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1,855 results for “winds”
Salinity, Turbidity, Wind from the E1 buoy 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 E1 meteo-oceanographic buoy in the Northern Adriatic Sea (north of Rimini city on a bottom depth of 10.5 m), Italy. Specifically, it encompasses measurements taken atmospheric parameters above the water surface and measurements at a defined nominal 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 buoy is located at 44,14° N; 12,57° 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 in this area over nearly a decade. The E1 buoy 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. </p>
Database of Participatory Practices and Social Innovations in Wind Energy Developments
<p>Inês Campos was responsible for designing the database, collecting data, and analyzing data. Flávio Oliveira also collaborated in the design of the database and data collection. </p>
Plausible 2050 offshore wind locations in the North Sea
<p>This dataset contains a set of zones and points representing plausible locations for offshore wind farms and individual turbines to have been built in the North Sea by the years 2030, 2040, and 2050, based off the national ambitions announced up to summer 2024.</p> <p>This version (version 3) is a major revision. Many wind farm zones and most turbines have moved. Column names have changed. See readme.pdf for further information and a changelog.</p> <p>A full description of how these coordinates were arrived at is currently under development as a journal article, and once it is available this readme will be updated to link to it. Check the "latest version" link in Zenodo to see if this has already happened.</p> <p>If using this version, please cite the dataset directly using the title and authors above and DOI 10.5281/zenodo.14222865</p> <p>Please do not use "OSW zones.png" for serious work; use the underlying data instead. The image is included so as to give a useful preview in Zenodo.</p>
Wind tunnel test data for the evaluation of the aerodynamic coefficients of an antenna mast with ancillaries.
<p>This dataset comprises measured data and results from static wind tunnel tests conducted in April 2024 at the Giovanni Solari Wind Tunnel Facility (GS-WinDyn). The tests aim to assess the drag, lift, and moment coefficients <span>of an antenna mast designed as a triangular lattice tower, equipped with both linear and discrete ancillary components.</span> The wind tunnel experiments are carried out under both smooth and turbulent flow conditions using a scaled 3D model of the antenna mast. Five ancillary configurations, based on predominant patterns observed, are tested. Drag forces, lift forces and moments are measured using two six-component force balances attached to the ends of the model, while downstream three-component velocity data is captured by a Cobra probe. For each configuration, aerodynamic coefficients are determined for angles of attack ranging from 0° to 360°, with increments of up to 10°. The dataset provides the measured data and the obtained aerodynamic coefficients and it has significant reuse potential in several applications: comparison with experimental wind tunnel data, validation of analytical and numerical CFD models with similar configurations, estimation of wind loads due to ancillary structures, and characterization of wake effects.</p>
Temperature and wind field data for Fendt, Germany
<p>This dataset includes high-resolution temperature and wind vector data from a network of sensors within a 20x20x9m [LxWxH] domain. The observations included air temperature by Distributed Temperature Sensing (DTS), surface temperature by thermal infrared imaging (TIR; surface brightness temperature), horizontal and vertical profiles of air temperature and wind vectors (EC; two-axial and three-axial sonic anemometers; temperature derived from speed of sound) and a vertical profile of air temperature (TT; aspirated temperature in a shielded enclosure).</p> <p>The data were collected at the DE-Fen observatory, Fendt-Peissenberg, Germany, during the ScaleX 2016 Campaign, Jun-Aug 2016 (<a href="https://scalex.imk-ifu.kit.edu">https://scalex.imk-ifu.kit.edu</a>).</p> <p> </p>
