Initiation of Deep Convection over an Idealized Mesoscale Convergence Line
Bibliographic record
Abstract
Abstract This study performs cloud-resolving simulations of cumulus convection over an idealized surface-based convergence zone to investigate the mechanisms and sensitivities of deep convection initiation forced by mesoscale ascent. The surface convergence forms in response to a localized diurnal heating anomaly over an otherwise homogeneous and unheated surface, producing a strong boundary layer updraft over the center of the heat source. This updraft gives rise to a line of cumuli that gradually deepen and, in some cases, transition into deep convection. To statistically investigate the factors controlling this transition, a new thermal-tracking algorithm is developed to follow incipient cumulus cores as they ascend through the troposphere. This tool is used to isolate the impacts of key environmental parameters (cloud-layer lapse rate, midlevel humidity, etc.) and initial core parameters near cloud base (horizontal area, vertical velocity, etc.) on the ultimate cloud-top height. In general, the initial core size determines which thermals in a given cloud field will undergo the deepest ascent, and the sensitivity of cloud depth to initial core parameters increases in environments that are more hostile to deep convection. Diurnal midlevel moistening from detraining cumuli above the convergence line produces a small but robust enhancement in cloud-top height, particularly for smaller cores.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".