Impact of hygroscopic CCN and turbulence on cloud dropletgrowth: A parcel-DNS approach
Bibliographic record
Abstract
Abstract. This paper investigates the relative importance of turbulence, hygroscopicity of cloud condensation nuclei (CCN), and aerosol loading on early cloud development. A parcel-DNS hybrid approach is developed to seamlessly simulate the evolution of cloud droplets in warm clouds. The results show that turbulence and CCN hygroscopicity have a dominant effect on the formation of large droplets. When CCN hygroscopicity is considered, condensational growth has a strong effect in the first minute, providing sufficient collector droplets. In the meantime, turbulence effectively accelerates the collisions among the collector droplets and the small droplets and continues to broaden the droplet size distribution (DSD). In contrast, seeding of extra aerosols modulates the growth of small droplets by inhibiting condensational growth while the growth of large droplets remains unaffected, resulting in a similar tail of the DSD. Overall, seeding reduces the LWC and effective radius but increases the relative dispersion. This opposing trend of the bulk properties suggests that the traditional Kessler-type or Sundqvist-type autoconversion parameterizations which mainly depend on the LWC or mean radius might not represent the drizzle formation process well. Properties related to the width or the shape of the DSD are also needed, suggesting that the Berry-and-Reinhardt scheme is conceptually better.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".