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57 results for “waves and winds”

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zenodo52/100

Laboratory measurements of wind, waves, and turbulence in hurricane conditions in the ASIST wind-wave facility

Laboratory measurements of wind, waves, and turbulence in hurricane conditions, collected in September and October of 2018 and January of 2019 in the ASIST wind-wave facility, in the SUSTAIN laboratory at the University of Miami. This dataset includes two experiments, one with fresh water ("fresh") and another with seawater ("salt"), each in 10-m winds from 0 to approximately 42 m/s. Data include: <ul> <li>3-dimensional wind velocity at 20 Hz sampling frequency from Campbell Scientific IRGASON sonic anemometer (collected in 2018)</li> <li>2-dimensional (along-tank and vertical) wind velocity at 1000 Hz sampling frequency from TSI IFA-300 hot film anemometer (collected in 2018)</li> <li>1-dimensional (along-tank) wind velocity at 10 Hz sampling frequency from a pitot anemometer (collected in 2018 and 2019)</li> <li>3-dimensional water velocity in the bottom 5 cm of the tank at 100 Hz sampling velocity from Nortek Vectrino velocimeter. (collected in 2018)</li> <li>Water elevation at 20 Hz sampling frequency at 6 locations in the tank from Senix Toughsonic 30 ultrasonic distance meters (collected in 2019)</li> <li>Along-tank static air pressure difference at 10 Hz sampling frequency from Baratron MKS 226 differential pressure transducer (collected in 2019)</li> </ul> All data is in NetCDF4 format. Experiment set up and positions of instruments are documented in more detail in Curcic and Haus (2020), Revised estimates of ocean surface drag in strong winds, Geophysical Research Letters, <a href="https://agupubs.onlinelibrary.wiley.com/doi/10.1029/2020GL087647">https://agupubs.onlinelibrary.wiley.com/doi/10.1029/2020GL087647</a>. Produced as part of the National Science Foundation Award #1745384, titled "Air-Sea Momentum Transfer in Extreme Wind Conditions". Contact: Milan Curcic <mcurcic@miami.edu>

