Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
75
datasets available to search
ShareScore release 0.9.0
Dataset results
75 results for “gravity waves”
Dataset of "Gravity Wave Activity in the Atmosphere of Mars During the 2018 Global Dust Storm: Simulations With a High-Resolution Model" (1/2)
<p>This dataset contains the GrADS data of high-resolution Mars GCM results used for figures in the paper "Gravity Wave Activity in the Atmosphere of Mars During the 2018 Global Dust Storm: Simulations With a High-Resolution Model" by T. Kuroda, A.S. Medvedev and E. Yiğit.</p> <p>Each file with the name starting 'data' contains two-dimensional (X: longitude, Y: latitude) data of surface pressure (Ps) (unit: hPa) and dust opacity in infrared wavelength (tau), and three-dimensional (X: longitude, Y: latitude, Z:sigma-level) data of temperature (T) (unit: K), zonal wind velocity (u) (unit: m/s), meridional wind velocity (v) (unit: m/s) and vertical wind velocity (w) (unit: m/s), in snapshots of every 1/6 Sol for the periods of 30 degrees in Ls per a file as described below. The dust scenario implemented for producing this dataset is taken from Montabone et al. (2020), which is based on the observed dust opacity in Mars Year 24 (MY34).</p> <p>data180rdc-my34.tar.xz: for Ls=180-210 (49 Sols)</p> <p>data210rdc-my34.tar.xz: for Ls=210-240 (47 Sols)</p> <p>data240rdc-my34.tar.xz: for Ls=240-270 (46 Sols)</p> <p>The .tar.xz files can be extracted in Linux with 'tar Jxvf' command, and .grd and .ctl files with the same stem are generated.</p> <p>The file 'flux61ls5-my34.tar.xz' contains the three-dimensional fluxes and physical parameters calculated from the model output with the MY34 dust scenario. The contents are (T')^2, (u')^2, (v')^2, u'v', u'w', v'w' T(bar), u(bar), v(bar), squared Brunt-Vaisala frequency, and geopotential height. (bar) denotes the sum of the total wavenumber s=0-60 components, and the dash denotes the deviation from (bar), i.e. sum of the total wavenumber s=61-106 components. There are 36 time grids between Ls=182.5 and Ls=357.5 with the step of Ls=5 degrees. Kinetic and potential energies can be derived from these values using the formulae in the paper.</p> <p>The file 'flux61ls5-lowdust.tar.xz' is the same as 'flux61ls5-my34.tar.xz', except the model output with the 'low-dust' scenario (Kuroda et al., 2019; Kuroda, 2019a, 2019b).</p> <p>The file 'scripts.zip' contains the FORTRAN scripts to derive the fluxes and physical parameters equivalent to the file 'flux61ls5-my34.tar.xz' from the model outputs in this dataset and Kuroda (2020), i.e. data180rdc-my34.tar.xz, data210rdc-my34.tar.xz, data240rdc-my34.tar.xz, data270rdc-my34.tar.xz, data300rdc-my34.tar.xz and data330rdc-my34.tar.xz. Also, the fluxes and physical parameters equivalent to the file 'flux61ls5-lowdust.tar.xz' can be derived with those scripts from the model outputs data180rdc.tar.xz, data210rdc.tar.xz, data240rdc.tar.xz, data270rdc.tar.xz, data300rdc.tar.xz and data330rdc.tar.xz which are available in Kuroda (2019a, 2019b).</p>
Data and Codes for "Explainable Offline-Online Training of Neural Networks for Parameterizations: A 1D Gravity Wave-QBO Testbed in the Small-data Regime" by Pahlavan et al. (2023)
<p>This is part of the code and data related to the paper entitled Explainable Offline-Online Training of Neural Networks for Parameterizations: A 1D Gravity Wave-QBO Testbed in the Small-data Regime, available at https://arxiv.org/abs/2309.09024.</p><p>The original sources of the codes are the v1.0.0 version of open source software EnsembleKalmanProcesses.jl for EKI analysis, accessible at zenodo.org/records/7806813, and the \emph{qbo1d} code for the 1D-QBO model simulations, accessible at github.com/DataWaveProject/qbo1d.git.</p>
SABER gravity wave absolute momentum fluxes for the global monsoon seasons 2016-2020
<p>This is a near-global dataset of gravity wave absolute momentum fluxes derived from SABER satellite observations using the method described in Ern et al. (2018). It covers the months of JJA and DJF, starting with December 2016, and ending with August 2020.</p> <p>Dimensions: 4 vertical levels, 37 longitudes, and 37 latitudes.</p> <p>Variables: gravity wave absolute momentum flux</p>
