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424 results for “gravity”
The global marine free air gravity anomaly model SDUST2022GRA
<p>SDUST2022GRA.nc is the global marine free air gravity anomaly model covering 80°S~82°N and 0~360°E on 1′×1′ grids. SDUST2022GRA is recovered from multi-radar and ICESat-2 laser altimeter data to investigate the contribution of ICESat-2.</p>
Supplementary material for 3D Acoustic-Elastic Coupling with Gravity: The Dynamics of the 2018 Palu, Sulawesi Earthquake and Tsunami
<p>This repository contains the supplementary files for our SC21 submission: "3D Acoustic-Elastic Coupling with Gravity: The Dynamics of the 2018 Palu, Sulawesi Earthquake and Tsunami".</p> <p>It contains the input data for all simulations. For more details, please refer to the included README.md files.</p> <p> </p> <p>The directory "seissol-sc21-revision-source-code" contains the version of SeisSol that we used.</p> <p> </p>
Ocean and ice without waves data for role of surface gravity waves in aquaplanet ocean climates
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Ocean and ice with waves data for role of surface gravity waves in aquaplanet ocean climates
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Ocean and ice spin-up data for role of surface gravity waves in aquaplanet ocean climates
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Atmospheric and surface gravity wave data for role of surface gravity waves in aquaplanet ocean climates
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FEHM source code modifications and executables for use with ocean-world gravity
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Dataset of "Gravity Wave Activity in the Atmosphere of Mars During the 2018 Global Dust Storm: Simulations With a High-Resolution Model" (2/2)
<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 contains two-dimensional (X: longitude, Y: latitude) data of surface pressure (Ps) and dust opacity in infrared wavelength (tau), and three-dimensional (X: longitude, Y: latitude, Z:sigma-level) data of temperature (T), zonal wind velocity (u), meridional wind velocity (v) and vertical wind velocity (w). Each tar.xz file contains snapshots of those data in every 1/6 Sol for Ls of 30 degrees. The dust scenario implemented for producing this dataset is taken from Montabone et al. (2020).</p> <p>data270rdc-my34.tar.xz: for Ls=270-300 (48 Sols)</p> <p>data300rdc-my34.tar.xz: for Ls=300-330 (51 Sols)</p> <p>data330rdc-my34.tar.xz: for Ls=330-360 (56 Sols)</p>
Internal gravity waves in a three solar mass star
<p>The movie shows the horizontal velocity (left) and horizontal temperature fluctuations (right) for the 2D hydrodynamical simulation of a three solar mass star at the zero-age main sequence. Both quantities are scaled by their respective horizontal mean value to account for the different amplitudes in the inner (lower amplitudes) and outer (higher amplitudes) parts of the model. The magnified regions show the convective core. The simulation has been performed with the fully compressible, time-implicit <a href="http://slh-code.org">Seven-League Hydro (SLH)</a> code.</p>
Dataset of "Effects of latitude-dependent gravity wave source variations on the middle and upper atmosphere"
<p>This is a dataset for three 60-day simulations with the CMAT2-GCM for June-July 2010 conditions, using the Whole atmosphere gravity wave parameterization by Yiğit et al. (2008).</p> <p>Dimensions: 66 vertical levels, 24 longitudes, and 91 latitudes. The bottom level is at 100 mb. </p> <p>Variables: There are seven physical variables. Daily-averaged zonal wind, temperature, geopotential height, zonal drag, gravity wave total heating/cooling rate, gravity wave-induced temperature fluctuations, and gravity wave absolute momentum flux. </p> <p>Simulations: A benchmark run "00n", a run with latitude-dependent gravity wave source spectrum with 50% increased flux at the lower boundary in both hemispheres "51n"; same as "52n" but 100% increased flux in the Southern Hemisphere only. </p>
Modified Gravity and the Black Hole Mass Gap
<p><strong>Reproduction package for the paper "Modified Gravity and the Black Hole Mass Gap".</strong></p> <p> </p> <p><strong>The package contains inlist and run_star_extra.f files for MESA that can be used to generate grids of ZAMS models for custom values of the gravitational constant. Also included are inlist and run_star_extra.f files for the PPISN test suite demonstrating how to change the value of G and how to load the ZAMS files during the post-pulse relaxation process. </strong></p>
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>
First release of the time-variable gravity model over North China (NC-IGP01T).
