Performance of the ground‐based total ozone network assessed using satellite data
Bibliographic record
Abstract
Dobson and Brewer spectrophotometer and filter ozonometer data available from the World Ozone and Ultraviolet Data Centre (WOUDC) were compared with satellite total ozone measurements from TOMS (onboard Nimbus 7, Meteor 3, and Earth Probe satellites), OMI (AURA satellite) and GOME (ERS‐2 satellite) instruments. Five characteristics of the difference with satellite data were calculated for each site and instrument type: the mean difference, the standard deviation of daily differences, the standard deviation of monthly differences, the amplitude of the seasonal component of the difference, and the range of annual values. All these characteristics were calculated for five 5‐year‐long bins and for each site separately for direct sun (DS) and zenith sky (ZS) ozone measurements. The main percentiles were estimated for the five characteristics of the difference and then used to establish criteria for “suspect” or “outlier” sites for each characteristic. About 61% of Dobson, 46% of Brewer, and 28% of filter stations located between 60°S and 60°N have no “suspect” or “outlier” characteristics. In nearly 90% of all cases, Dobson and Brewer sites demonstrated 5‐year mean differences with satellites to be within ±3% (for DS observations). The seasonal median difference between all Brewer DS measurements at 25°–60°N and GOME and OMI overpasses remained within ±0.5% over a period of more than 10 years. The satellite instrument performance was also analyzed to determine typical measurement uncertainties. It is demonstrated that systematic differences between the analyzed satellite instruments are typically within ±2% and very rarely are they outside the ±3% envelope. As the satellite instrument measurements appear to be better than ±3%, ground‐based instruments with precision values worse than ±3% are not particularly useful for the analyses of long‐term changes and comparison with numerical simulations.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".