The Stochastic Spinup of Vorticity in Spontaneous Tropical Cyclogenesis
Bibliographic record
Abstract
Abstract Cloud-permitting simulations have shown that tropical cyclones (TCs) can form spontaneously in a quiescent environment with uniform sea surface temperature. While several mesoscale feedbacks are known to amplify an existing midlevel vortex, how the noisy deep convection produces the initial midlevel vortex remains unclear. This paper develops a theoretical framework to understand the evolution of the midlevel mesoscale vorticity’s histogram in the first two days of spontaneous tropical cyclogenesis, which we call the “stochastic spinup stage.” The mesoscale vorticity is produced by two random processes related to deep convection: the random stretching of planetary vorticity f and the tilting of random vertical shear. With the central limit theorem, the mesoscale vorticity is modeled as the sum of three independent normal distributions, which include the cyclones produced by stretching, cyclones produced by tilting, and anticyclones produced by tilting. The theory predicts that the midlevel mesoscale vorticity obeys a normal distribution, and its standard deviation is universally proportional to the square root of the domain-averaged accumulated rainfall, agreeing with simulations. The theory also predicts a critical latitude below which tilting is dominant in producing mesoscale vorticity. Treating the magnitude of random vertical shear as a fitting parameter, the critical latitude is shown to be around 12°N. Because the magnitude of vertical shear should be larger in the real atmosphere, this result suggests that tilting is an important source of mesoscale vorticity fluctuation in the tropics.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".