<p>Time-variable gravity model over North China (NC-IGP01T)</p>
Standard Bouguer anomaly model achieved by multi-source Bouguer gravity anomaly Bayesian data fusion algorithm in Sichuan-Yunnan region
<p>* Method: Based on the equivalent source inversion and Bayesian uncertainty quantization theory, a new multi-source gravity data fusion algorithm is developed, which effectively solves the multi-source data fusion problem with different noise and datum.</p> <p>* Standard Bouguer anomaly is Fused from WGM2012 Bouguer gravity anomaly model and 394 gravity profile data measured in Sichuan-Yunnan region. Fusion anomaly results can eliminate datum draft between multi-source gravity and reduce incoherent noise.</p> <p>* Spatial resolution of the standard Bouguer anomaly is about 20 kilometers.</p> <p>* Correcting deviations means the difference between the fused standard Bouguer anomaly model and the WGM2012 Earth gravity model.</p>
System resistance of stainless steel portal frames under gravity loads
<p>This file includes data on system strength of stainless steel frames under gravity loads obtained from the finite element simulations carried out on six stainless steel frames. This data was used in the analysis carried out and reported in the publication: Input and output data is provided separately for each of the six frames investigated. Frames 1 and 2 correspond to austenitic stainless steel, Frames 3 and 4 to duplex stainless steel and Frames 5 and 6 to ferritic stainless steel.</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>
Data set to article "Synthetic inversions for density using seismic and gravity data" by Blom, Boehm and Fichtner
<p><strong>Data set to “Synthetic inversions for density using seismic and gravity data” by Nienke Blom, Christian Boehm and Andreas Fichtner</strong></p> <p>This data set relates to our paper <em>“Synthetic inversions for density using seismic and gravity data”</em><em>, </em><em>in which we discuss the imaging of density variations inside the Earth as a separate, independent parameter using seismic waveform tomography and gravity measurements</em>. The research consists of synthetic experiments conducted using a home-written MATLAB wave propagation code. The data set contains the code itself, the input files and output files for each of the experiments described in the manuscript and its supplementary material, all the figures, some extra material (such as a video of Figure 1 in the manuscript) and some scripts.</p> <p>Below I’ll give a description of the contents of this data set and how they are structured, followed by an overview of the experiments conducted for the paper.</p> <p>In this data set, the following things can be found:</p> <ul> <li> <p>There is a directory with all the figures: FIGURES. This contains the figures in *.pdf, *.eps and *.png formats.</p> </li> <li> <p>There is a directory FD2D_ADJOINT_CODE with in it the MATLAB code fd2d-adjoint. If you plan on using our code, it would be awfully kind if you'd make a reference both to the code and to this paper. It was a lot of work to develop the code and the experiments. NOTE: the code supplied here is a snapshot of the code taken in February 2017. A more up-to-date version might be found on github (www.github.com/Phlos/fd2d-adjoint)</p> </li> <li> <p>For each (series of) experiment(s) described in the paper, there is a directory T1, T2, …, Tn. This also holds for the supplementary tests, the folders for which are designated with the suffix .SUPPLEMENTARY.</p> </li> <li> <p>For Figure 1 in the manuscript, there is a directory Fig1.snapshots. In this directory, everything pertaining to the snapshots figure and its corresponding video can be found.</p> </li> <li> <p>There is a separate directory SCRIPTS with a couple of useful scripts that might be used in addition to the ones in the fd2d-adjoint code.</p> </li> </ul> <p><br> In each of the test directories T1...Tn, there are subdirectories for each experiment conducted within that test framework. Each of the subdirectories has a name Systematic.test-[xxx]. Within those Systematic.. directories, the following can be found:</p> <ul> <li> <p>an input file Systematic….input_parameters.m that can be copied to [fd2d-adjoint]/input/input_parameters.m in order to re-run the experiment. As the code has been under development while the tests were run, it may be that some input parameters are missing from the earlier experiments.</p> </li> <li> <p>A mat-file obs.all-vars.mat. If this file is copied to [fd2d-adjoint]/output/Systematic.test… , this saves the recalculation of the ‘obs’ data when the code is run.