A trajectory‐based estimate of the tropospheric ozone column using the residual method
Bibliographic record
Abstract
We estimate the tropospheric column ozone using a forward trajectory model to increase the horizontal resolution of the Aura Microwave Limb Sounder (MLS) derived stratospheric column ozone. Subtracting the MLS stratospheric column from Ozone Monitoring Instrument total column measurements gives the trajectory enhanced tropospheric ozone residual (TTOR). Because of different tropopause definitions, we validate the basic residual technique by computing the 200‐hPa‐to‐surface column and comparing it to the same product from ozonesondes and Tropospheric Emission Spectrometer measurements. Comparisons show good agreement in the tropics and reasonable agreement at middle latitudes, but there is a persistent low bias in the TTOR that may be due to a slight high bias in MLS stratospheric column. With the improved stratospheric column resolution, we note a strong correlation of extratropical tropospheric ozone column anomalies with probable troposphere‐stratosphere exchange events or folds. The folds can be identified by their colocation with strong horizontal tropopause gradients. TTOR anomalies due to folds may be mistaken for pollution events since folds often occur in the Atlantic and Pacific pollution corridors. We also compare the 200‐hPa‐to‐surface column with Global Modeling Initiative chemical model estimates of the same quantity. While the tropical comparisons are good, we note that chemical model variations in 200‐hPa‐to‐surface column at middle latitudes are much smaller than seen in the TTOR.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".