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57 results for “waves and winds”
Ocean surface wind estimation from waves based on small GPS buoy observations in a bay and the open ocean
<p>This is dataset of ocean surface wave and wind used in the paper "Ocean surface wind estimation from waves based on small GPS buoy observations in a bay and the open ocean" by Shimura et al. (2022, JGR-Oceans, <a href="https://doi.org/10.1029/2022JC018786">https://doi.org/10.1029/2022JC018786</a> ).</p> <p>"data_bayObservation.nc" contains the observed wind, estimated wind, and observed wave spectral data during the bay observations.</p> <p>"data_openOceanObservation.nc" contains the reanalysis wind, estimated wind, and observed wave spectral data during the open ocean observations.</p> <p> </p>
Data Intervals To Study Statistics of Whistler Waves in the Solar Wind
<p>This dataset contains a list of intervals that were used to study the statistics of whistler waves in the solar wind. It is created in companion to a manuscript submitted to ApJ.</p>
Data Supporting "Air-Sea Momentum Exchange with Explicit Wind-Wave-Current Coupling and Effects on Hurricane Structure and Impacts"
<p>This dataset produced the figures for the Air-Sea Momentum Exchange with Explicit Wind-Wave-Current Coupling and Effects on Hurricane Structure and Impacts Manusrcipt.</p>
SWAN simulations: Local wind-waves in partial sea ice cover
<p>This repository contains data and scripts associated with the study: <em>Scaling simulations of local wind-waves amid sea ice floes </em>by S. Brenner & C. Horvat.</p>
Simulation datasets for: Intense surface winds from gravity wave breaking in simulations of a destructive macroburst
<p>Shortly after 0600 UTC (midnight local time) 9 June 2020, a convective line produced severe winds across parts of northeast Colorado that caused extensive damage, especially in the town of Akron. High-resolution observations showed gusts exceeding 50 m s<sup>−1</sup>, accompanied by extremely large pressure fluctuations, including a 5-hPa pressure surge in 19 s immediately following the strongest winds and a 15-hPa pressure drop in the following 3 min. Numerical simulations of this event (using the WRF Model) and with horizontally homogeneous initial conditions (using Cloud Model 1) reveal that the severe winds in this event were associated with gravity wave dynamics. In a very stable postfrontal environment, elevated convection initiated and led to a long-lived gravity wave. Strong low-level vertical wind shear supported the amplification and eventual breaking of this wave, resulting in at least two sequential strong downbursts. This wave-breaking mechanism is different from the usual downburst mechanism associated with negative buoyancy resulting from latent cooling. The model output reproduces key features of the high-resolution observations, including similar convective structures, large temperature and pressure fluctuations, and intense near-surface wind speeds. The findings of this study reveal a series of previously unexplored mesoscale and storm-scale processes that can result in destructive winds.</p> <p><strong>Significance Statement </strong></p> <p>Downbursts of intense wind can produce significant damage, as was the case on 9 June 2020 in Akron, Colorado. Past research on downbursts has shown that they occur when raindrops, graupel, and hail in thunderstorms evaporate and melt, cooling the air and causing it to sink rapidly. In this research, we used numerical models of the atmosphere, along with high-resolution observations, to show that the Akron downburst was different. Unlike typical lines of thunderstorms, those responsible for the Akron macroburst produced a wave in the atmosphere, which broke, resulting in rapidly sinking air and severe surface winds.</p>
Wave, Flow, and Sediment Dynamics under Strong Winds on a tidal beach
<p>The data are saved as matlab data file.</p> <p>1) ssc_fit.mat is used for producing figure 2.</p> <p>2) Reynolds_shear_stress.mat is used for producing figures 3 and 10.</p> <p>3) hydrodynamics.mat is used for producing figure 4.</p> <p>4) shear_stress_&_ssc.mat is uesd for producing figures 5 and 8.</p> <p>5) SSF.mat is used for producing figures 6 and 11.</p> <p>6) breaking_wave_criteria.mat is used for producing figure 7.</p> <p>7) mob_number.mat is used for producing figure 9.</p>
Simulation datasets for: Intense surface winds from gravity wave breaking in simulations of a destructive macroburst
Open the record for dataset details and reuse information.
