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SAMI3 data in netCDF format (2019-Mar-16)
<p>SAMI3 (Sami3 is Also a Model of the Ionosphere) is a seamless, three-dimensional, physics-based model of the ionosphere (Huba et al, 2008). It is based on SAMI2, a two-dimensional model of the ionosphere (Huba et al., 2000). <br><br>SAMI3 models the plasma and chemical evolution of seven ion species (H⁺, He⁺, N⁺, O⁺, N⁺₂, NO⁺ and O⁺₂). The temperature equation is solved for three ion species (H⁺, He⁺ and O⁺) and for the electrons. Ion inertia is included in the ion momentum equation for motion along the geomagnetic field. This is important in modeling the topside ionosphere and plasmasphere where the plasma becomes collisionless. <br><br>SAMI3 includes 21 chemical reactions and radiative recombination, and uses a nonorthogonal, nonuniform, fixed grid for the magnetic latitude range +/- 89 degrees.. <br><br><b>Drivers</b><br>Neutral composition, temperature, and winds: NRLMSISE00 (Picone et al., 2002) and HWM14 (Drob et al., 2015). <br>Solar radiation: Flare Irradiance Spectral Model version 2 (FISM v2)<br>Magnetic field: Richmond apex model [Richmond, 1995]. <br>Neutral wind dynamo electric field: Determined from the solution of a 2D potential equation [Huba et at., 2008]. <br>For the SAMI3/Weimer configuration: High latitude electric field: calculated from the empirical Weimer model for the potential. <br>For the SAMI3/AMPERE configuration: High latitude electric field: calculated using the Magnetosphere-Ionosphere Coupling solver (MIX) developed by Merkin and Lyon (2010). The inputs to MIX are SAMI3's internal conductances, plus field-aligned current observations from Active Magnetosphere and Planetary Electrodynamics Response Experiment (AMPERE), derived from the 66+ satellite Iridium NEXT constellation's engineering magnetometer data. This potential calculation is described in Chartier et al (2022). <br><br>For ease of use, SAMI3 output is remapped to a regular grid using the Earth System Modeling Framework by Hill et al (2004)</p>
SAMI3 data in netCDF format (2019-Mar-02)
<p>SAMI3 (Sami3 is Also a Model of the Ionosphere) is a seamless, three-dimensional, physics-based model of the ionosphere (Huba et al, 2008). It is based on SAMI2, a two-dimensional model of the ionosphere (Huba et al., 2000). <br><br>SAMI3 models the plasma and chemical evolution of seven ion species (H⁺, He⁺, N⁺, O⁺, N⁺₂, NO⁺ and O⁺₂). The temperature equation is solved for three ion species (H⁺, He⁺ and O⁺) and for the electrons. Ion inertia is included in the ion momentum equation for motion along the geomagnetic field. This is important in modeling the topside ionosphere and plasmasphere where the plasma becomes collisionless. <br><br>SAMI3 includes 21 chemical reactions and radiative recombination, and uses a nonorthogonal, nonuniform, fixed grid for the magnetic latitude range +/- 89 degrees.. <br><br><b>Drivers</b><br>Neutral composition, temperature, and winds: NRLMSISE00 (Picone et al., 2002) and HWM14 (Drob et al., 2015). <br>Solar radiation: Flare Irradiance Spectral Model version 2 (FISM v2)<br>Magnetic field: Richmond apex model [Richmond, 1995]. <br>Neutral wind dynamo electric field: Determined from the solution of a 2D potential equation [Huba et at., 2008]. <br>For the SAMI3/Weimer configuration: High latitude electric field: calculated from the empirical Weimer model for the potential. <br>For the SAMI3/AMPERE configuration: High latitude electric field: calculated using the Magnetosphere-Ionosphere Coupling solver (MIX) developed by Merkin and Lyon (2010). The inputs to MIX are SAMI3's internal conductances, plus field-aligned current observations from Active Magnetosphere and Planetary Electrodynamics Response Experiment (AMPERE), derived from the 66+ satellite Iridium NEXT constellation's engineering magnetometer data. This potential calculation is described in Chartier et al (2022). <br><br>For ease of use, SAMI3 output is remapped to a regular grid using the Earth System Modeling Framework by Hill et al (2004)</p>
SAMI3 data in netCDF format (2019-Mar-05)
<p>SAMI3 (Sami3 is Also a Model of the Ionosphere) is a seamless, three-dimensional, physics-based model of the ionosphere (Huba et al, 2008). It is based on SAMI2, a two-dimensional model of the ionosphere (Huba et al., 2000). <br><br>SAMI3 models the plasma and chemical evolution of seven ion species (H⁺, He⁺, N⁺, O⁺, N⁺₂, NO⁺ and O⁺₂). The temperature equation is solved for three ion species (H⁺, He⁺ and O⁺) and for the electrons. Ion inertia is included in the ion momentum equation for motion along the geomagnetic field. This is important in modeling the topside ionosphere and plasmasphere where the plasma becomes collisionless. <br><br>SAMI3 includes 21 chemical reactions and radiative recombination, and uses a nonorthogonal, nonuniform, fixed grid for the magnetic latitude range +/- 89 degrees.. <br><br><b>Drivers</b><br>Neutral composition, temperature, and winds: NRLMSISE00 (Picone et al., 2002) and HWM14 (Drob et al., 2015). <br>Solar radiation: Flare Irradiance Spectral Model version 2 (FISM v2)<br>Magnetic field: Richmond apex model [Richmond, 1995]. <br>Neutral wind dynamo electric field: Determined from the solution of a 2D potential equation [Huba et at., 2008]. <br>For the SAMI3/Weimer configuration: High latitude electric field: calculated from the empirical Weimer model for the potential. <br>For the SAMI3/AMPERE configuration: High latitude electric field: calculated using the Magnetosphere-Ionosphere Coupling solver (MIX) developed by Merkin and Lyon (2010). The inputs to MIX are SAMI3's internal conductances, plus field-aligned current observations from Active Magnetosphere and Planetary Electrodynamics Response Experiment (AMPERE), derived from the 66+ satellite Iridium NEXT constellation's engineering magnetometer data. This potential calculation is described in Chartier et al (2022). <br><br>For ease of use, SAMI3 output is remapped to a regular grid using the Earth System Modeling Framework by Hill et al (2004)</p>