Poker Flat Incoherent Scatter Radar (PFISR) Observations of E-region Neutral Winds
<p>Updated: 12-15-2021</p> <p><strong>RULES OF THE ROAD:</strong></p> <p>You are welcome to use the data 'as is', however, please inform me via email if you plan to use the dataset. There are a number of small issues with the dataset that are best discussed. We are interested in publications that use the data and derived values that are presented within the dataset. <strong>If you plan to publish these results, please circulate a draft by me (SRK) and we would appreciate an offer of co-authorship or at minimum an acknowledgement. You should include the NSF funding numbers NSF AGS - 1853408</strong></p> <p> </p> <p>As a general warning, the data from PFISR are quite noisy and you may need to perform significant averaging to produce usable results. Again, please contact me and we can discuss this in more detail.</p> <p>Version v0.6.4.2021.07.12 - This was the final processed version at the time that the final report was submitted to the NSF.</p> <p> </p> <p><strong>--------------- Previous from before ------------------</strong></p> <p>This file contains Poker Flat Incoherent Scatter Radar (PFISR) E-region Neutral Winds Data. These data correspond to monthly data files that include the E-region neutral winds and other parameters for the from March 2013-June 2019.</p> <p><strong>Publications of the Joule Heating Results:</strong></p> <p>https://doi.org/10.1029/2021JA029371</p> <p>https://doi.org/10.1029/2021JA029719</p> <p> </p> <p><strong>Publication of Neutral Wind Results:</strong></p> <p>Hopefully we will have something in 2021. </p> <p> </p> <p><strong>RAW ISR Data:</strong> These data were processed from the following files found in: https://data.amisr.com/database/tmp/Kaeppler/winds/ and https://data.amisr.com/database/tmp/Kaeppler/missing_IPY.tar.gz Please note that the error on the line of sight velocities may have been overestimated in these data and we scaled them by a eVLOS/sqrt(10). Interested persons should contact Ashton Reimer or Roger Varney at SRI International for more information about these data, please see amisr.com</p> <p>Truthfully, the ISR data should eventually be reprocessed and then the winds algorithm run over it again. This is a step for future work.</p> <p> </p> <p><strong>Processing Code is available upon request via email.</strong></p> <p> </p> <p><strong>File Documentation:</strong></p> <p> </p> <p><strong>Please see the change log:</strong></p> <p>Purpose: This is the overarching program and functions which process the<br> E region neutral winds from the fitted AC and LP data from PFISR.<br> This is a conversion fo process_eregwinds_srk.py which was originally written by<br> Nicolls into a more formal python class structure.</p> <p>2017-10-05 - v0.2</p> <p>The ProcessEregionNeutralWinds.py file has been validated against process_eregwinds_srk.py<br> using 20161121.001_ac_3min-fitcal.h5, 20170301.013_ac_3min-fitcal.h5, 20170302.001_ac_3min-fitcal.h5.<br> The program to run these is ComparePrograms.py. At this point these program match.<br> I am going to start diverging the code base, first subtly in the Joule Heating<br> since I found that Mike just looped over Nbeams, which isn't quite right, you need to loop<br> over the beams that were selected.</p> <p>Changes from this point forward will produce different results.</p> <p>2017-10-10 - v0.3.2017.10.10</p> <p>Version v0.3, I made some IO changes but I may start processing some data with this version.</p> <p>Version v0.4 - lots of small edits made to the IO and the plotting software. It all seems to work<br> I have also included the SNR and Ne into the monthly plots and other information.<br> Made processing smoother.</p> <p>03 13 2018 - added solar local time converion</p> <p>v0.4.1 - 09 08 2018 added some ability to extract out the raw electron and SNR densities for each altitude bin<br> v0.4.2 - 10 15 2018 added in obtaining the F-region flows - want to check against the electric field.