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61 results for “gravity model”
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>
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>
Crustal thicknesses, Moho depths and 3-D density anomaly model for GJI paper: Crustal structure of onshore-offshore Atlantic Canada and environs from constrained 3-D gravity inversion using variable mesh depths by J. Kim Welford
<p>The files are provided as ascii text files in terms of both latitudes/longitudes and eastings/northings. For the 3-D density anomaly model, it is provided with columns of x, y, z, and absolute density. The conversions from latitudes/longitudes to eastings/northings for all of the models and maps in this work are computed with ellipsoid WGS-84 and UTM zone 19 using Generic Mapping Tools.</p>
SDUST2021GRA: Global marine gravity anomaly model recovered from Ka-band and Ku-band satellite altimeter data
<p>SDUST2021GRA is the global marine gravity anomaly model on a grid of 1′×1′, which is established from the altimeter data of <strong> </strong>Ka-band and Ku-band altimetry satellite including HY-2A. Its spatial coverage is 80°S-80°N. Assessed by the shipborne gravity data, the accuracy of SDUST2021GRA in the global is 2.37 mGal, and that in the open ocean is about 1.5 mGal.</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>
EGFs, gravity and crustal models data around the Solonker suture zone in NE China
<p>The observed EGFs, complete Bouguer gravity anomalies data and the 3-D crustal Vs and density models from our joint inversion around the Solonker suture zone in NE China.</p>
Forward Asteroseismic Modeling of Stars with a Convective Core from Gravity-mode Oscillations: Parameter Estimation and Stellar Model Selection
<p>MESA inlists associated with <a href="https://ui.adsabs.harvard.edu/#abs/arXiv:1806.06869">Aerts et al. (2018)</a>. MESA version 10108.</p> <p>Publication DOI: <a href="https://doi.org/10.3847/1538-4365/aaccfb">10.3847/1538-4365/aaccfb</a></p>
Binary asteroseismic modelling: isochrone-cloud methodology and application to Kepler gravity mode pulsators
<p>MESA inlists associated with <a href="https://ui.adsabs.harvard.edu/#abs/2018MNRAS.tmp.2555J/abstract">Johnston et al. (2019)</a>. MESA version 10108.</p> <p>Publication DOI: <a href="https://doi.org/10.1093/mnras/sty2671">10.1093/mnras/sty2671</a></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>
Model and the related code for gravity field determination from the third invariant of the GOCE gravity gradient tensors: I3GG V1.0th
<p>The model and the related code for gravity field determination from the third invariant of the GOCE gravity gradient tensors: I3GG V1.0th</p>
Gravity change data used in the paper "Insights into the magmatic feeding system of the 2021 eruption at Cumbre Vieja (La Palma, Canary Islands) inferred from gravity data modeling"
<p>Gravity changes data observed in the network between July 2021 and January 2022 </p> <p>Reference:</p> <p>Insights into the magmatic feeding system of the 2021 eruption at Cumbre Vieja (La Palma, Canary Islands) inferred from gravity data modeling <br> F. G. Montesinos1,7, S. Sainz-Maza2,7, D. Gómez-Ortiz3, J. Arnoso4,7, I. Blanco-Montenegro5,7, M. Benavent1,7 E. Vélez4,7, N. Sánchez6 and T. Martín-Crespo3</p> <p>1 Facultad de CC. Matemáticas, Universidad Complutense de Madrid. Plaza de Ciencias 3, 28040 Madrid, Spain.<br> 2 Observatorio Geofísico Central (IGN). C/ Alfonso XII, 3. 28014 Madrid, Spain.<br> 3 Dpt. Biología y Geología, Física y Química Inorgánica, ESCET, Universidad Rey Juan Carlos. C/Tulipán s/n, 28933 Móstoles, Madrid, Spain.<br> 4 Instituto de Geociencias (IGEO), CSIC-UCM. C/ Doctor Severo Ochoa, 7. 28040 Madrid, Spain.<br> 5 Departamento de Física, Escuela Politécnica Superior, Universidad de Burgos. Avda. de Cantabria s/n, 09006 Burgos, Spain.<br> 6 Instituto Geológico y Minero de España (IGME, CSIC), Unidad Territorial de Canarias, Alonso Alvarado, 43, 2A, 35003 Las Palmas de Gran Canaria, Spain.<br> 7 Research Group ‘Geodesia’, Universidad Complutense de Madrid, Spain.</p> <p><br> Corresponding author: Fuensanta G. Montesinos (fuensant@ucm.es)</p> <p>This research is supported by the project PID2019-104726GB-I00/AEI/10.13039/501100011033 funded by the Spanish Research Agency. Further, the University Complutense of Madrid (grants Financiación Grupos 2021, UCM 2022-GRFN14/22) and the Spanish Ministry of Science and Innovation (RD 1078/2021, funding for research activities of the CSIC-PIE project CSIC-LAPALMA-07) supported this research.</p>
A global marine gravity anomaly model SDUST2022GRA_IS2
<p>A global marine gravity anomaly model SDUST2022GRA_IS2 recovered from along- and across-track altimeter data of ICESat-2</p>
Data for model analysis in "Beyond the growth rate of cosmic structure: Testing modified gravity models with an extra degree of freedom", arXiv:1502.03710
<p>SQLite databases containing theoretical predictions for the model comparison in arXiv:1502:03710.</p>
Data for "Machine Learning Parameterization of Subgrid-Scale Orographic Gravity Wave Drag in a Middle-Atmosphere General Circulation Model" by Lu et al., submitted to JAMES, 2022.
<p>The NetCDF data file involving the decision tree strucutre attributes of the random forest emulator.</p> <p>gcm_regressors/<br> The data file involving the decision tree strucutre attributes (in NetCDF format)</p>
Gravity Wave Morphology During the 2018 Sudden Stratospheric Warming Simulated by a Whole Neutral Atmosphere General Circulation Model
<p>This dataset includes a complete set of raw data, metadata and saved session data which is necessary for re-producing figures in a paper entitled "Gravity Wave Morphology During the 2018 Sudden Stratospheric Warming Simulated by a Whole Neutral Atmosphere General Circulation Model" submitted to the Journal of Geophysical Research - Atmosphere.</p>
Dataset for "Gravity model explained by the radiation model on a population landscape"
<p>This dataset contains simulated data used in the paper "Gravity model explained by the radiation model on a population landscape".</p>
EGFs, gravity and crustal models data around the JPH volcanic area in NE China
<p>The observed EGFs, complete Bouguer gravity anomalies data and the 3-D crustal Vs and density models from our joint inversion around the Jingpohu volcanic area in Northeast China. </p>
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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)
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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.