Influences of drizzle on stratocumulus cloudiness and organization
Bibliographic record
Abstract
Abstract Large‐eddy simulations are used to study the influence of drizzle on stratocumulus organization, based on measurements made as part of the Second Dynamics and Chemistry of the Marine Stratocumulus field study‐II. Cloud droplet number concentration ( N c ) is prescribed and considered as the proxy for different aerosol loadings. Our study shows that the amount of cloudiness does not decrease linearly with precipitation rate. An N c threshold is observed below which the removal of cloud water via precipitation efficiently reduces cloud depth, allowing evaporation to become efficient and quickly remove the remaining thin clouds, facilitating a fast transition from closed cells to open cells. Using Fourier analysis, stratocumulus length scales are found to increase with drizzle rates. Raindrop evaporation below 300 m lowers the cloud bases and amplifies moisture variances in the subcloud layer, while it does not alter the horizontal scales in the cloud layer, suggesting that moist cold pool dynamic forcings are not essential for mesoscale organization of stratocumulus. The cloud scales are greatly increased when the boundary layer is too deep to maintain well mixed.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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 teacher head, 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".