Convective Momentum Transport and Multiscale Organization in Simulated Shear Parallel Mesoscale Convective Systems
Bibliographic record
Abstract
Abstract The way in which moist convection interacts with large scale flows is a major contemporary research issue. Organized mesoscale systems are, in particular, important for the interactions between convection and the ambient shear. Here we present numerical simulations of mesoscale systems evolving in a background shear using the Research and Weather Forecasting (WRF) model. We are particularly interested in the long time integration, allowing the systems to repeatedly develop and die and effectively interact with the background shear. Starting with a typical African and equatorial jet-shear, the simulated solution goes through various phases or stages. First, a transient state, consisting of scattered squall-like systems that are aligned perpendicular to the background shear, develops and then evolves into a regime of multiscale mesoscale systems with large stratiform anvils. During the latter period the background wind changes substantially through the effect of both up scale and down scale convective momentum transport (CMT). At this stage, the systems become aligned parallel to the wind shear, with elongated stratiform anvils in which meso-beta scale convective cells evolve and propagate in the shear direction, relative to the stratiform anvils. These results are reminiscent of the development of shear parallel mesoscale convective systems observed for instance in the Eastern Pacific ITCZ and corroborate recent theoretical results obtained with a simple multi-cloud model. As such they have important implications for the parameterization of CMT in climate models.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".