opencc-by-4.0May 2020View details →

zenodo48/100

Wind WAVES TDSF Dataset

Wind Spacecraft: The Wind spacecraft (<a href="https://wind.nasa.gov">https://wind.nasa.gov</a>) was launched on November 1, 1994 and currently orbits the first Lagrange point between the Earth and sun.  A comprehensive review can be found in <a href="https://ui.adsabs.harvard.edu/abs/2021RvGeo..5900714W/abstract">Wilson et al. [2021]</a>.  It holds a suite of instruments from gamma ray detectors to quasi-static magnetic field instruments, Bo.  The instruments used for this data product are the fluxgate magnetometer (MFI) [<a href="https://ui.adsabs.harvard.edu/abs/1995SSRv...71..207L/abstract">Lepping et al., 1995</a>] and the radio receivers (WAVES) [<a href="https://ui.adsabs.harvard.edu/abs/1995SSRv...71..231B/abstract">Bougeret et al., 1995</a>].  The MFI measures 3-vector Bo at ~11 samples per second (sps); WAVES observes electromagnetic radiation from ~4 kHz to >12 MHz which provides an observation of the upper hybrid line (also called the plasma line) used to define the total electron density and also takes time series snapshot/waveform captures of electric and magnetic field fluctuations, called TDS bursts herein. WAVES Instrument: The WAVES experiment [<a href="https://ui.adsabs.harvard.edu/abs/1995SSRv...71..231B/abstract">Bougeret et al., 1995</a>] on the Wind spacecraft is composed of three orthogonal electric field antenna and three orthogonal search coil magnetometers.  The electric fields are measured through five different receivers: Low Frequency FFT receiver called FFT (0.3 Hz to 11 kHz), Thermal Noise Receiver called TNR (4-256 kHz), Radio receiver band 1 called RAD1 (20-1040 kHz), Radio receiver band 2 called RAD2 (1.075-13.825 MHz), and the Time Domain Sampler (TDS).  The electric field antenna are dipole antennas with two orthogonal antennas in the spin plane and one spin axis stacer antenna. The TDS receiver allows one to examine the electromagnetic waves observed by Wind as time series waveform captures. There are two modes of operation, TDS Fast (TDSF) and TDS Slow (TDSS). TDSF returns 2048 data points for two channels of the electric field, typically Ex and Ey (i.e. spin plane components), with little to no gain below ~120 Hz (the data herein has been high pass filtered above ~150 Hz for this reason). TDSS returns four channels with three electric(magnetic) field components and one magnetic(electric) component.  The search coils show a gain roll off ~3.3 Hz [e.g., see <a href="https://ui.adsabs.harvard.edu/abs/2010JGRA..11512104W/abstract">Wilson et al., 2010</a>; <a href="https://ui.adsabs.harvard.edu/abs/2012GeoRL..39.8109W/abstract">Wilson et al., 2012</a>; <a href="https://ui.adsabs.harvard.edu/abs/2013JGRA..118....5W/abstract">Wilson et al., 2013</a> and references therein for more details]. The original calibration of the electric field antenna found that the effective antenna lengths are roughly 41.1 m, 3.79 m, and 2.17 m for the X, Y, and Z antenna, respectively.  The +Ex antenna was broken twice during the mission as of June 26, 2020.  The first break occurred on August 3, 2000 around ~21:00 UTC and the second on September 24, 2002 around ~23:00 UTC.  These breaks reduced the effective antenna length of Ex from ~41 m to 27 m after the first break and ~25 m after the second break [e.g., see <a href="https://ui.adsabs.harvard.edu/abs/2014GeoRL..41..266M/abstract">Malaspina et al., 2014</a>; <a href="https://ui.adsabs.harvard.edu/abs/2016JGRA..121.9369M/abstract">Malaspina & Wilson, 2016</a>]. TDS Bursts: TDS bursts are waveform captures/snapshots of electric and magnetic field data.  The data is triggered by the largest amplitude waves which exceed a specific threshold and are then stored in a memory buffer.  The bursts are ranked according to a quality filter which mostly depends upon amplitude.  Due to the age of the spacecraft and ubiquity of large amplitude electromagnetic and electrostatic waves, the memory buffer often fills up before dumping onto the magnetic tape drive.  If the memory buffer is full, then the bottom ranked TDS burst is erased every time a new TDS burst is sampled.  That is, the newest TDS burst sampled by the instrument is always stored and if it ranks higher than any other in the list, it will be kept.  This results in the bottom ranked burst always being erased.  Earlier in the mission, there were also so called honesty bursts, which were taken periodically to test whether the triggers were working properly.  It was found that the TDSF triggered properly, but not the TDSS.  So the TDSS was set to trigger off of the Ex signals. A TDS burst from the Wind/WAVES instrument is always 2048 time steps for each channel.  The sample rate for TDSF bursts ranges from 1875 samples/second (sps) to 120,000 sps.  Every TDS burst is marked a unique set of numbers (unique on any given date) to help distinguish it from others and to ensure any set of channels are appropriately connected to each other.  For instance, during one spacecraft downlink interval there may be 95% of the TDS bursts with a complete set of channels (i.e., TDSF has two channels, TDSS has four) while the remaining 5% can be missing channels (just example numbers, not quantitatively accurate).  