adsabs/AstroCLIMB huggingface dataset:
Paper: The Distribution of Molecular Water in the Lunar South Polar Region Based upon 6 μm Spectroscopic Imaging
Authors: William T. Reach, Paul G. Lucey, Casey I. Honniball, Anicia Arredondo, Erick R. Malaret
Figure caption: Spectrum of the 6.1 μm line from a single pixel of the spectral cube. The upper curve with the small blue dots is the reference-corrected spectrum. The orange curve is the baseline fit, with the green circles indicating where the baseline was fitted. These curves use the right-hand flux scale. The lower curve with the large blue dots is the baseline-divided spectrum, and the vertical dotted gray lines show the wavelengths that were summed to generate the H2O image.
Paper: Modeling the Formation of Selk Impact Crater on Titan: Implications for Dragonfly
Authors: Shigeru Wakita, 茂 脇田, Brandon C. Johnson, Jason M. Soderblom, Jahnavi Shah, Catherine D. Neish, Jordan K. Steckloff
Figure caption: Similar to Figure 1, but for the various methane clathrate thicknesses. Each line represents a different methane clathrate thickness case with a corresponding temperature profile following Kalousová & Sotin (2020). Note that the temperature gradient in the conductive lid of methane clathrate is ∼23, ∼15, and ∼10 K km−1 for the 5, 10, and 15 km thick cases, respectively. Since the thermal softening parameter of methane clathrate (i.e., the dependence on temperature) differs from that of water ice (see Table 1), the yield strength of water ice is slightly higher than that of methane clathrate at the boundary of the 5 km case (see red dashed–dotted line).
Paper: Modeling the Formation of Selk Impact Crater on Titan: Implications for Dragonfly
Authors: Shigeru Wakita, 茂 脇田, Brandon C. Johnson, Jason M. Soderblom, Jahnavi Shah, Catherine D. Neish, Jordan K. Steckloff
Figure caption: Temperature profile (left) and yield strength (right) plotted vs. ice-shell depth. Each line represents a different temperature gradient in the conductive ice layer (see legend). Note that we plot cases which have the convective temperature of 255 K. The shaded region illustrates the 10 km thick methane clathrate layer. Note that the jump in the yield strength indicates the boundary between methane clathrate and water ice.
Paper: Modeling the Formation of Selk Impact Crater on Titan: Implications for Dragonfly
Authors: Shigeru Wakita, 茂 脇田, Brandon C. Johnson, Jason M. Soderblom, Jahnavi Shah, Catherine D. Neish, Jordan K. Steckloff
Figure caption: Hugoniot curve for water ice (black solid line). The blue dashed line depicts the solidus, and the green dashed line depicts the liquidus. We take a threshold pressure from the intersect between the solid and dashed lines (see symbols).
Paper: The Influence of Cloud Condensation Nucleus Coagulation on the Venus Cloud Structure
Authors: Kevin McGouldrick, Erika L. Barth
Figure caption: The size distribution ﹩\left(\tfrac{{dN}}{d\mathrm{ln}r}\right)﹩ of CCN and droplets as functions of radius at four selected altitudes at the end time of the coag00 simulation (equ profile), compared with LCPS data. A vertical dashed line is drawn at a radius of 0.3 μm, indicating the smallest radius of particle LCPS was capable of detecting. Note that LCPS data points extend to smaller radii than this cutoff because the observational data presented here are determined from the modal fits derived by Knollenberg & Hunten (1980).
Paper: The Influence of Cloud Condensation Nucleus Coagulation on the Venus Cloud Structure
Authors: Kevin McGouldrick, Erika L. Barth
Figure caption: The mass loading (left) and effective radius (right) for each of the five latitudes simulated here (see the VIRA temperature profiles in Figure 1), with a diurnal period of 7.0 days and nominal CCN coagulation. The vertical profiles at the end of the simulations are compared with the observations by LCPS (near the equator). Note that there is an undisplayed off-scale LCPS mass-loading data point of 29.5 mg m−3 at 49 km. Note also that the simulations plotted here represent coag00 through coag04. The mass loading and effective radius that are derived from the model output data are available as data behind the figure CSV files: Fig_02_DBF_Mload.csv and Fig_02_DBF_Reff.csv.