SAMI3 data in netCDF format (2019-Mar-01)
<p>SAMI3 (Sami3 is Also a Model of the Ionosphere) is a seamless, three-dimensional, physics-based model of the ionosphere (Huba et al, 2008). It is based on SAMI2, a two-dimensional model of the ionosphere (Huba et al., 2000). <br><br>SAMI3 models the plasma and chemical evolution of seven ion species (H⁺, He⁺, N⁺, O⁺, N⁺₂, NO⁺ and O⁺₂). The temperature equation is solved for three ion species (H⁺, He⁺ and O⁺) and for the electrons. Ion inertia is included in the ion momentum equation for motion along the geomagnetic field. This is important in modeling the topside ionosphere and plasmasphere where the plasma becomes collisionless. <br><br>SAMI3 includes 21 chemical reactions and radiative recombination, and uses a nonorthogonal, nonuniform, fixed grid for the magnetic latitude range +/- 89 degrees.. <br><br><b>Drivers</b><br>Neutral composition, temperature, and winds: NRLMSISE00 (Picone et al., 2002) and HWM14 (Drob et al., 2015). <br>Solar radiation: Flare Irradiance Spectral Model version 2 (FISM v2)<br>Magnetic field: Richmond apex model [Richmond, 1995]. <br>Neutral wind dynamo electric field: Determined from the solution of a 2D potential equation [Huba et at., 2008]. <br>For the SAMI3/Weimer configuration: High latitude electric field: calculated from the empirical Weimer model for the potential. <br>For the SAMI3/AMPERE configuration: High latitude electric field: calculated using the Magnetosphere-Ionosphere Coupling solver (MIX) developed by Merkin and Lyon (2010). The inputs to MIX are SAMI3's internal conductances, plus field-aligned current observations from Active Magnetosphere and Planetary Electrodynamics Response Experiment (AMPERE), derived from the 66+ satellite Iridium NEXT constellation's engineering magnetometer data. This potential calculation is described in Chartier et al (2022). <br><br>For ease of use, SAMI3 output is remapped to a regular grid using the Earth System Modeling Framework by Hill et al (2004)</p>
SAMI3 data in netCDF format (2019-Mar-13)
<p>SAMI3 (Sami3 is Also a Model of the Ionosphere) is a seamless, three-dimensional, physics-based model of the ionosphere (Huba et al, 2008). It is based on SAMI2, a two-dimensional model of the ionosphere (Huba et al., 2000). <br><br>SAMI3 models the plasma and chemical evolution of seven ion species (H⁺, He⁺, N⁺, O⁺, N⁺₂, NO⁺ and O⁺₂). The temperature equation is solved for three ion species (H⁺, He⁺ and O⁺) and for the electrons. Ion inertia is included in the ion momentum equation for motion along the geomagnetic field. This is important in modeling the topside ionosphere and plasmasphere where the plasma becomes collisionless. <br><br>SAMI3 includes 21 chemical reactions and radiative recombination, and uses a nonorthogonal, nonuniform, fixed grid for the magnetic latitude range +/- 89 degrees.. <br><br><b>Drivers</b><br>Neutral composition, temperature, and winds: NRLMSISE00 (Picone et al., 2002) and HWM14 (Drob et al., 2015). <br>Solar radiation: Flare Irradiance Spectral Model version 2 (FISM v2)<br>Magnetic field: Richmond apex model [Richmond, 1995]. <br>Neutral wind dynamo electric field: Determined from the solution of a 2D potential equation [Huba et at., 2008]. <br>For the SAMI3/Weimer configuration: High latitude electric field: calculated from the empirical Weimer model for the potential. <br>For the SAMI3/AMPERE configuration: High latitude electric field: calculated using the Magnetosphere-Ionosphere Coupling solver (MIX) developed by Merkin and Lyon (2010). The inputs to MIX are SAMI3's internal conductances, plus field-aligned current observations from Active Magnetosphere and Planetary Electrodynamics Response Experiment (AMPERE), derived from the 66+ satellite Iridium NEXT constellation's engineering magnetometer data. This potential calculation is described in Chartier et al (2022). <br><br>For ease of use, SAMI3 output is remapped to a regular grid using the Earth System Modeling Framework by Hill et al (2004)</p>
PITS Apparent Depth Profiles for Mars Global Cave Candidate Catalog (MGC3) Features
<p>Apparent depth profiles calculated by the Pit Topography from Shadows (PITS) tool for the majority of the features in the Mars Global Cave Candidate Catalog (MGC<sup>3</sup>). PITS is a Python framework for automatically calculating apparent depth profiles for Martian and Lunar pits from just a single cropped satellite image. These images can also be single- or multi-band, such as in the case of the Mars Reconnaissance Orbiter (MRO) HiRISE camera. You can learn more about PITS by reading its <a href="https://academic.oup.com/rasti/article/2/1/492/7241547">journal article</a> in RAS Techniques and Instruments, going to its <a href="https://github.com/dlecorre387/Pit-Topography-from-Shadows/">GitHub repository</a> or reading the following <a href="https://www.danlecorre.com/post/first-paper-published">post</a>.</p> <p>Since not all catalogued cave candidates on Mars will be pits, PITS has so far been applied to the following MGC<sup>3</sup> subcategories:</p> <ul> <li>Atypical Pit Craters (APCs).</li> </ul> <p>With plans to extend this to:</p> <ul> <li>Lava tube skylights,</li> <li>small rimless pits,</li> <li>generic, amorphous pits,</li> <li>and polar pits.</li> </ul> <p>This totals 123 apparent depth profiles in CSV format, which have been derived automatically by PITS for 88 APCs. Therefore, these profiles can be plotted as the user prefers, and/or used in combination with other data to reveal more about this particular APC on the surface of Mars.