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75 results for “gravity waves”
Impact of surface and sediment boundary conditions on acoustic-gravity waves propagation in a compressible free-surface oceanic wave guide
<p>Source code for the analytical development and numerical solving.</p>
Onset of non-linear internal gravity waves in intermediate-mass stars
<p>MESA inlists associated with <a href="https://ui.adsabs.harvard.edu/#abs/2019MNRAS.482.5500R/abstract">Onset of non-linear internal gravity waves in intermediate-mass stars</a></p>
Dataset of "Gravity waves – Toward connecting space weather and atmosphere-ionosphere coupling"
<p>The first file is the Ap file, describing the variation of the geomagnetic index used in the model simulations. The data files starting with sw are the model simulation files for the different model experiments. Each file is on a 2x15 (lat x lon) horizontal grid and on 66 pressure levels. There are 24 longitude and 91 latitudes. The files describe the simulations from 7th to 9th August 2018 in two hour time steps. The output variables in each file are zonal wind, geopotential height, and zonal gravity wave drag, in this given order. The files are formatted in the standard binary format for GraDS. </p>
Sensitivity of Gravity-wave Momentum Flux to Moisture in the Mei-Yu Front Systems
<p>The data and codes for ploting the figures in our manuscript are provided here.</p>
Data from: Spatiotemporal measurement of surfactant distribution on gravity–capillary waves
Materials adsorbed onto the surface of a fluid – for instance, crude oil, biogenic slicks or industrial/medical surfactants – will move in response to surface waves. Owing to the difficulty of non-invasive measurement of the spatial distribution of a molecular monolayer, little is known about the dynamics that couple the surface waves and the evolving density field. Here, we report measurements of the spatiotemporal dynamics of the density field of an insoluble surfactant driven by gravity–capillary waves in a shallow cylindrical container. Standing Faraday waves and travelling waves generated by the meniscus are superimposed to create a non-trivial surfactant density field. We measure both the height field of the surface using moiré imaging, and the density field of the surfactant via the fluorescence of NBD-tagged phosphatidylcholine, a lipid. Through phase averaging stroboscopically acquired images of the density field, we determine that the surfactant accumulates on the leading edge of the travelling meniscus waves and in the troughs of the standing Faraday waves. We fit the spatiotemporal variations in the two fields using an ansatz consisting of a superposition of Bessel functions, and report measurements of the wavenumbers and energy damping factors associated with the meniscus and Faraday waves, as well as the spatial and temporal phase shifts between them. While these measurements are largely consistent for both types of waves and both fields, it is notable that the damping factors for height and surfactant in the meniscus waves do not agree. This raises the possibility that there is a contribution from longitudinal waves in addition to the gravity–capillary waves.
Data for Martian Gravity Waves Observed by the Thermal Emission Imaging System (THEMIS) during Northern Summer
<p>README Data for <br> "Martian Gravity Waves Observed by the Thermal Emission Imaging System (THEMIS) during Northern Summer"<br> J. Michael Battalio et al.,<br> November 2022<br> (https://battalio.com)<br> michael[at]battalio[dot]com</p> <p>In this dataset are:<br> GW_readme.txt (this file)</p> <p>numpy compressed files<br> GWDaytime.npz<br> GWNighttime.npz<br> GWSpectrumMY##.npz, where "##" run the eight Mars years from 26 to 33, inclusive.<br> GWSpectrumNightMY##.npz, where "##" run the eight Mars years from 26 to 33, inclusive.</p> <p>Python notebook file:<br> THEMIS_GW_LOAD.ipynb (minimal notebook to read *.npz files)</p> <p>--The following arrays are in GWDaytime.npz:<br> gwArr, gwPeaks, gwArrMax, gwPeaksMax, gwNames, <br> gwArrSym, gwPeaksSym, gwArrSymMax, gwPeaksSymMax, gwNamesSym</p> <p>--The following arrays are in GWNighttime.npz:<br> gwArrNight, gwArrNightSym, gwPeaksNight, gwPeaksNightSym, gwArrNightMax, gwArrNightSymMax, gwPeaksNightMax, gwPeaksNightSymMax, gwNamesNight, gwNamesNightSym</p> <p>The arrays have the following format: Each row in gwArr, gwPeaks, gwArrMax, gwPeaksMax corresponds to a THEMIS swath or sub swath for the name given at the same column index as the gwNames vector. Similarly for gwArrSym, gwPeaksSym, gwArrSymMax, gwPeaksSymMax to gwNamesSym and for the GWNighttime file.