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opencc-by-4.0Jul 2021View details →

WINTERC-G: a global upper mantle thermochemical model from coupled geophysical–petrological inversion of seismic waveforms, heat flow, surface elevation and gravity satellite data

WINTERC-G: A global, temperature and compositional model of the lithosphere            and upper mantle. Version:  v5.4, December 2020, J. Fullea, S. Lebedev, Z. Martinec, N. Celli            Contact:  Javier Fullea (jfullea@ucm.es)           Facultad de Fisica,           Universidad Complutense de Madrid (UCM),           Spain           ////////           Geophysics Section,           Dublin Institute for Advanced Studies           Dublin, Ireland   TYPE:  This contains files with:  i) the model directly on the triangular grid solved for in the surface wave inversion.  ii) an interpolated grid at 0.5 deg lateral resolution for the density and density discontinuities used in the gravity field data inversion   If you have any questions regarding the methodology or the construction of the model, please contact the authors. If you use the model, we would request that you cite the reference indicated below, and appreciate your feedback regarding the model and its application. Citation: Fullea, J., Lebedev, S., Martinec, Z., & Celli, N. L. (2021). WINTERC-G: mapping the upper mantle thermochemical heterogeneity from coupled geophysical–petrological inversion of seismic waveforms, heat flow, surface elevation and gravity satellite data. Geophysical Journal International, 226(1), 146-191. ******************************* Summary: construction of the model. WINTERC-G is a Waveform tomography and Gravity (geoid and gravity anomalies and gradiometric measurements from ESA's GOCE mission) INversion model of the TEmpeRature and Composition of the lithosphere and upper mantle at global scale. WINTERC-G is based on upon the integrated geophysical-petrological approach LitMod (Afonso et al., 2008; Fullea et al. 2009) and, hence, all relevant mantle rock physical properties modelled (seismic velocities and density) are computed within a thermodynamically self-consistent framework allowing for a direct parameterization in terms of the temperature and composition of the lithosphere-upper mantle. The inversion is a two-step procedure. In a first step, we invert surface-wave, Rayleigh and Love fundamental mode dispersion curves from a high resolution global dataset measured using waveform inversion, along with surface heat flow and elevation (isostasy) for temperature and crustal structure using a point-wise, non-linear, gradient-search inversion over a triangular grid with an average 225 km lateral inter-knot spacing. In a second step we use a fully parallelized spherical harmonic formalism to invert satellite gravity field data in order to refine the initial crustal density and mantle composition distributions from the step 1 for a fixed temperature field. The parameter space in step 1 includes crust (densities and S-wave velocities for a three-layered crust)  and mantle variables (the depth of the thermal Lithosphere-Athenosphere-Boundary, the thickness of the sublithospheric thermal buffer, the sublithospheric temperatures at 3 different equispaced nodes down to 400 km, the lithospheric and sublithospheric mantle compositon, and the the radial anisotropy at the 3 crustal layers and at 56, 80, 110, 150, 200, 260, 330, and 400 km depths. The parameter space in step 2 is defined by the average crustal density, and the mantle composition in the lithosphere and sublithosphere. We use the output crustal density from step 1 as the initial value in step 2 inversion. Mantle densities are derived based on the output temperature field from step 1 (kept fixed) and the bulk mantle composition inversion variables.     ******************************* This archive contains the following files:   README (this file)   WINTERC-G_Vp-Vs.lis (triangular grid)   WINTERC-G_rad_anis_Vs.lis (triangular grid)   WINTERC-G_Temperature.lis (triangular grid)   WINTERC-G_Density.lis (triangular grid)   WINTERC-G_LAB.lis (triangular grid)   WINTERC_T_rho_1D.z (1D average model of temperature and density)   rho_*_out.xyz (0.5 deg egular grid for gravity field)   ETOPO2_km_continental.xyz (0.5 deg egular grid for gravity field)   ETOPO2_km_depth_Ice.xyz (0.5 deg egular grid for gravity field)   ETOPO2_km_depth_Bed.xyz (0.5 deg egular grid for gravity field)   Global_Moho_WINTERC-G.xyz (0.5 deg egular grid for gravity field) Files in the triangular grid with an average 225 km lateral inter-knot spacing (12232 grid points): * WINTERC-G_Vp-Vs.lis: Vp and Vs (in km/s) in all model columns with a vertical grid step of 2 km  Format for each column:  #Column number longitude latitude depth(km, <0 downwards) Vp (km/s) Vs(km/s)      5640         93.72     4.135       -5.0                3.91     2.11 * WINTERC-G_rad_anis_Vs.lis: radial anisotropy, (Vsh-Vsv)/Vs_iso (in %) in all model columns with a vertical grid step of 2 km   Format for each column:   #Column number longitude latitude depth(km, <0 downwards) anisotropy (%) * WINTERC-G_Temperature.lis: temperature (in ºC) in all model