Mixed‐phase clouds in a turbulent environment. Part 1: Large‐eddy simulation experiments
Bibliographic record
Abstract
Abstract Mixed‐phase clouds are thermodynamically unstable, i.e. with no other forcing ice will grow at the expense of supercooled liquid water, eventually leading to complete glaciation of the cloud. In the presence of dynamic forcing, e.g. regular motions or turbulent fluctuations, liquid water can be generated in an ice cloud. Earlier theoretical considerations have identified two necessary conditions that had to be satisfied to produce liquid water in a pre‐existing ice cloud: (i) the vertical velocity of an ice cloud parcel must exceed a threshold velocity and (ii) the vertical displacement of an ice cloud parcel must be above a threshold altitude to achieve water saturation. This article uses a large‐eddy simulation (LES) model to investigate whether satisfying these conditions alone can be used as a predictive tool for the occurrence of mixed‐phase clouds in a turbulent environment. It is shown that, in general for a range of microphysical assumptions, ice concentrations and thermodynamic conditions, identifying points that satisfy these two dynamic conditions results in a good estimate of the domain liquid cloud fraction and the evolution of the liquid cloud fraction over time from the LES. When relatively large liquid water contents are present, theory underpredicts liquid cloud fraction. Further, when ice is permitted to sediment, theory overpredicts liquid cloud fraction. Two modifications to the theory are suggested, and it is demonstrated how these reduce the deviation of predicted liquid cloud fraction from simulated cloud fraction.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".