The Extrapolation of Near-Surface Wind Speeds under Stable Stratification Using an Equilibrium-Based Single-Column Model Approach
Bibliographic record
Abstract
Abstract Classical approaches to modeling the near-surface (i.e., below 200 m) wind profile are equilibrium based (i.e., no time evolution) and either lack a physical basis or are based on surface-layer physics. In this study, the limits of the equilibrium approach in stable stratification are further tested by applying the method within a more physically comprehensive single-column model (SCM) framework. The SCM considered here is a highly idealized momentum and temperature budget model that uses a range of different parameterizations of turbulent fluxes. A 10-yr observational dataset obtained from the 213-m Cabauw tower in the Netherlands is used to drive the SCM and to assess model performance. Results from this study demonstrate several limitations of this SCM-based equilibrium approach. The existence of two physically meaningful equilibrium solutions for a given value of the surface turbulent temperature flux (used as a lower boundary in the SCM) generally results in either a tendency to underestimate stratification or the breakdown of the model because of runaway cooling and collapsed turbulence. Different representations of the geostrophic wind profile accounting for baroclinic effects caused by the strong land–sea temperature gradient at Cabauw are shown to have only a modest influence on the mean wind profile. The local internal boundary layer (IBL) at Cabauw results in a strong tendency for the SCM to overestimate wind speeds in weakly to moderately stable conditions. In very stable conditions (where the IBL influence was low), the equilibrium approach remained limited because of its inability to account for time-evolving phenomena such as the inertial oscillation and the low-level jet.
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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) | 0.001 | 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 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".