Comparison of radiative transfer models for limb‐viewing scattered sunlight measurements
Bibliographic record
Abstract
This study compares the limb scattered radiances calculated by six radiative transfer models for a variety of viewing conditions. Atmospheres that include molecular scattering, aerosol scattering, and ozone absorption are considered. All models treat single scattering accurately in full spherical geometry. Two “approximate spherical” models (CDI and LIMBTRAN) rely on the plane‐parallel atmosphere approximation to calculate the diffuse radiance field; the remaining four “spherical” models (Siro, MCC++, GSLS, and CDIPI) treat multiple scattering in a spherical atmosphere. Only three of the models (Siro, MCC++, and GSLS) have vector treatment with polarization. A brief comparison of vector radiances with the limb scattered radiances measured by the SOLSE and LORE instruments demonstrates agreement usually within 15% and always within 30%. The inclusion of polarization appears to have little effect on the level of agreement among the models (which agree to within 2% for this sample case). A more general comparison among calculated scalar radiances follows, including four solar zenith angles (20°, 60°, 80°, and 90°), three relative azimuth angles (20°, 90°, and 160°), and two surface albedos (0 and 0.95). The single scattered radiances agree to within 1% for almost every case. Comparisons of the total radiance show larger differences, with 2–4% spread among the results of the spherical models. The approximate spherical models show a positive radiance difference relative to the other models that increases with tangent height, reaching as much as 8% at 60 km. The rule used to divide the model atmosphere into discrete layers is shown to affect the calculated radiance, causing a height‐dependent difference of up to 1% for 1 km layer thickness.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".