Tropospheric ozone over the tropical Atlantic: A satellite perspective
Bibliographic record
Abstract
We use satellite sensor measurements to obtain a broad picture of the processes affecting tropical tropospheric O 3 production over Africa and the Atlantic in the early part of the year. Terra/MOPITT CO retrievals correlate well with biomass burning fire counts observed by the TRMM/VIRS instrument in Northern Hemisphere savanna regions and allow investigation of the subsequent convection of the biomass burning plume at the intertropical convergence zone and interhemispheric transport. Measurements of NO 2 from the ERS‐2/GOME instrument enable identification of two important tropical sources of this O 3 precursor, biomass burning and lightning. Good correlation is seen between NO 2 retrievals and TRMM/LIS lightning flash observations in southern African regions free of biomass burning, thus indicating a probable lightning source of NO x . The combination of MOPITT CO, GOME NO 2 , and TRMM fire and lightning flash counts provides a powerful tool for investigating the tropospheric production of O 3 precursors. These data are used in conjunction with the MOZART‐2 chemical transport model to investigate the early year tropical Atlantic tropospheric O 3 distribution using January 2001 as a case study. Inconsistencies between the various tropical tropospheric O 3 column products obtained from EP/TOMS data, and between these products, in situ measurements, and modeling, have led to questions about how the Northern Hemisphere biomass burning is connected to the TOMS derived O 3 maximum in the tropical southern Atlantic. We show that the early year tropical O 3 distribution is actually characterized by two maxima. The first arises due to biomass burning emissions, is located near the Northern Hemisphere fires, and is most evident in the lower troposphere. The second is located in the southern tropical Atlantic midtroposphere, and results from NO x produced by lightning over southern Africa and South America.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.001 | 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".