On the dependence of the OH <sup>*</sup> Meinel emission altitude on vibrational level: SCIAMACHY observations and model simulations
Bibliographic record
Abstract
Abstract. Measurements of the OH Meinel emissions in the terrestrial nightglow are one of the standard ground-based techniques to retrieve upper mesospheric temperatures. It is often assumed that the emission peak altitudes are not strongly dependent on the vibrational level, although this assumption is not based on convincing experimental evidence. In this study we use Envisat/SCIAMACHY (Scanning Imaging Absorption spectroMeter for Atmospheric CHartographY) observations in the near-IR spectral range to retrieve vertical volume emission rate profiles of the OH(3-1), OH(6-2) and OH(8-3) Meinel bands in order to investigate, whether systematic differences in emission peak altitudes can be observed between the different OH Meinel bands. The results indicate that the emission peak altitudes are different for the different vibrational levels, with bands originating from higher vibrational levels having higher emission peak altitudes. It is shown that this finding is consistent with the majority of the previously published results. The SCIAMACHY observations yield differences in emission peak altitudes of up to about 4 km between the OH(3-1) and the OH(8-3) band. The observations are complemented by model simulations of the fractional population of the different vibrational levels and of the vibrational level dependence of the emission peak altitude. The model simulations well reproduce the observed vibrational level dependence of the emission peak altitude – both qualitatively and quantitatively – if quenching by atomic oxygen as well as multi-quantum collisional relaxation by O2 is considered. If a linear relationship between emission peak altitude and vibrational level is assumed, then a peak altitude difference of roughly 0.5 km per vibrational level is inferred from both the SCIAMACHY observations and the model simulations.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".