Cloud detection in the upper troposphere‐lower stratosphere region via ACE imagers: A qualitative study
Bibliographic record
Abstract
Satellite‐based limb occultation measurements are well suited for the detection and mapping of polar stratospheric clouds (PSCs) and cirrus clouds. Usually, cloud signatures are detected on aerosol extinction profiles. In this paper, ACE two‐dimensional (2‐D) imager data are used to show PSCs and cirrus clouds. Clouds can be clearly seen, with a good vertical and horizontal resolution (1 km), during sunset and sunrise. In addition, we discovered significant differences between stratospheric (PSCs) and tropospheric (cirrus) clouds. PSCs appear as “symmetric” layers, no horizontal or vertical “structure” is detected within the PSC, suggesting that PSCs are uniform clouds with a very large horizontal extent. On the other hand, cirrus cloud image geometry is not well‐defined. In contrast to PSCs, cirrus clouds appear as irregular shaped clouds. These tropospheric clouds seem to have horizontal dimensions similar to the Sun on the image (25 km at the tangent point). The qualitative display of these different kinds of clouds, seen on the raw 2‐D imager data, proves the ability of the imagers to be an efficient cloud detector in the upper troposphere‐lower stratosphere (UTLS) region. Moreover, the structure of these clouds can be derived.
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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 | 0.001 | 0.001 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".