Secondary ice processes: Results from seven airborne field campaigns
Bibliographic record
Abstract
<!--!introduction!--> The secondary ice process (SIP) in cumulus clouds is a major microphysical process, which can result in rapid enhancement of ice particle concentration in the presence of pre-existing ice. We present results of airborne investigations of Cumulus Congestus clouds (CuCg) from seven field campaigns spanning the time period from 2011 – 2022: ICE-T, SEAC4RS, UAEREP (2), CAMP2Ex, SPICULE and ESCAPE. The SPEC Learjet research aircraft participated in all of the field projects accompanied by other research aircraft in five of the seven projects. The results presented are a synthesis of measurements of marine and continental CuCg from the Caribbean, Southeast U.S., South China Sea, UAE and U.S. High Plains. The composite data indicate that after the first primary ice typically formed at temperatures colder than about -8°C, subsequent production of ice via the fractured frozen drop (FFD) SIP was strongly related to the concentration of supercooled large drops (SLDs), with diameters from about 0.2 mm to a few mm. The concentration of SLDs is directly linked to the rate of collision-coalescence, which in turn depends primarily on the subcloud aerosol size distribution and cloud base temperature. Once the production of small ice is initiated via the FFD SIP, the differential in fall velocities between small ice and SLDs creates an avalanche process that rapidly freezes the supercooled region of an updraft core. Other SIP mechanisms, Hallett-Mossop and ice-ice collisions, are also considered. These studies provide the framework for a consistent picture of FFD SIP parameterization in weather prediction models.
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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) | 0.001 | 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".