Interpolation of LES Cloud Surfaces for Use in Direct Calculations of Entrainment and Detrainment
Bibliographic record
Abstract
Abstract Direct calculations of the entrainment and detrainment of air into and out of clouds require knowledge of the relative velocity difference between the air and the cloud surface. However, a discrete numerical model grid forces the distance moved by a cloud surface over a time step to be either zero or the width of a model grid cell. Here a method for the subgrid interpolation of a cloud surface on a discrete numerical model grid is presented. This method is used to calculate entrainment and detrainment rates for a large-eddy simulation (LES) model, which are compared with rates calculated via the direct flux method of Romps. The comparison shows good agreement between the two methods as long as the model clouds are well resolved by the model grid spacing. This limitation of this technique is offset by the ability to resolve fluxes on much finer temporal and spatial scales, making it suitable for calculating entrainment and detrainment profiles for individual 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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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 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".