</p> <p>Each depth profile's CSV file is named according to the HiRISE Reduced Data Record Version 1.1. (RDRV11) that it was calculated upon (e.g. ESP_011386_2065_RED_profile.csv for the red-band version of the HiRISE image ESP_011386_2065). Where there are multiple MGC3 APCs contained within a single image, the file names are numbered generally from the most northern to southernmost, or most westerly to easterly. ESRI shapefiles for the location of all APCs in each HiRISE image have been provided in polygon (containing the extents used to crop the larger HiRISE product) and point format in order to give context in these intances. </p> <p>As the headers suggest, the first four columns represent the shadow length (<em><span class="math-tex">\(L\)</span></em>), apparent depth (<em><span class="math-tex">\(h\)</span></em>), and the upper/lower bounds of <span class="math-tex">\(\Delta h\)</span>, respectively, before they have been corrected for non-zero emission angles (<span class="math-tex">\(\varepsilon\)</span>) at the time of image acquisition. Whereas the latter four columns represent the same quantities after <span class="math-tex">\(\varepsilon\)</span>-correction. How this correction is derived and applied is explained in the PITS journal article linked above.</p>
SAMI3 data in netCDF format (2019-Mar-30)
<p>SAMI3 (Sami3 is Also a Model of the Ionosphere) is a seamless, three-dimensional, physics-based model of the ionosphere (Huba et al, 2008). It is based on SAMI2, a two-dimensional model of the ionosphere (Huba et al., 2000).<br> <br> SAMI3 models the plasma and chemical evolution of seven ion species (H⁺, He⁺, N⁺, O⁺, N⁺₂, NO⁺ and O⁺₂). The temperature equation is solved for three ion species (H⁺, He⁺ and O⁺) and for the electrons. Ion inertia is included in the ion momentum equation for motion along the geomagnetic field. This is important in modeling the topside ionosphere and plasmasphere where the plasma becomes collisionless.<br> <br> SAMI3 includes 21 chemical reactions and radiative recombination, and uses a nonorthogonal, nonuniform, fixed grid for the magnetic latitude range +/- 89 degrees..<br> <br> <strong>Drivers</strong><br> Neutral composition, temperature, and winds: NRLMSISE00 (Picone et al., 2002) and HWM14 (Drob et al., 2015).<br> Solar radiation: Flare Irradiance Spectral Model version 2 (FISM v2)<br> Magnetic field: Richmond apex model [Richmond, 1995].<br> Neutral wind dynamo electric field: Determined from the solution of a 2D potential equation [Huba et at., 2008].<br> For the SAMI3/Weimer configuration: High latitude electric field: calculated from the empirical Weimer model for the potential.<br> For the SAMI3/AMPERE configuration: High latitude electric field: calculated using the Magnetosphere-Ionosphere Coupling solver (MIX) developed by Merkin and Lyon (2010). The inputs to MIX are SAMI3's internal conductances, plus field-aligned current observations from Active Magnetosphere and Planetary Electrodynamics Response Experiment (AMPERE), derived from the 66+ satellite Iridium NEXT constellation's engineering magnetometer data. This potential calculation is described in Chartier et al (2022).<br> <br> For ease of use, SAMI3 output is remapped to a regular grid using the Earth System Modeling Framework by Hill et al (2004)</p>
SAMI3 data in netCDF format (2019-Mar-31)
<p>SAMI3 (Sami3 is Also a Model of the Ionosphere) is a seamless, three-dimensional, physics-based model of the ionosphere (Huba et al, 2008). It is based on SAMI2, a two-dimensional model of the ionosphere (Huba et al., 2000).<br> <br> SAMI3 models the plasma and chemical evolution of seven ion species (H⁺, He⁺, N⁺, O⁺, N⁺₂, NO⁺ and O⁺₂). The temperature equation is solved for three ion species (H⁺, He⁺ and O⁺) and for the electrons. Ion inertia is included in the ion momentum equation for motion along the geomagnetic field. This is important in modeling the topside ionosphere and plasmasphere where the plasma becomes collisionless.<br> <br> SAMI3 includes 21 chemical reactions and radiative recombination, and uses a nonorthogonal, nonuniform, fixed grid for the magnetic latitude range +/- 89 degrees..<br> <br> <strong>Drivers</strong><br> Neutral composition, temperature, and winds: NRLMSISE00 (Picone et al., 2002) and HWM14 (Drob et al., 2015).<br> Solar radiation: Flare Irradiance Spectral Model version 2 (FISM v2)<br> Magnetic field: Richmond apex model [Richmond, 1995].<br> Neutral wind dynamo electric field: Determined from the solution of a 2D potential equation [Huba et at., 2008].<br> For the SAMI3/Weimer configuration: High latitude electric field: calculated from the empirical Weimer model for the potential.<br> For the SAMI3/AMPERE configuration: High latitude electric field: calculated using the Magnetosphere-Ionosphere Coupling solver (MIX) developed by Merkin and Lyon (2010). The inputs to MIX are SAMI3's internal conductances, plus field-aligned current observations from Active Magnetosphere and Planetary Electrodynamics Response Experiment (AMPERE), derived from the 66+ satellite Iridium NEXT constellation's engineering magnetometer data. This potential calculation is described in Chartier et al (2022).<br> <br> For ease of use, SAMI3 output is remapped to a regular grid using the Earth System Modeling Framework by Hill et al (2004)</p>
Canopy damage and recovery following Hurricane Maria using multitemporal lidar data, Mar-2017 - Mar-2020, Puerto Rico
