A Modeling Study of the Topographic Effects on Shallow Convective Clouds
Bibliographic record
Abstract
Abstract Shallow convective clouds (SCCs) play important roles in the Earth system. Previous studies mostly focus on SCCs over the oceans or plains. It is unclear how topography affects SCCs. In this study, the impacts of isolated ridges on the development of SCCs are investigated using large‐eddy simulations, where the maximum height and the half‐width of the ridge are systematically varied. In all simulations, the potential temperature over the ridge top is higher than over the plain, and the difference increases with the volume of the ridge. Upslope winds are only produced in simulations where the maximum slope angle is >0.5°. The vapor transport by upslope winds tends to increase the humidity over the ridge top. On the contrary, the dry air entrained from above the convective boundary layer tends to decrease the humidity over the ridge top. The upslope winds from the two sides of the ridge collide near the ridge top. This produces wide updrafts, and thereby facilitates the development of SCCs. As the ridge geometry varies, the variation of the depth of SCCs is collectively determined by the variations of the temperature, humidity, and updrafts. The depth of the SCCs increases with the maximum height of the ridge. It also increases as the half‐width increases from 2 to 8 km, but only slightly changes as the half‐width further increases to 16 km. The results of this study can potentially be used to implement the topographic effects in the parameterizations of SCCs.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".