An explicit study of aerosol mass conversion and its parameterization in warm rain formation of cumulus clouds
Bibliographic record
Abstract
Abstract. The life time of atmospheric aerosols is highly affected by in-cloud scavenging processes. Aerosol mass conversion from aerosols embedded in cloud droplets into aerosols embedded in raindrops is a pivotal pathway for wet removal of aerosols in clouds. The aerosol mass conversion rate in the bulk microphysics parameterizations is always assumed to be linearly related to the precipitation production rate, which includes the cloud water autoconversion rate and the cloud water accretion rate. The ratio of the aerosol mass concentration conversion rate to the cloud aerosol mass concentration has typically been considered to be the same as the ratio of the precipitation production rate to the cloud droplet mass concentration. However, the mass of an aerosol embedded in a cloud droplet is not linearly proportional to the mass of the cloud droplet. A simple linear relationship cannot be drawn between the precipitation production rate and the aerosol mass concentration conversion rate. In this paper, we studied the evolution of aerosol mass concentration conversion rates in a warm rain formation process with a 1.5-dimensional non-hydrostatic convective cloud and aerosol interaction model in the bin microphysics. We found that the ratio of the aerosol mass conversion rate to the cloud aerosol mass concentration can be statistically expressed by the ratio of the precipitation production rate to the cloud droplet mass concentration with an exponential function. We further gave some regression equations to determine aerosol conversions in the warm rain formation under different threshold radii of raindrops and different aerosol size distributions.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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 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".