Description and validation of a limb scatter retrieval method for Odin/OSIRIS
Bibliographic record
Abstract
In this paper we present the Modified Onion Peeling (MOP) inversion method, which is for the first time used to retrieve vertical profiles of stratospheric trace gases from Odin/OSIRIS limb scatter measurements. Since the original publication of the method in 2002, the method has undergone major modifications discussed here. The MOP method now uses a spectral microwindow for the NO2 retrieval, instead of the wide UV‐visible band used for the ozone, air, and aerosol retrievals. We give a brief description of the algorithm itself and show its performance with both simulated and real data. Retrieved ozone and NO2 profiles from the OSIRIS measurements were compared with data from the GOMOS and HALOE instruments. No more than 5% difference was found between OSIRIS daytime and GOMOS nighttime ozone profiles between 21 and 45 km. The difference between OSIRIS and HALOE sunset NO2 mixing ratio profiles was at most 25% between 20 and 40 km. The neutral air density was compared with the ECMWF analyzed data and around 5% difference was found at altitudes from 20 to 55 km. However, OSIRIS observations yield as much as 80% greater aerosols number density than GOMOS observations between 15 and 35 km. These validation results indicate that the quality of MOP ozone, NO2, and neutral air is good. The new version of the method introduced here is also easily expanded to retrieve additional species of interest.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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".