Direct entrainment and detrainment rate distributions of individual shallow cumulus clouds in an LES
Bibliographic record
Abstract
Abstract. Probability distribution functions of shallow cumulus cloud core entrainment and detrainment rates are calculated using 4362 individual cumulus clouds isolated from LES (large eddy simulation) using a cloud tracking algorithm. Calculation of the mutual information between fractional entrainment/detrainment and a variety of mean cloud core properties suggests that fractional entrainment rate is best predicted by the mean cloud buoyancy B and the environmental buoyancy lapse rate dθρ/dz at that level, while fractional detrainment is best predicted by the mean vertical velocity w and the critical mixing fraction χc. Fractional entrainment and detrainment rates are relatively insensitive to cloud core horizontal area, and the perimeter of horizontal cloud core sections display an a0.73 dependence. This implies that cloud core mass entrainment flux E is proportional to cloud core cross-sectional area instead of cloud core surface area, as is generally assumed. Empirical best-fit relations for ε(B, dθρ/dz and δ(w, χc) are found for both individual shallow cumulus clouds and cloud ensembles. It is found that clouds with high buoyancy in strong stratification experience low entrainment rates, while clouds with high vertical velocities and critical mixing fractions experience low detrainment rates.
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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 | 0.000 |
| 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) | 0.002 | 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".