columns with a vertical grid step of 2 km  Format for each column:   #Column number longitude latitude depth (km, <0 downwards) T (ºC)   dT (%)   dT(K)         6437         297.20    -2.524     -259.000             1431.9   -1.91     -27.9   The anomalies dT are in % and K with respect to the 1D model in WINTERC_T_rho_1D.z (column 2). * WINTERC-G_Density.lis: density (in kg/m3) in all model columns with a vertical grid step of 2 km  Format for each column:   #Column number longitude latitude depth(km, <0 downwards) rho (kg/m3) drho(%) drho(kg/m3)   The anomalies drho are in % and kg/m3 with respect to the 1D model in WINTERC_T_rho_1D.z (column 3). * WINTERC_T_rho_1D.z: 1D average model of temperature (column 2 in ºC) and density (column 3 in kg/m3) with a vertical grid step of 2 km      5.00000000       0.0000000000000000        6.0259973839110526    3.00000000       0.0000000000000000        38.960571309690394    1.00000000      0.33634006819423840        174.42296045978722   -1.00000000       3.8888495253719624        1692.8437489147236   -3.00000000       23.974111923225379        1863.8834351235944   -5.00000000       47.727920701943034        2568.2414495590924   -7.00000000       89.633398074381162        2819.8386016341910   -9.00000000       137.01489361657013        2839.5325893195904   -11.0000000       182.35233447017222        2897.6600872935287   -13.0000000       224.46247069572485        2945.2036923862997   -15.0000000       260.63395547331390        3069.6809340323475   -17.0000000       292.28175449521456        3132.4574175461721   -19.0000000       322.29571965406632        3145.5747337463940   -21.0000000       351.58698283375054        3157.2401512748038   -23.0000000       380.30002225705056        3177.0000420059773   -25.0000000       408.50259805632055        3183.6651032398490   -27.0000000       436.22632217636487        3190.9586785996116   -29.0000000       463.48733903170023        3198.9369509456310   -31.0000000       490.29841705549831        3209.7229872383764   -33.0000000       516.71149258457456        3221.6329506091679   ... Files in the interpolated regular grid at 0.5 deg lateral resolution used for gravity field data inversion:   * rho_c_out.xyz: average crustal density   * rho_submoho_out.xyz: mantle density below the Moho discontinuity   * rho_*_out.xyz: mantle density defined at different model depths: 20, 35, 56, 80, 110, 150, 200, 260, 330 and 400 km.   Format for the density files:   # longitude latitude density (kg/m3)     Files containing layer discontinuities:  * ETOPO2_km_continental.xyz: surface elevation including ice sheet and 0 in marine areas (km, <0 upwards)  * ETOPO2_km_depth_Ice.xyz: surface elevation including ice sheet (km, >0 downwards, <0 above sea level)  * ETOPO2_km_depth_Bed.xyz: bedrock surface elevation without ice sheet (km, >0 downwards, <0 above sea level)  * Global_Moho_WINTERC-G.xyz: crust-mantle discontinuity depth (km, >0 downwards)   Format for the discontinuity files:    # longitude latitude depth (km)     The gravity field in WINTERC-G is computed using an spherical harmonic formalism and a model discretization in 13 layers with laterally varying density. The first 7 layers are characterized by top and bottom boundaries with laterally varying radius whereas the last 6 layers are defined by top and bottom boundaries with constant radius: 1/ Water: from ETOPO2_km_continental.xyz to ETOPO2_km_depth_Ice.xyz with rho=1030 kg/m3 (constant vertically) 2/ Ice: from ETOPO2_km_depth_Ice.xyz to ETOPO2_km_depth_Bed.xyz  with rho=910 kg/m3 (constant vertically) 3/ Crust: from ETOPO2_km_depth_Bed to Global_Moho_WINTERC-G.xyz with rho=rho_c_out.xyz (constant vertically) 4/ submoho-20km: from Global_Moho_WINTERC-G.xyz to z_20km (file with 20 km everywhere except where z_moho>20km) with rho=rho_submoho_out.xyz (top) and rho=rho_20km_out.xyz (bottom) 5/ 20km-36km: from  z_20km (file with 20 km everywhere except where z_moho>20km) to z_36km (file with 36 km everywhere except where z_moho>36km)   with rho=rho_20km_out.xyz (top) and rho=rho_36km_out.xyz (bottom) 6/ 36km-56km: from  z_36km (file with 36 km everywhere except where z_moho>36km) to z_56km (file with 56 km everywhere except where z_moho>56km)   with rho=rho_36km_out.xyz (top) and rho=rho_56km_out.xyz (bottom) 7/ 56km-80km: from  z_56km (file with 56 km everywhere except where z_moho>56km) to 80 km depth with rho=rho_56km_out.xyz (top) and rho=rho_80km_out.xyz (bottom) The next 6 layers are computed using the constant radius option: 8/ 80km-110km: from z=80km to z=110 km with rho=rho_80km_out.xyz (top) and rho=rho_110km_out.xyz (bottom) 9/ 110km-150km: from z=110km to z=150 km with rho=rho_110km_out.xyz (top) and rho=rho_150km_out.xyz (bottom) 10/ 150km-200km: from z=150km to z=200 km with rho=rho_150km_out.xyz (top) and rho=rho_200km_out.xyz (bottom) 11/ 200km-260km: from z=200km to z=260 km with rho=rho_200km_out.xyz (top) and rho=rho_260km_out.xyz (bottom) 12/ 260km-330km: from z=260km to z=330 km with rho=rho_260km_out.xyz (top) and rho=rho_330km_out.xyz (bottom) 13/ 330km-400km: from z=330km to z=400 km with rho=rho_330km_out.xyz (top) and rho=rho_400km_out.xyz (bottom)

