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607 results for “wind data”
Wind speed and direction data from benchmark stations at the HJ Andrews Experimental Forest, 1973 to present
A three-level hydro-climatological network for data monitoring was established in 1994. The networks at each level are nested to form a coordinated program of data acquisition and measurement. A future vision of linking the benchmark meteorological stations with regional weather stations to expand the future scope of studies was also considered in designing this network. The first-level in this top-down approach consists of Benchmark Meteorological Stations (BMS) and Benchmark Stream Stations. The BMS are designed to represent the environment across the Andrews. These stations are intended to provide complete, long-term, high temporal resolution, meso-scale hydroclimatological data. The location of the BMS network is based on factors such as elevation, aspect, vegetation gradients, and accessibility. Collected meteorological parameters are generally standardized across the BMS as well as methods and instrumentation. Secondary Meteorological Stations also follow standardized methods and serve similar purposes but are somewhat limited in meteorological parameters collected. The Primary Meteorological Station (PRIMET), Central Meteorological Station (CENMET), Upper Lookout Meteorological Station (UPLMET), and Vanilla Leaf Meteorological Station (VANMET) are the four Benchmark Stations, Climatic Station at Watershed 2 (CS2MET) and the Hi-15 Meteorological Station (H15MET) are Secondary Stations. These wind parameters were previously part of database code MS001, but were separated out into their own database in 2024.
High-frequency dissolved oxygen, water temperature, wind speed, and radiation data; stream and in-lake nutrient concentration data; and daily metabolism and nutrient loading estimates for 16 lakes in North America and Northern Europe.
In lakes, ecosystem structure and processes are influenced by gross primary production (GPP), ecosystem respiration (R), and net ecosystem production (NEP). The rates of these metabolic processes are often controlled by resource availability, which often reflects catchment loads. Although the relationship between catchment loads and in-lake nutrient concentrations may be well defined in specific lakes, we explored how watershed vs. in-lake predictors of metabolism compare across lake types. To do this, we combined stream loads of carbon (C), nitrogen (N), and phosphorus (P) with high frequency in situ monitoring of lake metabolism and in-lake C, N, and P concentrations from 16 lakes spanning a range of latitudes (39 to 64 degrees N), inflowing stream (0 - 6 streams), and trophic status (oligotrophic to eutrophic). The data package includes high-frequency dissolved oxygen, water temperature, wind speed, and solar radiation data as well as daily estimates of GPP, R, and NEP derived from those data. In addition, the data package includes in-lake and stream concentrations of dissolved organic carbon, total nitrogen, and total phosphorus and stream discharge data. The package also includes estimates of daily carbon, nitrogen and phosphorus loading to each lake derived from the stream concentrations and discharge.
WILLOW - Norther: data set for the full-scale validation of model-based virtual sensing methods for an operational offshore wind turbine
<h1><em><strong>1. General description </strong></em></h1> <p>This data set contains as-build design information, as well as full-scale vibration response measurements from an operational offshore wind-turbine. The turbine is part of the Norther wind farm which is located in the Belgian North Sea<em> </em>and includes a total of 44 Vestas V164 (8.4MW) wind turbines on monopile foundations, see <a href="../api/records/11093262/draft/files/Fig1_Norther_locaction.png/content" target="_blank" rel="noopener noreferrer">Fig1_Norther_locaction.png</a>. This data set is intended to verify and validate model-based virtual sensing algorithms, using data as well as modeling information from a real turbine. </p> <h2><em><strong>1.1 Summary of the shared structural information</strong></em></h2> <p>The included information entails a detailed description of the geometric properties of the monopile and transition piece, distributed and lumped structural masses . All information shared in this record is conform the as-designed documentation. An example of the lumped masses considered in the model input files is presented in "<a href="../api/records/11093262/draft/files/Fig2_Sensor_Network.png/content" target="_blank" rel="noopener">Fig2_Sensor_Network.png"</a></p> <h2><em><strong>1.2 Summary of the shared geotechnical information</strong></em></h2> <p>Monopiles are distinguished by the significant role of soil-structure interaction. Ground reaction is most typically included in the structural model as non-linear p-y curves. Different p-y curves are available for a certain number of soils in the standards applicable to offshore structures (API RP 2GEO, 2011, and ISO 19901-4:2016(E), 2016).