Cloud climatology at the Southern Great Plains and the layer structure, drizzle, and atmospheric modes of continental stratus
Bibliographic record
Abstract
Long‐term (6.5 years) cloud observations from the Atmospheric Radiation Measurements (ARM) program Southern Great Plains (SGP) climate research facility in Oklahoma are used to develop detailed cloud climatology. Clouds are classified with respect to their altitude (low, middle, and high), vertical development, and the presence of multilayer clouds. Single‐layered cirrus, middle or low clouds were observed a total of 23% of the time the MilliMeter Cloud Radar (MMCR) was operating, and multilayer clouds were observed 20.5% of the time. Boundary layer clouds exhibit the strongest seasonal variability because of continental stratus associated with midlatitude frontal systems. Cirrus clouds are the most frequently observed cloud type and exhibit strong seasonal variability in cloud base height (higher cloud base during the summer months) and relatively constant cloud fraction. The majority of middle‐level clouds are shallow with vertical extent less than 1 km. No strong seasonal cycle in the fractional coverage of multilayer clouds is observed. Continental stratus clouds exhibit strong seasonal variability with maximum occurrence during the cold seasons. Nondrizzling stratus clouds exhibit a bimodal seasonal variability with maximum occurrences in the fall and spring, while drizzling stratus occur most frequently in the winter. Thermodynamic and dynamic variables from soundings and the European Centre for Medium‐Range Weather Forecasts Model (ECMWF) analyses at the SGP site illustrate an interesting coupling between strong large‐scale forcing and the formation of single‐layered (no other cloud layer is present) continental stratus clouds. Single‐layered stratus clouds (drizzling and nondrizzling) exhibit a strong correlation with positive ω at 500 mbar and strong northerly flow.
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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 | 0.001 | 0.001 |
| 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) | 0.003 | 0.001 |
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".