Importance of vertical velocity variations in the cloud droplet nucleation process of marine stratus clouds
Bibliographic record
Abstract
Eleven cloud cases through marine stratus, obtained during two field experiments in the North Atlantic Ocean, are used to study the sensitivity of cloud droplet nucleation to the vertical gust velocity. Selected cloud microphysical data, size‐distributed aerosol properties and particle chemistry are applied in an adiabatic parcel model. The nucleated cloud droplet number concentrations ( N ) predicted using the probability density function (PDF) of the measured in‐cloud vertical velocities are compared to predictions using a characteristic velocity value. In this study, the model‐predicted N from the PDF of the measured in‐cloud vertical velocities agrees with the observed maximum N ( N max ) to within 8.6%. The average N ( N avg ) can be related to N max using a power law (Leaitch et al., 1996). If a relationship between N max and N avg based on the measurements is applied to obtain the average N from the model‐predicted N , then the model‐predicted average N agrees with the observed average N to within 13.3%. When a characteristic vertical velocity (0.8 times the standard deviation of the vertical velocity distribution in this study) is used in the parcel model to simulate N , the model‐predicted N agrees with the observed maximum N to within 5.7% and the model‐predicted average N agrees with the observed average N within 8.8%. This indicates that using a characteristic value of the vertical velocity distribution instead of its PDF is a good approximation for simulating the nucleated cloud droplet number of marine stratus on a cloud scale.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 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".