Retrieval of daytime mesospheric ozone using OSIRIS observation of O <sub>2</sub> (a <sup>1</sup> ∆ <sub>g</sub> ) emission
Bibliographic record
Abstract
Abstract. Improving knowledge of the ozone global distributions in the mesosphere-lower thermosphere (MLT) is a crucial step in understanding the behaviour of the middle atmosphere. However, the ozone concentration under sunlit conditions in the MLT is often so low that its measurement requires instruments with very high sensitivity. Fortunately, the bright oxygen airglow can serve as a proxy to retrieve the daytime ozone density indirectly, due to the strong connection to ozone photolysis in the Hartley band. The OSIRIS IR imager (hereafter IRI), one of the instruments on the Odin satellite, routinely measures the oxygen infrared atmospheric band (IRA band) at 1.27 μm. In this paper, we will describe the detailed steps of retrieving the calibrated IRA band limb radiance, the volume emission rate of O2(a1∆g) and, finally, the ozone number density. This retrieval technique is applied to a one-year-sample IRI dataset. The resulting product is a completely new ozone dataset with very high along-track resolution. The performance of the retrieval technique is demonstrated by a comparison of the coincident ozone measurements from the same spacecraft, as well as zonal mean and monthly average comparisons between OS, SMR, MIPAS and ACE-FTS. The consistency of this IRI ozone dataset implies that such a retrieval technique can be further applied to all the measurements made throughout the 19 years-long mission, leading to a long-term, high resolution dataset in the middle atmosphere.
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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 | 0.000 |
| 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) | 0.001 | 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".