Dynamical influence of large valleys on the propagation of coastally trapped disturbances
Bibliographic record
Abstract
Abstract The influence on the propagation of coastally trapped disturbances (CTD) of large gaps in the bounding coastal mountains is studied via application of the Colorado State University Regional Atmospheric Modeling System (RAMS). By large gap it is meant that the width is of order 25–50% of the Rossby radius. In order to isolate the effects of the gaps, idealised simulations are used. Thus, RAMS is configured with a straight north‐south coastline with a ramp‐shaped coastal mountain range in which a gap is inserted and a gravity current‐like CTD generated through cooling the low level atmosphere at the southern end of the domain. Radiative, land surface and cloud parameterisations are turned off to avoid sea breeze generation and other thermal effects and to focus on the dynamical effect of the gaps. Four simulations are conducted: no gap in the coastal mountain barrier, an insertion of a narrow (80 km) gap, a wide (160 km) gap, and an idealised island in the barrier. It is found that the gaps influence CTD propagation by effectively stalling the propagation at their mouth for a period and subsequently weakening the gravity current‐like features (vertical extent, strength and abruptness of the wind and stratification changes at the leading edge). The idealised island has a less obvious impact since it is bounded by relatively narrow straits and does not allow communication with the model interior like the gaps do. Although lack of data and the nature of the idealised simulation prevents a detailed assessment of the simulations against real cases, qualitative comparisons with observed events in western North America and south‐eastern Australia are made. Application of these results to air quality and forecasting issues is discussed. Copyright © 2000 Royal Meteorological Society
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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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".