The data archive is here: http://dx.doi.org/10.15486/ngt/1797399 please use this DOI when citing this dataset. Hurricane Maria (Category 4) snapped and uprooted canopy trees, removed large branches, and defoliated vegetation across Puerto Rico. The magnitude of forest damages and the rates and mechanisms of forest recovery following Maria provide important benchmarks for understanding the ecology of extreme events. We used airborne lidar data acquired before (2017) and after Maria (2018, 2020) to quantify landscape-scale changes in forest structure along a 439-ha elevational gradient (100 to 800 m) in the Luquillo Experimental Forest. Damages from Maria were widespread, with 73% of the study area losing ≥1 m in canopy height (mean = -7.1 m). Taller forests at lower elevations suffered more damage than shorter forests above 600 m. Yet only 13% of the study area had canopy heights ≤2 m in 2018, a typical threshold for forest gaps, highlighting the importance of damaged trees and advanced regeneration on post-storm forest structure. Heterogeneous patterns of regrowth and recruitment yielded shorter and more open forests by 2020. Nearly 45% of forests experienced initial height loss (<-1 m, 2017-2018) followed by rapid height gain (>1 m, 2018-2020), whereas 21.6% of forests with initial height losses showed little or no height gain, and 17.8% of forests exhibited no structural changes >|1| m in either period. Canopy layers <10 m accounted for most increases in canopy height and fractional cover between 2018-2020, with gains split evenly between height growth and lateral crown expansion by surviving individuals. These findings benchmark rates of gap formation, crown expansion, and canopy closure following hurricane damage. Included in the attached zip file are four TIF and four KML files. Support for this work was provided by grants BSR-8811902, DEB-9411973, DEB-9705814 , DEB-0080538, DEB-0218039 , DEB-0620910 , DEB-1239764, DEB-1546686, and DEB-1831952 from the National
figure data for "Subsurface radiation environment of Mars and its implication for shielding protection of future habitats" by L.Röstel, J.Guo et al. 2020
<pre>This data of dose rates at different elevations above and below the Martian surface was modeled using the GEANT4-based AtRIS toolkit. Please refer to the following paper for reference and a detailed description of the model and scaling: „Subsurface radiation environment of Mars and its implication for shielding protection of future habitats“, L.Röstel, J.Guo et al. 2020 JGR: planets. List of files: AbsorbedDosePrimariesAR.txt - figures 2 in the paper EquivalentDosePrimariesAR.txt - figure 3 AbsorbedDoseSiliconSlabScenarios.txt - figure 4 AbsorbedDoseWaterSphereScenarios.txt - figure 5 EquivalentDoseWaterSphereScenarios.txt - figure 6 NeutronFlux.txt - figure 7 RequiredShieldingDepth.txt - figure 8</pre>
figure data for "Radiation environment and doses on Mars at Oxia Planum and Mawrth Vallis: support for exploration at sites with biosignature preservation potential", by F. Da Pieve, G. Gronoff, J. Guo et al (2020)
<p>The data are the tabular format of the plots in figures 2-7 of the paper submitted.</p> <p> </p>
Amundsen Sea MAR simulations forced by ERAinterim
<p><strong>MAR simulations produced by Marion Donat-Magnin at IGE, Grenoble, France.</strong></p> <p><br> This simulation is evaluated in the following article:</p> <p>Donat-Magnin, M., Jourdain, N. C., Gallée, H., Amory, C., Kittel, C., Fettweis, X., Wille, J. D., Favier, V., Drira, A., and Agosta, C. (2020). Interannual variability of summer surface mass balance and surface melting in the Amundsen sector, West Antarctica, The Cryosphere, 14, 229–249, <a href="https://doi.org/10.5194/tc-14-229-2020">https://doi.org/10.5194/tc-14-229-2020</a> </p> <p><br> Here are provided the monthly means over 1979-2017. Daily outputs available on demand.<br> <br> See netcdf metadata for more information. Note that what is called runoff in the outputs is not actually a runoff (into the ocean) but more the net production of liquid water at the surface (which can either form ponds or flow into the ocean).</p> <p> </p> <p>Monthly files provided on MAR grid (see MAR_grid10km.nc). We also provide climatological (1979-2017 average) surface mass balance (SMB), surface melt rates and net liquid water production ("runoff") on a standard 8km WGS84 stereographic grid (see files ending as mean_polar_stereo.nc).</p> <p> </p> <p>The following variables are provided:</p> <ul> <li>CC Cloud Cover</li> <li>LHF Latent Heat Flux</li> <li>LWD Long Wave Downward</li> <li>LWU Long Wave Upward</li> <li>QQp Specific Humidity (pressure levels)</li> <li>QQz Specific Humidity (height levels)</li> <li>RH Relative Humidity</li> <li>SHF Sensible Heat Flux</li> <li>SIC Sea ice cover</li> <li>SP Surface Pressure</li> <li>ST Surface Temperature</li> <li>SWD Short Wave Downward</li> <li>SWU Short Wave Upward</li> <li>TI1 Ice/Snow Temperature (snow-layer levels)</li> <li>TTz Temperature (height levels)</li> <li>UUp x-Wind Speed component (pressure levels)</li> <li>UUz x-Wind Speed component (height levels)</li> <li>VVp y-Wind Speed component (pressure levels)</li> <li>VVz y-Wind Speed component (height levels)</li> <li>UVp Horizontal Wind Speed (pressure levels)</li> <li>UVz Horizontal Wind Speed (height levels)</li> <li>ZZp Geopotential Height (pressure levels)</li> <li>mlt Surface melt rate</li> <li>rfz Refreezing rate</li> <li>rnf Rainfall</li> <li>rof Runoff (i.e. net production of surface liquid water)</li> <li>sbl Sublimation</li> <li>smb Surface Mass Balance</li> <li>snf Snowfall</li> </ul>
Datasets associated with Agostini, S., Houlbreque, F., Biscéré, T., Harvey, B. P., Heitzman, J. M., Takimoto, R., et al. (2020). Greater mitochondrial energy production provides resistance to ocean acidification in 'winning' hermatypic corals. Front. Mar. Sci. 7. doi:10.3389/fmars.2020.600836.
<p>Datasets associated with Agostini, S., Houlbreque, F., Biscéré, T., Harvey, B. P., Heitzman, J. M., Takimoto, R., et al. (2020). Greater mitochondrial energy production provides resistance to ocean acidification in ‘winning’ hermatypic corals. Front. Mar. Sci. 7. doi:10.3389/fmars.2020.600836.</p>
1988-2009 time-series of land-use/land-cover maps for the Mar Menor / Campo de Cartagena watershed by means of supervised classification of Landsat images.
