Tropical Cyclogenesis under the Influence of an Upper-Tropospheric Cold Low in Idealized Numerical Simulations
Bibliographic record
Abstract
Abstract Previous studies have indicated that approximately 20% of tropical cyclones (TCs) form under the influence of an upper-level disturbance globally. This study investigates tropical cyclogenesis (TCG) within a lower-tropospheric wave pouch (WP) in the presence of an upper-tropospheric cold low (CL) using idealized simulations in a resting environment. The β effect induces distinctly different wind and humidity asymmetries in the CL and WP owing to their differing vertical and horizontal structures. The CL exhibits a wetter western sector and a drier eastern sector, with stronger winds in the eastern sector. In contrast, the eastern sector of the WP, where a mesoscale vortex emerges, is slightly wetter than its western sector. This results in the strongest dry-air intrusion when the WP is positioned northeast of the CL and the weakest dry-air intrusion when the WP is located southwest of the CL. Additionally, when the WP is positioned northeast of the CL, the vortex developing from their vorticity superposition competes with the WP-induced vortex. The anticyclone southeast of the CL, induced by the Rossby energy dispersion, disrupts vorticity aggregation and hinders TCG. In contrast, when the WP is located southwest of the CL, the superposition of the WP and CL vorticity accelerates TCG. These combined mechanisms make the southwest quadrant of the CL the most favorable region for TCG, while the northeast quadrant being the least favorable. The sensitivity of TCG to the CL depth and the CL–WP separation distance are also investigated through further experiments.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".