opencc-by-4.0Jun 2024View details →

Magnetic, gravity and seismicity data for the Monchique intrusion and surroundings (SW Portugal, SW Iberia)

This dataset contains the following data:   1. Magnetic anomaly data (processed line data) acquired by drone-borne magnetometer for the Monchique area (.dat file) 2. Magnetic and gravity anomaly maps for the Monchique area  in SW Portugal, SW Iberia: <ul> <li>Magnetic anomaly (.tif and .grd files)</li> <li>Reduced to the pole (RTP) magnetic anomaly (.tif and .grd files)</li> <li>Free air gravity anomaly (.tif and .grd files)</li> <li>Complete Bouguer gravity anomaly, after terrain correction (.tif and .grd files)</li> </ul> 3. Seimicity data: <ul> <li>Relocated earthquakes that occurred between 01/01/2007 and 01/07/2023 in the Monchique area (.xlsx file)</li> <li>Focal mechanisms (moment tensor inversion solutions) of earthquakes occurred in the Monchique area (.xlsx file)</li> </ul>   For all details on data collection and processing please refer to: Neres, M., Camargo, G., Soares, A., Custódio, S., Bos, M., Vales, D., & Terrinha, P. (2024). Monchique alkaline magmatic intrusion (SW Iberia): Geophysical modeling and relationship with active seismicity and hydrothermalism. Tectonophysics. <a title="Persistent link using digital object identifier" href="https://doi.org/10.1016/j.tecto.2024.230426" target="_blank" rel="noreferrer noopener">https://doi.org/10.1016/j.tecto.2024.230426</a>

