A Multimoment Bulk Microphysics Parameterization. Part III: Control Simulation of a Hailstorm
Bibliographic record
Abstract
With continuous increase in the resolution of operational numerical weather prediction models, grid-scale saturation schemes that model cloud microphysics are becoming increasingly important. In Parts I and II of this study, the importance of the relative dispersion of the hydrometeor size distribution in bulk microphysics parameterizations was demonstrated and a closure approach for a three-moment scheme was proposed. In this paper, the full three-moment version of the new multimoment scheme is tested in a 3D simulation of a severe hailstorm. The modeled microphysical fields are examined, with particular attention paid to the simulated hail fields including the maximum hail sizes at the ground. A mesoscale model was initialized using synoptic analyses and successively nested to a resolution of 1 km. When compared to observations of the real storm from a nearby radar, the simulated storm reproduced several of the observed characteristics including the direction and speed of propagation, a bounded weak echo region, hook echo, mesocyclone, and a suspended overhang region. The magnitudes of radar reflectivity and surface precipitation are also well simulated. The mass contents, total number concentrations, equivalent reflectivities, and mean mass diameters of each hydrometeor category in the model were examined. The spatial distributions of the various hydrometeors throughout the storm appeared realistic and their values were consistent with published observations from other storms. Using the three predicted parameters of the gamma size distribution for hail, a method was introduced to determine the maximum hail size simulated from a bulk scheme that is physically observable. The observed storm produced golf ball–sized hail while the simulation produced walnut-sized hail at approximately the same time and location. The results suggest that because of the additional information provided about the size distribution, there is added value in prognosing the relative dispersion parameter of a given hydrometeor category in a bulk 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 | 0.001 |
| 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".