</p> <p>The required soil properties to define p-y curves according to the API framework are given in the soil profile provided in a separate Excel. Rather than symbols, the name of the soil properties is generally used as column header (e.g., <em>Undrained shear strength</em>). Therefore, it is straightforward to identify each soil parameter. The only soil parameter that might lead to confusion is:</p> <ul> <li><em>"epsilon50 [-]" </em>represents the vertical strain at half the maximum principal stress difference in a static undrained triaxial compression test on an undisturbed soil sample.</li> </ul> <p>It's worthy to note that estimates for the small shear strain stiffness, referred to as Gmax, are also included. Despite not being required as an input to define the API p-y curves, this parameter remains a key input for other soil reaction frameworks than the API (e.g., PISA). </p> <h2><em><strong>1.3 Summary of the shared measurement data</strong></em></h2> <p>Two sets of measurement data have been curated for validation purposes; the first interval has been collected during parked conditions, whereas the second interval has been collected during rated operational conditions. Both records have a length of 2 hours, and are subdivided into 10-minute data sets. Furthermore 1Hz SCADA data has been made available for the selected intervals. All different data sources are time synchronized and have been subjected to several internal quality checks. </p> <p>The sensor network on NRT-WTG is illustrated in in <strong>Fig. 2, </strong>whereas a description of the sensor types is presented in <strong>Tab.1.</strong> The acceleration sensors are installed in the horizontal plane, and measure tangential (Y) and orthogonal (X) to the wall, where the positive Y direction is pointing clockwise and the positive X direction is pointing inwards. All strain sensors are installed vertically and are located on the inside of the wall.</p> <table> <tbody> <tr> <td><strong>Data type </strong></td> <td><strong>Sensor type</strong></td> <td><strong>Fs (Hz)</strong></td> <td> <p><strong>Level mLAT (m)</strong></p> </td> <td><strong>Description </strong></td> </tr> <tr> <td>Acceleration (g) </td> <td>Piezo-electric acc. sensor (<strong>ACC</strong>)</td> <td>30</td> <td>15, 69, 97 </td> <td>3 Bi-directional accelerometers at different levels. LAT 15 installed at 240 degree heading; LAT 69 and 97 at 60 degree.</td> </tr> <tr> <td>Strain (micro strain)</td> <td>Resistive strain gauge (<strong>SG</strong>)</td> <td>30</td> <td>14</td> <td>6 SGs: equally spaced around the inner circumference of the can. Headings: 50, 110, 170, 230, 290, 350 degree.</td> </tr> <tr> <td>Strain (micro strain)</td> <td>Fiber-Bragg Grating strain gauge (<strong>FBG</strong>)</td> <td>100</td> <td>-17, -19</td> <td>2 FBGs per level at 165 and 255 degree respectively.</td> </tr> </tbody> </table> <p><strong>Table 1. Description of sensor types.</strong></p> <p>The FBG strain time series have been synchronized with the SG time series using using a cross-correlation based approach. Therefore the SG data has been used to genereate refrence strain time series at the headings of the FBG sensors; the FBG data is subsequently synchronized with regard to this reference time series. No synchronization of the acceleration data was needed, since these are collected using the same data aquisition system as the SG data. </p> <p>The SG strain time series have been calibrated and temperature compensated, whereas this is not the case for the FBG strain time series. The latter have a yet to be determined calibration offset. </p> <p>In conjunction to the sensor channels presented in <strong>Tab. 1</strong>, 1 Hz SCADA data is provided. A summary of the provided SCADA parameters, all sampled at 1Hz, is presented in <strong>Tab 2.