Orographic Effects on Supercell: Development and Structure, Intensity and Tracking
Bibliographic record
Abstract
Orographic effects on tornadic supercell development, propagation, and structure are investigated using Cloud Model 1 (CM1) with idealized bell-shaped mountains of various heights and a homogeneous fluid flow with a single sounding. It is found that blocking effects are dominative compared to the terrain-induced environmental heterogeneity downwind of the mountain. The orographic effect shifts the storm track towards the left of storm motion, particularly on the lee side of the mountain, when compared to the storm track in the control simulation with no mountain. The terrain blocking effect also enhances the supercells inflow, which was increased more than one hour before the storm approached the terrain peak. The enhanced inflow allows the central region of the storm to exhibit clouds with a greater density of hydrometeors than the control. Moreover, the enhanced inflow increases the areal extent of the supercells precipitation, which, in turn enhances the cold pool outflow serving to enhance the storm’s updraft until becoming strong enough to undercut and weaken the storm considerably. Another aspect of the orographic effects is that downslope winds produce or enhance low-level vertical vorticity directly under the updraft when the storm approaches the mountain peak.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".