Impacts of Predicted Liquid Fraction and Multiple Ice‐Phase Categories on the Simulation of Hail in the Predicted Particle Properties (P3) Microphysics Scheme
Bibliographic record
Abstract
Abstract Since its inception in 2015, the Predicted Particle Properties (P3) bulk microphysics scheme has undergone several major developments. Ice is now represented by a user‐specified number of freely‐evolving (non‐prescribed) categories; the liquid fraction of particles is predicted, thereby allowing for mixed‐phase particles and improved process rates; and the scheme is triple‐moment, which allows the size spectral width to vary independently. As such, P3 is now capable of representing key properties and microphysical processes that are important for hail. In this study, the impacts of some new capabilities of P3 on the simulation of hail amounts and sizes are examined in the context of idealized, high‐resolution (200‐m isotropic grid spacing) hailstorm simulations using a cloud‐resolving model. Sensitivity tests are conducted to examine the impacts of (a) the predicted liquid fraction, and (b) the number of generic ice‐phase categories (varied between one and four). Predicted liquid fraction leads to a more realistic treatment of melting and shedding, which decreases the mean ice (hail) sizes during melting compared to the original P3 scheme. In contrast, with an increasing number of ice‐phase categories, the problem of property dilution is mitigated, ultimately resulting in greater quantities of hail and larger sizes reaching the surface. It is argued that the latest version of the P3 scheme is now capable of realistically representing the major microphysical processes involved in the initiation, growth, and decay of hail.
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How this classification was reachedexpand
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 | 0.000 |
| 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 | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".