A Comparison of Equilibrium and Time-Evolving Approaches to Modeling the Wind Profile under Stable Stratification
Bibliographic record
Abstract
Abstract In this study, the authors contrast the modeling of the wind profile under stable stratification considering both equilibrium (i.e., constant in time) and time-evolving frameworks, as well as one-dimensional (1D) and 3D approaches. The models considered include an equilibrium-based single-column model (SCM), a time-evolving SCM, and a time-evolving 3D mesoscale model. Data obtained from the Cabauw meteorological tower in the Netherlands over a 10-yr period are used to drive the models and to assess model performance. First, a composite dataset of low-level jet (LLJ) case studies was used to demonstrate the ability of the time-evolving SCM and the mesoscale model to accurately simulate the evolving stratification, the inertial oscillation, and the LLJ. The equilibrium SCM did not accurately simulate the LLJ case studies. The mean performances of the different models in different stability classes over the 10-yr period were then compared. Both the equilibrium and time-evolving SCMs were found to overestimate wind speeds in weakly and moderately stable conditions because of the influence of an internal boundary layer but were found to be more accurate in the higher-stability classes. Frequent model breakdown and the tendency to underestimate stratification limited the usefulness of the equilibrium SCM. Despite its various limitations and simplified physics, the time-evolving SCM approach is found to perform comparably to the mesoscale model while using a fraction of the computational cost but requiring local observations. Consequently, an SCM approach may be useful in the context of commercial wind resource assessment.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".