A Turbulence-Based Model for Resolving Velocity and Temperature Profiles in the Atmospheric Surface Layer
Bibliographic record
Abstract
The goal of this paper is to demonstrate the advantages of coupling the integrated Monin-Obukhov similarity functions with an algebraic turbulence equation to resolve the profiles of temperature and velocity in the atmospheric surface layer. The latter equation is derived using the definition of turbulent viscosity from the k-ε turbulence model. The proposed surface-layer model is validated with eddy covariance and profile measurements from the CASES99 experiment and the results are compared with classic flux-profile techniques. In doing so, the relative benefits of the presented formulation become apparent. Firstly, the addition of a turbulence model improves convergence in the moderate to very stable regime. Secondly, the addition of an extra equation allows the four atmospheric parameters of interest ( u*, θ*, L and z0) to be resolved simultaneously. Furthermore, the definition of turbulent viscosity can be used to reformulate the Monin-Obukhov equations for the very stable limit.
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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 | 0.000 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".