Paper: The Influence of Cloud Condensation Nucleus Coagulation on the Venus Cloud Structure
Authors: Kevin McGouldrick, Erika L. Barth
Figure caption: Left: the mixing ratio of water vapor and sulfuric acid at the end of our nominal simulations for each of the five VIRA profiles studied here; recent observations of water vapor by Cottini et al. (2015; C2015) and Fedorova et al. (2016; F2016) are also shown, with observed ranges as error bars. Right: the relative humidity over the pure substances for water vapor and sulfuric acid vapor in the same simulations. Note that the sulfuric acid curves are dashed in the mixing ratio plot, while the water vapor curves are solid; in the relative humidity plot, it is the reverse (sulfuric acid curves are solid, while water vapor curves are dashed). Note that the simulations plotted here represent coag00 through coag04. The mixing ratios and relative humidities that are derived from the model output data are available as data behind the figure CSV files: Fig_03_DBF_MixRat.csv and Fig_03_DBF_RelHum.csv.
Paper: The Influence of Cloud Condensation Nucleus Coagulation on the Venus Cloud Structure
Authors: Kevin McGouldrick, Erika L. Barth
Figure caption: The quasi-steady-state particle size distributions found in the model domain at the end of our nominal simulation (coag00). In each case, ﹩\mathrm{log}\tfrac{{dN}}{d\mathrm{ln}r}﹩ is plotted as a contour plot vs. altitude (linear) and radius (log). At left we display only the CCN, in the middle panel is only the droplets, and at right is the interpolated and combined size distribution. The vertical dashed line in each panel is drawn at a radius of 0.3 μm, the smallest particle that LCPS was capable of detecting.
Paper: The Influence of Cloud Condensation Nucleus Coagulation on the Venus Cloud Structure
Authors: Kevin McGouldrick, Erika L. Barth
Figure caption: Left: the vertical profile of the acid mass fraction for the nominal simulations. Right: the vertical profile of aerosol extinction coefficient for each of the altitudes in our nominal simulations. The traditional separation between the upper cloud and the middle (and lower) cloud at 57 km is indicated as a horizontal dashed line. Note the significant reduction in the upper cloud opacity as the influence of photochemical production diminishes sharply with altitude in these column simulations. Note that the simulations plotted here represent coag00 through coag04. The acid mass fraction (equilibrium weight percent) and the extinction coefficients from the model outputs are available as data behind the figure CSV files: Fig_08a_DBF_Wtp.csv, Fig_XX_DBF_Bext_coag00.csv, Fig_XX_DBF_Bext_coag01.csv, Fig_XX_DBF_Bext_coag02.csv, Fig_XX_DBF_Bext_coag03.csv, and Fig_XX_DBF_Bext_coag04.csv.
Paper: The Influence of Cloud Condensation Nucleus Coagulation on the Venus Cloud Structure
Authors: Kevin McGouldrick, Erika L. Barth
Figure caption: Left: the five latitudinal temperature profiles used in our simulations, from VIRA. The five profiles are defined for latitudes 0°–30° (equ), 30°–45° (mid1), 45°–60° (mid2), 60°–75° (pol1), and 75°–90° (pol2). Right: the eddy diffusion coefficient profile used in all simulations.
Paper: The Influence of Cloud Condensation Nucleus Coagulation on the Venus Cloud Structure
Authors: Kevin McGouldrick, Erika L. Barth
Figure caption: The ﹩\mathrm{log}\tfrac{{dN}}{d\mathrm{ln}r}﹩ at selected altitudes plotted as functions of time and radius from the coag00 (equ profile) simulation. Note the stability of three distinct size modes in the condensational cloud (at model altitudes of 49.5 and 54.5 km) and the absence of a large size mode (i.e., Mode 3) in the photochemical clouds (model altitudes 59.5 and 64.5 km).
