Mean Cold Pool Size of Quasi-Equilibrium Convection. Part I: Why Do Cold Pools Collide?
Bibliographic record
Abstract
Abstract Precipitation-driven cold pools play an important role in organizing tropical convection. Previous studies of tropical convection in the radiative–convective equilibrium (RCE) setup found that cold pools tend to collide with each other and trigger new convection. It remains unclear why most cold pools do not have enough space to dissipate without collision. We explain it as the smaller mean cold pool radius Req compared to its maximum potential radius Rmax. The latter denotes the radius needed for a cold pool’s buoyancy deficit to be dissipated by surface heating. Applying an energy balance constraint leads to an analytical solution for their ratio Rmax/Req, which depends on the Bowen ratio, surface precipitation–evaporation ratio, and rain sedimentation efficiency. The theory predicts that in the regime of marine tropical convection where the Bowen ratio is much smaller than one, Req cannot reach Rmax, and cold pools must collide frequently. This prediction is supported by large-eddy simulations using varying rain evaporation rates. In Part II, we combine the energy balance constraint with a convective life cycle model to obtain a theory of the mean cold pool radius Req.
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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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".