Large horizontal gradients in atmospheric CO at the synoptic scale as seen by spaceborne Measurements of Pollution in the Troposphere
Bibliographic record
Abstract
We have examined the influence of synoptic processes on the distribution of atmospheric CO as observed by the Measurements of Pollution in the Troposphere (MOPITT) satellite instrument. In the MOPITT data, large horizontal gradients in CO, coherent at the synoptic scale, have been observed. The concentration of CO varies rapidly by as much as 50–100% across distances of ∼100 km, forming distinct boundaries in the CO distribution. These can last one to several days and span horizontal distances of 600–1000 km. On average, such events were observed in the MOPITT CO daily images once every 3–4 days over North America in spring and summer 2000. We focused on three case studies over North America in August 2000 to understand the mechanisms responsible for the large gradients in CO. Through an analysis of meteorological data from the National Centers for Environmental Prediction/National Center for Atmospheric Research Reanalysis, parcel trajectory modeling, and global three‐dimensional chemical transport modeling, we found that the large horizontal gradients typically reflect the differential vertical and horizontal transport of air with different chemical signatures. In the first case, the large gradients in CO over North Dakota resulted from the lifting ahead of a cold front that transported boundary layer air enriched with CO from forest fires in Montana, combined with the descent of clean air from the Canadian Prairies behind the front. In the second case, the large gradients over northeastern Texas were produced by the convective lifting over Arkansas of air with high concentrations of CO from the oxidation of volatile organic compounds and the onshore transport of clean air from the Gulf of Mexico. In the third case, we examined an example of outflow of surface pollution from North America by a cyclone. The largest gradients in this case were observed along the boundary between the boundary layer air transported by the warm conveyor belt ahead of the cold front and the clean air transported from the Atlantic by the semipermanent high‐pressure system in the central Atlantic. Our results demonstrate that MOPITT can capture the influence of synoptic processes on the horizontal and vertical distribution of CO. The large gradients in CO observed on synoptic scales represent valuable information that can be exploited to improve our understanding of atmospheric CO. In particular, these results suggest that the MOPITT observations provide a useful data set with which to address a range of issues from air quality on local/regional scales to long‐range transport of pollution on continental/global scales.
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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 | 0.000 | 0.001 |
| 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) | 0.000 | 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".