adsabs/AstroCLIMB huggingface dataset:
Paper: Impact Crater Morphometry on Pluto: Implications for Surface Composition and Evolution
Authors: Joshua E. Hedgepeth, Catherine D. Neish, Veronica J. Bray
Figure caption: Polar map (50°N–90°N latitude) of Pluto’s north pole with impact craters (D ≥ 15 km) mapped and colorized by relative depth. The map is shown without (left) and with (right) the geologic group boundaries. Darker red craters have higher relative depths, and brighter yellow craters have lower relative depths. Crater diameters are proportional to circle size but not to scale. The data behind the figure are available in a machine-readable format (see Figure 5).
Paper: Impact Crater Morphometry on Pluto: Implications for Surface Composition and Evolution
Authors: Joshua E. Hedgepeth, Catherine D. Neish, Veronica J. Bray
Figure caption: Global mosaic of Pluto’s complex impact craters (D ≥ 15 km) near the equator (−50° < latitude < 50°) colorized by relative depth, where darker red craters have high relative depths and brighter yellow craters have lower relative depths. The map is shown without (top) and with (bottom) the geologic group boundaries. Crater diameters are proportional to circle size but not to scale. The data behind the figure are available in a machine-readable format. (The data used to create this figure are available.)
Paper: Impact Crater Morphometry on Pluto: Implications for Surface Composition and Evolution
Authors: Joshua E. Hedgepeth, Catherine D. Neish, Veronica J. Bray
Figure caption: Cylindrical (top) and polar (bottom; latitude ≥ 50°) projection of Pluto from the “Pluto New Horizons LORRI MVIC—Global Mosaic” (300 mpp; Schenk et al. 2018). It is overlaid with red outlines of its geologic terrain as defined by White et al. (2021). Image courtesy New Horizons Team, USGS Astrogeology Science Center, https://astrogeology.usgs.gov/search/map/Pluto/NewHorizons/Pluto_NewHorizons_Global_Mosaic_300m_Jul2017.
Paper: Impact Crater Morphometry on Pluto: Implications for Surface Composition and Evolution
Authors: Joshua E. Hedgepeth, Catherine D. Neish, Veronica J. Bray
Figure caption: (Top) Large (D = 45 km, R = 0.716; A) and small (D = 15 km, R = 0.983; B) craters within Vega Terra shown with a LORRI image. (Bottom) Sample topographic plot of a profile between the points A–A′ and B–B′, where the x-axis is the distance from the crater center.
Paper: Impact Crater Morphometry on Pluto: Implications for Surface Composition and Evolution
Authors: Joshua E. Hedgepeth, Catherine D. Neish, Veronica J. Bray
Figure caption: Relative depth as a function of crater size (in kilometers; top) and vertical error in the DEM (bottom). Craters are color-coded by the geologic group in which they exist. Note that craters with R < 0 are anomalously deep. The data behind the figure are available in a machine-readable format (see Figure 5).
Paper: Impact Crater Morphometry on Pluto: Implications for Surface Composition and Evolution
Authors: Joshua E. Hedgepeth, Catherine D. Neish, Veronica J. Bray
Figure caption: Example impact crater shown in the LORRI mosaic of Pluto overlaid with the DEM. The craters were identified by their circular shape, elevated rims, central peaks, flat floor (due to the presence of nitrogen ice), and ejecta (if present).
Paper: Impact Crater Morphometry on Pluto: Implications for Surface Composition and Evolution
Authors: Joshua E. Hedgepeth, Catherine D. Neish, Veronica J. Bray
Figure caption: The eight-profile technique is employed to map crater topography (top). A sample topographic plot of profile A–A′ is shown, where the x-axis is the distance from the crater center (bottom). The red squares identify the rim, and the blue triangles mark the crater floor.