Data of 'Estimating Wind Speed and Direction Using Wave Spectra'
<p>This set contains data of a Spotter wave buoy deployment and of the RMSE of wind speed and direction estimates as described in 'Estimating Wind Speed and Direction Using Wave Spectra', submitted for review to Journal of Geophysical Research.</p>
Datatset from "Coastal flooding in the Maldives induced by mean sea-level rise and wind-waves: from global to local coastal modelling"
<p>Resulting downscaled wave fields from the WaveWatch III simulations for the four main wave directions identified and six return periods (10, 20, 50, 100, 500, and 1000 years).</p>
Supplementary material for: "On the selection of time-varying scenarios of wind and ocean waves: methodologies and applications in the North Tyrrhenian Sea"
<p>All the routines to perform the clustering of the hindcast data have been developed in Matlab.<br> The code Case_study.m allows to reproduce the examples outlined in chapter 3.2, Figs. 15-16 of the manuscript:</p> <p>"On the selection of time-varying scenarios of wind and ocean waves: methodologies and applications in the North Tyrrhenian Sea"</p> <p>Submitted to "Ocean Modelling" - Elsevier, and currently is available online.</p> <p>The code needs to be fed with the data stored into the "hind_param.mat" and "clusters.mat" files, attached to this repo.<br> Please note that the wave parameters stored into "hind_param.mat" cannot be employed for commercial purposes.<br> In case they are meant to be used for research, reference has to be made to:</p> <p>- Mentaschi, L., Besio, G., Cassola, F., & Mazzino, A. (2013).<br> Developing and validating a forecast/hindcast system for the Mediterranean Sea.<br> Journal of Coastal Research, 65(sp2), 1551-1556.</p> <p>- Mentaschi, L., Besio, G., Cassola, F., & Mazzino, A. (2015).<br> Performance evaluation of Wavewatch III in the Mediterranean Sea.<br> Ocean Modelling, 90, 82-94.</p> <p>For further inquires about the hindcast data, please contact meteocean@dicca.unige.it.</p> <p>Finally, results of the sensitivity analysis of CE and W2 with respect to varying number of clusters are stored into the<br> zip files "CE.zip" and "W2.zip", respectively (Figs. from 2 to 8 of the submitted manuscript).<br> Each *dat file refers to a particular set of the data inital conditions (see manuscript), and it is labeled as follows:<br> "*_[FIRST YEAR OF TRAINING]_[LAST YEAR OF TRAINING]_[FIRST YEAR OF VALIDATION]_[LAST YEAR OF VALIDATION]_<br> [TOTAL NUMBER OF TIME STEPS]_[TIME SHIFT BETWEEN SUCCESSIVE SCENARIOS]_*_[INITIAL TIME RESOLUTION]_*_<br> [VARIABLES EMPLOYED].dat"</p> <p>For further information, please contact Giulia Cremonini at:<br> giulia.cremonini@edu.unige.it</p>
Dataset for the paper "Parameter study and optimization of floating wind-wave co-generation system based on the Taguchi method"
<p>The Dataset is the result files for the paper "Parameter study and optimization of floating wind-wave co-generation system based on the Taguchi method"</p>
Simulated wind field outputs from WRF and significant wave height results from SWAN simulations.
<p>Supplementary materials for the article titled <em>“Super Typhoons Simulation: A Comparison of WRF and Empirical Parameterized Models for High Wind Speeds.”</em> The data include simulated wind field outputs and significant wave height results from SWAN simulations.</p>
Data for Paper: Wind-wave momentum flux in steep, strongly forced, surface gravity wave conditions
<p>Laboratory measurements of wind, waves, and airside static pressure under low to moderate wind forcing (U10 ~ 6 -16 m/s) collected in Oct 2022 in the SUSTAIN wind-wave facility at the University of Miami.</p> <p>This dataset includes 11 runs, all of which contain monochromatic waves generated by the wave paddles with various wind forcing exerted above. All data is in ".mat" formate readable via MATLAB.</p> <p>Experiment set up and positions of instruments are documented in more details in the manuscript Tan et al (2024): Wind-wave momentum flux in steep, strongly forced, surface gravity wave conditions.</p> <p> Fig_3: time series static pressure p sampled at 100 Hz and horizontal/vertical wind speed (u/w) sampled at 1000 Hz</p> <p>Fig_4: Frictional velocity u_star_Rn* obtained at differenet heights (h) using frictional velocity</p> <p>Fig_5: a folder that containes the phase-averaged, spline-interpolated static pressure (p2_total), X-coordinate (long-wave phase), Y coordinate (heights above the stationary water) and the u/w at respective heights to generate airflow streamlines</p> <p>Fig_6 and 7: NSS-based phase-averaged, spline-interpolated pressure (delta_P_new).</p> <p>Fig_8: phase-averaged form stress based on measurements and NSS for all 11 runs</p> <p>Fig_9: NSS-based form stress deviation from measured form stress (NSS miscal) against wind-steepness and wave age;</p> <p>Fig_10 and 11: wave growth rate (gamma) against wave age (Cp/ustar) and two other parameterization from Fig.10</p> <p>(The revised version contains the projection of Donelan (1999) and Yang et al. (2013)'s data to the U10/Cp parameterization in panel (b) per reviewer's suggestion);</p> <p>Fig_12: form stress values (tau_form) and form stress to total stress (tau_tot) ratio.