Improving ECC Ozonesonde Data Quality: Assessment of Current Methods and Outstanding Issues
Bibliographic record
Abstract
Abstract We review the current state of knowledge of ozonesonde uncertainty and bias, with reference to recent developments in laboratory and field experiments. In the past 20 years ozonesonde precision has improved by a factor of 2, primarily through the adoption of strict standard operating procedures. The uncertainty budget for the ozone partial pressure reading has contributions from stoichiometry, cell background current, pump efficiency and temperature, sensing solution type, and volume. Corrections to historical data for known issues may reduce biases but simultaneously introduce additional uncertainties. This paper describes a systematic approach to quantifying these uncertainties by considering the physical and chemical processes involved and attempts to place our estimates on a firm theoretical or empirical footing. New equations or tables for ozone/iodine conversion efficiency, humidity and temperature corrections to pump flow rate, and altitude‐dependent pump flow corrections are presented, as well as detailed discussion of stoichiometry and conversion efficiencies. The nature of the so‐called “background current” is considered in detail. Two other factors particularly affecting past measurements, uncertainties and biases in the pressure measurement, and the comparison of sonde profiles to spectrophotometric measurements of total column ozone, are also discussed. Several quality assurance issues remain, but are tractable problems that can be addressed with further research. This will be required if the present goal of better than 5% overall uncertainty throughout the global ozonesonde network is to be achieved.
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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 | 0.107 | 0.117 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".