Paper: Microwave Observations of Venus with CLASS
Authors: Sumit Dahal, Michael K. Brewer, Alex B. Akins, John W. Appel, Charles L. Bennett, Ricardo Bustos, Joseph Cleary, Jullianna D. Couto, Rahul Datta, Joseph Eimer, Thomas Essinger-Hileman, Jeffrey Iuliano, Yunyang Li, 云炀 李, Tobias A. Marriage, Carolina Núñez, Matthew A. Petroff, Rodrigo Reeves, Karwan Rostem, Rui Shi, 瑞 时, Deniz A. N. Valle, Duncan J. Watts, Janet L. Weiland, Edward J. Wollack, Zhilei Xu, 智磊 徐
Figure caption: The T Ven/T Jup ratio measurements (left) and the corresponding histograms (right) from the CLASS dichroic G-band instrument. The uncertainties (red bars) in the measured ratios (blue dots) are the combined errors obtained from the variance of baseline measurements away from the planets. The inverse-variance weighted mean ratios (dashed lines) are 1.822 ± 0.002 and 1.675 ± 0.003 for 150 and 220 GHz, respectively, where the uncertainties are the standard errors on the mean.
Paper: Microwave Observations of Venus with CLASS
Authors: Sumit Dahal, Michael K. Brewer, Alex B. Akins, John W. Appel, Charles L. Bennett, Ricardo Bustos, Joseph Cleary, Jullianna D. Couto, Rahul Datta, Joseph Eimer, Thomas Essinger-Hileman, Jeffrey Iuliano, Yunyang Li, 云炀 李, Tobias A. Marriage, Carolina Núñez, Matthew A. Petroff, Rodrigo Reeves, Karwan Rostem, Rui Shi, 瑞 时, Deniz A. N. Valle, Duncan J. Watts, Janet L. Weiland, Edward J. Wollack, Zhilei Xu, 智磊 徐
Figure caption: Disk-averaged Venus brightness temperature measurements at 1 mm–2 cm wavelengths. The data points, along with their references, are listed in Table 2. The magenta dashed–dotted curve is the reference Venusian atmospheric model determined from the radio occultation data, and the solid orange curves are obtained by scaling various model parameters (see Section 5 for details) from the reference model. The green dashed curve shows the scaled model that is most consistent with the CLASS and VLA measurements, which are among the most precise measurements to date in this wavelength range.
Paper: Microwave Observations of Venus with CLASS
Authors: Sumit Dahal, Michael K. Brewer, Alex B. Akins, John W. Appel, Charles L. Bennett, Ricardo Bustos, Joseph Cleary, Jullianna D. Couto, Rahul Datta, Joseph Eimer, Thomas Essinger-Hileman, Jeffrey Iuliano, Yunyang Li, 云炀 李, Tobias A. Marriage, Carolina Núñez, Matthew A. Petroff, Rodrigo Reeves, Karwan Rostem, Rui Shi, 瑞 时, Deniz A. N. Valle, Duncan J. Watts, Janet L. Weiland, Edward J. Wollack, Zhilei Xu, 智磊 徐
Figure caption: Measured array-averaged Venus brightness temperature vs. fractional solar illumination. The uncertainties on the data points are the standard errors on the mean. The flat lines (dashed black) centered at the absolute brightness temperature values from Table 1 fall within the 1σ uncertainty (red shading) of the linear best-fit lines (solid red). There is no statistically significant phase dependence of the measured temperatures at both frequency bands.
Paper: Grain Size Effects on UV–MIR (0.2–14 μm) Spectra of Carbonaceous Chondrite Groups
Authors: David C. Cantillo, Vishnu Reddy, Adam Battle, Benjamin N. L. Sharkey, Neil C. Pearson, Tanner Campbell, Akash Satpathy, Mario De Florio, Roberto Furfaro, Juan Sanchez
Figure caption: Principal component values for all six meteorites studied at different grain sizes are shown with the α-line boundary from DeMeo et al. (2009) that separates the S and C/X complexes. Grain size variations can be seen by using the dotted line interconnecting PC values of measurements of the same meteorite at different grain sizes. Marker size increases with grain size, which includes values of 45–90 μm, 90–150 μm, 150–300 μm, 300–500 μm, and 500–1000 μm. The Lunar-style space weathering trend from Binzel et al. (2019) is shown with blue dots and a corresponding arrow showing the direction of increasing space weathering. The carbonaceous space weathering trend from Lantz et al. (2018) is shown with the brown arrow and indicates that the measured weathering trend would move in either direction depending on the meteorite composition. It also shows that differentiating between space weathering and grain size effects would be challenging in PC space.