<p>Serie de mapas de usos y coberturas de la cuenca del Mar Menor (SE España): 2009, 2000, 1997 y 1998. Así como el documento completo de tesis en las que se generaron y analizaron.</p> <p>Time-series of land-use / land-cover maps of Mar Menor watershed (SE Spain): 2009, 2000, 1997 y 1998. As well as the complete thesis document in which they were generated and analyzed.</p>
MARSIS surface clutter simulations over Lucus Planun, Mars
<p>Mars Express MARSIS radargram simulations over Lucus Planum (Mars)</p> <p>This archive contains simulations of radar surface scattering for the MARSIS low-frequency radar over the area of Lucus Planum.</p> <p>Ground Penetrating Radar (GPR) is a well-established geophysical technique employed for more than five decades to investigate the terrestrial subsurface. It is based on the transmission of radar pulses at frequencies in the MF, HF and VHF portions of the electromagnetic spectrum into the surface, to detect reflected signals from subsurface structures (see e.g. Bogorodsky et al. 1985). Orbiting GPR have been successfully employed in planetary exploration (Phillips et al. 1973, Picardi et al. 2004, Seu et al. 2007, Ono et al. 2009), and are often called subsurface radar sounders. By detecting dielectric discontinuities associated with compositional and/or structural discontinuities, radar sounders are the only remote sensing instruments allowing the study of the subsurface of a planet from orbit.</p> <p>MARSIS is a synthetic-aperture, orbital sounding radar carried by the European Space Agency spacecraft Mars Express (Picardi et al. 2005). MARSIS is optimized for deep penetration, having detected echoes down to a depth of 3.7 km over the South Polar Layered Deposits (Plaut el al. 2007). MARSIS transmits through a dipole, which has negligible directivity, with the consequence that the radar pulse illuminates the entire surface beneath the spacecraft and not only the near-nadir portion from which subsurface echoes are expected. The electromagnetic wave can then be scattered by any roughness of the surface.</p> <p>If the surface of the body being sounded is not smooth at the wavelength scale, i.e. if the r.m.s. of topographic heights is greater than a fraction of the wavelength, then part of the incident radiation will be scattered in directions different from the specular one. This means that areas of the surface that are not directly beneath the radar can scatter part of the incident radiation back towards it, and thus produce surface echoes that will reach the radar after the echo coming from nadir, which can mask, or be mistaken for, subsurface echoes. This surface backscattering from off-nadir directions is called "clutter".</p> <p>To validate the detection of subsurface interfaces, numerical electromagnetic models of surface scattering, such as those by Nouvel et al. (2004), Russo et al. (2008) or Spagnuolo et al. (2011), have been used to produce simulations of surface echoes, which are then compared to real echoes detected by the radar. A code for the simulation of radar wave surface scattering has been developed, based on the work of Nouvel et al. (2004), using the MOLA topographic dataset (Smith et al. 2001) to represent the Martian surface as a collection of flat plates called facets. The radar echo is computed as the coherent sum of reflections from all facets illuminated by the radar. The computational burden of every simulation is very high but, thanks to a collaboration with CINECA, the code has been parallelized and ported for use in a Blue Gene/Q system. The code has been tested and its performance evaluated on the Fermi machine at CINECA.</p> <p>The data included in this archive simulate MARSIS observations over the area of Lucus Planum, in the equatorial region of Mars, and have been used in the paper "Radar sounding of Lucus Planum, Mars, by MARSIS" by Orosei, Rossi, Cantini, Caprarelli, Carter, Papiano, Cartacci, Cicchetti and Noschese, accepted for pubblication on Journal of Geophysical Research - Planets. A direct comparison of simulations with actual observations allows to unambiguously identify subsurface echoes in MARSIS radargrams. Echoes reaching the radar after nadir surface echoes will be identified as coming from the subsurface if they are not present in simulations. Conversely, any secondary echo that is present in both real and simulated data must be interpreted as coming from the surface.</p> <p>The numerical code for the simulation of surface scattering was developed at the Consorzio Interuniversitario per il Calcolo Automatico dell'Italia Nord-Orientale (CINECA) in Bologna, Italy. Simulations were produced thanks to the Partnership for Advanced Computing in Europe (PRACE), awarding us access to the SuperMUC computer at the Leibniz-Rechenzentrum, Garching, Germany through project 2013091832. Test simulations were run Jacobs University CLAMV HPC cluster, and we are grateful to Achim Gelessus for his support.</p> <p><br> REFERENCES</p> <p>Bogorodsky, V., Bentley, C., Gudmandsen, P. 1985. Radioglaciology. Reidel, Dordrecht. ISBN 90-277-1893-8</p> <p>Nouvel, J.-F., Herique, A., Kofman, W., Safaeinili, A. 2004. Radar signal simulation: Surface modeling with the Facet Method. Radio Science 39, 1013.</p> <p>Ono, T., Kumamoto, A., Nakagawa, H., Yamaguchi, Y., Oshigami, S., Yamaji, A., Kobayashi, T., Kasahara, Y., Oya, H. 2009. Lunar Radar Sounder Observations of Subsurface Layers Under the Nearside Maria of the Moon. Science 323, 909.</p> <p>Phillips, R.J., and 14 colleagues 1973. Apollo Lunar Sounder Experiment. NASA Spec. Pub. 330, (22) 1-26.</p> <p>Picardi, G., and 12 colleagues 2004. MARSIS: Mars Advanced Radar for Subsurface and Ionosphere Sounding, In: Mars Express: the scientific payload. ESA Publications Division, 51-69.