Matched asymptotics of a hurricane boundary layer
Bibliographic record
Abstract
<!--!introduction!--> Tropical cyclones are complex weather systems that result from the conversion of latent energy into kinetic energy. The boundary layer on top of the ocean surface plays a crucial role in the intensification process, acting as a transport layer for moisture, energy, and momentum. The feedback between the balanced flow in the bulk vortex and moisture fed into the vortex through frictional mass transport in the boundary layer is still not fully understood. This talk presents the results of a simplified model based on matched asymptotic analysis of a boundary layer beneath a symmetric tropical cyclone. The model introduces the concept of a convection-controlling layer (CCL) that couples the bulk vortex and the frictional layer, allowing for consistent matching of the two regimes. The CCL acts as a transition layer, modulating mass fluxes and translating the balanced gradient-wind flow that forces the boundary layer. The presentation provides insights into the asymptotic modeling techniques and shows physically relevant solutions of the double-boundary layer approach. The approach is only valid for symmetric vortices, but there is an outlook for extension to vortices under the influence of vertical wind shear.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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) | 0.004 | 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".