The Influence of Lateral Spreading upon Solitary Wave Formation by Internal Tides
Bibliographic record
Abstract
Abstract As internal tides propagate in the ocean, they carry and dissipate energy over hundreds and even thousands of kilometers. In relatively shallow seas, the low-vertical-mode internal tide can evolve to form solitary waves whose surface signature can be detected by satellites as regions of high and low surface reflectance. To gain insight into what processes lead to the observation of internal solitary waves by satellites, we perform fully nonlinear simulations in three dimensions to examine the evolution of horizontally propagating, vertical mode 1 internal tides as it depends on wave amplitude, ocean depth, and the spanwise extent of the waves. The background stratification is set up according to measurements in the South China Sea. The spanwise evolution of the three-dimensional waves is examined in terms of the lateral spreading, radius of curvature, and sea surface signature corresponding to a threshold in the surface horizontal convergent and divergent flow. We find that the lateral spreading of the waves varies little with initial wave amplitude and ocean depth but decreases exponentially with increasing initial lateral extent. The relative deepening of isopycnals associated with solitary wave formation is greater if the initial wave amplitude is larger, but the deepening is less for waves in deeper water and if their initial lateral extent is smaller resulting in more rapid lateral spreading. Thus, for solitary waves to create a sea surface signature visible to satellites, they must be generated with sufficiently large amplitude and a sufficiently large initial lateral extent before propagating in a sufficiently shallow ocean.
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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 | 0.002 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".