</p> <p>Picardi, G., and 33 colleagues 2005. Radar Soundings of the Subsurface of Mars. Science 310, 1925-1928.</p> <p>Plaut, J. J., and 23 colleagues 2007. Subsurface Radar Sounding of the South Polar Layered Deposits of Mars. Science 316, 92.</p> <p>Russo, F., Cutigni, M., Orosei, R., Taddei, C., Seu, R., Biccari, D., Giacomoni, E., Fuga, O., Flamini, E. 2008. An incoherent simulator for the SHARAD experiment. Radar Conference, 2008. RADAR '08. IEEE 26-30 May 2008, 1.</p> <p>Seu, R., and 11 colleagues 2007. SHARAD sounding radar on the Mars Reconnaissance Orbiter. Journal of Geophysical Research (Planets) 112, 5.</p> <p>Smith, D. E., and 23 colleagues 2001. Mars Orbiter Laser Altimeter (MOLA): Experiment summary after the first year of global mapping of Mars. J. Geophys. Res. 106, 23,689-23,722.</p> <p>Spagnuolo, M. G., Grings, F., Perna, P., Franco, M., Karszenbaum, H., Ramos, V. A. 2011. Multilayer simulations for accurate geological interpretations of SHARAD radargrams. Planetary and Space Science 59, 1222.</p>
Updated database of craters on Mars with pitted impact deposits
<p>This point-based database (provided in two formats: as a ESRI shapefile set and simple .csv) currently contains 309 craters on Mars that possess “crater-related pitted materials” (CRPM), which are consistent with impact deposits described in detail in Tornabene et al. (2007; 2012). The Tornabene et al. (2012) publication is the original source of the initial database of 204 craters, which was based on a survey by the Mars Reconnaissance Orbiter (MRO) over a period of late 2006 to early 2012. MRO continues to image the surface and its craters, as such the database has since grown from 204 to 309 entries to date. Despite this growth, the general characteristics of the crater population remains generally consistent with what is described in Tornabene et al. (2012) (e.g., size range, latitudinal and elevation distribution, etc.).</p> <p>When present, these pitted impact deposits represent the upper most surface of the crater-fill with the pits potentially representing top-down views of so-called degassing pipes observed only in eroded cross-sections at some terrestrial impact structures such as the Ries in Germany (e.g., Caudill et al., 2021). Therefore, the craters that contain pits and preserve them well are themselves amongst the very best-preserved and often youngest craters of their size-class on Mars. Indeed, some of these craters are observed to have far-reaching (10s to 100s of crater radii) thermal / secondary crater rays (e.g., Tornabene et al. 2006), which is considered to be a feature associated with only the best-preserved and youthful craters on planets/moons with solid surfaces.</p> <p>These craters have enabled us to place further constraints on the scaling of crater depth as a function of diameter for complex craters on Mars (Tornabene et al. 2018) and may even help us to ultimately determine where the only samples we have of Mars — the Martain Meteorites — come from.</p> <p>See README rtf file for further details on the database.</p> <p> </p> <p><strong>Versions</strong></p> <p><strong>8.21.2025: </strong>4th version - deleted 3 additional duplicates (Lunae, Oudemans and Toro) total entries is now 309<strong><br></strong></p> <p><strong>8.20.2025b</strong>: 3rd version upload - fixed 1 duplicate (312 entries), caught some additional updates with respect to new official crater names, and CTX image IDs.</p> <p><strong>8.20.2025</strong>: 2nd version with an increase to 313 entries with some updates to preservation ratings, image IDs, etc.</p> <p><strong>5.3.2023</strong>: 1st version uploaded with 300 entries</p> <p> </p> <p><strong>Main references (*original/source database):</strong></p> <p>*Tornabene, L.L., Osinski, G.R., McEwen, A.S., Boyce, J.M., Bray, V.J., Caudill, C.M., Grant, J.A., Hamilton, C.W., Mattson, S. and Mouginis-Mark, P.J., 2012. Widespread crater-related pitted materials on Mars: Further evidence for the role of target volatiles during the impact process. Icarus, 220(2), pp.348-368. https://doi.org/10.1016/j.icarus.2012.05.022</p> <p>Tornabene, L.L., McEwen, A.S., Osinski, G.R., Mouginis-Mark, P.J., Boyce, J.M., Williams, R.M.E., Wray, J.J. and Grant, J.A., 2007. Impact melting and the role of subsurface volatiles: Implications for the formation of valley networks and phyllosilicate-rich lithologies on early Mars. In International Conf. on Mars VII. Lunar Planet. Sci. Inst. Contri (Vol. 1353), Abstract# 3288.</p> <p><strong>Other references:</strong></p> <p>Tornabene, L.L., Moersch, J.E., McSween Jr, H.Y., McEwen, A.S., Piatek, J.L., Milam, K.A. and Christensen, P.R., 2006. Identification of large (2–10 km) rayed craters on Mars in THEMIS thermal infrared images: Implications for possible Martian meteorite source regions. Journal of Geophysical Research: Planets, 111(E10).</p> <p>Boyce, J.M., Wilson, L., Mouginis-Mark, P.J., Hamilton, C.W. and Tornabene, L.L., 2012. Origin of small pits in martian impact craters. Icarus, 221(1), pp.262-275.</p> <p>Denevi, B.W., Blewett, D.T., Buczkowski, D.L., Capaccioni, F., Capria, M.T., De Sanctis, M.C., Garry, W.B., Gaskell, R.W., Le Corre, L., Li, J.Y. and Marchi, S., 2012. Pitted terrain on Vesta and implications for the presence of volatiles. Science, 338(6104), pp.246-249.</p> <p>Sizemore, H.G., Platz, T., Schorghofer, N., Prettyman, T.H., De Sanctis, M.C., Crown, D.A., Schmedemann, N., Neesemann, A., Kneissl, T., Marchi, S. and Schenk, P.M., 2017. Pitted terrains on (1) Ceres and implications for shallow subsurface volatile distribution. Geophysical Research Letters, 44(13), pp.6570-6578.</p> <p>Tornabene, L.L., Watters, W.A., Osinski, G.R., Boyce, J.M., Harrison, T.N., Ling, V. and McEwen, A.S., 2018. A depth versus diameter scaling relationship for the best-preserved melt-bearing complex craters on Mars. Icarus, 299, pp.68-83.</p> <p>Caudill, C., Osinski, G.R., Greenberger, R.N., Tornabene, L.L., Longstaffe, F.J., Flemming, R.L. and Ehlmann, B.L., 2021. Origin of the degassing pipes at the Ries impact structure and implications for impact‐induced alteration on Mars and other planetary bodies. Meteoritics & Planetary Science, 56(2), pp.404-422.</p> <p>Michalik, T., Matz, K.D., Schröder, S.E., Jaumann, R., Stephan, K., Krohn, K., Preusker, F., Raymond, C.A., Russell, C.T. and Otto, K.A., 2021. The unique spectral and geomorphological characteristics of pitted impact deposits associated with Marcia crater on Vesta. Icarus, 369, p.114633.</p>
