Beyond ray tracing for internal waves. I. Small-amplitude anelastic waves
Bibliographic record
Abstract
We compute the transmission of small-amplitude two-dimensional anelastic internal waves in nonrotating inviscid fluid having arbitrarily specified stratification and background velocity. Whereas stability analyses of the flow involve solving an eigenvalue problem that relates the frequency to horizontal wavenumber, we focus upon the evolution of waves incident from below with independently specified frequency and horizontal wavenumber. A numerical method is developed to ensure that the wave field in the upper domain corresponds only to upward-propagating transmitted waves. Two particular applications are discussed. First, internal waves incident upon a piecewise-linear shear layer are examined and their transmission is computed as a function of the bulk Richardson number Rib and the ratio of the density scale height relative to the depth of the shear layer. The waves transmit partially across critical levels if they coincide with heights where the gradient Richardson number is less than 1/4. Transmission is larger if Rib is smaller. Decreasing the density scale height reduces the frequency and wavenumber range over which internal waves propagate, but this does not significantly affect the magnitude of transmission. Second, internal waves generated by flow over Jan Mayen island are examined. Although the waves are ducted, the waves are found to transmit partially through the top of the duct. The results are used to interpret the discrepancy between predictions of the Fourier-ray tracing model and fully nonlinear numerical simulations of Eckermann et al. [Mon. Weather Rev. 134, 2830 (2006)].
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.004 | 0.001 |
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".