Retrieval of stratospheric O<sub>3</sub> and NO<sub>2</sub> profiles from Odin Optical Spectrograph and Infrared Imager System (OSIRIS) limb‐scattered sunlight measurements
Bibliographic record
Abstract
Scientific studies of the major environmental questions of global warming and ozone depletion require global data sets of atmospheric constituents with relevant temporal and spatial resolution. In this paper, global number density profiles of O3 and NO2 are retrieved from Odin Optical Spectrograph and Infrared Imager System (Odin/OSIRIS) limb‐scattered sunlight measurements, using the maximum a posteriori estimator. Differential optical absorption spectroscopy is applied to OSIRIS radiances as an intermediate step, using the wavelength windows 571–617 nm for O3 and 435–451 nm for NO2. The method is computationally efficient for processing OSIRIS data on an operational basis. Results show that a 2–3 km height resolution is generally achievable between about 12 km and 45 km for O3 with an estimated accuracy of 13% at the peak and between about 15 km and 40 km for NO2 with an estimated accuracy of 10% at the peak. First validations of the retrieved data indicate a good agreement both with other retrieval techniques applied to OSIRIS measurements and with the results of other instruments. Once the validation has reached a confident level, the retrieved data will be used to study important stratospheric processes relevant to global environmental problems. The unique NO2 data set will be of particular interest for studies of nitrogen chemistry 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 | 0.000 | 0.000 |
| 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".