Ice Crystal Sizes in High Ice Water Content Clouds. Part II: Statistics of Mass Diameter Percentiles in Tropical Convection Observed during the HAIC/HIWC Project
Bibliographic record
Abstract
Abstract High ice water content (IWC) regions in mesoscale convective systems (MCSs) are a potential threat to commercial aviation, as they are suspected to cause in-service engine power-loss events and air data probe malfunctions. To investigate this, the high-altitude ice crystals (HAIC)/high ice water content (HIWC) projects set up a first field campaign in Darwin (Australia) in 2014. The airborne instrumentation was selected to provide the most accurate measurements of both the bulk total water content (TWC), using a specially developed isokinetic evaporator, and the individual ice crystals properties, using particle imaging probes. This study focuses on determining the size ranges of ice crystals responsible for the mass in high IWC regions, defined here as cloud regions with IWC greater than 1.5 g m−3. It is shown that for high IWC areas in most of the encountered MCSs, median mass diameters (MMDs) of ice crystals range from 250 to 500 μm and decrease with increasing TWC and decreasing temperature. At the same time, the mass contribution of the smallest crystals (below 100 μm) remains generally low (below 15%). In contrast, data from two flight missions in a long-lasting quasi-stationary tropical storm reveal that high IWC values can also be associated with MMDs in the range 400–800 μm and peak values of up to 2 mm. Ice crystal images suggest a major growth contribution by vapor deposition (columns, capped columns) even for such larger MMD values.
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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 | 0.001 |
| 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) | 0.000 | 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 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".