to plot the figures (in post_processing_codes/)<br> - tables of latent heat flux and horizontal wind values (Tables_LH_and_Uwind.pdf)<br> - a gif of the horizontal wind in the reference run (u_wind_run-t04_speed.gif)<br> - a gif of the vertical wind in the reference run (w_wind_run-t04_speed.gif)</p>
Potential vorticity and wind from ERA5 at several isentropic surfaces
<p>This datasets collects winds (u and v components) and potential vorticity from ERA5 at four isentropic surfaces: 475, 600, 700 and 800 K. Data are available daily and monthly. Potential vorticity and modified potential vorticity are stored. </p>
Dataset for the paper "Aircraft wake vortices affecting airport wind measurements"
<p>Dataset in support of the paper "Aircraft wake vortices affecting airport wind measurements". The dataset contains the results of the manual classification as discussed in section 2 and 3 of the paper, details can be found there.</p><p>For each take-off, one row exists in the dataset. The columns are:</p><ul><li><i>takeoff_no</i>: int, Incrementing integer</li><li><i>timestamp</i>: string, UTC time the flight passes by the anemometer</li><li><i>flight_id</i>: string, Unique identifier for the flight</li><li><i>typecode</i>: string, ICAO aircraft typecode of the flight</li><li><i>wtc</i>: string: ICAO wake turbulence category of the flight</li><li><i>groundspeed_kts</i>: float, Groundspeed [kts] at the moment of passing by the anemometer</li><li><i>alt_above_thr_m</i>: float, Altitude above runway threshold [m] at the moment of passing by the anemometer</li><li><i>wind_speed_kts</i>: float, Wind speed [kts]. Computed as a mean of the sensor values for a 2min window ending at the crossing timestamp</li><li><i>wind_dir_deg</i>: float, Wind direction [°]. Computed as a mean of the sensor values for a 2min window ending at the crossing timestamp</li><li><i>is_event_visual_assessor_1</i>: int, Classification of assessor 1 of wheather the flight caused a wake that hit the anemometer</li><li><i>is_event_visual_assessor_2</i>: int, Classification of assessor 2 of wheather the flight caused a wake that hit the anemometer</li><li><i>is_event_visual_assessor_3</i>: int, Classification of assessor 3 of wheather the flight caused a wake that hit the anemometer</li><li><i>is_event_visual_sum</i>: int, Sum of classifications of 3 assessors (0 to 3)</li><li><i>is_event_wake_model</i>: float, Classification of wheather the flight caused a wake that hit the anemometer based on P2P wake model output (only applied to flights with a sum of classifications of 2 and more)</li><li><i>is_event</i>: int, Final classification of wheather the flight caused a wake that hit the anemometer</li></ul><p> </p>
Data accompanying the manuscript "Assessing the Probability of Extremely Low Wind Energy Production in Europe at Sub-seasonal to Seasonal Time Scales"
<p>This dataset contains time series of wind energy production aggregated over France and Europe, obtained from a 1000-year climate simulation from the CESM model (version 1.2.2, Hurrel et al. 2013), coupled to a simple energy model to compute grid-point capacity factor from surface wind. Wind power is then computed by multiplying the capacity factor by the installed capacity, taken from 5 e-Highway scenarios (X5, X7, X10, X13 and X16), and integrated over the regions of interest. More details about the climate simulation, wind energy model and installed capacity scenarios can be found in the associated manuscript, "Assessing the Probability of Extremely Low Wind Energy Production in Europe at Sub-seasonal to Seasonal Time Scales" (Cozian et al. 2023).</p><p>The data is organized into 10 files for France and 10 files for Europe. In each case, the 10 files correspond to 10 batches of 100 years each, with 3-hourly output. Each file contains 5 time series corresponding to the 5 installed capacity scenarios.