Bulk microphysics parametrization of ice fraction for application in climate models
Bibliographic record
Abstract
Abstract Using in situ aircraft measurements of cloud microphysical properties collected in extratropical stratiform clouds during several field programs, a parametrization of the ice‐particle spectrum that includes small ice particles has been developed. This parametrization has been tested using a single prognostic equation developed by Tremblay et al. (1996) for application in a regional model. The addition of small ice‐particles significantly increases the vapour deposition‐rate when the natural atmosphere is assumed to be water saturated, and thus enhances the glaciation of simulated mixed‐phase cloud via the Bergeron–Findeisen process without significantly affecting the other cloud microphysical processes such as riming and particle‐sedimentation rates. After the water vapour pressure in mixed‐phase cloud was modified, based on the scheme of Lord et al. (1984), by weighting the saturation water vapour pressure with ice fraction, it was possible to simulate a more stable mixed‐phase cloud. It was also noted that the ice‐particle concentration (maximum dimension L > 100 µm ) in mixed‐phase cloud is lower on average by a factor of three, and, consequently, the parametrization should be corrected for this effect. After accounting for this effect, the parametrized ice‐fraction agreed well with observation. Copyright © 2006 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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".