A study of the approaches used to retrieve aerosol extinction, as applied to limb observations made by OSIRIS and SCIAMACHY
Bibliographic record
Abstract
Abstract. Limb scatter instruments in the UV–vis spectral range have provided long-term global records of stratospheric aerosol extinction important for climate records and modelling. While comparisons with occultation instruments show generally good agreement, the source and magnitude of the biases arising from retrieval assumptions, approximations in the radiative transfer modelling and inversion techniques have not been thoroughly characterized. This paper explores the biases between SCIAMACHY v1.4, OSIRIS v5.07 and SAGE II v7.00 aerosol extinctions through a series of coincident comparisons as well as simulation and retrieval studies to investigate the cause and magnitude of the various systematic differences. The effect of a priori profiles, particle size assumptions, radiative transfer modelling, inversion techniques and the different satellite datasets are explored. It is found that the assumed a priori profile can have a large effect near the normalization point, as well as systematic influence at lower altitudes. The error due to particle size assumptions is relatively small when averaged over a range of scattering angles, but individual errors depend on the particular scattering angle, particle size and measurement vector definition. Differences due to radiative transfer modelling introduce differences between the retrieved products of less than 10 % on average, but can introduce vertical structure. The combination of the different scenario simulations and the application of both algorithms to both datasets enable the origin of some of the systematic features such as high-altitude differences when compared to SAGE II to be explained.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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 teacher head, 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".