</p> <p>(The revised version contains U10 per reviewer's suggesion).</p> <p>This project was Funded in part by Office of Naval Research/Naval Research Laboratory base program unit 73-1Y91.</p> <p>Please cite our JGR: Oceans paper "Wind-wave momentum flux in steep, strongly forced,1 surface gravity wave conditions" if you were to use our dataset.</p> <p>Contact: Peisen Tan <pxt254@miami.edu> for different levels of raw data collected in this experiment.</p> <p>We kindly ask the readers who use our dataset to cite our paper:</p> <p><span>Tan, P.</span><span>, </span><span>Savelyev, I.</span><span>, </span><span>Laxague, N. J. M.</span><span>, </span><span>Haus, B. K.</span><span>, </span><span>Curcic, M.</span><span>, </span><span>Matt, S.</span><span>, et al. (</span><span>2025</span><span>). </span><span>Wind-wave momentum flux in steep, strongly forced, surface gravity wave conditions</span><span>. </span><em>Journal of Geophysical Research: Oceans</em><span>, </span><span>130</span><span>, e2024JC021616. </span><a href="https://doi.org/10.1029/2024JC021616">https://doi.org/10.1029/2024JC021616</a></p> <p>We would also appreciate if you can send us a copy of your manuscript if you have used our data. Thank you!</p>
Dataset for analysis of the value of energy for wave, wind and solar power
<p>Data and code for: Vrana, Til Kristian, & Svendsen, Harald G. (2021). Quantifying the Market Value of Wave Power compared to Wind&Solar - a case study. The 9th Renewable Power Generation Conference - RPG Dublin Online 2021 (RPG 2021), (https://doi.org/10.1049/icp.2021.1383)</p>
Dataset used in Sea surface wind structure observed by wave gliders during tropical cyclones
<p>Sea surface wind vector observed by three wave gliders deployed in the Western Pacific Ocean. The observation level is 1.2 meter.</p>
Large-eddy simulation of an atmospheric bore and associated gravity wave effects on wind farm performance in the Southern Great Plains
<p>Animations from weather radars (observations) and simulations (modeling) of an atmospheric bore and associated gravity waves during the AWAKEN field campaign from a case study on 06 June 2023. The first animation is reflectivity from the NEXRAD WSR-88D system at the Oklahoma City radar site (KTLX) operated by the National Weather Service. The second animation is wind speed at 95, 145, and 270 m agl from the Texas Tech X-Band radars at the AWAKEN site. The third and fourth animations are for simulation results using the Weather Research and Forecasting model (WRF) with two domains. The third animation is vertical velocity at 1 km agl and potential temperature at 200 m agl on domain d01, which has a horizontal grid spacing of 300 m. The fourth animation is hub-height wind speed and perturbation pressure along with simulated power output for 3 wind turbines in each of the four rows on domain d01, which has a horizontal grid spacing of 20 m. The wind turbines are NREL 2.8 MW turbines parameterized using a generalized actuator disk. </p> <p>These animations are included as supplementary material for the manuscript "Large-eddy simulation of an atmospheric bore and associated gravity wave effects on wind farm performance in the Southern Great Plains" submitted to <em>Wind Energy Science</em>. </p>
A wave detection procedure for electromagnetic cyclotron waves AND results (data) in case of the solar wind
<p>A detection procedure (produced via IDL) for electromagnetic cyclotron waves is presented,which is used to find low frequency waves in the solar wind over a period of 7 years. The procedure and the relevant results (data) are described in a manuscript entitled "Statistical study of low frequency electromagnetic cyclotron waves in the solar wind at 1 AU", which has been submitted to Journal of Geophysical Research - Space Physics for publication. More information about the procedure and data can be accessed by writing to the following address: zgqisp@163.com.</p>
GNSS and Wind Speed Dataset from North Sea Wave Glider 2016 Deployment
<p>5 Hz GNSS RINEX and 10 min wind speed data collected on-board an SV2 Wave Glider in the North Sea from 28 July to 10 August 2016 used in Penna et al, “Sea Surface Height Measurement Using a GNSS Wave Glider”, submitted to Geophysical Research Letters.</p> <p> </p>
Wave field in a wind and paddle tank: effect of a thin surface layer of fish oil
<p>Video showing the surface elevation field in a wind- and paddle-wave tank in clean water condition (tap water) and in water covered with a thin layer of fish oil. Reference wind speed 8 m/s and JONSWAP-like paddle spectrum (Hs = 0.062 cm, Tp =1.0 s) Experiments carried out in the flume of the First Institute of Oceanography (Qingdao, P.R. China).</p>
Wave field in a wind tank: effect of a thin surface layer of fish oil.
<p>Video showing the surface elevation field in a wind-wave tank in clean water condition (tap water) and in water covered with a thin layer of fish oil. Reference wind speed 6 m/s. Experiments carried out in the flume of the First Institute of Oceanography (Qingdao, P.R. China).</p>
ScienceDex guides
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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.