The Diurnal Variation of Elevated Convection over the Great Plains
Bibliographic record
Abstract
Abstract At night, the low-level water vapor and mass convergence that precedes convective rainfall is typically elevated about 1.5 km above the surface. We use 15 summers (2009–23) of meteorological data from the Rapid Refresh/Rapid Update Cycle analysis, and rainfall data from the Integrated Multi-satellitE Retrievals for GPM (IMERG) dataset, to show that elevated rain events over the Great Plains can be most clearly distinguished from surface-based events during their developmental stage, 0–3 h prior to peak rainfall. The upward shift in low-level water vapor and mass convergence that is a characteristic of elevated convection during the development phase usually occurs after local midnight and is accompanied by an increase in low-level stability, convective inhibition (CIN), and in the height of maximum convective available potential energy (CAPE). While the nocturnal low-level jet (NLLJ) plays a major role in promoting nocturnal convection by modifying the low-level stability and providing favorable wind and moisture conditions, the region of elevated nocturnal convection extends over much of the interior of North America, far beyond the immediate influence of the NLLJ. Upward shifts in water vapor convergence and CAPE also appear in rain-weighted composites against the mean low-level lapse rate. Water vapor convergence in the layer 1.5–3 km above the surface is more efficient at promoting nocturnal convection when it is positively correlated with CAPE at a similar altitude. Finally, we show that elevated nocturnal rain events exhibit stronger low-level warm anomalies, and weaker cold anomalies, than surface-based nocturnal rain events. Significance Statement This paper investigates elevated convection over the Great Plains, which involves the entrainment of air primarily from above the near-surface layer. Elevated convection becomes more common at night due to diurnal changes in the thermal structure and moisture. Nocturnal convection in this region, often elevated, is less predictable and requires further study. Many case studies and short-term field campaigns have examined nocturnal convection, but few have analyzed its vertical structure using large-scale datasets over many years. This study utilizes 15 summers of data from the Rapid Refresh/Rapid Update Cycle (RAP/RUC) analysis and the Integrated Multi-satellitE Retrievals for GPM (IMERG) rainfall dataset.
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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".