Ice crystal number concentration versus temperature for climate studies
Bibliographic record
Abstract
Abstract Ice crystal number concentration (Ni) is an important parameter, having a strong influence on the calculation of cloud optical and microphysical parameters. Cloud and precipitation parameterizations within climate and weather forecasting models, affecting the heat and moisture budget of the atmosphere, cannot be determined accurately if Ni is not estimated correctly. Previous studies of ice crystal number concentration versus temperature (T) have shown that Ni–T relationships are not unique. The present study uses observations made in the glaciated regions of stratiform clouds from two Arctic and two mid‐latitude field projects to study the Ni versus temperature relationship. Scatter plots of Ni versus T at the ice particle measurement level do not show a good correlation with T for ice crystals at sizes less than 1000 µm. For a given temperature, the variation in Ni is found to be up to two to three orders of magnitude for ice crystals with sizes larger than approximately 100 µm. A significant Ni–T relationship is found for precipitation sized particles with sizes greater than 1000 µm. The ice particle concentration for sizes between 100 and 1000 µm varied from 0.1 to 100 L−1, independent of geographic location where the measurements were made. Based on this work, it is concluded that modelling studies should be tested for the possible variations in Ni versus T. Copyright © 2001 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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".