Bibliographic record
Abstract
Abstract Tropical cyclone precursor vortices are convectively coupled vortices. They undergo significant changes in size and intensity before transitioning to a mature hurricane or typhoon. This paper designs a stochastic quasi-2D model to study the vortices’ formation and interaction. Based on the diagnostic result of a cloud-permitting simulation, we parameterize deep convection as random pulses whose probability of occurrence depends on the spatially smoothed vorticity. The dependence of convective probability on the vorticity field represents the mesoscale feedback. The smoothing represents the spontaneous spreading of convective activity by cold pools and other processes. Simulations show that the system exhibits two stages: the vortex formation stage and the vortex interaction stage. The vortex formation stage features the stochastic nucleation of vortices and their subsequent growth via the mesoscale feedback. The growth of mesoscale vorticity magnitude undergoes a power-law growth and then transitions to exponential growth. An analytical theory is proposed to capture this transition. The vortex interaction stage features vortex merging. The vortex size grows due to merging and spontaneous spreading of convective activity. When the vortex size grows sufficiently large, it is squeezed by the convection-induced convergent flow, which converts the growth in size to the growth in vorticity magnitude. This adjustment process corresponds to a bidirectional kinetic energy transfer, with the rotational wind producing an upscale energy transfer and the convergent wind producing a downscale energy transfer. This quasi-2D model provides a simple framework for understanding the multiscale interaction in tropical cyclogenesis.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".