A New Mechanism of Droplet Size Distribution Broadening during Diffusional Growth
Bibliographic record
Abstract
Abstract A new mechanism has been developed for size distribution broadening toward large droplet sizes. This mechanism may explain the rapid formation of large cloud droplets, which may subsequently trigger precipitation formation through the collision–coalescence process. The essence of the new mechanism consists of a sequence of mixing events between ascending and descending parcels. When adiabatically ascending and descending parcels having the same initial conditions at the cloud base arrive at the same level, they will have different droplet sizes and temperatures, as well as different supersaturations. Isobaric mixing between such parcels followed by further ascents and descents enables the enhanced growth of large droplets. The numerical simulation of this process suggests that the formation of large 30–40-μm droplets may occur within 20–30 min inside a shallow adiabatic stratiform layer. The dependencies of the rate of the droplet size distribution broadening on the intensity of the vertical fluctuations, their spatial amplitude, rate of mixing, droplet concentration, and other parameters are considered here. The effectiveness of this mechanism in different types of clouds is discussed.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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 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".