During another downlink interval, those missing channels may be returned if they are not overwritten.  During every downlink, the flight operations team at NASA Goddard Space Fligth Center (GSFC) generate level zero binary files from the raw telemetry data.  Those files are filled with data received on that date and the file name is labeled with that date.  There is no attempt to sort chronologically the data within so any given level zero file can have data from multiple dates within.  Thus, it is often necessary to load upwards of five days of level zero files to find as many full channel sets as possible.  The remaining unmatched channel sets comprise a much smaller fraction of the total. All data provided here are from TDSF, so only two channels.  Most of the time channel 1 will be associated with the Ex antenna and channel 2 with the Ey antenna.  The data are provided in the spinning instrument coordinate basis with associated angles necessary to rotate into a physically meaningful basis (e.g., GSE). TDS Time Stamps: Each TDS burst is tagged with a time stamp called a spacecraft event time or SCET.  The TDS datation time is sampled after the burst is acquired which requires a delay buffer.  The datation time requires two corrections.  The first correction arises from tagging the TDS datation with an associated spacecraft major frame in house keeping (HK) data.  The second correction removes the delay buffer duration.  Both inaccuracies are essentially artifacts of on ground derived values in the archives created by the WINDlib software (K. Goetz, Personal Communication, 2008) found at <a href="https://github.com/lynnbwilsoniii/Wind_Decom_Code">https://github.com/lynnbwilsoniii/Wind_Decom_Code</a>. The WAVES instrument's HK mode sends relevant low rate science back to ground once every spacecraft major frame.  If multiple TDS bursts occur in the same major frame, it is possible for the WINDlib software to assign them the same SCETs.  The reason being that this top-level SCET is only accurate to within +300 ms (in 120,000 sps mode) due to the issues described above (at lower sample rates, the error can be slightly larger).  The time stamp uncertainty is a positive definite value because it results from digitization rounding errors.  One can correct these issues to within +10 ms if using the proper HK data. *** The data stored here have not corrected the SCETs! *** The 300 ms uncertainty, due to the HK corrections mentioned above, results from WINDlib trying to recreate the time stamp after it has been telemetered back to ground.  If a burst stays in the TDS buffer for extended periods of time (i.e., >2 days), the interpolation done by WINDlib can make mistakes in the 11th significant digit.  The positive definite nature of this uncertainty is due to rounding errors associated with the onboard DPU (digital processing unit) clock rollover.  The DPU clock is a 24 bit integer clock sampling at &sim;50,018.8 Hz.  The clock rolls over at &sim;5366.691244092221 seconds, i.e., (16*224)/50,018.8. The sample rate is a temperature sensitive issue and thus subject to change over time.  From a sample of 384 different points on 14 different days, a statistical estimate of the rollover time is 5366.691124061162 ± 0.000478370049 seconds (calculated by Lynn B. Wilson III, 2008).  Note that the WAVES instrument team used UR8 times, which are the number of 86,400 second days from 1982-01-01/00:00:00.000 UTC. The method to correct the SCETs to within +10 ms, were one to do so, is given as follows: <ol> <li>Retrieve the DPU clock times, SCETs, UR8 times, and DPU Major Frame Numbers from the WINDlib libraries on the VAX/ALPHA systems for the TDSS(F) data of interest.</li> <li>Retrieve the same quantities from the HK data.</li> <li>Match the HK event number with the same DPU Major Frame Number as the TDSS(F) burst of interest.</li> <li>Find the difference in DPU clock times between the TDSS(F) burst of interest and the HK event with matching major frame number (Note: The TDSS(F) DPU clock time will always be greater than the HK DPU clock if they are the same DPU Major Frame Number and the DPU clock has not rolled over).</li> <li>Convert the difference to a UR8 time and add this to the HK UR8 time.  The new UR8 time is the corrected UR8 time to within +10 ms.</li> <li>Find the difference between the new UR8 time and the UR8 time WINDlib associates with the TDSS(F) burst. Add the difference to the DPU clock time assigned by WINDlib to get the corrected DPU clock time (Note: watch for the DPU clock rollover).</li> <li>Convert the new UR8 time to a SCET using either the IDL WINDlib libraries or TMLib (STEREO S/WAVES software) libraries of available functions.  This new SCET is accurate to within +10 ms.</li> </ol> One can find a UR8 to UTC conversion routine at <a href="https://github.com/lynnbwilsoniii/wind_3dp_pros">https://github.com/lynnbwilsoniii/wind_3dp_pros</a> in the ~/LYNN_PRO/Wind_WAVES_routines/ folder. Examples of good waveforms can be found in the notes PDF at <a href="https://wind.nasa.gov/docs/wind_waves.pdf">https://wind.nasa.gov/docs/wind_waves.pdf</a>. Data Set Description Each Zip file contains 300+ IDL save files; one for each day of the year with available data.  