opencc-by-4.0Oct 2024View details →

GRAVITY and CRIRES+ data for Gliese 229Bab

This Zenodo record contains the GRAVITY data and CRIRES+ RVs used in Xuan et al. 2024 to fit the orbit of the binary brown dwarf Gliese 229 BaBb. For the full author list and paper, please see <a href="https://www.nature.com/articles/s41586-024-08064-x">https://www.nature.com/articles/s41586-024-08064-x</a> After unzipping, there will be one ipynb file and a folder for the five epochs of GRAVITY data. The CRIRES+ RVs are listed in the Jupyter notebook. The notebook will start an orbit fit. It is recommended that the code be run with multiple CPUs.  The notebook is written in Julia, so users should install Julia first following resources provided in the notebook.

opencc-by-4.0Nov 2022View details →

Data for paper "An adaptive nonlinear iterative method for predicting seafloor topography from altimetry-derived gravity data"

LM is the linear inversion seafloor topography model NLM is the nonlinear inversion seafloor topography model PM is the prior seafloor topography model

opencc-by-4.0Dec 2019View details →

The dataset from a submitted journal entitled "Characterization of the Mamasa earthquake source in West Sulawesi based on the earthquake relocation data, gravity data, and coulomb stress change of Palu earthquake series"Dataset for paper

This dataset consists of four files, namely: 1. Coulomb Stress Input file. This data is input data for Coulomb 3.3 software 2. Double Couple Percentage. This table is used for the Spatio-temporal Compensated Linear Vector Dipole (CLVD) analysis 3. Gravity data. This data consists of coordinates, altitude, and Complete Bouguer Anomaly. 4. Residual comparison of before and after the relocation. This table is to ensure that our relocation is successful

opencc-by-4.0Nov 2023View details →

Gravity, Free-Air and Bouguer Anomaly Data in the Ivrea-Verbano Zone (Western Alps, Italy)

Gravity dataset collected in the Ivrea-Verbano Zone (IVZ, Western Alps, Italy). The data was collected in the frame of a gravity-based investigation and modelling of the Ivrea Geophysical Body. For citation and further details on the work see Scarponi et al. (2020, GJI): <a href="https://doi.org/10.1093/gji/ggaa263">https://doi.org/10.1093/gji/ggaa263</a>The file contains the gravity data collected in the IVZ region, including free-air anomaly and Bouguer gravity anomaly (in mGal).Longitude, Latitude coordinates are in degrees, elevation in meters.Uncertainty on the final gravity data products and gravity data is 1 mGal.---Data collection, as well as the associated research, were supported by the Swiss National Science Foundation (SNF) (grant numbers PP00P2_157627 and PP00P2_187199).

opencc-by-4.0Nov 2023View details →

SDUST2020MGCR: a global marine gravity change rate model determined from multi-satellite altimeter data

SDUST2020MGCR.nc is the global marine gravity change rate model covering 70°S~70°N and 0°~360°E on 5′×5′ grids. The dataset contains geospatial information (latitude, longitude), SDUST2020MGCR and an attachment data (GIA MGCR).