Mars Thermospheric Water Abundance in Mars Years 32-36
<p>This file contains the derived thermospheric water abundances and mixing ratios along with the associated solar longitude, latitude, solar zenith angle, local time and global dust optical depth.</p>
Multiple Evolution Modes of Megaripples in the Qaidam Basin and Implications for Ripple-Like Aeolian Landforms on Mars
<p>The dataset includes wind regime data for Golmud, Sebei, and the west bank of the Narin Gol River in the Qaidam Basin, as well as sediment grain size and morphological parameters of the megaripples. In addition, we provide R language source code for data processing and visualization.</p><p>The primary directory contains the data and the source code in the R language. The data includes sediment grain size, morphological parameters and wind regime analysis data of megaripples. Modifying the working path and installation package is necessary to call the R source code for data loading.</p>
Mars PCM temperature results from MY 36 LS = 0° to MY 37 LS = 60°
<p>We present here the numerical simulation results of the temperature in the Martian atmosphere from Martian Year (MY) 36 solar longitude (LS) 0° to MY 37 LS 60°. The data is based on the Mars PCM (version 6). A description of the model setting is presented in Fan et al. (2024), and a general description of the model architecture is given in Forget et al. (1999). The model has a 64×48×73 grid in longitude, latitude, and pressure levels, which corresponds to a horizontal resolution of 5.625° and 3.75° in longitude and latitude, respectively, and 73 hybrid terrain-following vertical pressure levels (σ-grid) from surface to ~2×10^-3 Pa. The run has 960 dynamical timesteps in each Martian day, and the physical timestep is 7.5 Martian minutes.The dust injection is semi-interactive regulated by the MY 36 and MY 37 dust scenarios.<br><br>The data is seven NetCDF files, each including two Martian months. They are self-explanatory with the meanings of variables included in their headers, which contain atmosphere temperature, surface temperature, and surface pressure in the simulation, together with information about the four-dimensional grid in longitude, latitude, pressure level, and time, and also the corresponding LS at each timestep.</p> <p>The variable names of the longitude, latitude, pressure level, and time of grid points and their units are <em>latitude</em> [degree], <em>longitude</em> [degree], <em>altitude</em> [Pa], and <em>Time</em> [sol], respectively. The names of the variables and their corresponding units and dimensions are below.<br>Temperature [K]: <em>temp</em>[<em>Time</em>, <em>altitude</em>, <em>latitude</em>, <em>longitude</em>]<br>Surface temperature [K]: <em>tsurf</em>[<em>Time</em>, <em>latitude</em>, <em>longitude</em>]<br>Surface pressure [Pa]: <em>ps</em>[<em>Time</em>, <em>latitude</em>, <em>longitude</em>]<br>Solar longitude [degree]: <em>Ls</em>[<em>Time</em>]</p> <p><br>Reference: (1) Forget et al. (1999) Improved general circulation models of the Martian atmosphere from the surface to above 80 km. Journal of Geophysical Research, 104(E10), 24155-24176. (2) Fan et al. (2025) Diurnal temperature variations and migrating thermal tides in the Martian lower atmosphere observed by the Emirates Mars InfraRed Spectrometer. Journal of Geophysical Research: Planets. <span>130</span>, e2025JE009092.</p>
Predicted times, spatial coordinates of bow shock crossings and shock geometry at Mars from the NASA/MAVEN mission, using spacecraft ephemerides and magnetic field data, with a predictor-corrector algorithm
<p><strong>CHARACTERISTICS</strong><br>Planet: <strong>Mars</strong><br>Radius: <strong>R<sub>M</sub> = 3389.5 km</strong> (volumetric mean planetary radius)<br>Spacecraft: <strong>NASA/Mars Atmosphere and Volatile Evolution (MAVEN)</strong><br>Spacecraft coordinates system: <strong>Mars Solar Orbital (MSO)</strong> equivalent to <em>Sun-State </em>coordinate system:</p> <ul> <li>+<em>X<sub>MSO</sub></em> points towards the Sun from the planet’s centre,</li> <li>+<em>Z<sub>MSO</sub></em> towards Mars’ North pole and perpendicular to the orbital plane defined as the <em>X<sub>MSO</sub></em>–<em>Y<sub>MSO</sub></em> plane passing through the centre of Mars,</li> <li><em>Y<sub>MSO</sub></em> completes the orthogonal system.</li> </ul> <p>Time span: <strong>01/11/2014 to 30/04/2024</strong> (Mars Years MY32 to MY36 included, part of MY37).<br>Total number N of candidate bow shock crossings in the database: <strong>N = 20107</strong></p> <p><strong>ORIGINAL DATASETS USED</strong><br>The original MAVEN/MAG data repository on which these algorithms were applied is available on NASA's Planetary Data System (PDS) at <a href="https://doi.org/10.17189/1414178">https://doi.org/10.17189/1414178</a>. For this study, 1-Hz magnetic field data was used.