This data set is not complete as the software used to retrieve and calibrate these TDS bursts did not have sufficient error handling to handle some of the more nuanced bit errors or major frame errors in some of the level zero files.  There is currently (as of June 27, 2020) an effort (by Keith Goetz et al.) to generate the entire TDSF and TDSS data set in one repository to be put on SPDF/CDAWeb as CDF files.  Once that data set is available, it will supercede and replace this one as it will be more complete and all SCETs will be corrected. When one restores any given IDL save file, they will find an IDL structure named struc.  Inside are several tags and for each date there are T number of TDSF bursts, each of which has K (= 2048) time stamps.  The structure contains the following tags: <ul> <li>SCETS:  [T]-Element [string] array of SCETs at start of TDS burst with format 'YYYY-MM-DD/hh:mm:ss.xxx'</li> <li>UNIX:  [T,K]-Element [double] array of quasi-Unix times (i.e., converted from UTC times without removing leap seconds)</li> <li>CH1EXDA_CH2EXYZAC_WAVES:  [T,K,5]-Element [float] array of electric fields [mV/m] in spinning WAVES coordinates, where the components are: <ul> <li>[*,*,0] = Ex in DC-coupled mode from channel 1</li> <li>[*,*,1] = Ex in AC-coupled mode from channel 1</li> <li>[*,*,2] = Ex in AC-coupled mode from channel 2</li> <li>[*,*,3] = Ey in AC-coupled mode from channel 2</li> <li>[*,*,4] = Ez in AC-coupled mode from channel 2</li> </ul> </li> <li>SRATE:  [T]-Element [float] array of sample rates for each TDSF burst [Hz]</li> <li>FILTER_FREQ:  [T]-Element [float] array of soft low pass filter frequencies [Hz]</li> <li>EVENT_NUM:  [T]-Element [long] array of TDS event numbers [N/A]</li> <li>UR8_START:  [T]-Element [double] array of UR8 times at start of TDS burst [# of 86,400 second days from 1982-01-01/00:00:00.000 UTC]</li> <li>EX_START_ANG:  [T]-Element [float] array of counter-clockwise angles between the +Ex antenna and the spacecraft-to-sun line at the start of each TDS burst [e.g., see <a href="https://ui.adsabs.harvard.edu/abs/2016JGRA..121.9369M/abstract">Malaspina & Wilson, 2016</a> for definitions]</li> <li>EX___END_ANG:  [T]-Element [float] array of counter-clockwise angles between the +Ex antenna and the spacecraft-to-sun line at the end of each TDS burst [e.g., see <a href="https://ui.adsabs.harvard.edu/abs/2016JGRA..121.9369M/abstract">Malaspina & Wilson, 2016</a> for definitions]</li> <li>THETA_AX:  [T]-Element [float] array of average counter-clockwise angles between the +Ex antenna and the Earth-to-sun line [e.g., see <a href="https://ui.adsabs.harvard.edu/abs/2016JGRA..121.9369M/abstract">Malaspina & Wilson, 2016</a> for definitions]</li> <li>BO_GSE:  [T,3]-Element [float] array of Bo 3-vectors [nT] in GSE coordinate basis at start of TDS bursts</li> <li>SC_GSE_POS:  [T,3]-Element [float] array of spacecraft position 3-vectors [RE] in GSE coordinate basis at start of TDS bursts</li> <li>SC_SPIN_RATE:  [T]-Element [float] array of spacecraft spin rates [deg/s] at start of TDS bursts</li> <li>JULIAN:  [T]-Element [double] array of Julian day numbers [N/A]</li> <li>EARTH_WAKE:  [T]-Element [byte] array of logical values defining whether spacecraft is in Earth's optical wake [1 = TRUE, 0 = FALSE]</li> <li>LUNAR_WAKE:  [T]-Element [byte] array of logical values defining whether spacecraft is in the lunar optical wake [1 = TRUE, 0 = FALSE]</li> <li>CHANNEL_1_LABS:  [T]-Element [string] array of channel 1 labels defining the source type shown in the CH1EXDA_CH2EXYZAC_WAVES tag</li> <li>CHANNEL_2_LABS:  [T]-Element [string] array of channel 2 labels defining the source type shown in the CH1EXDA_CH2EXYZAC_WAVES tag</li> <li>CHANNEL_1_INT:  [T]-Element [integer] array of channel 1 labels [obsolete]</li> <li>CHANNEL_2_INT:  [T]-Element [integer] array of channel 2 labels [obsolete]</li> <li>UNITS:  [19]-Element [string] array defining the units of each structure tag</li> <li>NOTES:  [11]-Element [string] array defining some useful things about the data set</li> </ul> Note that the angles have all been shifted by +360 degrees to avoid rollover issues.  The reason being that the spacecraft rotates such that EX_START_ANG is always larger than EX___END_ANG.  All angles herein are counter-clockwise angles in the GSE basis. Rotating Bo from GSE to WAVES coordinates Since the quasi-static magnetic field is given in GSE coordinates, one may want to examine the wave fields relative to the Bo direction.  If you examine the attached figure labeled GSE-Basis_to_WAVES_rotation_2.jpg, you will see how to rotated from GSE into WAVES coordinates.  The angle \(\phi\) in the image corresponds to the THETA_AX structure tag values.  The angle \(\pi\) in the image is the actual value of pi in radians.  This is is necessary to flip the GSE vectors to match the WAVES +z-axis, which is pointed toward the south ecliptic pole, not the north like Z-GSE. It is important to rotate Bo into WAVES rather than the electric fields into magnetic field-aligned coordinates since each antenna has different noise and response functions.