Paper: Grain Size Effects on UV–MIR (0.2–14 μm) Spectra of Carbonaceous Chondrite Groups
Authors: David C. Cantillo, Vishnu Reddy, Adam Battle, Benjamin N. L. Sharkey, Neil C. Pearson, Tanner Campbell, Akash Satpathy, Mario De Florio, Roberto Furfaro, Juan Sanchez
Figure caption: The plot of the offset in 9.5–11.5 μm Si–O stretching band peak center relative to the 45–90 μm powder for each of our seven studied meteorite samples across our five grain size bins and slab.
Paper: Grain Size Effects on UV–MIR (0.2–14 μm) Spectra of Carbonaceous Chondrite Groups
Authors: David C. Cantillo, Vishnu Reddy, Adam Battle, Benjamin N. L. Sharkey, Neil C. Pearson, Tanner Campbell, Akash Satpathy, Mario De Florio, Roberto Furfaro, Juan Sanchez
Figure caption: The optimal set of variables for anisotropic scattering ω, b, and c for Aguas Zarcas 45–90 μm from our modeling effort.
Paper: Grain Size Effects on UV–MIR (0.2–14 μm) Spectra of Carbonaceous Chondrite Groups
Authors: David C. Cantillo, Vishnu Reddy, Adam Battle, Benjamin N. L. Sharkey, Neil C. Pearson, Tanner Campbell, Akash Satpathy, Mario De Florio, Roberto Furfaro, Juan Sanchez
Figure caption: Regression models for the simulated single-scattering albedo for anisotropic scattering (top) and isotropic scattering (bottom).
Paper: Grain Size Effects on UV–MIR (0.2–14 μm) Spectra of Carbonaceous Chondrite Groups
Authors: David C. Cantillo, Vishnu Reddy, Adam Battle, Benjamin N. L. Sharkey, Neil C. Pearson, Tanner Campbell, Akash Satpathy, Mario De Florio, Roberto Furfaro, Juan Sanchez
Figure caption: The optimal set of variables for anisotropic scattering ω, b, and c for all grain sizes of Aguas Zarcas.
Paper: Grain Size Effects on UV–MIR (0.2–14 μm) Spectra of Carbonaceous Chondrite Groups
Authors: David C. Cantillo, Vishnu Reddy, Adam Battle, Benjamin N. L. Sharkey, Neil C. Pearson, Tanner Campbell, Akash Satpathy, Mario De Florio, Roberto Furfaro, Juan Sanchez
Figure caption: Modeled reflectance vs. observed reflectance (left) and chi-squared for goodness of fit testing (right) for Aguas Zarcas 45–90 μm anisotropic scattering.
Paper: Grain Size Effects on UV–MIR (0.2–14 μm) Spectra of Carbonaceous Chondrite Groups
Authors: David C. Cantillo, Vishnu Reddy, Adam Battle, Benjamin N. L. Sharkey, Neil C. Pearson, Tanner Campbell, Akash Satpathy, Mario De Florio, Roberto Furfaro, Juan Sanchez
Figure caption: Optimal variable ω for anisotropic scattering (left) and for isotropic scattering (right) for all grain sizes of Aguas Zarcas.
Paper: Roughness and Angularity of Fragments from Meteorite Disruption Experiments
Authors: Gabriel Gowman, Desireé Cotto-Figueroa, Andrew Ryan, Laurence A. J. Garvie, Christian G. Hoover, Erik Asphaug
Figure caption: Photograph of nine meteorite samples used in this analysis, along with a 1 cm square for scale reference. Top row: scale square, Allende—A-RF-1, A-RF-2, A-S-AllB-1, A-S-AllB-2. Bottom row: Tamdakht—T-RF-1, T-RF-2, T-RF-3, T-S-t3a-1, T-S-t3a-2.
Paper: Global Mapping of Fragmented Rocks on the Moon with a Neural Network: Implications for the Failure Mode of Rocks on Airless Surfaces
Authors: Ottaviano Rüsch, Valentin T. Bickel
Figure caption: Examples of detections for different morphological types. The large arrow denotes illumination direction. Panel widths vary in the range 30–60 m. (a) M1356624061RC, (b) M169845868RC, (c) M127240244LC, (d) M108203484RC, (e) M109780793RC, (f) M168523403LC, (g) M1193830064LC, (h) M110560726RC, (i) M108366721LC, (j) M139029855LC, (k) M169283456RC, (l) M110560726RC, (m) M109895309LC, (n) M110526893RC. Image credits to NASA/LROC/GSFC/ASU.
