Cloud characterization for trace gas retrievals over snow using O2-O2 and oxygen B-band absorption
Bibliographic record
Abstract
Geostationary measurements of trace gases provide valuable air quality data at unprecedented temporal scales. At high latitudes challenges begin to arise, such as lines of sight that stray from nadir, and (during winter) limited sunlight and pervasive snow cover. Motivated by the desire to fully take advantage of TEMPO (Tropospheric Emissions: Monitoring of Pollution) measurements over Canada, we investigate one of these issues: snow.A key challenge with measurements over snowy scenes is the similar reflectivity of snow and clouds. Trace gas algorithms rely on the contrast between surface and cloud reflectivities to estimate an effective cloud fraction which is necessary to characterize the light path for cloudy scenes. This snow-cloud ambiguity ultimately compromises the data quality, denying the opportunity to capitalize on the potential increase in surface sensitivity offered by the high reflectivity of snow. Here we present an algorithm that simultaneously uses O2-O2 and oxygen B-band absorption to extract cloud data for trace gas retrievals while reducing dependency on the surface-cloud reflectivity contrast.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".