Parameterization of effective ice particle size for high‐latitude clouds
Bibliographic record
Abstract
Abstract A parameterization has been developed for mean effective size Dge in terms of ice water content (IWC) and temperature using in situ measurements of ice crystal spectra, cloud particle shapes and particle cross‐sectional area A from four research projects conducted in latitudes north of 45° N. The cloud microphysical measurements were made using PMS 2D optical probes, a PMS forward scattering spectrometer probe (FSSP), and Nevzorov total water and liquid water content probes. The IWCs derived from particle spectra using three different methods were compared with IWC measured with the Nevzorov probe (IWCNev). The contribution of small particles to the total mass was estimated by integrating a gamma distribution function that was fitted to match the measured FSSP concentrations. The Dge was calculated from the derived IWC and total cross‐sectional area per unit volume Ac. This analysis indicates that there are significant differences among the schemes used to derive the IWC. It was found that the IWC derived based on the Cunningham scheme and IWCNev have the highest correlation: r2 = 0.78. After considering small particles, the derived IWC almost matched the IWCNev. The average estimated contribution of small particles to the Ac was 43%. The average estimated contribution of small particles to the total IWC, however, was 20%. Since Dge is directly proportional to the ratio IWC/Ac, the addition of small particles reduced the derived Dge considerably. The largest changes in Dge associated with small particles, however, occur at the coldest temperature and at low IWC, reaching up to 45% for temperatures less than −25° C. Generally, Dge and IWC increase with increasing temperature. Good agreement between the parameterized Dge and derived Dge from measurements were found when small particles were included. Copyright © 2002 Royal Meteorological Society.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".