</p> </li> <li> <p>A mat-file initial_misfits.mat. If this file is copied to [fd2d-adjoint]/output/Systematic.test… , this saves the recomputation of the initial misfits with respect to the obs data when the code is run.</p> </li> <li> <p>A file lbfgs_output_log.txt which monitors the misfit and gradient development across the iterations. If the inversion was restarted a couple of times, all of this remains in the logfile.</p> </li> <li> <p>For each iteration of the inversion iter[xxx], an iter[xxx].all-vars.mat file, which contains most of the matlab output files for this iteration.</p> </li> <li> <p>For each iteration of the inversion iter[xxx], some figures:</p> <ul> <li> <p>a model plot of the current model anomalies with respect to the background model iter[xxx].model-diff.rhovsvp.png.</p> </li> <li> <p>a gravity plot of the gravity vector difference between the current model and the background model iter[xxx].gravity_difference.png.</p> </li> <li> <p>a kernel plot of the total relative kernels (whether seis only or seis+grav) of the current model in rho-mu-lambda parametrisation: iter[xxx].rho-mu-lambda.png.</p> </li> </ul> </li> </ul> <p><br> </p> <p>Now follows a brief description of each of the (series of) tests conducted for the paper. The test numbers are mostly chronological, and so are the Systematic.test… subdirectories.</p> <ul> <li> <p><strong>Figure 1</strong>: shows snapshots of wave propagation past a density anomaly. The full data for this and the full video are given in the Fig1.snapshots. <em>Discussed in: Figure </em><em>1 of the manuscript.</em></p> </li> <li> <p><strong>T1: </strong><strong>reference.</strong> A reference test in which we assess to which density can be recovered as an independent parameter. <em>Discussed in: Figure </em><em>4</em></p> <ul> <li> <p>Reference experiment: Systematic.test-033</p> </li> </ul> </li> <li> <p><strong>T2: </strong><strong>ignored density.</strong> A test in which the effect is explored if density is ignored, i.e. if it is kept fixed to the starting model. <em>Discussed in: Figure </em><em>4</em></p> <ul> <li> <p>Fixing density: Systematic.test-040</p> </li> </ul> </li> <li> <p><strong>T3: </strong><strong>starting model</strong>. A series of test in which is explored to what extent the starting models of P and S seismic velocity influence the recovery of density. In the different sub-tests, different levels of information on P and S velocity are already present. <em>Discussed in: Figure </em><em>6</em></p> <ul> <li> <p>vs, vp 100% correct: Systematic.test-029</p> </li> <li> <p>vs, vp 75% correct: Systematic.test-037</p> </li> <li> <p>vs,vp 50% correct: Systematic.test-036</p> </li> </ul> </li> <li> <p><strong>T4: </strong><strong>fixed velocities</strong>. A series of tests in which is explored to what extent one can “get away with” only updating density, assuming that the models for P and S velocity are already sufficiently accurate. <em>Discussed in: Figure </em><em>7</em></p> <ul> <li> <p>vs,vp fixed at 50% correct: Systematic.test-038</p> </li> <li> <p>vs, vp fixed at 75% correct: Systematic.test-041</p> </li> <li> <p>vs, vp fixed at 100% correct: Systematic.test-039</p> </li> </ul> </li> <li> <p><strong>T5: </strong><strong>gravity</strong>. A set of tests in which the addition of gravity data to the (up until here purely) seismic inversion. Both the full gravity vector and its potential are used as gravity data. <em>Discussed in: Figure </em><em>8</em></p> <ul> <li> <p>seismic + full gravity vector (x,z) data: Systematic.test-045</p> </li> <li> <p>seismic + gravity potential data (‘geoid’): Systematic.test-046</p> </li> </ul> </li> <li> <p><strong>T6: noise</strong>. A series of tests in which the addition of noise to the seismic data is explored. Both correlated and uncorrelated noise are explored. Noise levels vary across frequencies. <em>Discussed in: Figure </em><em>9</em></p> <ul> <li> <p>correlated noise: Systematic.test-050</p> </li> <li> <p>uncorrelated noise: Systematic.test-052</p> </li> </ul> </li> <li> <p><strong>T7: impedance</strong>. A test in which the impedance contrast across anomaly boundaries are set to zero. It is explored to what extent the recovery of density relies on the presence of an impedance contrast. <em>Discussed in: Figure </em><em>10</em></p> <ul> <li> <p>no impedance contrast: Systematic.test-055</p> </li> </ul> </li> <li> <p><strong>T8: parametrisation (</strong><em><strong>supplementary</strong></em><strong>)</strong>. A test in which it is explored to what