opencc-by-4.0Jun 2020View details →

zenodo48/100

Hydroelastic response of the scaled model of a floating offshore wind turbine platform in waves: HELOFOW Project Database

This dataset contains the data measured during the HELOFOW model test campaign, performed at the Ocean and Hydrodynamic Engineering wave tank of Ecole Centrale Nantes (ECN): decay tests, regular wave tests and irregular waves tests. The preprocessed measured data is contained in MAT files. The model, the measurements and the tests are described in the appended Excel files. A Matlab(R) function is given as a short example to show how the MAT files are structured and how data may be handled for a plot.  As stated in the reference paper (Leroy et al., Ocean Engineering, 2022): "As the size of floating wind turbines continues to increase, floating platforms reach dimensions that make their elastic and hydro-elastic behaviour significant. Several works in connection with the numerical modelling of the elastic behaviour of these wind turbines have been carried out but few validation data are available. This study focuses on the hydro-elastic response of a large floating wind turbine, in regular waves and severe sea-states. A new experimental wind turbine model has been designed to represent a 1:40 Froude-scaled spar platform carrying the DTU 10 MW turbine. The main challenge is here to reproduce a 1st bending mode frequency and hydrodynamic loads representative of a realistic large floating wind turbine. The platform model is made of a flexible backbone, reproducing the correct flexibility, and light floaters fixed on it provide the correctly scaled geometry. This experimental model is tested in various conditions including regular waves of several periods and steepness, and irregular waves of various intensity, including extreme 50-year return period conditions."   This work was carried out within the framework of the WEAMEC, West Atlantic Marine Energy Community, and with funding from the Pays de la Loire Region and Europe (European Regional Development Fund).  HELOFOW project on <a href="https://www.weamec.fr/en/projects/helofow/">the WEAMEC website</a>.

opencc-by-4.0Feb 2022View details →

zenodo44/100

Adriatic Sea wind-wave climate years 1981-2010 and 2021-2050 (RCP4.5 and RCP8.5)

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). Reference periods: 1) Historical climate: years 1981-2010 2) Future climate: years 2021-2050 (IPCC scenario RCP4.5 and RCP8.5)

opencc-by-4.0Feb 2022View details →

zenodo44/100

Adriatic Sea wind and wave time series years 1981-2010 and 2021-2050 (RCP4.5 and RCP8.5)

