Impacts of Synoptic Weather Patterns and their Persistency on Free Tropospheric Carbon Monoxide Concentrations and Outflow in Eastern China
Bibliographic record
Abstract
Abstract The relationship between synoptic weather patterns (SWPs) and variability of air pollution has not been fully understood. In this study, we assess the sensitivity of carbon monoxide (CO) concentrations and outflow in the free troposphere over eastern China to SWPs and their persistency, using daily CO data from the spaceborne Atmospheric Infrared Sounder (AIRS) instrument from 2003 to 2015. The SWPs over eastern China are classified into eight patterns following the Kirchhofer approach. On a regional average over the 13 years, distinct differences are observed in CO distributions and outflows among the eight SWPs. CO concentrations tend to increase under three SWPs that are characterized with anticyclonic circulations and downdrafts in all or part of eastern China. In contrast, three SWPs related to the East Asian summer monsoon (EASM) tend to reduce CO concentrations. The remaining two SWPs, dominant in winter and autumn, respectively, tend to bring more clean episodes. CO outflow from eastern China is sensitive to the influence of the SWPs on zonal winds. CO outflow is enhanced under two SWPs that prevail in winter and spring. Under the other SWPs, CO outflow decreases or changes only slightly. CO concentrations either increase or decrease by 5–15% under the persistent control of a SWP for two to seven days. In most cases, the anomalies of CO concentrations and CO outflow tend to be amplified if a SWP persists for several days. This study statistically illustrates the synoptic influences on the regional distribution and transport of air pollutants.
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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 | 0.001 | 0.001 |
| 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 | 0.001 |
| 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".