Paper: The Influence of Cloud Condensation Nucleus Coagulation on the Venus Cloud Structure
Authors: Kevin McGouldrick, Erika L. Barth
Figure caption: The ratio of the magnitude of the vertical flux due to eddy diffusion to the photochemical production flux for sulfuric acid in our nominal simulations, at the five simulated latitudes. Values greater than 1 indicate that the diffusional flux exceeds the photochemical production flux, while values less than 1 indicate that the photochemistry dominates vertical transport. The relative gas fluxes derived from simulations are available as data behind the figure CSV file: Fig_04_DBF.csv.
Paper: The Distribution of Molecular Water in the Lunar South Polar Region Based upon 6 μm Spectroscopic Imaging
Authors: William T. Reach, Paul G. Lucey, Casey I. Honniball, Anicia Arredondo, Erick R. Malaret
Figure caption: Details of the water emission at Moretus. These SOFIA images of 6.1 μm H2O emission (left) and 6 μm continuum (center) are centered on the Moretus crater. The visible-light image (right) is from Change 2, using the Virtual Lunar Atlas (Chevalley & Legrand 2023), at approximately the same illumination conditions (by coincidence). The purple asterisk indicates the top of the central peak in the crater, and the purple arcs are guides to the locations of the north and south rims. These same markers are copied over to the left-hand panel. Both the rims and the central peak are shifted southward in the H2O image compared to the continuum image, consistent with a higher H2O abundance on the sides of topographic features that are less illuminated.
Paper: The Distribution of Molecular Water in the Lunar South Polar Region Based upon 6 μm Spectroscopic Imaging
Authors: William T. Reach, Paul G. Lucey, Casey I. Honniball, Anicia Arredondo, Erick R. Malaret
Figure caption: Distribution of water near the lunar south pole. (a) In false color, a stereographic projection of the 6.1 μm H2O emission. (b) In grayscale, an LRO image of the exact same area. The longitude 0° and 90° meridians are shown in both panels, and latitude circles are labeled panel (b). The pixels in the SOFIA image are elongated due to reprojection from the foreshortened, Earth-centered view of the pole, which was close to the lunar limb but just visible at the time of the SOFIA observation. Arcs (gray in panel (a) and purple in panel (b)) delineate some of the water emission features. Particularly noteworthy in this projection is bright water emission from just inside the northern rims of Cabeus and Haworth. The water emission appears to be from the crater floors just at the inner base of the northern rims; this also occurs at Shomberger.
Paper: Modeling the Formation of Selk Impact Crater on Titan: Implications for Dragonfly
Authors: Shigeru Wakita, 茂 脇田, Brandon C. Johnson, Jason M. Soderblom, Jahnavi Shah, Catherine D. Neish, Jordan K. Steckloff
Figure caption: Close-up view of the same impact simulation show in Figure 4. Each line shows the surface profile at different time steps; the solid line indicates t = 400 s and the dashed line t = 2000 s. Arrows indicate the rim location. Note the vertical scale is different from Figure 4.
Paper: Modeling the Formation of Selk Impact Crater on Titan: Implications for Dragonfly
Authors: Shigeru Wakita, 茂 脇田, Brandon C. Johnson, Jason M. Soderblom, Jahnavi Shah, Catherine D. Neish, Jordan K. Steckloff
Figure caption: Crater depth as a function of crater diameter. These craters are formed by 3–4 km diameter impactors on a 5–15 km thick methane clathrate layer with the corresponding temperature profiles from Kalousová & Sotin (2020; see legend). While the error bars correspond to an uncertainty of six cells, they are almost within the symbols. Note that we also added the horizontal error bar in the case of the very ambiguous rim (see blue square symbol, 15 km thick methane clathrate with 4 km diameter impactor), which makes it hard for us to define the crater rim. Selk crater is also plotted as a star with error bars (again almost within the symbol; Hedgepeth et al. 2020).