<br> v0.4.3 - 10 29 2018 added in some more altitude into the Joule Heating so I can make better figures<br> v0.4.4 - 11 20 2018 made some pretty major changes to IO to include consistent calculation of<br> Pedersen conductivity from FastConductivity.py. Made some changes to the Joule heating calculation and checked<br> formulas. It is worth checking again.</p> <p>v0.4.5 - 11 20 2018: added in Hall and Pedersen conductivities from fitted electron density data.<br> v0.4.6 - 12 03 2018: Tried to fix some of the double counting and time problems in testMakeMonthlyh5</p> <p>05 22 2019: added some statements to bypass the geophysical parameters. Also need in config file now.<br> Additionally wrote in IOEregionwinds a try except statement</p> <p>07 29 2019: Running the code for the 06 data reprocessed by Ashton</p> <p>v0.5.0 - 10-15-2019: put in some filtering on the LOS velocity discharging bad Chi square and bad error codes on the fit.</p> <p>v0.5.1 - 10-23-2019: changed chi square to 0.01 for lower boundary</p> <p>v0.5.3 - 12-02-2019: Added in that now passing in the Chi2 and Fitcode filtering by Config file<br> Bigger change that I am scaling the AC dVlos by some sort of factor while Ashton figures this out.<br> We decided that a conversative scaling would be to reduce the dVLOS by 1/sqrt(10).<br> The chi square produced in the data Ashton sent me typically was around 0.01, so the uncertaintiies on the LOS velocities<br> may be over estimated. So we are just changing this as a temporary fix while Ashton fixes the uncertainty estimation.</p> <p>v0.5.5 02 01 2020 - Added in calculation of Coriolis, Centrifugal, and Lorentz forcing<br> v0.5.5 02 10 2020 - Added a correction to qvert so that way I can calculate the lorentz term.<br> Found an error where qvert = 0 in the if statement goes to false.</p> <p>v0.5.5 02 15 2020 - Put in nuInscaler into the main program, scaling ALL kappas by the scaler number</p> <p>v0.5.6 02 28 2020 -- Added some more vlos diagostics and the calculation of the scale height. Added Altitude offset</p> <p>v0.5.6.2020.03.12_nuin_fracoff - testing putting in the Brekke formula for ion neutral collision frequency and took out frac</p> <p>v0.5.7.2020.04.10 - Put in Ashton's revised ion neutral collision frequency formulas into IO.<br> Also wrote a testscript and at least for the file I used was only different by 2.5%.<br> Revised where the mag data is being pulled from since the URL is deprecated<br> Added in Kappa which is now being interpolate - plan to see where kappa =1 is located for the paper.<br> commented out nuin scaler just so I am not chasing my tail</p> <p> test v0.5.7.2020.04.13_org commented back in original ion neutral collision frequency method<br> possible mistake that not summing up properly.</p> <p> test v0.5.7.2020.04.13_newnuin_orgsum_noTr800 - new formula for nuin except took off Tr>800.<br> I expect this should be almost the same as before since the formulas are basically the same.<br> did the original sum using frac[0] and frac[1] want to see if I am underestimating</p> <p> v0.5.7.2020.04.13_newnuin_orgsum_yesTr800 - same as above except now including Tr>800.</p> <p> 'v0.5.7.2020.04.13_newnuin_newsum_noTr800' - using the new sum now and new col freq</p> <p> v0.5.7.2020.04.13_updatedorg - updated original uses original method but including the NO term</p> <p>v0.6.0.2020.04.15 -- Now think I have the new ion neutral collision frequency working and validated.<br> Found a mistake in how I was calculating the ion neutral collision frequency that<br> the fraction weight I was using only included the O+ and O2+ terms and not NO+<br> Turns out I was basically weighting by about 0.5, so I was effectively reducing the<br> ion neutral collision frequency by about a factor of 0.5 or less...<br> From this point forward need to start using any results from > v0.6<br> This revision has changed previous results signi</p> <p>v0.6.0.2020.04.21 -- updated to now include the temperature correction for the O2+</p> <p>v0.6.1.2020.04.23 -- made a number of changes to the geomagnetic files and reprocessed from CDAweb.<br> Wrote new code to be able to process the files from CDAweb in the new format.<br> Also changed the geomagnetic data files</p> <p>v0.6.1.2020.06.07 -- changed the generation of Monthly files to hopefully be in order now<br> Added in missingIPY files given to me by ashton, maybe improve data covarege<br> Some work going to need to be done to make sure that all of the 10, 15, and 20 minute data are there.