opencc-by-4.0Mar 2024View details →

Gravity core XRF data from the Porcupine Abyssal Plain

X-ray Fluorescence (XRF) data from three gravity cores obtained during NOC expedition JC231 (2022) for the "Time-series studies at the Porcupine Abyssal Plain Sustained Observatory". These three gravity cores (GC050; GC073; GC076) were collected from different sites at the Porcupine Abyssal Plain (AESA Hill; AESA North Plain; PAP Central respectively).  <table> <tbody> <tr> <td>Ship/Platform</td> <td>Cruise Identifier</td> <td>Sample Identifier</td> <td>Site</td> <td>Number of Sections</td> <td>Section Identifier</td> <td>Date Sample Collected</td> <td>Decimal Latitude</td> <td>Decimal Longitude</td> <td>Water Depth (m)</td> <td>Sampling Device</td> <td>Storage Method</td> <td>Core Length (cm)</td> <td>Core Diameter (cm)</td> </tr> <tr> <td>RRS James Cook</td> <td>JC231</td> <td>GC050</td> <td>AESA Hill</td> <td>2</td> <td>Section 1</td> <td>09/05/2022</td> <td>48 59.103</td> <td>16 33.17</td> <td>4795</td> <td>Gravity core</td> <td>room temperature, dry (D)</td> <td>100</td> <td>6.5</td> </tr> <tr> <td>RRS James Cook</td> <td>JC231</td> <td>GC050</td> <td>AESA Hill</td> <td>2</td> <td>Section 2</td> <td>09/05/2022</td> <td>48 59.103</td> <td>16 33.17</td> <td>4795</td> <td>Gravity core</td> <td>room temperature, dry (D)</td> <td>50</td> <td>6.5</td> </tr> <tr> <td>RRS James Cook</td> <td>JC231</td> <td>GC073</td> <td>AESA North Plain</td> <td>3</td> <td>Section 1</td> <td>12/05/2022</td> <td>49 0.657</td> <td>16 33.221</td> <td>4846</td> <td>Gravity core</td> <td>room temperature, dry (D)</td> <td>100</td> <td>6.5</td> </tr> <tr> <td>RRS James Cook</td> <td>JC231</td> <td>GC073</td> <td>AESA North Plain</td> <td>3</td> <td>Section 2</td> <td>12/05/2022</td> <td>49 0.657</td> <td>16 33.221</td> <td>4846</td> <td>Gravity core</td> <td>room temperature, dry (D)</td> <td>100</td> <td>6.5</td> </tr> <tr> <td>RRS James Cook</td> <td>JC231</td> <td>GC073</td> <td>AESA North Plain</td> <td>3</td> <td>Section 3</td> <td>12/05/2022</td> <td>49 0.657</td> <td>16 33.221</td> <td>4846</td> <td>Gravity core</td> <td>room temperature, dry (D)</td> <td>100</td> <td>6.5</td> </tr> <tr> <td>RRS James Cook</td> <td>JC231</td> <td>GC076</td> <td>PAP Central </td> <td>3</td> <td>Section 1</td> <td>12/05/2022</td> <td>48 50.095</td> <td>16 31.331</td> <td>4843</td> <td>Gravity core</td> <td>room temperature, dry (D)</td> <td>100</td> <td>6.5</td> </tr> <tr> <td>RRS James Cook</td> <td>JC231</td> <td>GC076</td> <td>PAP Central </td> <td>3</td> <td>Section 2</td> <td>12/05/2022</td> <td>48 50.095</td> <td>16 31.331</td> <td>4843</td> <td>Gravity core</td> <td>room temperature, dry (D)</td> <td>100</td> <td>6.5</td> </tr> <tr> <td>RRS James Cook</td> <td>JC231</td> <td>GC076</td> <td>PAP Central </td> <td>3</td> <td>Section 3</td> <td>12/05/2022</td> <td>48 50.095</td> <td>16 31.331</td> <td>4843</td> <td>Gravity core</td> <td>room temperature, dry (D)</td> <td>80</td> <td>6.5</td> </tr> </tbody> </table>

opencc-by-4.0Oct 2021View details →

Probabilistic linear inversion of satellite gravity gradient data applied to the northeast Atlantic