</strong></p> <table> <tbody> <tr> <td><strong>Parameter</strong></td> <td><strong>Unit</strong></td> <td><strong>Description</strong></td> </tr> <tr> <td>Wind speed</td> <td>m/s</td> <td>Wind speed as recorded in the turbine SCADA</td> </tr> <tr> <td>Wind direction</td> <td>°</td> <td>Wind direction relative to North (0°) as recorded in the turbine SCADA</td> </tr> <tr> <td>Yaw angle</td> <td>°</td> <td>Yaw orientation of the nacelle relative to North (0°) as recorded in the turbine SCADA</td> </tr> <tr> <td>Pitch angle</td> <td>°</td> <td>Rotor blade pitch as recorded in the turbine SCADA</td> </tr> <tr> <td>Rotor speed</td> <td>rpm</td> <td>Rotor speed in rotations per minute as recorded in the turbine SCADA</td> </tr> <tr> <td>Power</td> <td>kW</td> <td>Active power of the turbine as recorded in the turbine SCADA</td> </tr> </tbody> </table> <p><strong>Table 2. </strong>List of provided SCADA parameters</p> <p> </p> <p>A summary of the selected intervals and relevant corresponding scada parameters is given in <strong>Tab 3</strong>.</p> <table> <tbody> <tr> <td><strong>Scenario </strong></td> <td><strong>T1 (UTC)</strong></td> <td><strong>T2 (UTC) </strong></td> <td><strong>Windspeed</strong></td> <td><strong>RPM </strong></td> <td><strong>Pitch </strong></td> </tr> <tr> <td>Parked</td> <td> <p>03/07 01:30</p> </td> <td> <p>03/07 03:30</p> </td> <td>< 4.5 m/s</td> <td>~1</td> <td>~18 °</td> </tr> <tr> <td>Rated</td> <td> <p>05/07 22:30</p> </td> <td> <p>06/07 00:30 </p> </td> <td>~15 m/s</td> <td>10.5</td> <td>8.1°</td> </tr> </tbody> </table> <p><strong>Table 3. </strong>Selected data intervals and relevant scada parameters</p> <p> </p> <h1><em><strong>2. Included in this version </strong></em></h1> <h2><em><strong>2.1 Version - 0.1.0</strong></em></h2> <ul> <li>Relevant Design information can be found in: <ul> <li>Geometry data for NRT-WTG: "WILLOW-Geometry_v4.xlsx"</li> <li>Best estimate soil profile: "WILLOW-BE_soil_profile.xlsx"</li> </ul> </li> <li>Acceleration, strain and scada data can be found in the following parquet files: <ul> <li>Measurement data for the parked case: "NRT-WTG_Parked.parquet.gz"</li> <li>Measurement data for the rated case: "NRT-WTG_Rated.parquet.gz"</li> </ul> </li> </ul> <p> </p> <h1><em><strong>3. Importing parquet files </strong></em></h1> <p>To import the measurement data into Python it is recommended to use pandas:</p> <pre>import pandas as pd<br># Read Parquet file with Pandas: relative_file_path = '<a href="../api/records/11093262/draft/files/NRT-WTG_Parked.parquet.gz/content" target="_blank" rel="noopener noreferrer">NRT-WTG_Parked.parquet.gz</a>' data = pd.read_parquet(relative_file_path ) <br><br>Once the dataframe has been imported, the users can process/re-arrange the raw data according the their needs; it should be noted that the imported dataframe contains NAN values - these are caused by the different sampling rates of the provided signals. </pre>
Data supplement for "Alignment of scanning lidars in offshore wind farms" - Wind Energy Science Journal
<p>These data are supplements for the calculations of the methods from the article "Alignment of scanning lidars in offshore wind farms".<br> The data was used to produce the results from the publication and is intended to be used here as sample data for illustrative purposes.</p>
Frøya wind data
<p>Herewith we present the dataset of wind measurements from a Skipheia meteorological station on the island of Frøya on the western coast of Norway, Trondelag.</p> <p>The site represents an exposed coastal wind climate with open sea, land and mixed fetch from various directions. UTM-coordinates of the Met-mast: 8.34251 E and 63.66638 N.</p> <p>Presented data were gathered between years 2009-2015;</p> <p>Hardware summary: 6 pairs of 2D sonic anemometers at 10, 16, 25, 40, 70, 100 m above the ground, independent temperature measurements at the same heights and near the ground; pressure and relative humidity from local meteostation (Sula, 20 km away).</p> <p>Database summary: approx. 180 000 of 10 min data samples of full data recovery. Wind speed and direction, temperature, pressure & relative humidity (from a nearby meteostation).