Wind and wave time series for 27 stations in the Adriatic Sea. Variables: - 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) - significant wave height (hs) and peak wave period (tp) from WAVEWATCH III v6.07 (2 km) forced with the scaled ERA5 wind fields Reference periods: - Historical climate: years 1981-2010 - Future climate: years 2021-2050 (IPCC scenario RCP4.5 and RCP8.5)

opencc-by-4.0Feb 2022View details →

zenodo44/100

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)

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 Natural Hazards and Earth System Sciences (NHESS) by Pranavam Ayyappan Pillai et al., 2022. 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.

opencc-by-4.0Mar 2022View details →

zenodo44/100

Wave and wind data from the Helsinki archipelago and Gulf of Finland

Data source: Finnish Meteorological Institute This is wave and meteorological data collected in the Helsinki archipelago and GoF durin 2012-2018. Each file contains data and metadata for one location. The Gulf of Finland (GoF) site has a separate file for all integrated data (WaveData_GoF_integrated.nc), while separate files (WaveData_GoF_spectra_2016a.nc etc) exist for the spectra. This is because both a DWR Mk-III and DWR4/ACM wave buoy was used, and they have different sampling frequencies. Coinciding wind data is embedded in each file. The data are described in the publication "The wave spectrum in archipelagos", Ocean Science, 2019, DOI: 10.5194/os-15-1469-2019

opencc-by-4.0Oct 2019View details →

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Tri-hourly dataset of wind and wave anomalies of the GFS and WAVEWATCH III models in the entire tropic region (TROPWA).

This dataset contains the anomalies of the total height and peak period of the waves and of the zonal and meridional components of the wind at 10 m above the sea surface obtained from the outputs of the GFS and WAVEWATCH III coupled global models in the entire tropical region (180°W to 178.75°E longitude/30°S to 30°N latitude), with a spatial resolution of 1.25°x1°. It is made up of two files in NetCDF format, where the data for wind anomalies (speed module and its zonal and meridional components) and wave anomalies (total wave height and peak period) are contained separately. This dataset was created in order to study all types of tropical storms, cold fronts and other physical processes that influence significant changes in wave parameters, as well as for the design and construction of coastal engineering works. The authors thanks to Tropical Atlantic Interdisciplinary Laboratory on physical, biogeochemical, ecological and human dynamics (IJL TAPIOCA).

opencc-by-4.0Jan 2019View details →

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Wind and Waves in Tropical Cyclones (1985-2022)

Dataset contains information on wind speed and wave height in Tropical Cyclones (TCs) obtained from the Best Track Data during the period from 1985 to 2022 and along-track altimeter measurements from 17 satellites in 1985-2018 (IMOS archive, Ribal and Young, 2019) and in 2020-2022 (CMEMS archive). For each TC, in which the maximum wind speed exceeded 30 m/s (1905 cases), files are created to combine altimetry data on the significant wave height and wind speed in the cyclone area (+-7 degrees from TC center) and information on each cyclone trajectory and its main characteristics (maximum wind speed, radius of maximum winds, heading velocity vector). To describe the radial distribution of wind speed, standard data on distances from the cyclone center to points with wind speeds of 34, 50, and 64 knots are approximated with the analytical function suggested by Holland (1980). For each cyclone, graphical files are provided to illustrate the evolution of every TC parameters, the quality of the wind prifile approximation, the location of altimeter tracks, and the along-track values of significant wave height and wind speed.  Files given in NetCDF and MAT formats contain -  TC coordinates, heading velocity and direction, maximum wind speed and radius of maximum wind speed every 3 hours -  parameters of wind radial distributions for TC central and far zone every 3 hours -  altimetry data: time and along-track significant wave height and wind speed in TC region (+-7 degrees from TC center) from satellites GEOSAT, ERS-1, TOPEX, ERS-2, GFO, ENVISAT, JASON-1, JASON-2, JASON-3, SARAL/AltiKa, CryoSat-2, HY-2A, HY-2B, CFOSAT, Sentinel-3A, Sentinel-3B, Sentinel-6А   To form the dataset, the NOAA archive with data on tropical cyclones (https://www.ncei.noaa.gov/data/international-best-track-archive-for-climate-stewardship-ibtracs/v04r00/access/netcdf/, DOI :10.48670/moi-00178) and archives CMEMS (https://resources.marine.copernicus.eu/products, DOI:10.48670/moi-00178) and IMOS (https://catalogue-imos. aodn.org.au/geonetwork/srv/rus/catalog.search#/metadata/c6d5c7b4-323e-4979-9364-cdfa59684163, DOI:10.26198/5c184f4a5cd2e) with altimetry data were used .

