Influence of corona discharge on the ozone budget in the tropical free troposphere: a case study of deep convection during GABRIEL
Bibliographic record
Abstract
Abstract. Convective redistribution of ozone and its precursors between the boundary layer (BL) and the free troposphere (FT) influences photochemistry, in particular in the middle and upper troposphere (UT). We present a case study of convective transport during the GABRIEL campaign over the tropical rain forest in Suriname in October 2005. During one measurement flight the inflow and outflow regions of a cumulonimbus cloud (Cb) have been characterized. We identified a distinct layer between 9 and 11 km altitude with enhanced mixing ratios of CO, O3, HOx, acetone and acetonitrile. The elevated O3 contradicts the expectation that convective transport brings low-ozone air from the boundary layer to the outflow region. Entrainment of ozone-rich air is estimated to account for 62% (range: 33–91%) of the observed O3. Ozone is enhanced by only 5–6% by photochemical production in the outflow due to enhanced NO from lightning, based on model calculations using observations including the first reported HOx measurements over the tropical rainforest. The "excess" ozone in the outflow is most probably due to direct production by corona discharge associated with lightning. We deduce a production rate of 5.12 × 1028 molecules O3 flash−1 (range: 9.89 × 1026–9.82 × 1028 molecules O3 flash−1), which is at the upper limit of the range reported previously.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".