Effects of Aerosols on the Precipitation of Convective Clouds: A Case Study in the Yangtze River Delta of China
Bibliographic record
Abstract
Abstract Aerosol is a critical factor affecting the atmospheric hydrological cycle and climate change. Acting as cloud condensation nuclei for cloud formation, aerosols have a significant impact on regional precipitation. This study uses the fully coupled chemistry module (Weather Research and Forecasting/Chem) within the Weather Research and Forecasting model to simulate convective cloud precipitation in the Yangtze River Delta of China. To investigate the impact of on precipitation, four numerical experiments are conducted. The base case uses the full emission inventory (which we call the 100% case), and the other three cases are designed based on reduced emissions for different percentages (which we call the 50% case, the 10% case, and the 1% case). Compared to the other cases, the grid point hour maximum precipitation of the 50% case is the largest, which can reach 44.1 mm/hr and has an increase of 5% over the 100% case. The strongest precipitation is delayed by about 1 hr in the 50% case, and precipitation area is increased by 6.5%. This study indicates that the influence of aerosols on regional precipitation is a nonlinear process, with a correlation coefficient of 0.52 ( p <0.01) showing a strong positive correlation between cloud condensation nuclei (>250 cm −3 , height of 0.5–3 km) and precipitation. Further analysis of the dynamics and microphysical processes of this convective precipitation shows that the 50% case has an area with higher rising velocity and bigger cloud water mixing ratio than the other cases but has a relatively low convective center. The formation of precipitation is mainly influenced by the accretion of snow by rain, but the role of the snow melting into rain cannot be ignored.
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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 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 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".