SABER gravity wave temperature variances for the global monsoon seasons 2016-2020
<p>This is a near-global dataset of gravity wave temperature variances at 50km altitude derived from SABER satellite observations using the method described in Ern et al. (2018). It covers the months of JJA and DJF, starting with December 2016, and ending with August 2020.</p> <p>Dimensions: 1 vertical level (z=50km), 37 longitudes, and 37 latitudes.</p> <p>Variables: gravity wave temperature variances</p>
Data for "Hunting for gravity waves in non-orographic winter storms using 3+ years of regional surface air pressure networks and radar observations"
<p>These data are shown in the figures included with the article "Hunting for gravity waves in non-orographic winter storms using 3+ years of regional surface air pressure networks and radar observations," submitted to Atmospheric Chemistry and Physics.</p>
Studying the scale selection of mixed Rossby-gravity waves: Idealized simulations with the TIGAR model
<p>Mixed Rossby-gravity waves peak in two atmospheric regions in reanalysis: the upper troposphere and the upper stratosphere. The scales of MRG waves are different in these two regions, which can be seen e.g. on real-time MRG wave vertical profiles (https://modes.cen.uni-hamburg.de/products#MRG). In order to understand the MRG wave scale selection in these regions, we run idealized simulations with the TIGAR model (Vasylkevych and Zagar, 2021) with a symmetric initial height perturbation with respect to the equator and zonal wind profiles derived from ERA5 reanalysis (Hersbach et al, 2020). In addition, we also run TIGAR simulations with symmetric initial height perturbation and idealized zonal jets centered at various latitudes.</p>
Different behavior of density perturbations between dayside and nightside in the Martian thermosphere and the ionosphere associated with atmospheric gravity waves
<div>---------------------</div> <div>GENERAL INFORMATION</div> <div>---------------------</div> <div> </div> <div>1. Title of Dataset: Different behavior of density perturbations between dayside and nightside in the Martian thermosphere and the ionosphere associated with atmospheric gravity waves</div> <div> </div> <div>2. Authors: Nakagawa, England, et al.</div> <div> </div> <div>3. Contact information: hiromu.nakagawa.c1@tohoku.ac.jp</div> <div> </div> <div>4. Date of data collection: April 2024</div> <div> </div> <div> </div> <div>---------------------</div> <div>DATA & FILE OVERVIEW</div> <div>---------------------</div> <div> </div> <div>1. Original MAVEN data access:</div> <div> </div> <div>The MAVEN/NGIMS (level-2, version-8, revision-1) are publicly available in ASCII format on the NASA Planetary Data System (PDS) at https://atmos.nmsu.edu/data_and_services/atmospheres_data/MAVEN/ngims.html.</div> <div> </div> <div>As for references in Figures 2-4, the MAVEN/MAG Calibrated data are publicly available in ASCII format on the NASA Planetary Data System (PDS) at https://pds-ppi.igpp.ucla.edu/search/view/?id=pds://PPI/maven.mag.calibrated.</div> <div> </div> <div> </div> <div>2. File List:</div> <div> </div> <div>[1] correlate_coefficients.txt</div> <div>[2] rmse_170-190_coupling_case.txt</div> <div>[3] rmse_190-210_coupling_case.txt</div> <div>[4] rmse_170-190_ion_specific_case.txt</div> <div>[5] rmse_190_210_ion_specific_case.txt</div> <div> </div> <div>---------------------</div> <div>DATA-SPECIFIC INFORMATION</div> <div>---------------------</div> <div> </div> <div>[1] correlate_coefficients.txt</div> <div>This includes the geometric information and the calculated correlate coefficient between neutrals and ions in all profiles applied in this study. The first header provides the information in the file: Orbit, Year, Month, Day, Hour, Minute, Unix time, SZA(deg), Lon(deg), Lat(deg), LST(hr), CC(CO2-N2), CC(CO2-CO2+). The geometric information corresponds to those at altitude around 190 km. The last two columns represents the correlate coefficients between CO2 and N2 and between CO2 and CO2+. </div> <div> </div> <div>[2] rmse_170-190_coupling_case.txt</div> <div>This includes RMSE (the unit is percentage) between the observed perturbations and the model fit. The first header provides the information in the file: unix_time, orbit number, sza, lon, lat, lst, rmse_neu_gw_%, rmse_neu_aw_%, rmse_ion_gw_%, rmse_ion_aw_%. The geometric information corresponds to those at altitude around 190 km. The last four RMSEs represents those with the model to fit with the gravity waves (gw) parameters and with the acoustic waves (aw) for the neutral (neu) perturbations (N2) and for the ion (ion) perturbations (CO2+) at altitude range between 170 km and 190 km for the case of ion-neutral coupling case. </div> <div> </div> <div>[3] rmse_190-210_coupling_case.txt</div> <div>This includes RMSE (the unit is percentage) between the observed perturbations and the model fit. The first header provides the information in the file: unix_time, orbit number, sza, lon, lat, lst, rmse_neu_gw_%, rmse_neu_aw_%, rmse_ion_gw_%, rmse_ion_aw_%. The geometric information corresponds to those at altitude around 190 km. The last four RMSEs represents those with the model to fit with the gravity waves (gw) parameters and with the acoustic waves (aw) for the neutral (neu) perturbations (N2) and for the ion (ion) perturbations (CO2+) at altitude range between 190 km and 210 km for the case of ion-neutral coupling case. </div> <div> </div> <div>[4] rmse_170-190_ion_specific_case.txt</div> <div>This includes RMSE (the unit is percentage) between the observed perturbations and the model fit. The first header provides the information in the file: unix_time, orbit number, sza, lon, lat, lst, rmse_neu_gw_%, rmse_neu_aw_%, rmse_ion_gw_%, rmse_ion_aw_%. The geometric information corresponds to those at altitude around 190 km. The last four RMSEs represents those with the model to fit with the gravity waves (gw) parameters and with the acoustic waves (aw) for the neutral (neu) perturbations (N2) and for the ion (ion) perturbations (CO2+) at altitude range between 170 km and 190 km for the case of ion-specific case. </div> <div> </div> <div>[5] rmse_190_210_ion_specific_case.txt</div> <div>This includes RMSE (the unit is percentage) between the observed perturbations and the model fit. The first header provides the information in the file: unix_time, orbit number, sza, lon, lat, lst, rmse_neu_gw_%, rmse_neu_aw_%, rmse_ion_gw_%, rmse_ion_aw_%. The geometric information corresponds to those at altitude around 190 km. The last four RMSEs represents those with the model to fit with the gravity waves (gw) parameters and with the acoustic waves (aw) for the neutral (neu) perturbations (N2) and for the ion (ion) perturbations (CO2+) at altitude range between 190 km and 210 km for the case of ion-specific case. </div> <div> </div> <div>---------------------</div> <div>METHODOLOGICAL INFORMATION</div> <div>---------------------</div> <div> </div> <div>Period to analysis: from March 2015 to August 2020 (orbit number from 713 to 11881)</div> <div> </div> <div>Number of files (CO2): 8,911</div> <div> </div> <div>Number of files (N2): 8,913</div> <div> </div> <div>Number of files (CO2+): 7,053(?)</div> <div> </div> <div>Data selection:</div> <div> </div> <div>1. Neutral species with inbound valid (IV)</div> <div> </div> <div>2. Ion species with SCP (quality flag=0)</div> <div> </div> <div>3. Simultaneous observations of CO2, N2, and CO2+ at altitudes 170-210 km</div> <div> </div> <div>4. All three species CO2, N2, and CO2+ are valid with data sample >10 points in a single orbit.</div> <div> </div> <div>Total number of files after data selection = 3,018</div> <div> </div> <div>Fitting:</div> <div> </div> <div>4th-order polynomial fit to extract the perturbation components of density in the range between 160 and 220 km.</div> <div> </div> <div> </div> <div>Correlation coefficient:</div> <div> </div> <div>The correlation coefficients between perturbations are calculated in the range between 170 and 210 km.</div> <div> </div> <div>Ion-neutral coupling cases whose correlation coefficient between CO2 and CO2+ larger than 0.7: Total number = 839</div> <div> </div> <div>Ion-specific case whose correlation coefficient between CO2 and CO2+ smaller than 0.2: Total number = 823</div> <div> </div> <div>#We also define CME cases based on Lee et al. (2017): Total number = 31 cases</div> <div> </div>