extent the inversion is affected if an inversion parametrisation using density and the elastic parameters mu and lambda is used, instead of the otherwise used parametrisation density-S velocity-P velocity. <em>Discussed in: </em><em>Supplementary </em><em>Figure </em><em>1,2 @ </em><em>Supplementary_material.pdf</em></p> <ul> <li> <p>inversion parametrisation rho-mu-lambda (reference target model): Systematic.test-032</p> </li> <li> <p>inversion parametrisation rho-mu-lambda with ‘scaling’ target model: Systematic.test-062a</p> </li> </ul> </li> <li> <p><strong>T9: scaling relations</strong>. A set of tests in which it is explored to what extent the recovery of density and seismic velocities is influenced if density is scaled to S velocity using a fixed scaling. <em>Discussed in: Figure </em><em>5</em></p> <ul> <li> <p>target model with density scaled to S velocity in different ways; all parameters free: Systematic.test-063</p> </li> <li> <p>same target model, but now density is scaled to S velocity with a fixed relationship: Systematic.test-067</p> </li> </ul> </li> <li> <p><strong>T10: anomaly strength (</strong><em><strong>supplementary</strong></em><strong>)</strong>. A set of tests in which the effect of the strength of the anomalies on the recovery of density and the other parameters is investigated. <em>Discussed in: </em><em>Supplementary </em><em>Figure </em><em>3-5 @ </em><em>Supplementary_material.pdf</em><em> </em></p> <ul> <li> <p>target model like reference case, but the anomalies 10% of PREM instead of 1%: Systematic.test-065</p> </li> <li> <p>target model like reference case, but the anomalies <em>in the upper mantle only</em> 10% of PREM instead of 1%: Systematic.test-064</p> </li> </ul> </li> </ul> <p><br> </p> <p>If you have any further questions, feel free to contact me.</p> <p>All the best,</p> <p>Nienke Blom, Utrecht University<br> n.a.blom@uu.nl<br> nienke.blom@posteo.net</p> <p> </p>
Global Gravity Field Model GAO2012
<p>Global gravity field model as combined solution from satellite missions and surface gravity field dataset</p>
Molodensky's truncation coefficients for cap integration in spectral gravity forward modelling
<p>Provided are Molodensky's truncation coefficients for cap-modified spectral gravity forward modelling from the <a href="https://doi.org/10.1007/s00190-019-01277-3">Bucha et al. (2019)</a> study. The coefficients are evaluated for</p> <ul> <li>the spherical distance of <em><span>\(\psi_0 = 100000\ \mathrm{m} / 6378137\ \mathrm{m}\)</span> </em>(100 km integration radius from the evaluation point),</li> <li>the reference sphere having the radius <span>\(R = 6378137\ \mathrm{m}\)</span>,</li> </ul> <ul> <li>the radius of the evaluation point<em> <span>\(r = 6378137\ \mathrm{m} + 7000\ \mathrm{m}\)</span></em>,</li> <li>harmonic degrees <span>\(n=0,\dots,21600\)</span>,</li> <li>topography powers <span>\(p=1,\dots,30\)</span>,</li> <li>radial derivatives <span>\(k=0,\dots,40\)</span>, and</li> <li>the first- and second-order horizontal derivatives.</li> </ul> <p>The coefficients were computed using 256 significant digits, ensuring 24-digit accuracy or better. After the evaluation, the coefficients were converted to double precision with 16 significant digits. Importantly, in some cases, the loss of significance errors may be encountered during the spherical harmonic synthesis when using the coefficients (see the reference below).</p> <p>Bucha, B., Hirt, C., Kuhn, M., 2019. <em>Cap integration in spectral gravity forward modelling up to the full gravity tensor</em>. Journal of Geodesy, <a href="https://doi.org/10.1007/s00190-019-01277-3">https://doi.org/10.1007/s00190-019-01277-3</a>.</p>
Refined Bathymetric Prediction based on Feature Extraction of Gravity Field Signals: BATHY-FE
<p>BATHY-FE is a refined global seafloor model derived from extraction of learnt bathymetric signatures inherent in gravity field signals. It spans longitudes -180 ~ 180, and latitudes -80 ~ 80. It contains more short-wavelength seafloor features, and is superior to existing bathymetric models in almost all marine regions, especially at regions close to the poles. It is a GMT readable grid file (i.e., a matrix of seafloor model, and vectors of longitudes and latitudes) with a spatial resolution of 15 arc-seconds.</p><p>Included in this repository are a MATLAB livescript and sample data in which a demonstration of the algorithm over a region north of Alaska and Canada is presented.</p>
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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.