</p> <p>Data description: Two data files of different formats are available: a ‘*.txt’ comma-separated values file and a native MATLAB ‘*.mat’ file. Both contain the same data, starting with the first column: timestamp, wind speed (m/s, columns WS1-WS12) for 6 anemometers pairs, wind direction (360 deg, columns WD1-WD12) for 6 anemometers pairs, temperature at 0.2 m (AT0), temperatures at levels of wind measurement (deg C, AT1-AT6), data from nearby meteostation Sula, pressure (hPa, PressureSula), relative humidity (%, RelHumSula), temperature (deg C, TempSula), wind direction (360 deg, WDSula) and wind speed (m/s, WSSula). Columns have headers describing the data (first row).</p> <p>Detailed site description with wind climate description can be found in attached analysis: Site analysys.pdf.</p> <p>Additional information and analysis can be found in listed below works, using data from Frøya site, or nearby sites:</p> <p><strong> </strong>Møller, M., Domagalski, P., and Sætran, L. R.: Comparing Abnormalities in Onshore and Offshore Vertical Wind Profiles, Wind Energ. Sci. https://wes.copernicus.org/articles/5/391/2020/ </p> <p>IEA Wind TCP Task 27 Compendium of IEA Wind TCP Task 27 Case Studies, Technical Report, Prepared by Ignacio Cruz Cruz, CIEMAT, Spain Trudy Forsyth, WAT, United States, October 2018; Chapter 1.8. <a href="https://community.ieawind.org/HigherLogic/System/DownloadDocumentFile.ashx?DocumentFileKey=8afc06ec-bb68-0be8-8481-6622e9e95ae7&forceDialog=0">https://community.ieawind.org/HigherLogic/System/DownloadDocumentFile.ashx?DocumentFileKey=8afc06ec-bb68-0be8-8481-6622e9e95ae7&forceDialog=0</a></p> <p>Domagalski, P., Bardal, L. M., & Satran, L. Vertical Wind Profiles in Non-neutral Conditions-Comparison of Models and Measurements from Froya. <em>Journal of Offshore Mechanics and Arctic Engineering,</em> doi: 10.1115/1.4041816, <a href="http://offshoremechanics.asmedigitalcollection.asme.org/article.aspx?articleid=2711333&resultClick=3">http://offshoremechanics.asmedigitalcollection.asme.org/article.aspx?articleid=2711333&resultClick=3</a></p> <p>Mathias Møller , Piotr Domagalski and Lars Roar Sætran, Characteristics of abnormal vertical wind profiles at a coastal site, <em>Journal of Physics: Conference Series</em>, IOPscience, under review (Feb 2019), DeepWind2019 conference poster available at: <a href="https://www.sintef.no/globalassets/project/eera-deepwind-2019/posters/c_moller_a4.pdf">https://www.sintef.no/globalassets/project/eera-deepwind-2019/posters/c_moller_a4.pdf</a></p> <p>Bardal, L. M., Onstad, A. E., Sætran, L. R., & Lund, J. A. (2018). Evaluation of methods for estimating atmospheric stability at two coastal sites. <em>Wind Engineering</em>, 0309524X18780378, <a href="https://doi.org/10.1177%2F0309524X18780378">https://doi.org/10.1177/0309524X18780378</a></p> <p>Bardal, L. M., & Sætran, L. R. (2016, September). Spatial correlation of atmospheric wind at scales relevant for large scale wind turbines. In <em>Journal of Physics: Conference Series</em> (Vol. 753, No. 3, p. 032033). IOP Publishing, doi:10.1088/1742-6596/753/3/032033, <a href="https://iopscience.iop.org/article/10.1088/1742-6596/753/3/032033/pdf">https://iopscience.iop.org/article/10.1088/1742-6596/753/3/032033/pdf</a></p> <p>Bardal, L. M., & Sætran, L. R. (2016). Wind gust factors in a coastal wind climate. <em>Energy Procedia,</em> 94, 417-424, <a href="https://doi.org/10.1016/j.egypro.2016.09.207">https://doi.org/10.1016/j.egypro.2016.09.207</a></p>
Metabolism dataset: one year of high-frequency temperature, dissolved oxygen, wind, photosynthetically active radiation observations and low-frequency nutrient data for 58 lakes in the Global Lake Ecological Observatory Network
Understanding controls on primary productivity is essential for describing ecosystems and their responses to environmental change. Lake primary production is strongly controlled by inputs of nutrients and colored dissolved organic matter. While past studies have developed mathematical models of this nutrient-color paradigm, broad empirical tests of these models are scarce. We compiled data from 58 diverse and globally distributed and mostly temperate lakes to test such a model and improve understanding and prediction of the controls on lake primary production. These lakes varied widely in size (0.02-2300 km2), pelagic gross primary production (20-8000 mg C m-2 d-1), and other characteristics. The data package includes high-frequency dissolved oxygen, water temperature, wind speed, and solar radiation data as well as daily estimates of GPP and ER derived from those data. In addition, the data package includes median in-lake and stream concentrations of dissolved organic carbon and total phosphorus for a subset of 18 of those lakes.
