Updated Altitudes for Martian Water-Ice Clouds above Gale Crater
Bibliographic record
Abstract
AbstractThe altitudes of Martian water-ice clouds above Gale crater are being calculated through the Cloud Altitude Observation (CAO) taken by the Mars Science Laboratory (MSL, Curiosity). Typically, a mission needs a lidar onboard to calculate altitude, but this observation utilizes local topography – Aeolis Mons – to follow shadows and determine an absolute wind velocity. Comparing this velocity to the angular wind velocity found in a paired vertical movie determines the cloud’s altitude. This method is useful for the community when a mission doesn’t have a lidar but nearby terrain to observe shadows. During Mars Year (MY) 35, 28 observations were collected and will be compared to Campbell et al. [1] results from MY 34 to determine any changes. IntroductionThe Aphelion Cloud Belt (ACB) season is a yearly equatorial cloud belt caused by cooler atmospheric temperatures. Occurring between solar longitude (Ls) 45°-150°, water-ice clouds are typically visible during two times of each sol, early morning and late afternoon [2,11]. Since 2012, MSL has been observing the ACB season in Gale crater (4.5°S, 137.4°E) with a variety of cloud observations to study seasonal and diurnal changes of different parameters such as seasonal and diurnal changes [1,8,9], cloud altitude [1], opacity [8,9,10] and ice crystal geometry [3].One parameter, altitude, is of interest to the atmospheric science community to determine the cloud’s relation to the Planetary Boundary Layer (PBL). Orbiters are able to constrain cloud altitudes through limb measurements but observations within 10 km of the surface are hampered by dust opacity [7]. This is difficult for studying the PBL in Gale crater from orbit as the PBL is very shallow during the ACB season, ranging between 2-3 km [5,12]. From the surface, cloud altitudes could be measured with a lidar [4, 13], but only a single Mars mission has carried this instrument and therefore we must employ other methods to find altitude.As described in Campbell et al. [1], a new atmospheric observation was implemented on MSL during the MY 34 ACB season. Typically used for dust-devil searching, the Navigation Camera (Navcam) took a movie pointed at Aeolis Mons observed shadows moving along the mountain cast by clouds above. These shadows were georeferenced with a digital terrain model and the distance travelled was used to determine an absolute wind velocity. Angular wind velocity is already determined using a vertically pointed movie that follows how clouds move with respect to the camera’s field of view [1]. When these two movies are paired together, the comparison between velocities is used to calculate altitude via trigonometry. Therefore, this paired movie combination facilitates a way to directly calculate cloud altitudes at Gale crater for the first time. For the remainder of MY 34 season, these observations were paired whenever possible and resulted in 9 detections of clouds and their shadows, with the results reported in [1].Following this success, a new MSL observation was designed, optimized, and implemented to observe water-ice cloud shadows and deduce the water-ice cloud altitudes for the MY 35 ACB season and named the Cloud Altitude Observation (CAO). Cloud Altitude ObservationThe CAO is performed in the ACB season on a weekly cadence. It consists of two movies – Cloud Shadow Movie (CSM) and Zenith Movie (ZM) – taken one after the other by the Navcam. Both are made up of eight 1022x1022 pixeled images and span 240 seconds each. The CSM (Figure 1, top) points directly at Aeolis Mons to capture shadow motion which is used to determine an absolute wind velocity. The ZM (Figure 1, bottom) points directly above the rover and is used to determine an angular velocity based on the camera’s field of view. Comparing these two velocities allows the altitude of the clouds to be calculated.Results and DiscussionA total of 28 observations were collected between Ls 47°-163° and will be compared to MY 34 results [1] to determine any seasonal changes. Processing and analysis of these observations are ongoing, but the entire MY 35 ACB season's data will be processed and analyzed by the time of the conference.The MY 35 ACB season is unique in that it followed a Global Dust Storm (GDS) that occurred from Ls 188°-250° [6]. These storms are large enough to envelope the entire planet and affect the local and global environment. MSL has the unique ability to continue operations during a GDS and detected dust optical depths of up to an order of magnitude larger than normally observed [6]. Studying how the cloud altitudes change before/after the GDS will help determine if there are any long-term effects to the water-ice clouds. Once the MY 36 ACB season concludes, it will be added to the current data set to further determine any seasonal changes.References[1] Campbell et al., Planet. Space Sci. 182. 104785. 2020.[2] Clancy et al., Icarus. 122, 36-62. 1996.[3] Cooper et al., P&SS. 168, 62-72. 2019.[4] Dickinson et al., Geophys. Res. Lett. 37, L18203. 2010.[5] Guzewich et al., JGR-Planets, 122, 2779–2792. 2017.[6] Guzewich et al., Geophys. Res. Lett., 46, 71-79. 2019.[7] Kleinbohl et al., JGR-Planets, 114, E10006. 2009.[8] Kloos et al., Adv Sp Res. 58, 1223-1240. 2016.[9] Kloos et al., JGR-Planets. 123, 233–245. 2018.[10] Moores, et al., Adv. Space Res. 55, 2217–2238. 2015b.[11] Tamppari et al., JGR-Planets. 108, 5073. 2003.[12] Tyler, Barnes, Int. J. Mars Sci. Explor. 8, 58–77. 2013.[13] Whiteway et al., Science. 325. 68. 2009.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".