Impacts of wind profile shear and curvature on the parameterized orographic gravity wave stress in the Weather Research and Forecasting model
Bibliographic record
Abstract
Abstract The parameterization of orographic gravity wave drag (OGWD) in the Weather Research and Forecasting model is extended by including the second‐order Wentzel–Kramers–Brillouin (WKB) corrections to the surface wave momentum flux (SWMF) caused by wind profile shear (WSHR) and curvature (WCUR) effects. Simulations of the atmospheric circulation are performed to study the behaviour and impact of the WKB corrections. In January, the SWMF is weakened in the Northern Hemisphere (NH) midlatitudes by the WSHR term, while the WCUR acts to enhance the SWMF over Antarctica. In July, the WSHR corrections are similar to those in January, whereas the WCUR term produces corrections of opposite sign in the high latitudes of each hemisphere. The latter is attributed to the increase of near‐surface winds in the cold season which reverses the low‐level wind profile curvature. The seasonal reversal of the WCUR term contradicts previous findings obtained from offline evaluation using reanalysis datasets. This may be due to the different OGWD parameterization schemes used, or it may suggest a sensitivity to the height at which the wind profile effects are evaluated. Changes in the SWMF can affect the vertical distribution of parameterized OGWD. In January, the OGWD in the NH midlatitudes is decreased in the lower troposphere but increased in the upper troposphere. This is because a reduced SWMF inhibits wave breaking in the lower troposphere. Therefore, more wave momentum flux (WMF) is transported to the upper troposphere which enhances wave breaking there. The increased upper‐tropospheric wave breaking in turn decreases the WMF propagating into the stratosphere where the OGWD is reduced. In July, the reduction of SWMF over Antarctica is more notable than in the NH midlatitudes in January. Consequently, the OGWD is weakened in the upper troposphere over Antarctica.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".