Interpretation of airborne CASPOL measurements using methods developed in the CLOUD chamber
Bibliographic record
Abstract
It is thought that cirrus clouds have a warming influence on the atmosphere. The presence of small(<50 μm) ice crystals in cirrus can complicate matters leading to a net cooling feedback on climate. Additionally, in mixed phase clouds, detection and quantification of small ice particles continue to pose a challenge for classification and derivation of Ice Water Content (IWC). Remote sensing techniques of cloud water and ice particles continue to require in-situ airborne measurements for validation. It was shown in previous studies that it is possible to classify such particles by their unique polarisation signature. The Cloud Aerosol Spectrometer with Polarisation (CASPOL) allows a semi-quantitative derivation of the spherical and aspherical fractions of particles. In this study we combine single, particle-by-particle, polarisation measurements with path averaged depolarisation measurements from chamber experiments at the European Organisation for Nuclear Research (CERN) to improve determination of particle specific polarisation response. We then use this comparison to implement a laboratory developed discrimination method for CASPOL airborne measurements collected as part of the Aerosol-Cloud-Coupling-and-Climate-Interactions-in-the-Arctic (ACCACIA) and the Cirrus-Coupled-Cloud-Radiation-Experiment (CIRCCREX) field campaigns. Results from homogeneously mixed chamber experiments showed good agreement between single particle polarisation and path averaged "remote" depolarisation measurements. However, contributions from larger particles (>50 μm), can lead to discrepancies. Analysis of the aircraft cloud data showed that CASPOL derived aspherical fraction periods in cirrus clouds agreed with image shape analysis collected using a high resolution CCD imaging spectrometer (3-View Cloud Particle Imager, 3V -CPI).
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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) | 0.001 | 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".