</p> <p><strong>METHOD</strong><br>To construct this database from the original datasets above, the predictor and predictor-corrector algorithms used are described in:<br>Simon Wedlund, C., Volwerk, M., Beth, A., Mazelle, C., Möstl, C., Halekas, J., Gruesbeck, J. and Rojas-Castillo, D., (2022), A Fast Bow Shock Location Predictor-Estimator From 2D and 3D Analytical Models: Application to Mars and the MAVEN mission, <em>Journal of Geophysical Research</em>, <strong>127</strong>, 1-33, e2021JA029942, <a href="https://doi. org/10.1029/2021JA029942">https://doi. org/10.1029/2021JA029942</a>. </p> <p>Also available at: <a href="https://doi.org/10.1002/essoar.10507942.1">https://doi.org/10.1002/essoar.10507942.1 </a> and as arXiv e-print: <a href="https://doi.org/10.48550/arXiv.2109.04366">https://doi.org/10.48550/arXiv.2109.04366</a></p> <p>These algorithms consist of two consecutive steps: </p> <ol> <li>Predictor geometric algorithm based on J. Gruesbeck's 3D model (<a href="https://doi.org/10.1029/2018JA025366">Gruesbeck et al. 2018</a>) for prediction of Mars bow shock position</li> <li>Corrector algorithm based on magnetic field measurements (magnitude and fluctuations).</li> </ol> <p><strong>REMARK ON VERSIONS</strong><br>From Version 3 onwards, we also provide the angle between the average Interplanetary Magnetic Field (IMF) vector upstream of the shock and the shock normal, noted \(\theta_{Bn}\)(ThetaBn). Assuming a smooth shock surface and the 3D model of Gruesbeck et al. (2018, all points), this gives a first indication of the geometry of the shock, so that:</p> <ul> <li>45<sup>∘</sup><<em>θ</em><sub><em>B</em><em>n</em></sub><135<sup>∘</sup>: quasi-perpendicular shock condition</li> <li><em>θ</em><sub><em>B</em><em>n</em></sub>≤45<sup>∘</sup> and <em>θ</em><sub><em>B</em><em>n</em></sub>≥135<sup>∘</sup>: quasi-parallel shock condition</li> </ul> <p>Uncertainty on these angles is estimated to be ± 5º. </p> <p>From Version 4 onwards, we also added the solar longitude Ls (in degrees).</p> <p>For details, see Simon Wedlund et al. (2022) above, §2.3 pp. 10-12. Note that due to minor adjustments in the code, some of the ThetaBn angles calculated here for the examples of Fig. 6 in Simon Wedlund et al. (2022) may slightly differ from the values quoted in the paper.</p> <p><strong>VARIABLES DESCRIPTION</strong><br>This database contains the following ASCII variables:</p> <ul> <li>Bow shock times in MAVEN's database (1-s resolution): <em>T</em><sub>bs</sub></li> <li>Mars Solar Orbital coordinates of the shock, in units of Mars radius <em>R</em><sub><em>M</em> </sub>(<em>R<sub>M</sub></em> = 3389.5 km):<br><em>X<sub>MSO</sub></em>,<sub> </sub><em>Y<sub>MSO</sub></em>, <em>Z<sub>MSO</sub></em> and Euclidean distance \(R_{MSO} = \sqrt{X_{MSO}^2 + Y_{MSO}^2 + Z_{MSO}^2}\) (in <em>R<sub>M</sub></em>)</li> <li>Solar Zenith angle in degrees: <em>SZA</em> = \(\tan^{-1}{Y_{MSO}^2+Z_{MSO}^2 \over X_{MSO}^2}\) (in º) </li> <li>Angle between average B-field direction and shock normal assuming a smooth shock surface \(\theta_{Bn}\) (ThetaBn, in º) <ul> <li>45 < ThetaBn < 135 deg: quasi-⊥ shock</li> <li>ThetaBn ≤45 deg & ThetaBn ≥ 135 deg: quasi-|| shock</li> </ul> </li> <li>Solar longitude Ls, in degrees.</li> <li>Flag for crossing: <ul> <li>sheath \(\longrightarrow\) solar wind, flag = 0.</li> <li>solar wind \(\longrightarrow\) sheath, flag = 1.</li> </ul> </li> </ul> <p><strong>WARNING</strong><br>This database is based on an automatic statistical geometrical estimate, further refined by constraints on magnetic field. It is aimed at giving a first approximation of the shock area times in the MAVEN data. It is particularly suited to statistical studies and region identification in the MAVEN datasets. As such, this database should be used as a <em>first indicator</em> of the shock location, and <em>with</em> <em>caution</em>: it <strong>CANNOT</strong>, and <strong>WILL NOT </strong>substitute, especially in case studies, for a careful analysis of the full magnetometer and plasma suite bow shock signatures. Moreover, the algorithm is optimised for detecting the first disturbance observed in the magnetic field immediately ahead of the shock's foot (in the foreshock area), and not for the detection of other structures in the shock, such as the shock ramp. The "shock" location is therefore given here with typical uncertainties of about 0.075 R<sub>M</sub> (with R<sub>M</sub> = 3389.5 km, i.e., about 250 km in the radial direction). Finally, for multiple shock crossings, the algorithm chooses the first occurrence of the shock starting from the undisturbed solar wind.</p> <p>Current formatting optimised for MATLAB.</p> <p><strong>ACKNOWLEDGEMENTS</strong><br>C. Simon Wedlund and M. Volwerk thank the Austrian Science Fund (FWF) project P32035-N36. C. Möstl thanks the Austrian Science Fund FWF projects P31659-N27, P31521-N27. A. Beth thanks the Swedish National Space Agency (SNSA) and its support with the grant 108/18. This database was notably used to add to the Helio4Cast database which monitors solar wind parameters in the solar system (<a href="https://doi.org/10.6084/m9.figshare.6356420">https://doi.org/10.6084/m9.figshare.6356420</a>). Helio4Cast is available at <a href="http://www.helioforecast.space/icmecat">www.helioforecast.space/icmeca</a>t and <a href="http://www.helioforecast.space/sircat">www.helioforecast.space/sircat</a>. </p> <p><strong>LICENSE AND RIGHTS</strong><br>This database is shared under a Creative Commons CC-BY-4.0 license.</p> <p>Version 1 (c) Cyril Simon Wedlund @ Space Research Institute of Graz (IWF), <br> Austrian Academy of Sciences (ÖAW), 2021-09-08<br>Version 2 (c) CSW @ ÖAW/IWF, 2021-11-30 -- Addition of R_MSO and SZA<br>Version 3 (c) CSW @ ÖAW/IWF, 2022-02-09 -- Addition of ThetaBn<br>Version 4 (c) CSW @ ÖAW/IWF, 2025-03-20 -- Addition of Ls, Bx, By, Bz and Bt.</p> <p> </p> <p><br>Contact email: cyril.simon.wedlund@gmail.com</p>
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