</p> <p>v0.6.1.2020.06.15_Weijia -- Updated the data for Weijia's study in particular since we are missing a lot of IPY data for 02-04 2013 and 2014.</p> <p>'v0.6.2.2020.07.01' -- Updated the data with new IPY27 mode for 2013 and 2014 Ashton processed. Also now put in mechanical Joule heating term.<br> Put in the conductance and conductivity now too.</p> <p>v0.6.2.2020.07.30 -- Made some changes to IO since Weijia noticed the mechanical heating terms were missing from the monthly files.</p> <p>v0.6.3.2020.10.19 -- Tried to elimated all extra instance of nuinscaler, and also output that variable. Added in variables<br> To get the Ti, Tn, ion neutral collision frequency along the vertical beam for diagnostic purposes<br> included dVest for F-region plasma drifts for Rafael</p> <p>v0.6.4.2020.11.20 -- Extracted some more parameters including F107 and the Hall and Pedersen Drags</p> <p>v0.6.4.2021.07.21 -- Final Run of data for NSF project</p> <p> </p>
Wind Speed vs Spanish Power Prices
<p>Average, min and max daily OMIE power prices (Spanish market) with corresponding wind average speed and maximum speed for each day. Units: €/MWh (Power Price), km/h (wind speed).</p>
Post-processed dataset from 50000 numerical simulations of monopile-supported NREL 5MW wind turbine in OpenFAST
<p>The dataset contains two separate files: NREL_Trainset40000.mat and NREL_Testset10000.mat.</p> <p>The stored input enviormental and operational parameters are:</p> <ul> <li>Significant wave height, m (Hs), peak period, s (Tp), wave direction, deg (Wave_dir);</li> <li>Wind speed, m/s (Vw_mean, Vw_std), wind direction, deg (Wdir_mean, Wdir_std);</li> <li>Turbine rotational speed, rpm (Rpm_mean, Rpm_std), blade pitch, deg (Pitch_mean, Pitch_std), turbine yaw angle, deg (Yaw_mean, Yaw_std).</li> </ul> <p>The output of the simulations includes the time series, sampled at 50 Hz, of the reaction force and bending moments at the mudline:</p> <ul> <li>Fzz, N</li> <li>Mxx, Nm</li> <li>Myy, Nm</li> </ul> <p>contact: nandar.hlaing@uliege.be</p>
Adriatic Sea wind-wave climate years 1981-2010 and 2021-2050 (RCP4.5 and RCP8.5)
<p>Adriatic Sea mean annual 50th, 90th, 95th and 99th percentiles of the significant wave height (Hs) from WAVEWATCH III v6.07 (2 km) forced with 1-hour ERA5 wind fields statistically scaled to QQ-match COSMO-CLM fields (available at https://doi.org/10.5281/zenodo.6021380).</p> <p>Reference periods:</p> <p>1) Historical climate: years 1981-2010</p> <p>2) Future climate: years 2021-2050 (IPCC scenario RCP4.5 and RCP8.5)</p>
Adriatic Sea wind climate years 1981-2010 and 2021-2050 (RCP4.5 and RCP8.5)
<p>Adriatic Sea mean annual 50th, 90th, 95th and 99th percentiles of the sea surface wind speed (10-m height U10) from 1-hour ERA5 fields (25 km) statistically scaled to QQ-match COSMO-CLM fields (8 km).</p> <p>Reference periods:</p> <p>1) Historical climate: years 1981-2010</p> <p>2) Future climate: years 2021-2050 (IPCC scenario RCP4.5 and RCP8.5)</p>
Adriatic Sea wind and wave time series years 1981-2010 and 2021-2050 (RCP4.5 and RCP8.5)
<p>Wind and wave time series for 27 stations in the Adriatic Sea.</p> <p>Variables:</p> <p>- 10-m height wind speed (wnd) and wind direction (wnddir) from 1-hour ERA5 fields (25 km) statistically scaled to QQ-match COSMO-CLM fields (8 km)</p> <p>- significant wave height (hs) and peak wave period (tp) from WAVEWATCH III v6.07 (2 km) forced with the scaled ERA5 wind fields</p> <p>Reference periods:</p> <p>- Historical climate: years 1981-2010</p> <p>- Future climate: years 2021-2050 (IPCC scenario RCP4.5 and RCP8.5)</p>
Data/ codes used in the the Natural Hazards and Earth System Sciences (NHESS) publication titled "Wind-Wave Characteristics and extremes along the Emilia-Romagna coast" by Pranavam Ayyappan Pillai et al. (2022)
