Quantification of the Effects of Shattering on Airborne Ice Particle Measurements
Bibliographic record
Abstract
Abstract Ice particle shattering poses a serious problem to the airborne characterization of ice cloud microstructure. Shattered ice fragments may contaminate particle measurements, resulting in artificially high concentrations of small ice. The ubiquitous observation of small ice particles has been debated over the last three decades. The present work is focused on the study of the effect of shattering based on the results of the Airborne Icing Instrumentation Evaluation (AIIE) experiment flight campaign. Quantitative characterization of the shattering effect was studied by comparing measurements from pairs of identical probes, one modified to mitigate shattering using tips designed for this study (K-tips) and the other in the standard manufacturer’s configuration. The study focused on three probes: the forward scattering spectrometer probe (FSSP), the optical array probe (OAP-2DC), and the cloud imaging probe (CIP). It has been shown that the overestimation errors of the number concentration in size distributions measured by 2D probes increase with decreasing size, mainly affecting particles smaller than approximately 500 μm. It was found that shattering artifacts may increase measured particle number concentration by 1 to 2 orders of magnitude. However, the associated increase of the extinction coefficient and ice water content derived from 2D data is estimated at only 20%–30%. Existing antishattering algorithms alone are incapable of filtering out all shattering artifacts from OAP-2DC and CIP measurements. FSSP measurements can be completely dominated by shattering artifacts, and it is not recommended to use this instrument for measurements in ice clouds, except in special circumstances. Because of the large impact of shattering on ice measurements, the historical data collected by FSSP and OAP-2DC should be reexamined by the cloud physics community.
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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 | 0.000 | 0.000 |
| 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".