Statistical Analysis of Ice Microphysical Properties in Tropical Mesoscale Convective Systems Derived from Cloud Radar and In-Situ Microphysical Observations
Bibliographic record
Abstract
Abstract. This study presents a statistical analysis of the properties of ice hydrometeors in tropical mesoscale convective systems observed during four different aircraft campaigns. Among the instruments on board the aircraft, we focus on the synergy of a 94 GHz cloud radar and 2 optical array probes (OAP; measuring hydrometeor sizes from 10 µm to about 1 cm). For two campaigns, an accurate simultaneous measurement of the ice water content is available, while for the two others, ice water content is retrieved from the synergy of the radar reflectivity measurements and hydrometeor size and morphological retrievals from OAP probes. The statistics of ice hydrometeor properties is calculated as a function of radar reflectivity factor measurement percentiles and temperature. Hence, MCS microphysical properties (ice water content, visible extinction, mass-size relationship coefficients, total concentrations and second and third moment of hydrometeors size distribution) are sorted in temperature (thus altitude) zones, and subsequently each individual campaign is analysed with respect to median microphysical properties of the global dataset (merging all 4 campaign datasets). The study demonstrates that ice water content, visible extinction, total crystal concentration, and second and third moments of hydrometeors size distributions are similar in all 4 type of MCS for IWC larger than 0.1 g m−3. Finally, two parameterizations are developed for deep convective systems. The first one concerns the calculation of the visible extinction as a function of temperature and ice water content. The second one concerns the calculation of hydrometeor size distributions as a function of ice water content and temperature that can be used in numerical weather prediction.
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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 | 0.002 |
| 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".