Diurnal effects in limb scatter observations
Bibliographic record
Abstract
Instruments that measure UV/visible scattered light from the Earth's limb are emerging as an important class of sensors capable of providing high‐quality profiles of aerosols and trace gases from the upper troposphere to the mesosphere. Critical to the inversion of limb scatter observations is the forward radiative transfer model. A fast and accurate radiative transfer model, VECTOR (Vector Orders‐of‐scattering Radiative transfer model), is presented that is able to account for the diurnal variation of species such as NO2 and BrO along the observing line of sight and the incoming solar beam. VECTOR has been used to quantify for the first time diurnal effect errors in NO2 and BrO with application to OSIRIS (Optical Spectrograph and Infra‐Red Imager System) and SCIAMACHY (Scanning Imaging Absorption Spectrometer for Atmospheric Chartography), two limb viewing satellite instruments. For a solar zenith angle near 90° at the tangent point, errors can exceed 50% for NO2 and 100% for BrO in the lower stratosphere, with the largest errors generally occurring when viewing across, and at large angles to, the terminator. These results applied to OSIRIS NO2 and SCIAMACHY BrO reveal that diurnal effect errors are generally small (<10%). Yet 1 out of every 6 OSIRIS NO2 profiles experiences large (10–35%) errors and 1 out of every 11 SCIAMACHY BrO profiles experiences large (10–100%, or larger) errors in the lower stratosphere.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".