New methodology applied to deriving total ozone and other atmospheric variables from global irradiance spectra
Bibliographic record
Abstract
A new sampling and analysis method for acquiring low‐noise spectra using the Brewer spectrophotometer is applied to ground‐based spectral measurements of global ultraviolet radiation. The new technique substantially reduces noise caused by changing atmospheric conditions that can occur during the sampling period. Routine measurements made at Toronto between 1996 and 2006 were used to develop a statistical model for determining total ozone and other variables from the global spectral data. Long‐term comparison of global measurements with direct measurements demonstrates that global scan data can be used to measure total ozone with an accuracy better than ±1% for a wide variety of total ozone amounts and vertical profiles under clear skies. Other information such as ozone temperature and instrument wavelength stability can also be extracted. The measurements compared with model results show good agreement for clear‐sky conditions. The results indicate that atmospheric aerosols and clouds can enhance absorption of UV radiation by ozone. For aerosol optical depth of one, the absorption enhancement is about 2.0% at air mass value of 1 and drops to 0.5% enhancement for air mass values between 2 and 3. For clouds the enhancement is generally small in winter months but can be substantial in summer months. The statistical relationship used for measuring total ozone using spectral global irradiance data is given. Comparisons of total ozone and ozone temperature derived from global scans with those from direct Sun scans are shown, and the effects of aerosols and clouds on derived total ozone from global data are discussed.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".