% MATLAB scripts to calculate and plot figures as in manuscript by % % Minakov, A., & Gaina, C. (2021). % Probabilistic linear inversion of satellite gravity gradient data applied % to the northeast Atlantic. Journal of Geophysical Research: Solid Earth, % 126, e2021JB021854. https://doi.org/10.1029/2021JB021854 %  % Last modified by alexamin@uio.no, 26/11/2021 % % version v1.1 %  % Contents of arhcive % /data  contains requiried and generated datasets  % /fig   folder for output figures  % /plot  scripts to produce figures  % /tools additional matlab tools and routines % Dataset in ..data/GOCE_NEATLANTIC is structure containing the full model %  %      Cm: [6670×6670 double] posterior model covariance matrix %       m: [29×23×10 double] mean denstity perturbation model %      Cd: [667×667 double] data covariance matrix %       d: [29×23 double] data vector (Trr) %       r: [1×10 double] distance %     lat: [29×1 double] latitute %     lon: [23×1 double] longitude % % Run  /plot/fig_results.m to produce all figures  % % Some scripts require GMT (Wessel et al. 2019) and SHBUNDLE (Sneeuw et al. 2018) software to be installed % and corresponding folders must be added to the matlab search path. % Also ScientificColorMaps7 by F. Crameri (2021) maybe required and have been included in the archive.

opencc-by-4.0Jan 2022View details →

Fatiando a Terra Data: Southern Africa - Ground-based gravity

This is a public domain compilation of ground measurements of gravity from Southern Africa. The observations are the absolute gravity values in mGal. The horizontal datum is not specified and heights are referenced to "sea level", which we will interpret as the geoid (which realization is likely not relevant since the uncertainty in the height is probably larger than geoid model differences). Note: This is a processed and formatted version of the source dataset below. It's meant for use in documentation and tutorials of the <a href="https://www.fatiando.org">Fatiando a Terra</a> project. Please cite the original authors when using this dataset. Changes made: Keep only coordinates, absolute gravity, and the (sea-level) observation height. Remove some points below sea-level (a bit suspicious and are potentially flawed heights from shipborne measurements). Convert from a custom text format to compressed CSV. Source: <a href="https://www.ngdc.noaa.gov/mgg/gravity/">NOAA NCEI</a> Source license: <a href="https://ngdc.noaa.gov/ngdcinfo/privacy.html">public domain</a> Repository: <a href="https://github.com/fatiando-data/southern-africa-gravity">https://github.com/fatiando-data/southern-africa-gravity </a>

opencc-by-4.0Jan 2022View details →

Fatiando a Terra Data: Earth - Gravity grid at 10 arc-minute resolution

Global 10 arc-minute resolution grids of gravity acceleration (gravitational and centrifugal) at 10 km geometric height. Note: This is a processed and formatted version of the source dataset below. It's meant for use in documentation and tutorials of the <a href="https://www.fatiando.org">Fatiando a Terra</a> project. Please cite the original authors when using this dataset. Changes made: Convert the grid from the ASCII format of ICGEM to CF-compliant netCDF. Add relevant metadata, including names, units, datum, etc. Fix grid coordinates to be generated by <code>numpy.linspace</code> instead <code>numpy.arange</code> (or the equivalent used by ICGEM internally) to guarantee equal spacing to a higher accuracy. Export to compressed netCDF. Source: <a href="https://doi.org/10.5880/icgem.2015.1">EIGEN-6C4</a> spherical harmonic model (generated by the <a href="http://icgem.gfz-potsdam.de/home">ICGEM calculation service</a>) Source license: <a href="https://doi.org/10.5880/icgem.2015.1">CC-BY</a> Repository: <a href="https://github.com/fatiando-data/earth-gravity-10arcmin">https://github.com/fatiando-data/earth-gravity-10arcmin</a>

opencc-by-4.0May 2022View details →

Fatiando a Terra Data: Bushveld, Southern Africa - Observed and preprocessed gravity

