Evaluation of the Capability of ExoMars-TGO NOMAD Infrared Nadir Channel for Water Ice Clouds Detection on Mars
Bibliographic record
Abstract
As part of the payload of the 2016 ExoMars Trace Gas Orbiter (TGO) mission, the Nadir and Occultation for MArs Discovery (NOMAD) suite instrument has been observing the Martian atmosphere since March 2018. NOMAD is mainly dedicated to the study of trace atmospheric species taking advantage of a high-spectral resolution. We demonstrate that when NOMAD is observing in nadir mode, i.e., when the line-of-sight points to the centre of Mars, it can be also exploited to detect ice. In this study we present a method based on the investigation of nadir observations of the NOMAD infrared channel, acquired during Mars Years 34 and 35 (March 2018 to February 2021). We take advantage of the strong water ice absorption band at 2.7 µm by selecting the diffraction orders 167, 168, and 169. We derive the Frost and Clouds Index (FCI), which is a good proxy for ice mapping, and obtain latitudinal-seasonal maps for water ice clouds. FCI is sensitive to the Polar Hood clouds. Nevertheless, detections in the Aphelion Cloud Belt (ACB) are limited. This is consistent with previous observations showing different physical properties between the two main Martian atmospheric structures and making the ACB less detectable in the infrared. We hence derive the infrared nadir channel sensitivity limit for the detection of these clouds.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".