</p><h4>References</h4><ul><li>Hurrell J W, Holland M M, Gent P R, Ghan S, Kay J E, Kushner P J, Lamarque J F, Large W G, Lawrence D, Lindsay K, Lipscomb W H, Long M C, Mahowald N, Marsh D R, Neale R B, Rasch P, Vavrus S, Vertenstein M, Bader D, Collins W D, Hack J J, Kiehl J and Marshall S (2013). The community earth system model: A framework for collaborative research. Bulletin of the American Meteorological Society, 94, 1339–1360. <a href="https://doi.org/10.1175/BAMS-D-12-00121.1">https://doi.org/10.1175/BAMS-D-12-00121.1</a></li><li>e-Highway 2050 (2015). Europe's future secure and sustainable electricity infrastructure. <a href="https://docs.entsoe.eu/baltic-conf/bites/www.e-highway2050.eu/results">https://docs.entsoe.eu/baltic-conf/bites/www.e-highway2050.eu/results</a></li><li>Cozian B, Herbert C and Bouchet F (2023). Assessing the Probability of Extremely Low Wind Energy Production in Europe at Sub-seasonal to Seasonal Time Scales. <a href="https://doi.org/10.48550/arXiv.2311.13526">https://doi.org/10.48550/arXiv.2311.13526</a></li></ul>
Melting glass fibres recovered from wind turbine blades into new glass fibres for wind turbine blades: Dataset
<p><strong><span>Melting glass fibres recovered from wind turbine blades into new glass fibres for wind turbine blades: Dataset.</span></strong></p> <p><span>In the study titled “Melting glass fibres recovered from wind turbine blades into new glass fibres for wind turbine blades”, four different types of glass fibres were manufactured with varying fractions of recycled fibre powder, 0 wt%, 1.64 wt%, 1.90 wt% or 1.96 wt%. Furthermore, these glass fibre types were used to manufacture composite specimens and characterised by static tension tests in fibre and transverse directions. </span></p> <p><span>This dataset is a collection of 13 Excel files.</span></p> <p><span>The “Glass fibre properties and Weibull analysis” Excel file summarise the single fibre tensile testing of glass fibre types and strength analysis using unimodal 2-parameter Weibull theory. There are four sheets in the Excel files for 0 wt%, 1.64 wt%, 1.90 wt% or 1.96 wt% glass fibres. </span></p> <p><span>The Excel files “Single glass fibres-Stress-strain curves-0 %, 1.64 %, 1.90 %, 1.96 %” contains the raw data of individual fibres obtained from the single fibre tensile testing experiments.</span></p> <p><span>There are eight Excel files containing the raw data of static tensile tests in the fibre and transverse direction of the composites made with glass fibres were manufactured with varying fractions of recycled fibre powder, 0 wt%, 1.64 wt%, 1.90 wt% or 1.96 wt%.</span></p>
Salinity, Turbidity, Wind from the S1-GB pylon at the LTER site Delta del Po and Costa Romagnola (2012-2021)
<p>The present database comprises observations spanning from 2012 to 2021, focusing on abiotic parameters collected from the S1-GB dynamic pylon, in the Northern Adriatic Sea (around 7 miles offshore within the Po Delta on a bottom depth of 22.5 m), Italy. Specifically, it encompasses measurements on atmospheric parameters above the water surface and measurements at a defined depth (https://vocab.nerc.ac.uk/collection/P01/current/ADEPZZ01/) of salinity (URI: https://vocab.nerc.ac.uk/collection/OD1/current/SAL/) in PSU (Practical Salinity Units), turbidity (URI: http://vocab.nerc.ac.uk/collection/P25/current/TURB/) in NTU (Nephelometric Turbidity Units; http://vocab.nerc.ac.uk/collection/P06/current/USTU/), wind speed (URI: http://vocab.nerc.ac.uk/collection/P25/current/WINDS/) in m/s (meters per second; http://vocab.nerc.ac.uk/collection/P06/current/PMPS/), and wind from direction (URI: http://vocab.nerc.ac.uk/standard_name/wind_from_direction/) in degrees (angular degrees, 0 represents the true north; http://vocab.nerc.ac.uk/collection/P06/current/UAAA/). The S1-GB pylon is situated at 44,74° N; 12,45° E (WGS-84 coordinate system) and is managed by the Institute of Marine Science of the National Research Council (ISMAR-CNR) in Bologna. The dataset relies on a Comma Separated Values (CSV) file and it is composed by 82391 records offering an invaluable insight into the dynamic characteristics of the marine environment over nearly a decade. The S1-GB pylon is part of the site “Delta del Po and Costa Romagnola”, which belongs to the Long Term Ecological Research national and international networks (LTER-Italy, LTER-Europe and ILTER) and eLTER-RI. The site contributes also to the DANUBIUS and JERICO Research Infrastructures.</p>
ScienceDex guides
Understand access before you commit
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.