A Multimoment Bulk Microphysics Parameterization. Part I: Analysis of the Role of the Spectral Shape Parameter
Bibliographic record
Abstract
Abstract With increasing computer power, explicit microphysics schemes are becoming increasingly important in atmospheric models. Many schemes have followed the approach of Kessler in which one moment of the hydrometeor size distribution, proportional to the mass content, is predicted. More recently, the two-moment method has been introduced in which both the mass and the total number concentration of the hydrometeor categories are independently predicted. In bulk schemes, the size spectrum of each hydrometeor category is often described by a three-parameter gamma distribution function, N(D) = N0Dαe−λD. Two-moment schemes generally treat N0 and λ as prognostic parameters while holding α constant. In this paper, the role of the spectral shape parameter, α, is investigated by examining its effects on sedimentation and microphysical growth rates. An approach is introduced for a two-moment scheme where α is allowed to vary diagnostically as a function of the mean-mass diameter. Comparisons are made between calculations using various bulk approaches—a one-moment, a two-moment, and a three-moment method—and an analytic bin model. It is found that the size-sorting mechanism, which exists in a bulk scheme when different fall velocities are applied to advect the different predicted moments, is significantly different amongst the schemes. The shape parameter plays an important role in determining the rate of size sorting. Likewise, instantaneous growth rates related to the moments are shown to be significantly affected by this parameter.
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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 | 0.002 |
| 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".