This dataset contains ground gravity observations over the area that comprises the Bushveld Igenous Complex in Southern Africa, including preprocessed gravity fields such as the gravity disturbance and the bouguer gravity disturbance (topography-free gravity disturbance). In addition, the dataset contains the heights of the observation points referenced on the WGS84 reference ellipsoid and over the mean sea-level (what can be considered to be the geoid). This dataset was built upon a portion of the Southern Africa gravity compilation available through <a href="https://www.ngdc.noaa.gov/mgg/gravity/">NOAA NCEI</a>. Note: This is a processed and formatted version of the source dataset below. It's meant for use in documentation and tutorials of the <a href="https://www.fatiando.org">Fatiando a Terra</a> project. Please cite the original authors when using this dataset. Changes made: <ul> <li>The original data were cropped to a region bounded by 25 and 32 degrees on longitude and -27 and -23 degrees on latitude.</li> <li>Geometric observation heights were obtained by adding geoid heights to the original observation heights referenced on the mean sea-level. The geoid heights on each observation point were obtained by interpolation of the geoid available in doi: <a href="https://doi.org/10.5281/zenodo.5882205">10.5281/zenodo.5882205</a>.</li> <li>Gravity disturbances were computed by removing the normal gravity of the WGS84 ellipsoid computed through <a href="https://www.fatiando.org/boule">Boule</a>.</li> <li>Bouguer gravity disturbances were computed by forward modelling the topography using <a href="https://www.fatiando.org/harmonica">Harmonica</a> starting from the topography grid provided in doi: <a href="https://doi.org/10.5281/zenodo.6481379">10.5281/zenodo.6481379</a> and using densities of 2670 kg/m³ above the ellipsoid and 1040 - 2670 kg/m³ below the ellipsoid.</li> </ul> Source: <a href="https://www.ngdc.noaa.gov/mgg/gravity/">NOAA NCEI</a> (gravity) and <a href="https://doi.org/10.7289/V5C8276M">ETOPO1</a> (topography) Source license: <a href="https://ngdc.noaa.gov/ngdcinfo/privacy.html">public domain</a> (gravity) and <a href="https://ngdc.noaa.gov/mgg/global/dem_faq.html#sec-2.4">public domain</a> (topography) Repository: <a href="https://github.com/fatiando-data/bushveld-gravity">https://github.com/fatiando-data/bushveld-gravity</a>

opencc-zeroMay 2021View details →

Ocean and ice with waves data for role of surface gravity waves in aquaplanet ocean climates

This data corresponds to the runs analysed in the manscript: Role of Surface Gravity Waves in Aquaplanet Ocean Climates (JAMES, 2021). In this work, we present a set of idealised numerical experiments that demonstrate the thermodynamic and dynamic implications of surface gravity waves for the oceanic climate of an aquaplanet. We study the impact of accounting for modulations by such waves upon air-sea momentum fluxes, Langmuir circulation and the Stokes-Coriolis force. This dataset is made up of atmospheric, oceanic and surface gravity wave simulations. When uncompressed the total dataset is 1.6 TB, the ocean and ice with waves component is 564 GB. See below for further details. See the related works section for the corresponding datasets.

opencc-zeroMay 2021View details →

Ocean and ice without waves data for role of surface gravity waves in aquaplanet ocean climates

This data corresponds to the runs analysed in the manscript: Role of Surface Gravity Waves in Aquaplanet Ocean Climates (JAMES, 2021). In this work, we present a set of idealised numerical experiments that demonstrate the thermodynamic and dynamic implications of surface gravity waves for the oceanic climate of an aquaplanet. We study the impact of accounting for modulations by such waves upon air-sea momentum fluxes, Langmuir circulation and the Stokes-Coriolis force. This dataset is made up of atmospheric, oceanic and surface gravity wave simulations. When uncompressed the total dataset is 1.6 TB, the ocean and ice without waves component is 484 GB. See below for further details. See the related works section for the corresponding datasets.

opencc-by-4.0Mar 2023View details →

Data files for Atmospheric Gravity Wave and Instability Observations from the International Space Station using the Near InfraRed Airglow Camera (NIRAC)