<p>The archive contains datasets and codes used in the manuscript titled "Wind-Wave Characteristics and extremes along the Emilia-Romagna coast", and published in the journal <em>Natural Hazards and Earth System Sciences</em> (<em>NHESS</em>) by Pranavam Ayyappan Pillai et al., 2022.</p> <p>Pranavam Ayyappan Pillai, U., Pinardi, N., Federico, I., Causio, S., Trotta, F., Unguendoli, S., and Valentini, A.: Wind-Wave Characteristics and extremes along the Emilia-Romagna coast, Nat. Hazards Earth Syst. Sci. Discuss. https://doi.org/10.5194/nhess-2022-103, 2022.</p>
Experimental Assessment of the Thermal Strain Distribution in Nb3Sn React & Wind Conductor Prototype for European DEMO
<p>The measured data, processed data and metadata related to the publication "Experimental Assessment of the Thermal Strain Distribution in Nb3Sn React & Wind Conductor Prototype for European DEMO" (doi: 10.1109/TASC.2022.3141699) are uploaded. The raw data correspond to susceptibility measurement as a function of temperature. Out of this measurement, strand distribution in the superconducting cable is determined by analysis.</p> <p>This work was supported by the Swiss National Science Foundation (SNF) under contract number 200021_179134.</p>
Data from the field experiment on katabatic winds on a steep slope (Grand Colon, French Alps), February 2019
<p>These are the data from the field experiment described in the paper 'Katabatic winds over steep slopes: overview of a field experiment designed to investigate slope-normal velocity and near surface turbulence' by CHARRONDIERE, C., BRUN, C., COHARD, J.M., SICART, J.E., OBLIGADO, M., BIRON, R., COULAUD, C. & GUYARD, H. (2022), Boundary-Layer Meteorol. 187, 29-54.</p> <p> </p>
Data for: Wind-induced hypolimnetic upwelling between the multi-depth basins of Lake Geneva during winter: An overlooked deepwater renewal mechanism?
<p>Combining field observations, 3D hydrodynamic modeling and particle tracking, we investigated wind-driven interbasin exchange, and in particular hypolimnetic upwelling, between the deep <em>Grand Lac</em> (max. depth 309 m) and shallow <em>Petit Lac</em> (max. depth 75 m) basins of Lake Geneva (Switzerland/France) during the weakly stratified fall/winter period 2018-2019.</p> <p><br> The data include measurements from moored Acoustic Doppler Current Profilers (ADCPs) and vertical thermistor lines along with the corresponding 3D modeling and particle tracking results.</p> <p><br> The three-dimensional model used in this study is based on the MIT General Circulation Model (MITgcm, http://mitgcm.org/, https://doi.org/10.1029/96JC02775).</p> <p><br> The particle tracking code is based on ctracker (https://doi.org/10.5281/zenodo.1034118)</p>
Dataset for Heavy snowfall event over the Swiss Alps: Did wind shear impact secondary ice production?
<p>The change in wind direction and speed with height, referred to as vertical wind shear, causes enhanced turbulence in the atmosphere. As a result, there are enhanced interactions between ice particles that break up during collisions in clouds which could cause heavy snowfall. For example, intense dual-polarization Doppler signatures in conjunction with strong vertical wind shear were observed by an X-band weather radar during a wintertime high-intensity precipitation event over the Swiss Alps. An enhancement of differential phase shift (Kdp > 1◦ km−1) around −15◦C suggested that a large population of oblate ice particles was present in the atmosphere. Here, we show that ice–graupel collisions are a likely origin of this population, probably enhanced by turbulence. We perform sensitivity simulations that include ice–graupel collisions of a cold frontal passage to investigate whether these simulations can capture the event better and whether the vertical wind shear had an impact on the secondary ice production (SIP) rate. The simulations are conducted with the Consortium for Small-scale Modeling (COSMO), at a 1km horizontal grid spacing in the Davos region in Switzerland. The rime splintering simulations could not reproduce the high ice crystal number concentrations, produced too large ice particles and therefore overestimated the radar reflectivity. The collisional-breakup simulations reproduced both the measured horizontal reflectivity and the ground-based observations of hydrometeor number concentration more accurately (∼ 20L−1). During 14:30–15:45UTC, the vertical wind shear strengthened by 60% within the region favorable for SIP. Calculation of the mutual information between the SIP rate and vertical wind shear and updraft velocity suggests that the SIP rate is best predicted by the vertical wind shear rather than the updraft velocity. The ice–graupel simulations were insensitive to the parameters in the model that control the size threshold for the conversion from ice to graupel and snow to graupel.</p>
