Thermally-driven circulation and convection over a mountainous tropical island
Bibliographic record
Abstract
Observational data from the 2011 Dominica Experiment (DOMEX) and cloud-resolving numerical simulations are exploited in order to acquire a better understanding of controlling parameters of thermally-driven circulation and convection over a mountainous tropical island. Four weak (<4 m/s) background wind days are investigated to obtain a preliminary diagnosis of the conditions favorable for diurnally-forced shallow cumulus convection over the island. The observations suggest that the degree of solar heating before the first cumulus development largely controls the amount of low-level forcing and the vigor of shallow cumulus, rather than the moist stability of the background flow.A “golden” case from DOMEX with a clear diurnal cycle in cumulus convection is studied using quasi-idealized numerical simulations (with full model physics and a realistic terrain profile) to better understand the mechanisms and sensitivities of island thermal circulations and cumulus convection. Simulations at different grid spacings reveal that large-eddy grid spacing (~100 m) provides the most accurate representation of the in-situ measurements from DOMEX and other observations. Sensitivity tests reveal that mechanical forcing played little role in convection initiation on this day since the Froude number (Fr) was < 1. Surprisingly, even though thermal circulations develop earlier over a mountainous island, they are ultimately weaker than those over a flat island possibly due to their elevated outflows undergo stable descent over a high terrain. Background wind velocity also has a significant impact on thermal circulations, which tend to weaken as the cross-barrier wind increases. Cloud shadowing and precipitation both have a negative feedback on thermal circulations, with the former being the stronger mechanism. In addition, cloud latent-heat release over the island strengthens thermal circulations, which also explains why circulations intensify when cumulus vigor is enhanced by greater moist instability.The simulations allow for the evaluation of thermal-circulation-strength predictions from thermodynamic heat engine theory. While the theory predicts the strength of thermal circulations reasonably well over a mountainous terrain, it fails to capture the sensitivity to terrain height likely because its assumption that the entire circulation is confined within the mixed layer is invalid.
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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 | 0.000 |
| 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) | 0.000 | 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".