Balance of Emission and Dynamical Controls on Ozone During KORUS-AQ from Multi-Constituent Satellite Data Assimilation
Bibliographic record
Abstract
Global multiconstituent concentration and emission fields obtained from the assimilation of the satellite retrievals of ozone, CO, NO2, HNO3, and SO2 from the Ozone Monitoring Instrument (OMI), Global Ozone Monitoring Experiment 2, Measurements of Pollution in the Troposphere, Microwave Limb Sounder, and Atmospheric Infrared Sounder (AIRS)/OMI are used to understand the processes controlling air pollution during the KoreaUnited States Air Quality (KORUSAQ) campaign. Estimated emissions in South Korea were 0.42 Tg N for NOx and 1.1 Tg CO for CO, which were 40% and 83% higher, respectively, than the a priori bottomup inventories, and increased mean ozone concentration by up to 7.5 ± 1.6 ppbv. The observed boundary layer ozone exceeded 90 ppbv over Seoul under stagnant phases, whereas it was approximately 60 ppbv during dynamical conditions given equivalent emissions. Chemical reanalysis showed that mean ozone concentration was persistently higher over Seoul (75.10 ± 7.6 ppbv) than the broader KORUSAQ domain (70.5 ± 9.2 ppbv) at 700 hPa. Large bias reductions (>75%) in the free tropospheric OH show that multiplespecies assimilation is critical for balanced tropospheric chemistry analysis and emissions. The assimilation performance was dependent on the particular phase. While the evaluation of data assimilation fields shows an improved agreement with aircraft measurements in ozone (to less than 5 ppbv biases), CO, NO2, SO2, PAN, and OH profiles, lower tropospheric ozone analysis error was largest at stagnant conditions, whereas the model errors were mostly removed by data assimilation under dynamic weather conditions. Assimilation of new AIRS/OMI ozone profiles allowed for additional error reductions, especially under dynamic weather conditions. Our results show the important balance of dynamics and emissions both on pollution and the chemical assimilation system performance.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".