Paper: Modeling the Formation of Selk Impact Crater on Titan: Implications for Dragonfly
Authors: Shigeru Wakita, 茂 脇田, Brandon C. Johnson, Jason M. Soderblom, Jahnavi Shah, Catherine D. Neish, Jordan K. Steckloff
Figure caption: Distribution of tracer particles, colored by their peak pressure. Unmelted particles are indicated by gray color, and the black dashed lines depict material boundaries. These craters are formed by 4 km diameter impactors with the temperature profile of Kalousová & Sotin (2020; see square symbols in Figure 7).
Paper: Modeling the Formation of Selk Impact Crater on Titan: Implications for Dragonfly
Authors: Shigeru Wakita, 茂 脇田, Brandon C. Johnson, Jason M. Soderblom, Jahnavi Shah, Catherine D. Neish, Jordan K. Steckloff
Figure caption: Distribution of melt volume as a function of the radial distance. These are products of 4 km diameter impactors with the temperature profile of Kalousová & Sotin (2020; see square symbols in Figure 7). The dashed line depicts the incipient melt (5.34 GPa), and the solid line depicts the complete melt (8.69 GPa). Note that we take the bin size in the horizontal direction as 2 km. The box in the upper right indicates the total volume of incipient melt (V  I ) and complete melt (V  C ).
Paper: Modeling the Formation of Selk Impact Crater on Titan: Implications for Dragonfly
Authors: Shigeru Wakita, 茂 脇田, Brandon C. Johnson, Jason M. Soderblom, Jahnavi Shah, Catherine D. Neish, Jordan K. Steckloff
Figure caption: The averaged surface profile for our best-case simulation for producing a Selk-like crater. The impact condition is a 4 km diameter impactor hitting a target with a 10 km thick methane clathrate layer and the associated temperature profile from Kalousová & Sotin (2020). The thick line denotes the averaged surface profile with a bin size of 2 km (40 cells) using the two surface profiles at different time steps. We take t = 400 s as the profile from 45 km and beyond, as this best represents the morphology of the rim prior to overflow (this is the time at which we determine the rim location (∼45 km)). For the profile from the center to 40 km, we use the profile at t = 3000 s as the crater interior has stabilized by this time; this is demonstrated by the relatively flat crater floor compared to t = 2000 s (their depths are almost identical). For the profile of the intermediate region (40–45 km), we interpolate those profiles. The shaded region denotes three cells from the original surface (six cells in total height).
Paper: Modeling the Formation of Selk Impact Crater on Titan: Implications for Dragonfly
Authors: Shigeru Wakita, 茂 脇田, Brandon C. Johnson, Jason M. Soderblom, Jahnavi Shah, Catherine D. Neish, Jordan K. Steckloff
Figure caption: Time series of crater formation for a 4 km diameter impactor hitting a 10 km thick methane clathrate layer over water ice with a temperature gradient of 15 K km−1. Color indicates the temperature in Kelvin, and the white dotted lines represent the material boundary. Arrows indicate the rim location (see also Figure 5). Supplemental movie Figure A1 covers the time sequences that are not shown here.
Paper: Modeling the Formation of Selk Impact Crater on Titan: Implications for Dragonfly
Authors: Shigeru Wakita, 茂 脇田, Brandon C. Johnson, Jason M. Soderblom, Jahnavi Shah, Catherine D. Neish, Jordan K. Steckloff
Figure caption: Same data in Figure 8, but showing the provenance plots. Tracer particles plotted at their original preimpact locations.
Paper: Modeling the Formation of Selk Impact Crater on Titan: Implications for Dragonfly
Authors: Shigeru Wakita, 茂 脇田, Brandon C. Johnson, Jason M. Soderblom, Jahnavi Shah, Catherine D. Neish, Jordan K. Steckloff
Figure caption: Similar plots to those shown in Figure 4, but for the different temperature gradient cases: (A) 5 K km−1, (B) 10 K km−1, and (C) 20 K km−1. Color indicates the temperature in Kelvin, and the white dotted lines represent the material boundary.