PIE LTER, Wind sensor data, 15 minute intervals, from the Ipswich Bay Yacht Club pier located in Ipswich, MA, year 2022.
Wind sensor measurements (wind speed and wind direction) for 2022 at the Ipswich Bay Yacht Club, Ipswich, MA, 15 minute average measurements.
SBC LTER: Wind data near Santa Barbara, from both land stations and ocean buoys
This data package includes wind data (e.g. wind speed, wind direction, wind velocity, air temperature, etc.) from 54 stations near Santa Barbara, CA since 1981. The wind data is collected from 3 agencies: Santa Barbara County Air Pollution Control District, California Department of Water Resources, and National Data Buoy Center. The data collected from the National Data Buoy Center also include the water temperature data. This data package is expected to update annually.
One-minute average horizontal wind velocity data (not corrected for air-flow distortion) from the Antarctic Circumnavigation Expedition (ACE) 2016/2017 legs 0 to 4.
<p><strong>Dataset abstract</strong></p> <p>This dataset contains the one-minute average horizontal wind velocity data from the Antarctic Circumnavigation Expedition (ACE) 2016/2017 legs 0 to 4. The data has been filtered for spurious observations and the true wind correction has been redone using the quality checked one-minute ship track velocity data. This data set has not been corrected for air-flow distortion, which was caused by the ship's super structure. The flow-distortion corrected data should be used for studies interested in the actual true wind speed near the ship's location.</p> <p><strong>Dataset contents</strong></p> <ul> <li>wind-observations-stbd-uncorrected-5min-legs0-4.csv, data file, comma-separated values</li> <li>wind-observations-port-uncorrected-5min-legs0-4.csv, data file, comma-separated values</li> <li>data_file_header, metadata, text format</li> <li>README.txt, metadata, text format</li> </ul> <p><strong>Dataset license</strong></p> <p>This one-minute averaged wind velocity dataset is made available under the Creative Commons Attribution 4.0 International License (CC BY 4.0) whose full text can be found at https://creativecommons.org/licenses/by/4.0/</p>
Na Wind-Temperature Lidar Data at Andes Lidar Observatory on 3/1/2016
<p>Measurement made by the Na Wind-Temperature Lidar at the Andes Lidar Observatory in Cerro Pachón, Chile. It includes Na density, temperature, zonal, meridional, and vertical wind, from 80 to 115 km altitude at 0.5 km interval and from 23.8 UT 2/29/2016 to 8.9 UT 3/1/2016 at 0.1 hour interval. Errors of these values are also included. -999 represents missing value. </p>
Historical Weather, Load, Wind, and Solar Data for the Salt River Project
<p>We created and curated a dataset of historical (1980-2019) hourly meteorology, load, wind, and solar data for the Salt River Project (SRP) region. The data was created by PNNL's <a href="https://godeeep.pnnl.gov/">GODEEEP</a> project. Each row in the dataset is a single hour and each column is a variable. All meteorological variables are spatially-averaged over the SRP service territory. The variables and their units are as follows:</p><ol><li>"Time_UTC"; Coordinated Universal Time (UTC); Time of day.</li><li>"T2"; Fahrenheit; 2-m air temperature.</li><li>"Q2"; kg/kg; 2-m water vapor mixing ratio.</li><li>"SWDOWN"; W/m^2; Downwelling shortwave radiative flux at the surface.</li><li>"GLW"; W/m^2; Downwelling longwave radiative flux at the surface.</li><li>"WSPD"; m/s; 10-m wind speed.</li><li>"Scaled_2019_Load"; MWh; Simulated hourly demand for electricity that is scaled to 2019 levels of annual energy. This load estimate does not account for historical changes in population and economics within the SRP service territory. It is included to make it easier to isolate weather impacts on load without having to consider long-term changes.