Supersaturation and diffusional droplet growth in liquid clouds: Polydisperse spectra
Bibliographic record
Abstract
Abstract Evolution of droplet size distribution (DSD) due to the water vapor diffusion in a vertically moving adiabatic parcels is investigated. Analytical expressions for height dependences of the main DSD parameters and DSD moments are obtained. The asymptotic behavior of the DSD parameters at large heights above cloud base is determined. It is shown that during diffusion growth, the width and the relative dispersion of the DSD decrease with height as z− 1/3 and z− 2/3, respectively. The paper presents examples of DSD evolution in cases DSD forms on aerosols with a three‐mode lognormal distribution. The aerosol distribution parameters used in the study correspond to four aerosol types: “Marine,” “Clean continental,” “Background,” and extremely polluted “Urban.” The vertical profiles of DSD parameters are compared with the asymptotic profiles. It is shown that in case of polydisperse DSD evolution, the vertical profile of supersaturation within several hundred meters above the cloud base can be approximated by a supersaturation profile corresponding to the “equivalent” monodisperse DSD. The initial radius of this equivalent DSD is equal to the mean radius of polydisperse DSD (haze size distribution) at cloud base, which is estimated using the Kohler theory. This result of the relation between the polydisperse and monodisperse solutions is universal. A new equation for estimation of supersaturation maximum for polydisperse case is obtained. The obtained analytical expressions and numerical results are useful for understanding the mechanisms of DSD formation in clouds and for parameterization of warm microphysical processes in cloud models.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".