The files in this set are data obtained from  the NIRAC airglow imager on the International Space Station. The files are  named for a JGR paper by J. Hecht et al.  entitled  Atmospheric Gravity Wave and Instability Observations from the International Space Station using the Near InfraRed Airglow Camera (NIRAC).  These files are for plots in Figures 5,7,10,11,17,18, and 19 in the submitted paper. The files are published here so as to be available for review. This paper should appear in JGR Atmospheres sometime in late 2023 or early 2024. The files that  are text files are meant to be  read with IDL as discussed in the readme file.

opencc-by-4.0Mar 2023View details →

Insights into the Magmatic Feeding System of the 2021 Eruption at Cumbre Vieja (La Palma, Canary Islands) Inferred from Gravity Data Modeling. Remote Sens. 2023, 15, 1936. https://doi.org/10.3390/rs15071936

Paper: Insights into the magmatic feeding system of the 2021 eruption at Cumbre Vieja (La Palma, Canary Islands) inferred from gravity data modeling  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 1 Facultad de CC. Matemáticas, Universidad Complutense de Madrid. Plaza de Ciencias 3, 28040 Madrid, Spain. 2 Observatorio Geofísico Central (IGN). C/ Alfonso XII, 3. 28014 Madrid, Spain. 3 Dpt. Biología y Geología, Física y Química Inorgánica, ESCET, Universidad Rey Juan Carlos. C/Tulipán s/n, 28933 Móstoles, Madrid, Spain. 4 Instituto de Geociencias (IGEO), CSIC-UCM. C/ Doctor Severo Ochoa, 7. 28040 Madrid, Spain. 5 Departamento de Física, Escuela Politécnica Superior, Universidad de Burgos. Avda. de Cantabria s/n, 09006 Burgos, Spain. 6 Instituto Geológico y Minero de España (IGME, CSIC), Unidad Territorial de Canarias, Alonso Alvarado, 43, 2A, 35003 Las Palmas de Gran Canaria, Spain. 7 Research Group ‘Geodesia’, Universidad Complutense de Madrid, Spain. Corresponding author: Fuensanta G. Montesinos (fuensant@ucm.es) 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. ------------------------------------------------------------------------------------------------ Responsible Researchers: - Fuensanta González Montesinos, Facultad de CC. Matemáticas, Universidad Complutense de Madrid. Spain fuensant@ucm.esResponsible Researchers:  -  José Arnoso Sampedro,  Instituto de Geociencias (CSIC-UCM), Spain jose_arnoso@csic.es   >> The use of this data set is limited to academic or research purposes and it have to be referenced Zone:Cumbre Vieja (La Palma Island, Spain) Geodetic Coordinates Datum WGS84 Gravity(mGal) and Bouguer Gravity anomaly GRS80 (mGal)(Terrain density 2450 kg/m3) The file GravityCumbreVieja_FGMontesinos_et_al.dat includes the values of gravity and complete Bouguer gravity anomaly (GRS80) calculated for the land gravity stations at the Cumbre Vieja area (La Palma Island, Spain). The gravity values were observed in 142 land gravity stations (Figure 3 in the manuscript) by our group in 2005 and 2021 surveys The positions of the stations were selected to cover most of the Cumbre Vieja area, and the coordinates were obtained by differential GPS (WGS84 Datum). The gravity observations were processed taking into account the usual corrections (instrument height, drift, jumps, etc.). The tidal correction was calculated from gravity tide measurements made in several islands of the Canary Archipelago. All the gravity values referred to absolute gravity stations (Table S1). The procedure to obtain the terrain correction and the Bouguer anomaly map is explained in the manuscript and in the supporting information.

opencc-by-4.0May 2023View details →

A haptic illusion created by gravity - Data and Codes

Dataset and code related to the study A haptic illusion created by gravity by Opsomer L, Delhaye BP, Théate V, Thonnard J-L, and Lefèvre P.

publicMay 2021View details →

Ocean and ice without waves data for role of surface gravity waves in aquaplanet ocean climates

Open the record for dataset details and reuse information.

publicMay 2021View details →

Ocean and ice with waves data for role of surface gravity waves in aquaplanet ocean climates

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