Observation of very short period atmospheric gravity waves in the lower Ionosphere using Very Low Frequency waves
<p>This is the VLF frequency data recorded at PARI station owned by Prof Morris Cohen, at Georgia Institute of Technology, Atlanta, USA.</p> <p>Data is for six transmitter signals, DHO, NAA, NAU, NLK, NPM, NML with both amplitude and Phase</p>
Data from: Fundamental properties of adjoint model and adjoint sensitivity under fully-nonlinear hydrostatic internal gravity waves
<p>This folder contains data from</p> <p>Shimizu, K. (2024), Fundamental properties of adjoint model and adjoint sensitivity under fully-nonlinear hydrostatic internal gravity waves, Journal of Geophysical Research: Oceans, 129, e2023JC020577. https://doi.org/10.1029/2023JC020577</p> <p>Its contents are briefly described in ReadMe.txt.</p>
Attenuating surface gravity waves with mechanical metamaterials
<p>Videos showing simulations of one or more submerged oscillators attenuating surface gravity waves, related to the publication </p> <p><a href="https://aip.scitation.org/author/de+Vita%2C+F">F. De Vita</a><em>, </em><a href="https://aip.scitation.org/author/de+Lillo%2C+F">F. De Lillo</a><em>, </em><a href="https://aip.scitation.org/author/Bosia%2C+F">F. Bosia</a><em>, and </em><a href="https://aip.scitation.org/author/Onorato%2C+M">M. Onorato</a>, "Attenuating surface gravity waves with mechanical metamaterials", Physics of Fluids 33, 047113 (2021) <a href="https://doi.org/10.1063/5.0048613">https://doi.org/10.1063/5.0048613</a></p> <p>Also included are Data relative to Figs. 3, 5, 6, 8 and gnuplot scripts to generate the figures.</p>
Primary Versus Secondary Gravity Wave Responses at F-region Heights Generated by a Convective Source
<p>Simulation outputs for the paper "Primary Versus Secondary Gravity Wave Responses at F-region Heights Generated by a Convective Source" by Heale et al.</p> <p>The .mat file consist of 3D (t,x,z) arrays of the temperature perturbation (K) for the full amplitude (T_fullamp), 1/4 amplitude (T_quarteramp), and 1/100th amplitude (T_hundrethamp) simulations. The time resolution is 60 seconds and the spatial resolution is 1km. The .mat file also included an x array and height array (km)</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>
Dataset and supporting materials of "Numerical simulation of orographic gravity waves observed over Syowa Station. Part 1: Wave propagation and breaking in the troposphere and lower stratosphere" by Kohma et al.
<p>Dataset and supporting materials of "Numerical simulation of orographic gravity waves observed over Syowa Station. Part 1: Wave propagation and breaking in the troposphere and lower stratosphere" by Kohma, M., K. Sato, D. C. Fritts, & T. S. Lund.</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.
SOUTHTRAC-GW: An airborne field campaign to explore gravity wave 1 dynamics at the world's strongest hotspot
<p>This contains the information on the zonal winds measured by the meteor radar at Tierra del Fuego in Argentina during July - December, 2019. Please contact the author for more information.</p>
Impact of a gravity wave process on the upper stratospheric ozone valley on the Qinghai-Tibetan Plateau
<p>The data sets are the results of WRF simulation and are used to plot the figures in this paper (Figure 8 to 11)</p>
Dataset for "Explainable Offline-Online Training of Neural Networks for Parameterizations: A 1D Gravity Wave-QBO Testbed in the Small-data Regime" by Pahlavan et al. (2023)
Open the record for dataset details and reuse information.
Dataset for "Contributions of Parameterized Gravity Waves and Resolved Equatorial Waves to the QBO Period in a Future Climate of CESM2" by Lee et al. (2024)
Open the record for dataset details and reuse information.
A possible mechanism for the stratospheric influence on tropical cyclone intensity: Downward control by gravity waves
Open the record for dataset details and reuse information.
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>
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.