Arctic low-level boundary layer clouds: in situ measurements and simulations of mono- and bimodal supercooled droplet size distributions at the top layer of liquid phase clouds
Bibliographic record
Abstract
Abstract. Aircraft borne optical in situ size distribution measurements were performed within Arctic boundary layer clouds with a special emphasis on the cloud top layer during the VERtical Distribution of Ice in Arctic clouds (VERDI) campaign in April and May 2012. An instrumented Basler BT-67 research aircraft operated out of Inuvik over the Mackenzie River delta and the Beaufort Sea in the Northwest Territories of Canada. Besides the cloud particle and hydrometeor size spectrometers the aircraft was equipped with instrumentation for aerosol, radiation and other parameters. Inside the cloud, droplet size distributions with monomodal shapes were observed for predominantly liquid-phase Arctic stratocumulus. With increasing altitude inside the cloud the droplet mean diameters grew from 10 to 20 μm. In the upper transition zone (i.e., adjacent to the cloud-free air aloft) changes from monomodal to bimodal droplet size distributions (Mode 1 with 20 μm and Mode 2 with 10 μm diameter) were observed. It is shown that droplets of both modes co-exist in the same (small) air volume and the bimodal shape of the measured size distributions cannot be explained as an observational artifact caused by accumulating data point populations from different air volumes. The formation of the second size mode can be explained by (a) entrainment and activation/condensation of fresh aerosol particles, or (b) by differential evaporation processes occurring with cloud droplets engulfed in different eddies. Activation of entrained particles seemed a viable possibility as a layer of dry Arctic enhanced background aerosol (which was detected directly above the stratus cloud) might form a second mode of small cloud droplets. However, theoretical considerations and model calculations (adopting direct numerical simulation, DNS) revealed that, instead, turbulent mixing and evaporation of larger droplets are the most likely reasons for the formation of the second droplet size mode in the uppermost region of the clouds.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".