Convective Cloud Bands Downwind of Mesoscale Mountain Ridges
Bibliographic record
Abstract
Abstract Elongated and quasi-stationary cloud bands capable of producing heavy precipitation have recently been observed in the lee of midlatitude mountain ridges. Herein, idealized explicit-convection simulations are used to investigate such bands. A methodical sampling of environmental parameter space reveals that the bands are favored by a multilayer upstream static-stability profile, with a conditionally unstable midlevel layer overlying an absolutely stable surface-based layer. Such profiles promote the formation of leeside hydraulic jumps, with deep upright ascent that initiates elevated moist convection. Over smooth ridges, isolated bands develop past each ridge end due to a local superposition of cross-barrier and along-barrier pressure gradients. This superposition enhances leeside vertical displacements compared to parcels traversing the ridge midsection. In the Northern Hemisphere, the Coriolis force favors the left band over the right band (relative to the incoming flow) due to opposite-signed relative-vorticity perturbations past the two ridge ends. Whereas the negative vorticity anomaly past the left end enhances forcing for ascent, the positive vorticity anomaly past the right end suppresses it. For the environmental flows considered herein, the simulated bands are the most persistent over medium-height (1.5-km high) ridges, which force stronger leeside ascent than taller or shorter ridges. Over more rugged terrain, additional bands form past deep gaps or valleys, again due to a local superposition of horizontal pressure gradients. In contrast to some recent studies of orographic cloud bands, these simulated bands owe their existence to the release of moist static instability, indicating that neither slantwise nor inertial instability is required for their formation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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 teacher head, 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".