Adapting Two-Moment Microphysics Schemes across Model Resolutions: Subgrid Cloud and Precipitation Fraction and Microphysical Sub–Time Step
Bibliographic record
Abstract
Abstract Two-moment multiclass microphysics schemes are very promising tools to be used in high-resolution NWP models. However, they must be adapted for coarser resolutions. Here, a twofold solution is proposed—namely, a simple representation of subgrid cloud and precipitation fraction—as well as a microphysical sub-time-stepping method. The scheme is easy to implement, allows supersaturation in ice cloud, and exhibits flexibility for adoption across model grid spacing. It is implemented in the Milbrandt and Yau two-moment microphysics scheme with prognostic precipitation in the context of a simple 1D kinematic model as well as a mesoscale NWP model [the Canadian regional Global Environmental Multiscale model (GEM)]. Sensitivity tests were performed and the results highlighting the advantages and disadvantages of the two-moment multiclass cloud scheme relative to the classical Sundqvist scheme. The respective roles of subgrid cloud fraction, precipitation fraction, and time splitting were also studied. When compared to the Cloud–Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO)/CloudSat-retrieved cloud mask, cloud fraction, and ice water content, it is found that the proposed solutions significantly improve the behavior of the Milbrandt and Yau microphysics scheme at the regional NWP scale, suggesting that the subgrid cloud and precipitation fraction technique can be used across model resolutions.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".