Small-Scale Moist Turbulence in Numerically Generated Convective Clouds
Bibliographic record
Abstract
Abstract The authors present simulations of cloud-free and cloudy, nonprecipitating, convective turbulence at spatial resolutions down to Δx = 2.6 m for a domain size of (1 km)3. The runs are analyzed with attention focused on the dynamical differences between resolutions and the presence or absence of moisture, as well as on the small-scale variability of the liquid water spectra in the cloudy cases. Because of evaporation and condensation, liquid water content does not act like a passive scalar. Much of the evaporation occurs in highly turbulent cloud-top mixing where differences in variances and kurtoses of real-space vorticity probability density functions between cloudy and cloud-free runs are also found. The cloudy cases have higher variance and lower kurtosis values than their cloud-free counterparts. The lower kurtosis values mean fewer high-intensity vortices for the cloudy cases, which is most likely due to the loss of buoyancy as evaporation occurs during entrainment events. This effect is associated with a change in the liquid water content spectra found in regions of cloud decay. Conditional sampling of these regions shows increased small-scale variability, above the background increase due to the bottleneck effect, of liquid water content spectra; this is not found in other cloudy regions. This may help explain recent measurements of enhanced small-scale liquid water content variability in aircraft measurements of stratocumulus clouds.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".