opencc-by-4.0Mar 2023View details →

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Pertubation Profiles Dataset used for "Convection-generated gravity waves in the tropical lower stratosphere from Aeolus wind profiling and ERA5 reanalysis"

These are the perturbation profiles, from 5km to 29.5km, with a 500m grid. In the study, we picked up the data between tropopause-1km to 22km, which was then squared, smoothed, and averaged into one value. We used a 14 points moving average for the smoothing. The data is from 2018-09 to 2022-09, based on the Aeolus L2B Rayleigh clear wind, using only quality flag 1 data. Please email me at mathieu.ratynski@estaca.eu if you're interested in the 100m resolution version, used in the final version of the manuscript.

opencc-by-4.0Jul 2023View details →

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Vertical Wind and Temperature Gravity Wave Perturbations Derived from Na Lidar Observations

The gravity wave perturbations associated with vertical wind and temperature in the mesopause region for heat flux calculations.

opencc-by-4.0Jul 2020View details →

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AIMS - Earth Observation Satellite Data of Wave and Wind in the Tyrrhenian Sea

This dataset is part of the AIMS project (Artificial Intelligence to Monitor our Seas), which has the vision to develop and validate novel Artificial Intelligence (AI) algorithms to unlock the true potential of remote monitoring and enable a faster transition to a climate neutral society and economy: the AI algorithms will leverage the advantages of usual monitoring methodologies of the features of waves and offshore wind, and eventually overcome their intrinsic limitations. The value and resolution of sparse measurements of satellites and unevenly-distributed in-situ instruments will be increased, hence leading to a significant reduction of the cost and execution time of data collection, ultimately making knowledge wider and more accessible. In particular, this dataset aggregates earth observation satellite data from 10 different satellites, measureing the significant wave height and the wind speed at 10 meters above sea leavel in the Tyrrhenian Sea, from January 2021 to May 2024.

opencc-by-4.0May 2024View details →

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Wind and Wave Measurements in the Oslo Fjord

Measurements of surface waves were conducted with the aim of measuring waves in the capillary-gravity regime as part of a master's thesis. An in-house built sensor equipped with an IMU, which measures acceleration and angular velocity in the unit's frame of reference, was used for a period of 26 hours. The sensor has a length of 2.5 cm and a width of 2 cm. The setup included the preprogrammed logger, SparkFun OpenLog Artemis, to record the sensor data.  Wind measurements were also made during the same period using a commercial 3-cup anemometer, although wind direction was not recorded. The equipment was mounted on a jetty at Lindøya, in the inner Oslo Fjord.

opencc-by-4.0Jun 2024View details →

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Diurnal waves forced by horizontal convergence of near-surface winds on Mars

This site provides public access to data used in the following journal article:  D. Hinson and J. Wilson (2023). Diurnal waves forced by horizontal convergence of near-surface winds on Mars, Icarus 394, 115420, doi: 10.1016/j.icarus.2022.115420  <a href="https://ntrs.nasa.gov/api/citations/20230001896/downloads/20230001896-Hinson_2022_Diurnal_waves%5B1%5D.pdf">https://ntrs.nasa.gov/api/citations/20230001896/downloads/20230001896-Hinson_2022_Diurnal_waves%5B1%5D.pdf</a>

opencc-by-4.0Dec 2022View details →

zenodo40/100

Dataset in support of "Laboratory wave and stress measurements quantify the aerodynamic sheltering in extreme winds" by Tan et al. (2023, JGR: Oceans)