Paper: Global Mapping of Fragmented Rocks on the Moon with a Neural Network: Implications for the Failure Mode of Rocks on Airless Surfaces
Authors: Ottaviano Rüsch, Valentin T. Bickel
Figure caption: Examples of detections for different morphological types. The large arrow denotes illumination direction. Panel widths vary in the range 30–60 m. The view for the detections in (o,p) has been enlarged to 300 m width. (a) M1299030725RC, (b) M110649693RC, (c) M1280467964LC, (d) M1130300625RC, (e) M110126713LC, (f) M1325718594RC, (g) M111668133RC, (h) M137278395RC, (i) M168869503RC, (j) M107809965LC, (k) M1343664677RC, (l) M127070409LC, (m) M1221580467RC, (n) M168442030LC, (o,p) M137420922LC. Image credits to NASA/LROC/GSFC/ASU.
Paper: Global Mapping of Fragmented Rocks on the Moon with a Neural Network: Implications for the Failure Mode of Rocks on Airless Surfaces
Authors: Ottaviano Rüsch, Valentin T. Bickel
Figure caption: Examples of detections for different morphological types. The large arrow denotes illumination direction. Panel widths vary in the range 30–60 m. (a) M174889254LC, (b) M176602625RC, (c), M124999473RC, (d) M1251998743RC, (e) M152702349RC, (f) M126595195LC, (g) M1162548409LC, (h) M1221615756RC, (i) M1222952209LC, (j) M112671946LC, (k) M108292443LC, (l) M1296942537RC, (m) M170905869LC, (n) M172460917LC, (o) M157364573RC, (p) M175408129RC. Image credits to NASA/LROC/GSFC/ASU.
Paper: Global Mapping of Fragmented Rocks on the Moon with a Neural Network: Implications for the Failure Mode of Rocks on Airless Surfaces
Authors: Ottaviano Rüsch, Valentin T. Bickel
Figure caption: Examples of positive and negative labels used for the training of the neural network. Note how the full range of boulder disintegration is covered, from fractured all the way to catastrophically shattered (1 through 25). Negative labels include intact boulders, craters, and linear features unrelated to boulder disintegration (26–30). Image credits to NASA/LROC/GSFC/ASU.
Paper: Global Mapping of Fragmented Rocks on the Moon with a Neural Network: Implications for the Failure Mode of Rocks on Airless Surfaces
Authors: Ottaviano Rüsch, Valentin T. Bickel
Figure caption: Summary of all produced clusters, including archetype description, number of features, mean estimated diameter of the feature (not of single fragments; using the approach described by Bickel et al. 2018), and an exemplary NAC crop-out. Note that the given example for heterogeneous clusters is not representative of the full range of features. The estimated mean diameter between cluster classes is above the implemented range of +/−10% scaling used in label augmentation, hence excluding that differences between boulder classes of similar archetype but different diameter are an artifact of label augmentation. Image credits to NASA/LROC/GSFC/ASU.
Paper: Global Mapping of Fragmented Rocks on the Moon with a Neural Network: Implications for the Failure Mode of Rocks on Airless Surfaces
Authors: Ottaviano Rüsch, Valentin T. Bickel
Figure caption: Spatial distribution of clusters 2 (predominantly large fractured boulders), 13 (predominantly small fractured boulders), and 8 (predominantly impact craters), shown as black dots. A few selected, globally distinct hotspots are highlighted by black circles; host location age is indicated by a single letter: C = Copernican, E = Eratosthenian, I = Imbrian, N = Nectarian. Insets show details of the Copernicus and Mare Humorum regions, indicated by white circles on the map. WAC global mosaic in the background; WAC image credits to NASA/LROC/GSFC/ASU.
Paper: Global Mapping of Fragmented Rocks on the Moon with a Neural Network: Implications for the Failure Mode of Rocks on Airless Surfaces
Authors: Ottaviano Rüsch, Valentin T. Bickel
Figure caption: Comparison of Diviner rock abundance maps and cluster 2 and cluster 13 heat maps for the Copernicus crater and Mare Humorum. The fractured features in Copernicus and Mare Humorum seem to correspond to large (∼ > 10 m) fractured blocks and to small fragments (<∼7 m) of shattered blocks, respectively, while featuring relatively similar Diviner rock abundance values.
