Three‐Moment Representation of Rain in a Bulk Microphysics Model
Bibliographic record
Abstract
Abstract A bulk three‐moment representation for rain microphysics is developed and implemented in the Predicted Particle Properties (P3) microphysics scheme. In addition, a new parameterization for rain self‐collection and collisional breakup (RSCB) is presented using a lookup table approach, based on the Spectral‐Bin Model (SBM). To quantify the impacts of sedimentation, evaporation, and RSCB on drop size distributions (DSDs), a rain shaft model is applied to a wide range of atmospheric scenarios (i.e., initial conditions and regimes) and compared against results from the SBM. DSD shapes are mainly determined by both sedimentation and evaporation, except in heavy rain where the impact of RSCB on DSD shape becomes more important than evaporation. The new parameterization for RSCB has a considerable impact on the mean drop size, improving the agreement between P3 and SBM. Only 4% of the original two‐moment rainshaft simulations have mean drop sizes and rain rates within ±20% of the SBM results, but this increases to more than 95% agreement when the three‐moment rain representation is used together with the new parameterization for RSCB. Generally, the improvement is more significant for heavy rain than for light drizzle. Remaining differences between bin and bulk model are attributable to treatments of evaporation, and the restriction to gamma DSDs in P3.
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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 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".