Data introduction:     There are three datasets used in this research: dataset 1 from Wind-Only (WO) experiment, dataset 2 from JONSWAP experiment with 10-cm significant wave height (J10), and dataset 3 from monochromatic wave experiment with 7.5-cm amplitude (M7.5).     Each dataset contains quality-controlled data of the respective experiment mentioned above. The data files are in the mat (MATLAB) format. There are 9 mat files in each dataset, and each file represents data collected under a specific wind forcing condition, with the fan frequency in the 10-50 Hz range with 5 Hz interval.     Each file contains four variables: seg (water elevation time series collected by the wave-wire with the units of m, demeaned and detrended), U (along-tank, downwind component of wind sampled by the IRGASON anemometer with the units of m/s), V (cross-tank component of wind sampled by the IRGASON anemometer with the units of m/s), and W (vertical component of wind collected by the IRGASON anemometer with the units of m/s). All four variables were collected at a sampling frequency of 20 Hz.

opencc-by-4.0Mar 2023View details →

zenodo40/100

Data set from long-term wave, wind and response monitoring of the Bergsøysund Bridge

Wind, wave, displacement and acceleration data have been collected in a measurement campaign on the Bergsøysund Bridge between the years 2014 and 2018. The data set is now available in this open-access research entry, for free access and download. The data is collected in two h5-files (hierachical data format), with sampling rates 2 Hz and 10 Hz, downsampled from the raw sampling rate of 200 Hz. Note that the data has undergone some minimal signal processing and adjustment, in line with that applied to the Hardanger Bridge data described in Fenerci et al. (2021). Tools and examples for import, data visualization and initial analysis are given in the opyndata Python package available on GitHub (Kvåle, 2022). Furthermore, a document briefly describing the hierarchy and structure of the data, is given. For more details on the measurement system and the bridge, it is referred to Kvåle and Øiseth (2017). The updated, copyrighted version of the appended preprint is published by ASCE with the following DOI: <a href="https://doi.org/10.1061/JSENDH.STENG-12095">10.1061/JSENDH.STENG-12095</a>

opencc-by-4.0Jan 2022View details →

zenodo40/100

Experimental Database of deterministic wave prediction built from synchronous measurements from an X-band pulse radar and met-ocean sensors deployed on the Floatgen floating wind turbine and its vicinity on SEM-REV test site.

This dataset is a deliverable of the FLOATECH project, funded under the European Union’s Horizon 2020 research and innovation programme under grant agreement No 101007142. The aim of this dataset is a result of the field experiments carried out at the Floatgen FOWT located at the SEM-REV test site.

opencc-by-4.0Dec 2022View details →

dryad40/100

Data for: Ocean surface wave slopes and wind-wave alignment observed in Hurricane Idalia

Open the record for dataset details and reuse information.

publicFeb 2025View details →

zenodo36/100

ASIT Wind and Waves 16-17 October 2019

Measurements made from the Air-Sea Interaction Tower south of Martha's Vineyard on 16-17 October 2019. Waves were measured by Lidar at 10 Hz. Wind speed at 10 m height is a 20 min average.

opencc-by-4.0Dec 2021View details →

zenodo36/100

Atmospheric visibility inferred from continuous-wave Doppler wind lidar, data set

Visibility data from Pershore, UK, between 2018 and 2020

opencc-by-4.0Mar 2022View details →

ScienceDex guides

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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.

Compare curated 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.

allen-brain-atlas

neuroscienceopenDocumentation, web resources, and API references are available online.

Last verified 2026-04-30Open record

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.

abode-home-cage

behavioral-neuroscienceopenThe DataShare record exposes download links for annotations, documentation, license text, and the zipped per-snippet data directory.

Last verified 2026-04-30Open record

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.

dandi-nwb

electrophysiologyopenPublished Dandiset metadata and archive endpoints are available through the production DANDI API.

Last verified 2026-04-30Open record

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.

ibl

behavioral-neuroscienceopenPublic sessions can be searched and loaded from the IBL public data server through ONE.

Last verified 2026-04-29Open record

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.

openneuro

neuroscienceopenPublished datasets are available on demand over the internet.

Last verified 2026-04-29Open record