Paper: Global Mapping of Fragmented Rocks on the Moon with a Neural Network: Implications for the Failure Mode of Rocks on Airless Surfaces
Authors: Ottaviano Rüsch, Valentin T. Bickel
Figure caption: Summary and relationships of lunar boulder morphologies under erosion. Relationships between morphologies can be described in terms of exposure time after emplacement and impact fragmentation energy. Before fragmentation, changes of morphology are limited to submillimeter surficial modifications. After fragmentation (where it occurs), changes of morphology due to increase in exposure age (connected by dashed lines) correspond to a loss of high reflectance, and/or decrease of small fragment number and/or development of debris apron. Lines a and b represent the end-member morphologies of the “core” and “cones” failure types, respectively. Survivor boulders that are not fragmented by a large meteoroid continue to develop debris aprons by abrasion. Specific studies are R22: Rüsch et al. (2022), PR22: Patzek & Rüsch (2022), and RW22: Rüsch & Wöhler (2022). Image credits to NASA/LROC/GSFC/ASU.
Paper: Impact Crater Morphometry on Pluto: Implications for Surface Composition and Evolution
Authors: Joshua E. Hedgepeth, Catherine D. Neish, Veronica J. Bray
Figure caption: (Top) Large (D = 130 km, R = 0.588; A) and small (D = 20 km, R = 0.134; B) craters within Cthulhu Macula shown with a LORRI image. (Bottom) Sample topographic plot of a profile between the points A–A′ and B–B′, where the x-axis is the distance from the crater center.
Paper: Impact Crater Morphometry on Pluto: Implications for Surface Composition and Evolution
Authors: Joshua E. Hedgepeth, Catherine D. Neish, Veronica J. Bray
Figure caption: (Top) Anomalously deep crater within Cthulhu Macula (D = 35 km, R = −0.626; A) and the Wright group (D = 20 km, R = −0.256; B) shown with a LORRI image. (Bottom) Sample topographic plot of a profile between the points A–A′ and B–B′, where the x-axis is the distance from the crater center.
Paper: A Multiannual Record of Convective Instability in Mars’s Middle Atmosphere from the Mars Climate Sounder
Authors: Nicholas G. Heavens, Alexey Pankine, J. Michael Battalio, Corwin Wright, David M. Kass, Armin Kleinböhl
Figure caption: Example convective instability diagnosis: (a) Cross section of the retrieved temperature (K) profiles along the dayside portion of a recentered orbit (Heavens et al. 2018); (b) Cross section of the diagnosed probability (%) of convective instability for the cross section in (a); (c) Retrieved temperature (K) profile (black solid line) with 1σ uncertainty (red dashed lines) corresponding to the highest probability of convective instability in the cross section in (b); (d) Lapse rate (K/km) profile (solid black line) and 1σ uncertainty (red dashed lines) compared with the dry adiabat (solid blue line) for the retrieved temperature profile in (c).
Paper: A Multiannual Record of Convective Instability in Mars’s Middle Atmosphere from the Mars Climate Sounder
Authors: Nicholas G. Heavens, Alexey Pankine, J. Michael Battalio, Corwin Wright, David M. Kass, Armin Kleinböhl
Figure caption: Example convective instability diagnosis: (a) Cross section of the retrieved temperature (K) profiles along the dayside portion of a recentered orbit (Heavens et al. 2018); (b) Cross section of the diagnosed probability (%) of convective instability for the cross section in (a); (c) Retrieved temperature (K) profile (black solid line) with 1σ uncertainty (red dashed lines) corresponding to the highest probability of convective instability in the cross section in (b); (d) Lapse rate (K/km) profile (solid black line) and 1σ uncertainty (red dashed lines) compared with the dry adiabat (solid blue line) for the retrieved temperature profile in (c). The inset zooms into the convective instability.
Paper: A Multiannual Record of Convective Instability in Mars’s Middle Atmosphere from the Mars Climate Sounder
Authors: Nicholas G. Heavens, Alexey Pankine, J. Michael Battalio, Corwin Wright, David M. Kass, Armin Kleinböhl
Figure caption: Mean spatial distribution of GW activity expressed as ﹩{\mathrm{log}}_{10}\hat{{{\rm{\Omega }}}_{\mathrm{GW}}}﹩ (K2 K−2) viewed from nightside off-nadir observations in the MRO-MCS A3 channel (sensitive to ≈25 km altitude) during L  s  = 0°–30°, MY 29–33, 35–36 averaged at 1° resolution for: (a) all observations; (b) observations excluding those flagged as being too near A1 loops. White space indicates that no data is available in the averaging bin. The lower end of the plotting color scale is saturated at 10−6 K2 K−2, around an order of magnitude greater than the approximate uncertainty in the planet’s coldest areas (the polar caps).
