Prediction of Graupel Density in a Bulk Microphysics Scheme
Bibliographic record
Abstract
Abstract A method to predict the bulk density of graupel ρ g has been added to the two-moment Milbrandt–Yau bulk microphysics scheme. The simulation of graupel using the modified scheme is illustrated through idealized simulations of a mesoscale convective system using a 2D kinematic model with a prescribed flow field and different peak updraft speeds. To examine the relative impact of the various approaches to represent rimed ice, simulations were run for various graupel-only and graupel-plus-hail configurations. Because of the direct feedback of ρ g to terminal fall speeds, the modified scheme produces a much different spatial distribution of graupel, with more mass concentrated in the convective region resulting in changes to the surface precipitation at all locations. With a strong updraft, the model can now produce solid precipitation at the surface in the convective region without a separate hail category. It is shown that a single rimed-ice category is capable of representing a realistically wide range of graupel characteristics in various atmospheric conditions without the need for a priori parameter settings. Sensitivity tests were conducted to examine various aspects of the scheme that affect the simulated ρ g . Specific parameterizations pertaining to other hydrometeor categories now have a direct impact on the simulation of graupel, including the assumed aerosol distribution for droplet nucleation, which affects the drop sizes of both cloud and rain, and the mass–size relation for snow, which affects its density and hence the embryo density of graupel converted from snow due to riming.
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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".