Dependence of Ice Microphysical Properties On Environmental Parameters: Results from HAIC-HIWC Cayenne Field Campaign
Bibliographic record
Abstract
Abstract High Ice Water Content (HIWC) regions above tropical mesoscale convective systems are investigated using data from the second collaboration of the High Altitude Ice Crystals and High Ice Water Content projects (HAIC-HIWC) based in Cayenne, French Guiana in 2015. Observations from in-situ cloud probes on the French Falcon 20 determine the microphysical and thermodynamic properties of such regions. Data from a 2-D stereo probe and precipitation imaging probe show how statistical distributions of ice crystal mass median diameter (MMD), ice water content (IWC), and total number concentration (Nt) for particles with maximum dimension (Dmax) > 55 μm vary with environmental conditions, temperature (T), and convective properties such as vertical velocity (w), MCS age, distance away from convective peak (L), and surface characteristics.IWCis significantly correlated withw, whereasMMDdecreases andNtincreases with decreasingTconsistent with aggregation, sedimentation and vapor deposition processes at lower altitudes.MMDtypically increases withIWCwhenIWC< 0.5 g m-3, but decreases withIWCwhenIWC> 0.5 g m-3for -15 °C ≤T≤ -5 °C. Trends also depend on environmental conditions, such as presence of convective updrafts that are the ice crystal source,MMDbeing larger in older MCSs consistent with aggregation and less injection of small crystals into anvils, andIWCs decrease with increasingLat lowerT. The relationship betweenIWCandMMDdepends on environmental conditions, with correlations decreasing with decreasingT. The strength of correlation betweenIWCandNtincreases asTdecreases.
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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 | 0.001 | 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 | 0.001 | 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".