Data produced in Lutz et al. (2023): Investigating the Linkage Between Spiral Trough Morphology and Cloud Coverage on the Martian North Polar Layered Deposits

North Polar Layered Deposit spiral trough geomorphology metric data (2023Trough_AllData.xlsx) This dataset contains the trough metric data calculated from the 3,192 trough profiles analyzed in this study as an Excel file. The dataset is split into tabs; the All_Data tab, which contain the metric values calculated for all trough profiles, and regional tabs, based on the regions identified in Smith & Holt (2015), and labeled R1-7a (excluding R0, 6, and 7b, as they do not contain troughs that we mapped). Each profile is identified by a unique number ranging between 1-3,192, labeled as Trough_Profile in all tabs of the dataset. The All_Data tab contains location data for each trough profile analyzed in the study, recorded as the center latitude of the profile, labeled CENTROID_X, and the center longitude of the profile, labeled CENTROID_Y. The values of each trough metric for all trough profiles are also recorded in this tab, including equator-facing trough wall slope (labeled as EQ_Slope), pole-facing trough wall slope (labeled as Pole_Slope), equator-facing trough wall relief (labeled as EQ_Relief), pole-facing trough wall relief (labeled as Pole_Relief) the difference between the two relief values (labeled as Relief_Diff), trough width (labeled as Width), and trough depth (labeled as Depth). Each regional tab contains information on which trough each trough profile is connected to, labeled as Trough_Number, where profiles from the same trough have matching number values. The values of each trough metric for trough profiles present in the region identified by the tab’s title are also recorded in this tab, including equator-facing trough wall slope (labeled as EQ_Slope), pole-facing trough wall slope (labeled as Pole_Slope), equator-facing trough wall relief (labeled as EQ_Relief), pole-facing trough wall relief (labeled as Pole_Relief) the difference between the two relief values (labeled as Relief_Diff), trough width (labeled as Width), and trough depth (labeled as Depth). North Polar Layered Deposit spiral trough profiles (trough_profiles.zip) This dataset contains the trough profile figures of the 3,192 trough profiles analyzed in this study as .PNG images. Each figure is titled with a unique number ranging between 1-3,192, identifying which profile the figure corresponds to (labeled as Trough Profile Profile_XXXX.txt, where the XXXX is the unique profile number). The x-axis of each figure is the extent of the trough profile in meters (labeled Extent (m)) and the y-axis of each figure is the elevation of the trough profile in meters (labeled Elevation (m)). The legend of each figure labels the original trough profile extracted MOLA data as a blue line, the polynomial curve fit to the original data as an orange line, the calculated minimum point on the profile as a green dot, the calculated left shoulder point on the profile as a red dot, and the calculated right shoulder point on the profile as a purple dot. A cloud atlas of the Martian North Polar Layered Deposits using THEMIS VIS imagery from Mars years 26-35 (All_VIS_Images.xlsx) This dataset contains our updated cloud atlas for the 13,857 THEMIS VIS images analyzed in this study as an Excel file. Each image is identified by their product ID (V########), labeled as file_ID in the dataset, which are used to request that specific image from the Planetary Data System (PDS). Each image also has their corresponding Mars year, ranging from a value of 25-35 and labeled as mars_year in the dataset. Each image also has their solar longitude (Ls), which is the Mars-Sun angle measured from the Northern Hemisphere of Mars where the northern spring equinox is Ls=0, the northern summer solstice is Ls=90, the northern autumn equinox is Ls=180, and the northern winter solstice is Ls=270. The solar longitude is labeled as solar_long in the dataset. The location data for each image is also recorded in the dataset as the center latitude of the image, labeled center_lat, and the center longitude of the image, labeled center_long. The classification done by this study is also recorded in the dataset. This includes which NPLD region, based on the regions identified in Smith & Holt (2015), ranging from 0-7b, based on which region most of the image lied in and labeled NPLD Region. It also includes the image noise ranking (labeled Image Noise Rank in the dataset), which was used to quantify image quality, where we assigned a metric 0,1, or 2 to each image, gauging whether the image was visually clear enough to distinguish clouds. This metric was based primarily on if surface features could be visibly distinguished in the image (e.g., pitting, craters, trough wall layers, trough edges, striations, etc.). A ranking of 0 meant the surface features were clear and high-resolution; 1 meant surface features were visible but less resolved in some way (e.g., slightly blurred, washed out, blocked by some visual artifacts, etc.); 2 meant the surface features were not at all distinct or blocked out by large visual artifacts, and the image was classified as too noisy to credibly decide if clouds were or were not present. Images were then classified as either having cloud presence or absence, on a yes/no scale (labeled Clouds? (y/n?/n) in the dataset). To state “yes”, the cloud’s edge must be distinct from the NPLD surface, so as not to confuse the cloud with other surface features. If there was doubt if the feature is a cloud (e.g., due to a soft or no cloud boundary with the surface and/or image defects on top of the potential cloud), that image is classified as “no?” in this analysis, indicating there may be clouds present but we did not feel confident enough in the image quality to state “yes”. If clouds were identified they were classified into three broad categories in the following section labeled Cloud type, if visible: trough-parallel clouds (similar to the low-altitude clouds with an elongated structure located parallel to the NPLD troughs identified in Smith et al. (2013)), wispy clouds, and general cloudiness. When visible, other related cloud features were noted, such as the presence of undulations, or linear cloud structures. Images classified as “no?” had their possible cloud type identified as well, though the noise complicating the image was also noted.