Unraveling the complex local-scale flows influencing ozone patterns in the southern Great Lakes of North America
Bibliographic record
Abstract
Abstract. This study examines the complexity of various processes influencing summertime ozone levels in the southern Great Lakes region of North America. Results from the Border Air Quality and Meteorology (BAQS-Met) field campaign in the summer of 2007 are examined with respect to land-lake differences and local meteorology using a large array of ground-based measurements, aircraft data and simulation results from a high resolution (2.5 km) regional air-quality model, AURAMS. Analyses of average ozone mixing ratio from the entire BAQS-Met intensive campaign period support previous findings that ozone levels are higher over the southern Great Lakes than over the adjacent land. However, there is great heterogeneity in the spatial distribution of surface ozone over the lakes, particularly over Lake Erie during the day, with higher levels located over the southwestern end of the lake. Results suggest that some of these increased ozone levels are due to local emission sources in large nearby urban centers. The land-lake differences in ozone mixing ratios are most pronounced during the night in a shallow inversion layer of about 200 m above the surface. After sunrise, these differences have a limited effect on the total mass of ozone over the lakes during the day time, though they may cause elevated ozone levels in the lake breeze air. A large reservoir layer of ozone is predicted by the AURAMS model over Lake Erie at night, centered between 600–1000 m above ground and extending into the land over Cleveland. The model also predicts a vertical circulation during the day with an updraft over Detroit-Windsor and downdraft over Lake St. Clair, which transports ozone up to 1500 m above ground and results in high ozone over the lake. Oscillations in ground level ozone mixing ratios were observed on several nights and several ground monitoring sites, with amplitudes of up to 40 ppbv and time periods of 15–40 min.
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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 | 0.000 | 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".