Generation and Trapping of Gravity Waves from Convection with Comparison to Parameterization
Bibliographic record
Abstract
Abstract Some parameterizations of gravity wave mean flow forcing in global circulation models (GCMs) add realism by describing wave generation by tropospheric convection. Because the convection in GCMs is itself a parameterized process, these convectively generated wave parameterizations necessarily use many simplifying assumptions. In this work, the authors use a realistic simulation of wave generation by convection described in previous work, which was validated by observations from the Darwin Area Wave Experiment (DAWEX), to test these assumptions and to suggest some possible improvements to the parameterizations. In particular, the authors find that wave trapping in the troposphere significantly modifies the spectrum of vertically propagating waves entering the stratosphere above convective wave sources, and offer a linear method for computing wave transmission and reflection effects on the spectrum suitable for inclusion in the parameterizations. The wave fluxes originate from both a time-varying heating mechanism and an obstacle effect mechanism acting in the simulation. Methods for including both mechanisms in the parameterizations are described. Waves emanating from the obstacle effect remain very sensitive to the depth of penetration of latent heating cells into an overlying shear zone, which will continue to make it difficult to accurately parameterize in a GCM where the convective cells are not resolved.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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".