Investigating the Effects of Orography and Ambient Wind on Deep Convection over Tropical Islands
Bibliographic record
Abstract
Abstract Using an observation–model synthesis, we investigate variations of cumulus convection over islands with varying characteristics and under varying environmental conditions to clarify controls on convective vigor. In this paper, we define the intensity of deep convection as the average 40-dBZ-radar echo-top height or 85-GHz brightness temperature among raining precipitation features having 40-dBZ echoes (RPF40s) and the frequency of deep convection as the fraction of RPF40s to the total number of precipitation features. Examination of the Tropical Rainfall Measuring Mission (TRMM) satellite database (1994–2015) of 272 tropical and subtropical islands reveals a modest weakening of convective intensity with increased terrain height h or ambient wind U (for a given island area A) and a strengthening with increasing A. Quasi-idealized, convection-permitting simulations broadly reproduce these sensitivities to h and A, but not that to U. In both observations and simulations, intensity increases with the island-averaged convective available potential energy (CAPE). Because CAPE generally decreases over taller islands that protrude deeper into the free troposphere, convective intensity varies inversely with h. The frequency increases with the total island area over which both large CAPE and strong near-surface horizontal convergence coincide. This trend favors higher frequencies over larger islands with complex (but shallow) terrain. The model’s inability to reproduce the observed decrease of convective intensity with U stems from a negative observed correlation between CAPE and U that was neglected in the simulations. Thus, as with h, the negative observed trend between intensity and U ultimately stems from the impacts of CAPE on convective intensity.
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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 | 0.000 |
| Scholarly communication | 0.001 | 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".