Paper: The Influence of Cloud Condensation Nucleus Coagulation on the Venus Cloud Structure
Authors: Kevin McGouldrick, Erika L. Barth
Figure caption: Variation in aerosol opacity at 1.735 μm over time for the equ profile model in which CCN self-coagulation is prohibited. Left: contour plot of aerosol extinction coefficient (β ext) vs. altitude over the last 2 yr of the simulation. Right: vertically integrated aerosol opacity for the upper cloud (z > 57 km), the lower cloud (z ≤ 57 km), and the total cloud over the last 2 yr of the simulation. Note the evident short-term opacity variations in the upper cloud arising from the diurnal nature of the photochemical production and exhibiting a 7.0-day period. Also note the longer-timescale variations in both the upper and the lower cloud opacity that have arisen as a consequence of the suppressed coagulation efficiency. The extinction coefficients from the model outputs are available as data behind the figure CSV file: Fig_XX_DBF_Bext_coag05.csv.
Paper: The Influence of Cloud Condensation Nucleus Coagulation on the Venus Cloud Structure
Authors: Kevin McGouldrick, Erika L. Barth
Figure caption: Contour plots of the mass loading (left) and effective radius (right) as functions of altitude and time for the equatorial (equ) profile simulation (coag05) with suppressed CCN self-coagulation. Significant excursions in mass loading, but especially effective radius, are evident, with transitions occurring around 500, 1100, 1300, and 1400 days, with shorter and more fleeting variations also evident. Note that we set the upper limit of the color bar scale to 6.5 μm, in order to highlight the excursions of the effective radius above this value, since the actual variation in the effective radius is only about 10%. The mass loading and effective radius that are derived from the model output data are available as data behind the figure CSV file: Fig_13_DBF_McReff.csv.
Paper: The Influence of Cloud Condensation Nucleus Coagulation on the Venus Cloud Structure
Authors: Kevin McGouldrick, Erika L. Barth
Figure caption: Comparison between the time evolution of the extinction coefficient and vertically integrated opacity over the last 2 yr of the nonequatorial latitude simulations for the 7.0-day photochemical diurnal period: mid1: coag06, mid2: coag07, pol1: coag08, pol2: coag09. The extinction coefficients from the model outputs are available as data behind the figure CSV files: Fig_XX_DBF_Bext_coag06.csv, Fig_XX_DBF_Bext_coag07.csv, Fig_XX_DBF_Bext_coag08.csv, and Fig_XX_DBF_Bext_coag09.csv.
Paper: The Influence of Cloud Condensation Nucleus Coagulation on the Venus Cloud Structure
Authors: Kevin McGouldrick, Erika L. Barth
Figure caption: Simulations coag05 (equ latitude profile) and coag09 (pol2 latitude profile) run for an additional 8 yr of model time (a total of 4383 days) to demonstrate that, though the magnitude of variation evolves with time, the simulations still have achieved a quasi-steady state in which the amplitude of the variations is not significantly decaying or growing with time. The extinction coefficients from the model outputs are available as data behind the figure CSV files: Fig_XX_DBF_Bext_coag49.csv, and Fig_XX_DBF_Bext_coag49.csv.
Paper: The Influence of Cloud Condensation Nucleus Coagulation on the Venus Cloud Structure
Authors: Kevin McGouldrick, Erika L. Barth
Figure caption: Comparison between the time evolution of the extinction coefficient and vertically integrated opacity over the last 50 days of the equatorial latitude simulation for three photochemical diurnal periods: 7.0 days (coag00), 7.5 days (coag10), and 8.0 days (coag20). The column-integrated opacities are plotted relative to that at the end time of the simulations (i.e., τ(t) − τ(1461 days)). The extinction coefficients from the model outputs are available as data behind the figure CSV files: Fig_XX_DBF_Bext_coag00.csv, Fig_XX_DBF_Bext_coag10.csv, and Fig_XX_DBF_Bext_coag20.csv.
