The improved Trajectory-mapped Ozonesonde dataset for the Stratosphere and Troposphere (TOST): update, validation and applications
Bibliographic record
Abstract
A global-scale horizontally and vertically resolved ozone climatology provides detailed insights into ozone variability. Here, the seasonal, annual and decadal monthly Trajectory-mapped Ozonesonde dataset for the Stratosphere and Troposphere (TOST) ozone climatology is improved and updated over 1970–2021. TOST is gridded at 5°×5°×1 km (latitude, longitude and altitude) from the surface to 26 km by the geometric coordinate and from the surface to 20 hPa at 26 pressure levels by the pressure coordinate, with the most recent ozonesonde data re-evaluated following the ASOPOS-2 guidelines (Smit and Thompson, 2021). Comparison between ozonesonde and trajectory-derived ozone shows good agreement for each decade, altitude and station, with relative differences (RDs) of 2 %–4 % in the troposphere and 0.5 % in the stratosphere. TOST also aligns well with aircraft, the Satellite Aerosol and Gas Experiment (SAGE) and the Microwave Limb Sounder (MLS) datasets. The updated TOST improves data coverage in all latitude bands and altitudes and reduces RD by 14 %–17 % compared to the previous version, taking advances in trajectory simulations and twice as many ozonesonde profiles. Higher uncertainties in TOST are where data are sparse, i.e., southern high latitudes, tropics and pre-1980s, and where variability is high, i.e., at the surface and upper troposphere–lower stratosphere (UTLS). Caution should therefore be taken when using TOST in these spaces and times. TOST captures global ozone distributions and temporal variations, showing an overall non-significant change in lower stratospheric ozone after 1998. TOST offers users a dataset with a long record, global coverage and high vertical resolution.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".