Ground-based assessment of the bias and long-term stability of fourteen limb and occultation ozone profile data records
Bibliographic record
Abstract
Abstract. The ozone profile records of a large number of limb and occultation satellite instruments are widely used to address several key questions in ozone research. Further progress in some domains depends on a more detailed understanding of these data sets, especially of their long-term stability and their mutual consistency. To this end, we make a systematic assessment of fourteen limb and occultation sounders that, together, provide more than three decades of global ozone profile measurements. In particular, we consider the latest operational Level-2 records by SAGE II, SAGE III, HALOE, UARS MLS, Aura MLS, POAM II, POAM III, OSIRIS, SMR, GOMOS, MIPAS, SCIAMACHY, ACE-FTS and MAESTRO. Central to our work is a harmonized and robust analysis of the comparisons against the ground-based ozonesonde and stratospheric ozone lidar networks. It allows us to investigate, from the ground up to the stratopause, the following main aspects of data quality: long-term stability, overall bias, and short-term variability, together with their dependence on geophysical parameters and profile representation. In addition, it permits us to quantify the overall consistency between the ozone profilers. Generally, we find that between 20–40 km, the satellite ozone measurement biases are smaller than ±5 %, the short-term variabilities are better than 5–12 % and the drifts are at most ±5 % decade−1 (and ±3 % decade−1 for a few records). The agreement with ground-based data degrades somewhat towards the stratopause and especially towards the tropopause, where natural variability and low ozone abundancies impede a more precise analysis. A few records deviate from the preceding general remarks, in part of the stratosphere; we identify biases of 10 % and more (POAM II and SCIAMACHY), markedly higher single-profile variability (SMR and SCIAMACHY), and significant long-term drifts (SCIAMACHY, OSIRIS, HALOE, and possibly GOMOS and SMR as well). Furthermore, we reflect on the repercussions of our findings for the construction, analysis and interpretation of merged data records. Most notably, the discrepancies between several recent ozone profile trend assessments can be mostly explained by instrumental drift. This clearly demonstrates the need for systematic comprehensive multi-instrument comparison analyses.
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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 | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 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) | 0.000 | 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".