</li><li>"Load"; MWh; Simulated hourly demand for electricity.</li><li>"Agua_Fria_Solar_Capacity"; N/A; Solar capacity factor for the SRP Agua Fria project with plant configurations taken from the EIA-860 database.</li><li>"Phoenix_Solar_Capacity"; N/A; Solar capacity factor for hypothetical solar plants derived using the grid cell nearest to Phoenix, AZ.</li><li>"Flagstaff_Solar_Capacity"; N/A; Solar capacity factor for hypothetical solar plants derived using the grid cell nearest to Flagstaff, AZ.</li><li>"Phoenix_Wind_Capacity"; N/A; Wind capacity factor for hypothetical 80-m plants derived using the grid cell nearest to Phoenix, AZ.</li><li>"Flagstaff_Wind_Capacity"; N/A; Wind capacity factor for hypothetical 80-m plants derived using the grid cell nearest to Flagstaff, AZ.</li></ol>
Wind measurement data from the publication: "Development of a load model validation framework applied to synthetic turbulent wind field evaluation"
<h3>Dataset description:</h3> <p>This datasat represents supplementary material used in the contribution "Development of a load model validation framework applied to<br>synthetic turbulent wind field evaluation" by Meyer, Huhn and Gottschall.</p> <p>Wind measurements from the Testfeld BHV are made available. For installation details, see the mentioned reference.</p> <p> </p> <h3>File description:</h3> <ul> <li>Lidar_HWS.nc - Horizontal wind speed measurements (10 min averages) from a WindCube V2 vertical profiler for one day with a low-level jet occurrence ( <div> <div>2021-04-20)</div> </div> </li> <li>Cups_HWS.nc - Horizontal wind speed measurements (10 min averages) from cup anemometer installed on a met mast for the same day</li> <li>Ensemble_averaged_Spectra.nc - Ensemble averaged spectra for neutral and near neutral situations from a Gill Windmaster at 110m above ground level, used to fit the Mann and KSEC model parameters</li> </ul> <h3> </h3> <h3>Referencing:</h3> <p>When used, please cite like the following:</p> <p>Meyer, Paul J., Matthias L. Huhn, and Julia Gottschall. 2024. "Development of a Load Model Validation Framework Applied to Synthetic Turbulent Wind Field Evaluation" <em>Energies</em> 17, no. 4: 797. https://doi.org/10.3390/en17040797</p> <p> </p> <p> </p>
Additional evidence for a pulsar wind nebula in SN 1987A from multi-epoch X-ray data and MHD modelling
<p>This is a basic reproduction package for the paper "Additional evidence for a pulsar wind nebula in the hearth of sN 1987A from multi-epoch X-ray data and MHD modeling" by Greco et al. 2022. It aims to provide the most important data products to check and reproduce the main results of the paper.</p>
Na Wind-Temperature Lidar Data at Andes Lidar Observatory on 10/30/2016
<p>Measurement made by the Na Wind-Temperature Lidar at the Andes Lidar Observatory in Cerro Pachón, Chile. It includes Na density, temperature, zonal, meridional, and vertical wind, from 80 to 115 km altitude at 0.5-km intervals and from 23.7 UT 10/29/2016 to 8.9 UT 10/30/2016 at 0.1-hour intervals. Errors of these values are also included. -999 represents missing values. </p>
QBO: monthly zonal stratospheric winds from tropical radiosonde data (mainly Singapore)
