Stability Dependence of the Turbulent Dissipation Rate in the Convective Atmospheric Boundary Layer
Bibliographic record
Abstract
Abstract Turbulent dissipation rate (ɛ) is a crucial parameter in turbulence theory, and an essential component of higher‐order planetary boundary layer schemes for numerical weather prediction and climate models. It is most often modeled diagnostically based on the dissipation scaling ɛ ∝ e3/2/L, where e and L are the turbulence kinetic energy (TKE) and the size of the largest turbulent eddies, respectively. Utilizing three‐month‐long vertically‐extended observations accompanied by high resolution large‐eddy simulations, scaling‐based ɛ‐models are evaluated, focusing on their stability dependence under daytime convective conditions. The analysis uncovers biases in the parameterized ɛ profiles that cannot be corrected through tuning of model constants. The biases are attributed to the limited and even opposing stability dependence of the modeled dissipation length. Close examination reveals violation of the dissipation scaling by the inclusion of TKE associated with organized convection. A self‐similar dissipation length is obtained when only the isotropic component of TKE is considered.
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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 | 0.001 |
| 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) | 0.000 | 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".