A triple-moment bulk microphysics scheme for the explicit simulation of hail /
Bibliographic record
Abstract
Damage from large hail is a costly problem in Canada and other parts of the world. With continuous increase in computer power, numerical weather prediction (NWP) models are getting closer to resolving the convective scale, where it is appropriate to employ explicit microphysics schemes. This dissertation examines the potential to predict hail using an explicit microphysics scheme in an NWP model. To this end, the requirements of a bulk scheme to model the hydrometeor size distribution are investigated. A number of schemes in the literature use the double-moment method with a three-parameter gamma distribution function to represent the size spectrum. In general, two of the parameters vary with the predicted moments while the third, normally the shape parameter, is held constant. In a simple 1D context, the role of the shape parameter is analyzed by comparing results from bulk schemes using different numbers of predicted moments and an analytic bin model. It is shown that this parameter is important in the overall prediction of the size distribution by affecting both the instantaneous growth rates and the sedimentation. In view of this, two alternatives to the fixed-value approach are presented. One is a double-moment method, where the shape parameter is diagnosed from the predicted moments; the other is a triple-moment approach, where all distribution parameters are fully prognosed. A new microphysics scheme using the proposed approaches has been designed and interfaced with the Mesoscale Compressible Community model (MC2). High-resolution (1 km) simulations of a severe hailstorm are conducted. The control simulation using the full triple-moment version of the scheme is compared to radar observations and is shown to realistically simulate the observed storm, including the spatial distribution and sizes of hail at the ground. Experiments are performed to determine the sensitivity of the different approaches on the simulation of hail. The results indicated that the triple-moment scheme gives the best results. For a double-moment scheme, the diagnostic approach for the shape parameter exhibits distinct improvement over the fixed-value approach. It is also shown that double-moment schemes are dramatically better in reproducing the control simulation than single-moment schemes, owing largely to the ability of multi-moment schemes to account for the effects of size-sorting.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".