<p><strong>Monthly Tropical Stratospheric Zonal Winds from Radiosondes</strong></p> <p><strong>Data Source and Processing:</strong></p> <p>Monthly mean zonal wind data for the tropics are provided as a service for the global QBO and trend analysis communities. The original data source and processing chain were established by the Free University of Berlin (FUB). Currently, the data is processed at the Karlsruhe Institute of Technology (KIT, ROR:04t3en479), Institute of Meteorology and Climate Research (IMK), Germany with the tools developed at FUB.</p> <p><strong>Data Description:</strong></p> <p>The dataset includes monthly mean zonal wind values at pressure levels 100, 90, 80, 70, 60, 50, 45, 40, 35, 30, 25, 20, 15, 12, and 10 hPa, derived from radiosonde observations at four equatorial stations:</p> <ul> <li>Kiribati (Canton Island) - data from 1953 to 1967 (closed)</li> <li>Maldives (Gan Island) - data from 1967 to 1975 (closed)</li> <li>Singapore (Payalebar) - data from 1975 to 1989</li> <li>Singapore (Changi) - data from 1989 onwards</li> </ul> <p><strong>Important Notes:</strong></p> <ul> <li>Values for 100 hPa from October 1967 are solely from Singapore (Changi).</li> <li>Values for 100 hPa before October 1967 are from Kiribati (Canton Island) when available.</li> <li>Detailed information about the radiosonde stations and their periods of operation is provided below.</li> </ul> <p><strong>Additional Information:</strong></p> <ul> <li>Access the data in other formats also published here: <a href="https://www.atmohub.kit.edu/english/807.php" target="_blank" rel="noopener noreferrer">https://www.atmohub.kit.edu/english/807.php</a></li> </ul> <p><strong>Detailed List of Radiosonde Stations:</strong></p> <div> <div> <div> <div> <table> <tbody> <tr> <th>Station Name</th> <th>Location (Lat, Lon)</th> <th>Data Period</th> <th>Pressure Levels (hPa)</th> </tr> <tr> <td>Kiribati (Canton Island)</td> <td>-2.7667, -171.7167</td> <td>1953 - 1967 (closed)</td> <td> <p>Above 100 (until August 1967)</p> <p>100 (until September 1967)</p> </td> </tr> <tr> <td>Maldives (Gan Island)</td> <td>-0.6933, 73.1556</td> <td>1967 - 1975 (closed)</td> <td>Above 100 (September 1967 to December 1975)</td> </tr> <tr> <td>Singapore (Payalebar)</td> <td>1.3667, 103.9167</td> <td>1975 - 1989</td> <td>Above 100 (January 1976 to May 1989)</td> </tr> <tr> <td>Singapore Upper Air Observatory</td> <td>1.3404, 103.8879</td> <td>1989 - present</td> <td> <p>100 (October 1967 to May 1989), </p> <p>All levels from June 1989</p> </td> </tr> </tbody> </table> </div> </div> </div> </div> <div> </div>
Data for "Saturation of destratifying and restratifying instabilities during down front wind events: a case study in the Irminger Sea"
<p>This archive contains processed data used in the study "Saturation of destratifying and restratifying instabilities during down front wind events: a case study in the Irminger Sea".</p> <p>We are grateful for the financial support of the Natural Environment Research Council (grants NE/L002612/1 and NE/T013494/1).</p> <p>This work used the ARCHER2 UK National Supercomputing Service (https://www.archer2.ac.uk).</p> <p>We would also like to thank Andrew Coward for providing computational support.</p> <p>The results contain modified Copernicus Climate Change Service information 2020. Neither the European Commission nor ECMWF is responsible for any use that may be made of the Copernicus information or data it contains.</p> <p>The results contain modified GEBCO data produced by the GEBCO Compilation Group (2023) GEBCO 2023 Grid (doi:10.5285/f98b053b-0cbc-6c23-e053-6c86abc0af7b)</p>
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.
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.
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>
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Allen Brain Atlas
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DANDI Archive for NWB datasets
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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
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