Statistical Comparison of Regional-Scale Tropospheric Aerosol Extinction Coefficient across China Based on CALIPSO Data
Bibliographic record
Abstract
Two years of CALIPSO level 2 version 2 data were analyzed to obtain regional distributions of vertical profiles of the aerosol extinction coefficient (AEC) across China. 10 typical geographical regions were selected for comparison, which cover various aerosol pollution levels, climate zones, and underlying surfaces. The whole troposphere was split into two-layers: the Lower- and the Upper-layer—separated by the maximum boundary layer height—which represent the boundary layer and the upper tropospheric layer, respectively. The annual average of the column-average AEC in the 10 regions ranged from 0.066 to 0.243 km–1 in the Lower-layer and from 0.022 to 0.059 km–1 in the Upper-layer. The regional AEC in the Lower-layer was the highest in central and eastern China, followed by that in the Sichuan Basin, the Yunnan-Guizhou Plateau, the capital economic circle, the Pearl River Delta, the desert region, the Tibetan Plateau, northeast China, the northwest semi-arid plateau, and, finally, the East China Sea. The seasonal AEC in the Lower-layer was the highest during spring in the desert and marine regions, during the summer on the Tibetan Plateau, and during autumn or winter in the other regions. The regional and seasonal patterns of AEC in the Lower-layer agreed to a large extent with known regional distributions of surface-layer PM2.5 distributions and dominant seasonal emission sources in their respective regions. Regional and seasonal patterns in the Upper-layer were slightly different from those in the Lower-layer due to different transport pathways of aerosol pollution in different regions. The proportion of occurrence under different pollution conditions and the number of polluted days were also estimated separately for the Lower- and the Upper-layer, based on AEC vertical profiles for each region.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 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 | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 teacher head, 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".