Ozone Corrections for Rayleigh-Scatter Temperature Determinations in the Middle Atmosphere
Bibliographic record
Abstract
A well-established technique for the determination of temperature in the middle atmosphere is the retrieval of temperature profiles from density profiles of air. The measurement of air density profiles from the ground and from space are typically determined from measurements of Rayleigh-scattered light. Most researchers using the Rayleigh-scatter temperature technique do not state whether they correct their measurements for the absorption of light due to ozone in the upper stratosphere. Such corrections may have been less significant for initial studies of temperature, but with the current need for temperature measurements of sufficient quality to access atmospheric change, these corrections take on an added importance. Significant improvement to the temperature measurements in the stratosphere are shown to result by including this effect for any reasonable choice of ozone profile. Simple correction functions are presented for temperature measurements, appropriate for low, middle, and high latitudes evaluated at two of the three most common Rayleigh-scatter wavelengths, 532 and 589 nm. Though the 350-nm wavelength is also commonly used for Rayleigh-scatter measurements, the effects of ozone at this wavelength are found to be negligible. The correction functions increase the temperature in the middle stratosphere by about as much as 4 K, with the largest corrections at the low latitudes and the longer wavelength, 589 nm. Deviations from these baseline values due to seasonal variability in the ozone profile change the temperature correction by less than 10%. Despite increases of two to three times of ozone in the mesosphere during the spring season, mesospheric temperatures are found to not require any correction for ozone. Hence, even without explicit knowledge of the ozone profile, experimenters can still significantly improve their Rayleigh-scatter stratospheric temperature measurements compared to not correcting for ozone absorption.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".