The Simulation of a Hailstorm Using a Multi-Moment Bulk Microphysics Scheme
Bibliographic record
Abstract
In Part I of this study, the three-moment version of a bulk microphysics scheme (Milbrandt and Yau, 2005b) was used successfully to simulate a severe hailstorm. The model was able to reproduce many of the observed gross characteristics, including the reflectivity structure and the maximum hail sizes at the ground. In this paper, we compare a series of sensitivity experiments using various one-moment and two-moment versions of the scheme with the three-moment version to explore the effects of predicting additional moments. 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. There is evidently some added value in predicting a third moment in a bulk microphysics scheme, particularly for simulating the maximum hail sizes. However, the two-moment simulations which used a diagnostic relation to prescribe the spectral shape parameter, α, closely reproduced the spatial pattern, quantity, and phase of the precipitation at the surface as well as the overall storm structure and peak values of several hydrometeor fields. The two-moment simulations using fixed values of α, on the other hand, differed more from the control. The runs using onemoment versions of the scheme were poor at reproducing the control simulation. Our results show that there is a dramatic improvement in simulation quality by going from a one-moment to a two-moment scheme. For the specific storm case under investigation, provided that the shape parameter varies appropriately as a diagnostic variable, a two-moment scheme is capable of reproducing most of the important aspects of the storm. However, to simulate the maximum hail sizes, a three-moment scheme is superior.
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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 | 0.000 | 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".