The role of orography in convection initiation over Hainan Island
Bibliographic record
Abstract
Abstract Semi‐idealized simulations are conducted to investigate orographic impacts on deep‐convection initiation (CI) over Hainan Island, prior to island‐scale sea‐breeze convergence. A control simulation and sensitivity tests are conducted to reproduce Hainan's characteristic diurnal cycle and quantify CI‐related processes. In these tests, diurnal heating is found to be necessary for CI, highlighting the importance of thermal (rather than mechanical) forcing. Analysis focuses on a set of Gaussian mountain (GM) simulations that simplify the island terrain greatly but reproduce the CI from the full‐terrain simulations reasonably. The heated GM case develops a much stronger updraft and more favorable thermodynamic conditions for CI in the mountain lee than corresponding unheated cases. This difference stems primarily from up‐mountain directed buoyancy forcing, which opposes the decelerative pressure gradient force (PGF) and friction over the windward slope, allowing more low‐level flow to ascend the mountain. Due to this increased windward adiabatic ascent, the PGF strengthens over the crest to drive stronger cross‐barrier flow. In the lee, the buoyancy and PGF act in concert to force a strong flow reversal and a deep layer of moist, humid air ascending the slope. Along the collision zone between the cross‐barrier flow over the crest and the leeside reversed flow, an intense subcloud updraft forces CI. In contrast, simulations without diurnal heating produce stronger leeside drying and much weaker leeside updrafts in a shallower boundary layer, which fails to cause CI. The insights derived from the GM simulations carry over to simulations with the full Hainan terrain.
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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 | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".