Ice Crystal Size Distributions in Tropical Mesoscale Convective Systems in the Vicinity of Darwin, Australia: Results from the HAIC/HIWC Campaign
Bibliographic record
Abstract
Abstract Total ice water content (IWC) derived from an isokinetic evaporator probe and ice crystal particle size distributions (PSDs) measured by a two-dimensional stereo probe and precipitation imaging probe installed on an aircraft during the 2014 European High Altitude Ice Crystals–North American High IWC field campaign (HAIC/HIWC) were used to characterize regions of high IWC consisting mainly of small ice crystals (HIWC_S) with IWC ≥ 1.0 g m−3 and median mass diameter (MMD) < 0.5 mm. A novel fitting routine developed to automatically determine whether a unimodal, bimodal, or trimodal gamma distribution best fits a PSD was used to compare characteristics of HIWC_S and other PSDs (e.g., multimodality, gamma fit parameters) for HIWC_S simulations. The variation of these characteristics and bulk properties (MMD, IWC) was regressed with temperature, IWC, and vertical velocity. HIWC_S regions were most pronounced in updraft cores. The three modes of the PSD reveal different dominant processes contributing to ice growth: nucleation for maximum dimension D < 0.15 mm, diffusion for 0.15 < D < 1.0 mm, and aggregation for D > 1.0 mm. The frequency of trimodal distributions increased with temperature. The volumes of equally plausible parameters derived in the phase space of gamma fit parameters increased with temperature for unimodal distributions and, for temperatures less than −27°C, for multimodal distributions. Bimodal distributions with 0.4 mm in the larger mode were most common in updraft cores and HIWC_S regions; bimodal distributions with 0.4 mm in the smaller mode were least common in convective cores.
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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) | 0.001 | 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".