Improving Kain-Fritsch convective parameterization using direct calculations of entrainment and detrainment in shallow and deep convection
Bibliographic record
Abstract
Direct calculation of entrainment and detrainment rates is used to evaluate the Kain-Fritsch convective parameterization scheme in shallow and deep convection.From the large-eddy simulation results, it is found that the Kain-Fritsch estimates of entrainment and detrainment in shallow convection do not accurately reflect the mixing dynamics observed in the high-resolution model.Moreover, the fractional entrainment and detrainment rates are found to be solely dependent on the cloud core buoyancy and vertical velocity, contradicting the assumptions made in the traditional parameterization schemes.A number of parameterizations for fractional entrainment and detrainment rates are presented, as the dynamics that govern the mixing processes appear to be largely different in shallow and deep convection.Nevertheless, the suggested parameterizations can accurately represent turbulent mixing processes in shallow and deep cumulus clouds.iii ABR ÉG É Le calcul direct des taux d'entraînement et de détraînement est utilisé pour évaluer le schéma de paramétrisation de la convection Kain-Fritsch en convection profonde et peu profonde.A partir des résultats de la simulation grande échelle, il est constaté que les estimations Kain-Fritsch d'entraînement et de détraînement á convection peu profonde ne reflétent pas avec précision la dynamique de mélange observée dans le modéle á haute résolution.En plus, les taux fractionnels d'entraînement et de détraînement dépendent uniquement de la flottabilité de la noyau de nuage et de la vitesse verticale, ce qui contredit les hypothéses retenues dans les schémas de paramétrisation traditionnels.Un certain nombre de paramétrages pour les taux fractionnels d'entraînement et de détraînement sont présents, puisque les dynamiques qui régissent les processus de mélange semblent être largement différente entre la convection profonde et peu profonde.Néanmoins, les paramétrages proposés peuvent représenter avec précision les processus de mélange turbulent dans les nuages cumulus peu profondes et profondes.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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 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".