The non‐local character of turbulence asymmetry in the convective atmospheric boundary layer
Bibliographic record
Abstract
The inadequacy of conventional gradient diffusion in closure modelling of turbulent heat fluxes within the convective atmospheric boundary layer is often alleviated by accounting for non‐local transport effects, such as Deardorff's counter‐gradient models, Wyngaard's transport asymmetry closures or mass‐flux parametrization. This concept of large‐eddy flux transport is examined here with the principal aim of unifying these seemingly different models. Using large‐eddy simulation (LES) runs for the atmospheric boundary layer, spanning weakly to strongly convective conditions, a generic diagnostic framework that encodes the role of third‐order moments in non‐local transport is developed and tested. The premise is that these non‐local effects are responsible for the inherent asymmetry in vertical transport and hence the necessary non‐Gaussian nature of the joint probability density function (JPDF) of vertical velocity and potential temperature must account for these effects. Conditional sampling (quadrant analysis) of this JPDF and the imbalance between the flow mechanisms of ejections and sweeps are used to characterize this asymmetry, which is then linked to the third‐order moments using a cumulant‐discard method for the Gram–Charlier expansion of the JPDF. While the concept of ejection‐sweep events used here is not a simple extension of that commonly used in the surface layer, their connection to third‐order moments shows that the concepts of bottom‐up/top‐down diffusion or updraught/downdraught models are accounted for by various quadrants of the JPDF. An analogy between mass‐flux models and the relaxed eddy accumulation method reveals that there is a seemingly implicit assumption of a Gaussian JPDF in the former.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".