Paper: The Influence of Cloud Condensation Nucleus Coagulation on the Venus Cloud Structure
Authors: Kevin McGouldrick, Erika L. Barth
Figure caption: Time series plots of the evolution of the aerosol opacity over the last 2 yr of the nominal simulation at equatorial latitudes. Left: a contour plot of the extinction coefficient as a function of altitude and time. Right: the vertically integrated opacity for the upper cloud above 57 km (blue), the lower and middle clouds below 57 km (red), and the total cloud (black). The extinction coefficients from the model outputs are available as data behind the figure CSV file: Fig_XX_DBF_Bext_coag00.csv.
Paper: The Influence of Cloud Condensation Nucleus Coagulation on the Venus Cloud Structure
Authors: Kevin McGouldrick, Erika L. Barth
Figure caption: As in Figure 3, the mixing ratios of water vapor and sulfuric acid vapor (left) and the relative humidity of sulfuric acid vapor (right) for the quasi-steady-state conditions at the end of our simulation in which we suppressed CCN self-coagulation. Note that the simulations plotted here represent coag05 through coag09. The mixing ratios and relative humidities that are derived from the model output data are available as data behind the figure CSV files: Fig_12_DBF_MixRat.csv and Fig_12_DBF_RelHum.csv.
Paper: The Influence of Cloud Condensation Nucleus Coagulation on the Venus Cloud Structure
Authors: Kevin McGouldrick, Erika L. Barth
Figure caption: As in Figure 2, the mass loading (left) and the effective radius (right) for the quasi-steady-state conditions at the end of our simulations at the pol2 latitude profile, within the Venusian polar vortex. Here the photochemical diurnal period is taken to be 2.5 days, and we vary the CCN self-coagulation and bottom boundary of the sulfuric acid mixing ratio. Note that the simulations plotted here represent coag34, coag39, coag3b, and coag3d. The mass loading and effective radius that are derived from the model output data are available as data behind the figure CSV files: Fig_22_DBF_Mload.csv and Fig_22_DBF_Reff.csv.
Paper: The Influence of Cloud Condensation Nucleus Coagulation on the Venus Cloud Structure
Authors: Kevin McGouldrick, Erika L. Barth
Figure caption: Comparison between the time evolution of the extinction coefficient and vertically integrated opacity over the last 2 yr of the pol2 latitude profile simulation for the 2.5-day diurnal period. coag34 and coag39 compare the Coag cases for the nominal sulfuric acid mixing ratio boundary condition, while coag3b and coag3d do so for the case with a 5 ppmv boundary condition. The extinction coefficients from the model outputs are available as data behind the figure CSV filesFig_XX_DBF_Bext_coag34.csv, Fig_XX_DBF_Bext_coag39.csv, Fig_XX_DBF_Bext_coag3b.csv, and Fig_XX_DBF_Bext_coag3d.csv.
Paper: The Influence of Cloud Condensation Nucleus Coagulation on the Venus Cloud Structure
Authors: Kevin McGouldrick, Erika L. Barth
Figure caption: Comparison between the time evolution of the extinction coefficient and vertically integrated opacity over the last 2 yr of the equatorial latitude (equ) profile for three photochemical diurnal periods: 7.0 days (coag05), 7.5 days (coag15), 8.0 days (coag25). The extinction coefficients from the model outputs are available as data behind the figure CSV files: Fig_XX_DBF_Bext_coag05.csv, Fig_XX_DBF_Bext_coag15.csv, and Fig_XX_DBF_Bext_coag25.csv.
Paper: The Influence of Cloud Condensation Nucleus Coagulation on the Venus Cloud Structure
Authors: Kevin McGouldrick, Erika L. Barth
Figure caption: The quasi-steady-state particle size distributions found in the model domain at a simulation time of 1100 days (low-opacity case) for simulation coag05 (equ profile). Further details are given in the caption for Figure 6.
Paper: The Influence of Cloud Condensation Nucleus Coagulation on the Venus Cloud Structure
Authors: Kevin McGouldrick, Erika L. Barth
Figure caption: The total particle size distribution (CCN+droplets) for four select altitudes in the coag05 simulation, over the last 2 yr of the simulation.