Examining Cloud Properties at Gale Crater with MSL and TGO/CaSSIS
Bibliographic record
Abstract
The Mars Science Laboratory (MSL) has been observing clouds around Gale Crater since its landing in 2012 (MY 31). While MSL takes a number of different cloud observations, the Zenith and Suprahorizon Movies (ZM & SHM) are notable for being captured since nearly the start of the mission at all times of year and throughout the sunlit hours. These observations have been used to characterize the clouds’ variability, behaviour, altitudes and various scattering properties including opacity [E.g. 1,2,3,4]. Cloud opacities over Gale show very little interannual or diurnal variability, especially during the Aphelion Cloud Belt (ACB) season, a repeated yearly period of increased water-ice cloud formation around Mars aphelion [4].MSL-derived cloud opacities have been compared with water-ice cloud opacity retrievals from MARCI on board the Mars Reconnaissance Orbiter (MRO) and EXI on board the Emirates Mars Mission [4]. However, both instruments have very large fields of view, and MRO’s sun-synchronous orbit means that it only captures images at the same time of day. Our goal is to derive opacities from another orbital dataset using cloud images from the Colour and Stereo Surface Imaging System (CaSSIS) and examine and compare cloud properties at Gale Crater.CaSSIS is the main imaging system of the ExoMars Trace Gas Orbiter (TGO). It takes colour-infrared images from an altitude of ~400 km at a resolution of ~4 m/px using four broadband filters spanning 400-1100 nm. The images may be up to ~9.5 km wide and ~50 km long [5]. As TGO is not in a sun-synchronous orbit, it is able to capture images at various times of day and a wider range of incidence and phase angles. Its high resolution enables us to easily determine the size and spacing of the cloud features we observe from the ground with MSL. From the spacings for these features, an altitude may be inferred, something we attempt to constrain from the ground with MSL [2].Figure 1: Temporal distribution of MSL cloud movies and CaSSIS images acquired over Gale Crater. Note that not all observations contain clouds.We are examining the nearly 75 images taken by CaSSIS over Gale Crater for the presence of clouds. From these images we can derive the cloud opacities and compare these values with those previously derived from surface observations by MSL [4]. We are also interested in comparing images taken both from the surface and from orbit at near the same time. Additionally, as the ACB season is the best time of year to reliably capture cloud images, we intend to image the Gale Crater area with CaSSIS in hopes of coordinating with MSL cloud movies. [1] J. E. Moores et al., “Atmospheric movies acquired at the Mars Science Laboratory landing site: Cloud morphology, frequency and significance to the Gale Crater water cycle and Phoenix mission results,” Advances in Space Research, vol. 55, no. 9, pp. 2217–2238, 2015, doi: 10.1016/j.asr.2015.02.007.[2] C. L. Campbell et al., “Estimating the altitudes of Martian water-ice clouds above the Mars Science Laboratory rover landing site,” Planetary and Space Science, vol. 182, p. 104785, Mar. 2020, doi: 10.1016/j.pss.2019.104785.[3] A. C. Innanen, C. W. Hayes, B. E. Koch Nichol, and J. E. Moores, “Four Mars Years of ACB Phase Function Observations from the Mars Science Laboratory Show Low Interannual and Diurnal Variability and Suggest Irregular Water–ice Crystal Geometry,” Icarus, vol. 429, p. 116437, Mar. 2025, doi: 10.1016/j.icarus.2024.116437.[4] C. W. Hayes, J. L. Kloos, A. C. Innanen, C. L. Campbell, H. M. Sapers, and J. E. Moores, “Five Mars Years of Cloud Observations at Gale Crater: Opacities, Variability, and Ice Crystal Habits,” Planet. Sci. J., vol. 5, no. 2, p. 51, Feb. 2024, doi: 10.3847/PSJ/ad2202.[5] N. Thomas et al., “The Colour and Stereo Surface Imaging System (CaSSIS) for the ExoMars Trace Gas Orbiter,” Space Sci Rev, vol. 212, no. 3, pp. 1897–1944, Nov. 2017, doi: 10.1007/s11214-017-0421-1.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".