An analysis of the Hines and Warner–McIntyre–Scinocca non‐orographic gravity wave drag parametrizations
Bibliographic record
Abstract
A column model based on CIRA wind and temperature profiles is employed to assess the characteristics of the Hines Doppler‐spread and Warner–McIntyre–Scinocca (WMS) non‐orographic drag parametrizations for internal gravity waves. The “Alexander–Dunkerton” variant of the WMS scheme is also briefly considered. This study goes into more detail than previous comparisons by performing a spectral analysis of the momentum deposition and drag, and by examining the ability of each scheme to reproduce a high vertical wavenumber tail consistent with atmospheric measurements. We find several undesirable characteristics in the drag produced by the Hines scheme. For typical midlatitude wind profiles, it produces an abrupt onset of large accelerations that vary strongly from one layer to the next. It is also unable to reproduce spectra consistent with observed wave saturation at high vertical vertical wavenumbers, even for the windless case. The WMS scheme has the ability to reproduce the observed spectral tail for the case of no background wind. In the presence of typical CIRA midlatitude background winds, however, it is demonstrated that the WMS saturation threshold generally does not follow the observed spectral behaviour at high vertical wavenumbers, except for one specific frequency dependence not considered in previous work. Doppler shifting is also found to interfere with the production of high‐wavenumber spectral tails consistent with observations.
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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 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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".