Techno-economic details of fixed-bottom offshore wind projects deployed in the European markets
<p>Version (with all files) - Updated version (research article is accepted).</p> <p>Publishing Date: July 10, 2022</p> <p>This dataset describes the techno-economic information of fixed-bottom offshore wind projects deployed in the North Sea region (DK, NL, BE, DE, and the UK). </p> <p>Contents: </p> <p>1) Offshore wind farm project prices and technical characteristics (farm size, turbine rated power, water depth, etc.,)</p> <p>2) Offshore wind farm capacity factor and cumulative energy generation</p> <p>3) Monopile weight </p> <p>4) Offshore wind farm installation duration </p> <p>5) UK offshore wind farms' transmission system cost</p> <p> </p>
Icing Wind Tunnel Measurements of Supercooled Large Droplets Using the 12 mm Total Water Content Cone of the Nevzorov Probe: Measurement Data
<p>This repository contains the measurement data that was used for the publication "Icing Wind Tunnel Measurements of Supercooled Large Droplets Using the 12 mm Total Water Content Cone of the Nevzorov Probe".</p>
EOOffshore: CCMP v0.2.1.NRT Wind Data for the Irish Continental Shelf Region
<p><a href="https://eooffshore.github.io">EOOffshore</a> is a <a href="https://www.seai.ie/">Sustainable Energy Authority of Ireland (SEAI)</a> funded <a href="https://www.seai.ie/data-and-insights/seai-research/research-projects/details/building-upon-copernicus-earth-observation-services-to-augment-wind-measurement-coverage-of-the-oredp-offshore-renewable-energy-assessment-areas">project</a>, which commenced in June 2020 in the <a href="https://www.ucd.ie/physics/">School of Physics</a> in <a href="https://www.ucd.ie/">University College Dublin (UCD)</a>. It presents a case study that demonstrates the utility of the <a href="https://pangeo.io/">Pangeo</a> software ecosystem in the development of offshore wind speed and power density estimates, increasing wind measurement coverage of offshore renewable energy assessment areas in the <a href="https://www.marine.ie/Home/site-area/irelands-marine-resource/real-map-ireland">Irish Continental Shelf (ICS)</a> region. It has involved the creation of a new <a href="https://eooffshore.github.io/datasets.html">wind data catalog</a> for this region, consisting of a collection of analysis-ready, cloud-optimized (ARCO) datasets featuring up to 21 years of available in situ, reanalysis, and satellite observation wind data products.</p> <p>This particular catalog data set (<em>eooffshore_ics_ccmp_v02_1_nrt_wind.zarr</em>) contains 2015-2021 Cross-Calibrated Multi-Platform (CCMP) v0.2.1.NRT 6-hourly wind products for the ICS region, where wind speed and direction are calculated from the <em>uwnd</em> and <em>vwnd</em> variables. The source data products are generated by <a href="https://www.remss.com/measurements/ccmp/">Remote Sensing Systems (RSS)</a>. This CCMP data set was used in the EOOffshore project outputs presented (<em><a href="https://meetingorganizer.copernicus.org/EGU22/EGU22-2746.html">Scalable Offshore Wind Analysis With Pangeo</a></em>) at the <em><a href="https://meetingorganizer.copernicus.org/EGU22/session/42046">Meeting Exascale Computing Challenges with Compression and Pangeo</a></em> <a href="https://www.egu22.eu/">2022 EGU General Assembly</a> session.</p> <p>Example usage of the CCMP data set in EOOffshore:</p> <ul> <li><a href="https://eooffshore.github.io/CCMP_ICS_Wind_Data.html">CCMP Wind Data for Irish Continental Shelf region</a></li> <li><a href="https://eooffshore.github.io/Offshore_Wind_AOI.html">Offshore Wind in Irish Areas Of Interest</a></li> <li><a href="https://eooffshore.github.io/Comparison_Wind_Power.html">Comparison of Offshore Wind Speed Extrapolation and Power Density Estimation</a></li> </ul> <p>Note:</p> <ul> <li>This <a href="https://rda.ucar.edu/datasets/ds745.1/">NCAR/UCAR Research Data Archive page</a> states that the CCMP license is CC-BY-4.0. A separate CCMP data set has been previously used in the <a href="https://gallery.pangeo.io/repos/cgentemann/pangeo_ccmp/">NASA CCMP Winds Pangeo Gallery notebook</a>.</li> </ul>