</p> <p>gwArr, gwArrMax, gwArrNight, gwArrNightMax, gwArrSym, gwArrSymMax, gwArrNightSym, gwPeaksNightSym array columns are:<br> Swath centroid Longitude, swath centroid Latitude, Spectrum maximum, baseline orientation bin of maximum, baseline length bin of maximum, empty, areocentric longitude, local time, mean of whole spectrum, standard deviation of whole spectrum, Mars year, median of whole spectrum</p> <p>gwPeaks, gwPeaksMax, gwPeaksSym, gwPeaksSymMax, gwPeaksNightMax, gwPeaksNightSymMax, gwPeaksNight, gwPeaksNightSym array dimensions are: Dim1 corresponds to each THEMIS swath given by gwNames, gwNamesSym, gwNamesNight, or gwNamesNightSym. Dim2 corresponds to each of the top-10 peaks in the spectrum (Note: Peak one is identical to the peak given in column 3 of the "Arr" variables.). Dim 3 provides the baseline orientation bin of the corresponding peak, baseline length bin of the corresponding peak, the value of the peak.</p> <p><br> --The following arrays are in each GGWSpectrumMY##.npz:<br> gwNamesMY##,gwNamesSymMY##,spectrumArrMY##,spectrumArrMaxMY##,spectrumArrSymMY##,spectrumArrSymMaxMY##</p> <p>spectrumArrMY##,spectrumArrMaxMY##,spectrumArrSymMY##,spectrumArrSymMaxMY## array dimensions are: Dim1 corresponds to each THEMIS swath given by gwNamesMY##, gwNamesSymMY##. Dim2 corresponds to the baseline orientation from 90° east to 90° west of a due north-south orientation. Dim 3 provides the baseline length from 0 to 500 km for full swaths or 0 to 60 km for symmetric swaths.</p> <p>--Similarly, the following arrays are each GGWSpectrumNightMY##.npz:<br> gwNamesNightMY##,gwNamesNightSymMY##,spectrumArrNightMY##,spectrumArrNightMaxMY##,spectrumArrNightSymMY##,spectrumArrNightSymMaxMY## The variable dimensions follow the same convention as the daytime files.</p>
Results of the CCM SOCOLv3 runs with and without orographic gravity waves (OGW) parameterizations
<p>1. Model run without any OGW parameterization:</p> <p>T_no_OGW.nc – Temperature (K)</p> <p>U_no_OGW.nc – Eastward zonal wind (m/s)</p> <p> </p> <p>2. Model run with the OGW parameterization of Lott and Miller:</p> <p>T_Lott_Miller.nc – Temperature (K)</p> <p>U_Lott_Miller.nc – Eastward zonal wind (m/s)</p> <p> </p> <p>3. Model run with the OGW parameterization of the Saint-Petersburg University (SPBU): </p> <p>T_SPBU.nc – Temperature (K)</p> <p>U_SPBU.nc – Eastward zonal wind (m/s)</p> <p>-------------------------------------------------------------------------------------------------</p> <p> (monthly and zonal mean values averaged over 2009-2018 time period) </p>
Data from: Spatiotemporal measurement of surfactant distribution on gravity–capillary waves
Open the record for dataset details and reuse information.
Conjugate effect of the 2011 Tohoku reflected tsunami-driven gravity waves in the ionosphere
<p>The ground-based GNSS TEC observations for the study of "Conjugate effect of the 2011 Tohoku reflected tsunami-driven gravity waves in the ionosphere" by Chou et al. (2021). All of the output data is processed by Matlab.</p> <p><a href="https://zenodo.org/api/files/dff37136-7c43-4ad9-94e9-d16da3241338/DTEC20110700460_300_geonet_gps_30.mat">DTEC20110700460_300_geonet_gps_30.mat</a> is the filtered GPS TEC data (04-60 min bandpass filtering) over Japan for Figure 1.</p> <p><a href="https://zenodo.org/api/files/dff37136-7c43-4ad9-94e9-d16da3241338/DTEC20110700460_300_gps_australia_glonass_30.mat">DTEC20110700460_300_gps_australia_glonass_30.mat</a> and <a href="https://zenodo.org/api/files/dff37136-7c43-4ad9-94e9-d16da3241338/DTEC20110700460_300_gps_australia_gps_30.mat">DTEC20110700460_300_gps_australia_gps_30.mat</a> are the filtered GNSS TEC data (04-60 min bandpass filtering) over Australia for Figure 1.</p> <p>*070.mat files are the TEC data obtained from the GPS receivers in Japan and Australia for Figure 2.</p> <p>PET070.mat file (PET station) is the TEC data for the supporting information.</p> <p> </p>
Data for "Data Imbalance, Uncertainty Quantification, and Generalization via Transfer Learning in Data-driven Parameterizations: Lessons from the Emulation of Gravity Wave Momentum Transport in WACCM"
Open the record for dataset details and reuse information.
Generation of Current-wave-supported Sediment Gravity Flow Promoted by Wave-induced Liquefaction on an Abandoned Estuary Delta
<p>This dataset includes sediment concentrations,velocities, and residual pore pressure data measured at two sites in the abandoned Yellow River Delta of the North Yellow Sea, China.</p>
Concentric Primary Gravity Waves in the Ionosphere Over the Continental United States on 7 October 2016 from Hurricane Matthew
<p>the reconstructed 3D ionospheric electron density disturbance</p>
Martian Dust Storms and Gravity Waves: Disentangling Water Transport to the Upper Atmosphere
<p>The data for GRL article figures.</p>
Wind-generated Gravity Waves Retrieval from Sea Surface Elevation by Airborne Ka-band Interferometric Altimeter
<p>This dataset contains WSSE simulation data , AirKaIA data and sea state data used in the paper "Wind-generated Gravity Waves Retrieval from Sea Surface Elevation by Airborne Ka-band Interferometric Altimeter".</p>
Wind-generated Gravity Waves Retrieval from Sea Surface Elevation by Airborne Ka-band Interferometric Altimeter
<p>This dataset contains WSSE simulation data , AirKaIA data and sea state data used in the paper "Wind-generated Gravity Waves Retrieval from Sea Surface Elevation by Airborne Ka-band Interferometric Altimeter".</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.