Parameterization of Cloud Microphysics Based on the Prediction of Bulk Ice Particle Properties. Part I: Scheme Description and Idealized Tests
Bibliographic record
Abstract
Abstract A method for the parameterization of ice-phase microphysics is proposed and used to develop a new bulk microphysics scheme. All ice-phase particles are represented by several physical properties that evolve freely in time and space. The scheme prognoses four ice mixing ratio variables, total mass, rime mass, rime volume, and number, allowing 4 degrees of freedom for representing the particle properties using a single category. This approach represents a significant departure from traditional microphysics schemes in which ice-phase hydrometeors are partitioned into various predefined categories (e.g., cloud ice, snow, and graupel) with prescribed characteristics. The liquid-phase component of the new scheme uses a standard two-moment, two-category approach. The proposed method and a complete description of the new predicted particle properties (P3) scheme are provided. Results from idealized model simulations of a two-dimensional squall line are presented that illustrate overall behavior of the scheme. Despite its use of a single ice-phase category, the scheme simulates a realistically wide range of particle characteristics in different regions of the squall line, consistent with observed ice particles in real squall lines. Sensitivity tests show that both the prediction of the rime mass fraction and the rime density are important for the simulation of the squall-line structure and precipitation.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".