Parameterization of Cloud Microphysics Based on the Prediction of Bulk Ice Particle Properties. Part III: Introduction of Multiple Free Categories
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract The predicted particle properties (P3) scheme introduced in Part I of this series represents all ice hydrometeors using a single “free” category, in which the bulk properties evolve smoothly through changes in the prognostic variables, allowing for the representation of any type of ice particle. In this study, P3 has been expanded to include multiple free ice-phase categories allowing particle populations with different sets of bulk properties to coexist, thereby reducing the detrimental effects of property dilution. The modified version of P3 is the first scheme to parameterize ice-phase microphysics using multiple free categories. The multicategory P3 scheme is described and its overall behavior is illustrated. It is shown using an idealized 1D kinematic model that the overall simulation of total ice mass, reflectivity, and surface precipitation converges with additional categories. The correct treatment of the rime splintering process, which promotes multiple ice modes, is shown to require at least two categories in order to be included without introducing problems associated with property dilution. Squall-line simulations using a 3D dynamical model with one, two, and three ice categories produce reasonable reflectivity structures and precipitation rates compared to radar observations. In the multicategory simulations, ice hydrometeors from different categories and with different bulk properties are shown to coexist at the same points, with effects on reflectivity structure and precipitation. The new scheme thus appears to work reasonably in a full 3D model and is ready to be tested more widely for research and operational applications.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it