A Multimoment Bulk Microphysics Parameterization. Part IV: Sensitivity Experiments
Bibliographic record
Abstract
This is the fourth in a series of papers exploring the effects of the number of predicted moments in bulk microphysics schemes. In Part III, the three-moment version of a new multimoment scheme was used to simulate a severe hailstorm. The model successfully reproduced many of the observed gross characteristics, including the reflectivity structure and the maximum hail sizes at the ground. In this paper, the authors compare a series of sensitivity experiments using various one- and two-moment versions of the scheme with the three-moment version to explore the effects of predicting additional moments on the simulated hydrometeor fields, precipitation, and storm dynamics. Six sensitivity runs were performed. They varied in their ability to reproduce the precipitation pattern, storm structure, and peak values of microphysical fields of the control simulation. The two-moment simulations, which used diagnostic relations to prescribe the relative dispersion parameter, α, closely reproduced the spatial pattern, quantity, and phase of the precipitation at the surface as well as the overall storm structure, propagation speed, and peak values of several hydrometeor fields. The two-moment simulations, which used fixed values of α, on the other hand, differed more from the control. The runs using one-moment versions of the scheme were considerably different from each other and were poor at reproducing the control simulation. The results suggest that there is a dramatic improvement in the simulation moving from one- to two-moment schemes. For the case studied, it was found that if maximum particle size is not of concern, a two-moment scheme with a diagnostic dispersion parameter can reproduce most of the important aspects in a hailstorm simulation with a three-moment scheme.
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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".