EOOffshore: Sentinel-1 Wind Data for the Irish Continental Shelf Region
<p><a href="https://eooffshore.github.io">EOOffshore</a> is a <a href="https://www.seai.ie/">Sustainable Energy Authority of Ireland (SEAI)</a> funded <a href="https://www.seai.ie/data-and-insights/seai-research/research-projects/details/building-upon-copernicus-earth-observation-services-to-augment-wind-measurement-coverage-of-the-oredp-offshore-renewable-energy-assessment-areas">project</a>, which commenced in June 2020 in the <a href="https://www.ucd.ie/physics/">School of Physics</a> in <a href="https://www.ucd.ie/">University College Dublin (UCD)</a>. It presents a case study that demonstrates the utility of the <a href="https://pangeo.io/">Pangeo</a> software ecosystem in the development of offshore wind speed and power density estimates, increasing wind measurement coverage of offshore renewable energy assessment areas in the <a href="https://www.marine.ie/Home/site-area/irelands-marine-resource/real-map-ireland">Irish Continental Shelf (ICS)</a> region. It has involved the creation of a new <a href="https://eooffshore.github.io/datasets.html">wind data catalog</a> for this region, consisting of a collection of analysis-ready, cloud-optimized (ARCO) datasets featuring up to 21 years of available in situ, reanalysis, and satellite observation wind data products.</p> <p>The <a href="https://www.copernicus.eu/en/about-copernicus">European Union Copernicus Earth Observation (EO) programme</a> and services are based on data collected from EO satellites, in particular, the <a href="https://sentinels.copernicus.eu/web/sentinel/home">Sentinel satellite missions</a>. This includes the <a href="https://sentinel.esa.int/web/sentinel/missions/sentinel-1">Sentinel-1 mission</a>, which consists of C-band Synthetic Aperture Radar (SAR) imaging satellites in polar orbit. One of its main objectives is the provision of ocean monitoring services, where its <a href="https://sentinel.esa.int/web/sentinel/user-guides/sentinel-1-sar/product-types-processing-levels/level-2">Level-2 Ocean (OCN)</a> products include an Ocean WInd field (OWI) component. This provides gridded estimates of wind speed and direction at 10 m above the surface, with a typical spatial resolution of 1 km. This particular catalog data set (<em>eooffshore_ics_level3_sentinel1_ocn.zarr.tar.gz</em>) contains 2015-2021 OCN wind products for the ICS region, which were retrieved from the <a href="https://scihub.copernicus.eu/">Copernicus Open Access Hub (COAH)</a> and the <a href="https://search.asf.alaska.edu/#/">Alaska Satellite Facility (ASF)</a>. The data set was used in the EOOffshore project outputs presented (<em><a href="https://meetingorganizer.copernicus.org/EGU22/EGU22-2746.html">Scalable Offshore Wind Analysis With Pangeo</a></em>) at the <em><a href="https://meetingorganizer.copernicus.org/EGU22/session/42046">Meeting Exascale Computing Challenges with Compression and Pangeo</a></em> <a href="https://www.egu22.eu/">2022 EGU General Assembly</a> session.</p> <p>Description and example usage of the Sentinel-1 data set in EOOffshore:</p> <ul> <li><a href="https://eooffshore.github.io/Sentinel-1_ICS_Wind_Data.html">Sentinel-1 Wind Data for Irish Continental Shelf region</a></li> <li><a href="https://eooffshore.github.io/Offshore_Wind_AOI.html">Offshore Wind in Irish Areas Of Interest</a></li> <li><a href="https://eooffshore.github.io/Comparison_Wind_Power.html">Comparison of Offshore Wind Speed Extrapolation and Power Density Estimation</a></li> </ul> <p>As requested by the <a href="https://sentinels.copernicus.eu/documents/247904/690755/Sentinel_Data_Legal_Notice">Legal Notice on the use of Copernicus Sentinel Data and Service Information</a>, this data set:</p> <ul> <li>Contains modified Copernicus Sentinel data [2015 - 2021]</li> </ul>
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.