Convectively Transported Water Vapor Plumes in the Midlatitude Lower Stratosphere
Bibliographic record
Abstract
Abstract Deep convective overshooting has been shown to transport water vapor into the midlatitude lower stratosphere. However, it has not been demonstrated how the convective water vapor plumes evolve after the overshoots collapse. Furthermore, there is a lack of characterization of the convective water vapor plumes, nor is it clear whether satellite instruments can observe the characteristics. We use a high‐resolution numerical weather prediction model to study a convective system over North America. Multiple overshoots transport water vapor in the overworld stratosphere, forming a moist layer between 16.2 and 16.8 km (389.8–399.7 K), with horizontal diameters of about 300–400 km, and a maximum water vapor mixing ratio of 10.0 ppmv (4.3 ppmv anomaly). Lagrangian trajectories and mass integrations show the overworld water vapor plumes are maintained after the convective system weakens. In the lowermost stratosphere (LMS), water vapor plumes are less stable and ice is present, because there is perturbation by ongoing convection. Lagrangian trajectories and mass integrations show the LMS parcels partly return to the troposphere, and that the LMS water vapor mass is reduced by half after the convection weakens. On average, the LMS moistening is between 15.0 and 15.8 km (362.6–382.0 K), with horizontal diameters of about 150 km, and a maximum water vapor mixing ratio of 31.1 ppmv (18.4 ppmv anomaly). Although current satellites have difficulty observing the fine structure of the convective water vapor plumes, a new satellite instrument under development (SHOW) with 1‐km vertical and 100‐km horizontal resolution will be able to verify the plume characteristics.
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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 | 0.000 |
| 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 | 0.000 | 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".