Modeling Study of Ice Formation in Warm-Based Precipitating Shallow Cumulus Clouds
Bibliographic record
Abstract
Abstract Observations of large concentrations of ice particles in the dissipating stage of warm-based precipitating shallow cumulus clouds point to the limitations of scientists’ understanding of the physics of such clouds and the possible role of cloud dynamics. The most commonly accepted mechanisms of ice splinter production in the riming process have limitations to properly explain the rapid production of ice bursts. A more detailed description of the temporal and spatial evolution of hydrometeors and their interaction with cloud condensation nuclei and ice nuclei is needed to understand this phenomenon. A cloud model with bin-resolved microphysics can describe the time-dependent evolution of liquid droplets and ice particles and provide insights into how the physics and dynamics and their interaction may result in ice initiation and ice multiplication. The authors developed a 1.5-dimensional nonhydrostatic convective cloud and aerosol interaction model with spectral (bin) microphysics. The number and mass concentrations of aerosols, including ice nuclei and cloud condensation nuclei, were explicitly followed. Since both in situ observations of bioaerosols and laboratory experiments pointed to efficient nucleation capabilities at relative warm temperatures, it was assumed that ice-nucleating bioaerosols are involved in primary ice particle formation in condensation and immersion modes. Results show that bioaerosols can be the source of primary ice pellets, which in turn lead to high ice concentrations.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".