Validation of SCIAMACHY limb NO <sub>2</sub> profiles using solar occultation measurements
Bibliographic record
Abstract
Abstract. The increasing amounts of reactive nitrogen in the stratosphere necessitate accurate global measurements of stratospheric nitrogen dioxide (NO2). Over the past decade, the SCIAMACHY (SCanning Imaging Absorption spectroMeter for Atmospheric CHartographY) instrument on ENVISAT (European Environmental Satellite) has been providing global coverage of stratospheric NO2 every 6 days. In this study, the vertical distributions of NO2 retrieved from SCIAMACHY limb measurements of the scattered solar light are validated by comparison with NO2 products from three different satellite instruments (SAGE II, HALOE and ACE-FTS). The retrieval algorithm based on the information operator approach is discussed, and the sensitivity of the SCIAMACHY NO2 limb retrievals is investigated. The photochemical corrections needed to make this validation feasible, and the chosen collocation criteria are described. For each instrument, a time period of two years is analyzed with several hundreds of collocation pairs for each year. As NO2 is highly variable, the comparisons are performed for five latitudinal bins and four seasons. In the 20 to 40 km altitude range, mean relative differences between SCIAMACHY and other instruments are found to be typically within 20 to 30%. The mean partial NO2 columns in this altitude range agree typically within 15% (both global monthly and zonal annual means). Larger differences are seen for SAGE II comparisons, which is consistent with the results presented by other authors. For SAGE II and ACE-FTS, the observed differences can be partially attributed to the diurnal effect error.
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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 | 0.001 | 0.001 |
| 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".