Marine Boundary Layer Cloud Observations in the Azores
Bibliographic record
Abstract
The recent deployment of the Atmospheric Radiation Measurement Program (ARM) Mobile Facility at Graciosa Island, Azores, in the context of the Clouds, Aerosol and Precipitation in the Marine Boundary Layer (CAP-MBL) field campaign added the most extensive (19 months) and comprehensive dataset of marine boundary layer (MBL) clouds to date. Cloud occurrence is high (60%–80%), with a summertime minimum. Liquid precipitation is frequently present (30%–40%), mainly in the form of virga. Boundary layer clouds are the most frequently observed cloud type (40%–50%) with a maximum of occurrence during the summer and fall months under the presence of anticyclonic conditions. Cumulus clouds are the most frequently occurring MBL cloud type (20%) with cumulus under stratocumulus layers (10%–30%) and single-layer stratocumulus (0%–10%) following in frequency of occurrence. A stable transition layer in the subcloud layer is commonly observed (92% of the soundings). Cumulus cloud bases and stratocumulus cloud tops correlate very well with the top of the transition layer and the inversion base, respectively. Drizzling stratocumulus layers are thicker (350–400 m) and have higher liquid water path (75–150 g m −2 ) than their nondrizzling counterparts (100–250 m and 30–75 g m −2 , respectively). The variance of the vertical air motion is maximum near the cloud base and is higher at night. The updraft mass flux is around 0.17 kg m −2 s −1 with 40%–60% explained by coherent updraft structures. Despite a high frequency of stratocumulus clouds in the Azores, the MBL is almost never well mixed and is often cumulus coupled.
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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) | 0.001 | 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".