Paper: A Multiannual Record of Convective Instability in Mars’s Middle Atmosphere from the Mars Climate Sounder
Authors: Nicholas G. Heavens, Alexey Pankine, J. Michael Battalio, Corwin Wright, David M. Kass, Armin Kleinböhl
Figure caption: The number of nightside diagnoses of convective instability made in the Mars Year and L  s  indicated. Observations flagged because of the likely presence of a loop structure are excluded. Red letters mark the estimated peak of activity for the major dust event compilation described in Section 2.3 and documented in Table 1.
Paper: A Multiannual Record of Convective Instability in Mars’s Middle Atmosphere from the Mars Climate Sounder
Authors: Nicholas G. Heavens, Alexey Pankine, J. Michael Battalio, Corwin Wright, David M. Kass, Armin Kleinböhl
Figure caption: Zonal mean probability (%) of convective instability, MY 29–33, MY 35–36 on the dayside for the labeled L  s  intervals. The red dotted lines indicate the vertical range of sampling. The black contour indicates the mean probability of instability >1%. Binning resolution in latitude is 2°.
Paper: A Multiannual Record of Convective Instability in Mars’s Middle Atmosphere from the Mars Climate Sounder
Authors: Nicholas G. Heavens, Alexey Pankine, J. Michael Battalio, Corwin Wright, David M. Kass, Armin Kleinböhl
Figure caption: The mean probability of convective instability on the nightside (%) for a vertical cross section averaged over 45°–65° N. The average is weighted by the cosine of the latitude. Red letters mark the estimated peak of activity for the major dust event compilation described in Section 2.3. Binning resolution in L  s  is 2°. Zonal averages are only shown when data is available in all 12 of the 30° bins in longitude.
Paper: A Multiannual Record of Convective Instability in Mars’s Middle Atmosphere from the Mars Climate Sounder
Authors: Nicholas G. Heavens, Alexey Pankine, J. Michael Battalio, Corwin Wright, David M. Kass, Armin Kleinböhl
Figure caption: Mean probability of convective instability, MY 29–33, MY 35–36 for the labeled time of day, L  s  range, and latitude range. Binning resolution in latitude and longitude is 2°
Paper: A Multiannual Record of Convective Instability in Mars’s Middle Atmosphere from the Mars Climate Sounder
Authors: Nicholas G. Heavens, Alexey Pankine, J. Michael Battalio, Corwin Wright, David M. Kass, Armin Kleinböhl
Figure caption: The mean probability of convective instability (%) on the nightside for a vertical cross section averaged over 15° S–15° N. The average is weighted by the cosine of the latitude. Red letters mark the estimated peak of activity for the major dust event compilation described in Section 2.3. Binning resolution in L  s  is 2°. Zonal averages are only shown when data is available in all 12 of the 30° bins in longitude.
Paper: A Multiannual Record of Convective Instability in Mars’s Middle Atmosphere from the Mars Climate Sounder
Authors: Nicholas G. Heavens, Alexey Pankine, J. Michael Battalio, Corwin Wright, David M. Kass, Armin Kleinböhl
Figure caption: Zonal mean probability (%) of convective instability, MY 29–33, MY 35–36 on the nightside for the labeled L  s  intervals. The red dotted lines indicate the vertical range of sampling. The black contour indicates the mean probability of instability >1%. Binning resolution in latitude is 2°.
Paper: A Multiannual Record of Convective Instability in Mars’s Middle Atmosphere from the Mars Climate Sounder
Authors: Nicholas G. Heavens, Alexey Pankine, J. Michael Battalio, Corwin Wright, David M. Kass, Armin Kleinböhl
Figure caption: The mean probability of convective instability on the dayside (%) for a vertical cross section averaged over 60°−90° N. The average is weighted by the cosine of the latitude. Red letters mark the estimated peak of activity for the major dust event compilation described in Section 2.3. Binning resolution in L  s  is 2°. Zonal averages are only shown when data is available in all 12 of the 30° bins in longitude.