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 necessitates 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, which is otherwise difficult to achieve with other systems (e.g. balloon measurements, solar occultation). In this study, the vertical distributions of NO2 retrieved from limb measurements of the scattered solar light from the SCIAMACHY instrument are validated using NO2 products from three different satellite instruments (SAGE II, HALOE and ACE-FTS). The retrieval approach, as well as the sensitivity of the SCIAMACHY NO2 limb data product are discussed, and the photochemical corrections needed to make this validation feasible, as well as 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 and instrument. The agreement between SCIAMACHY and each instrument is found to be better than 10 % between 22–24 km and 40 km. Additionally, NO2 amounts in three different latitude regions are validated individually, with considerably better agreements in high and middle latitudes compared to tropics. Differences with SAGE II and ACE-FTS below 20 km are consistent with those expected from the diurnal effect.
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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".