Improvements to the parametrization of snow in <scp>AROME</scp> in the context of ice crystal icing
Bibliographic record
Abstract
Abstract Ice crystal icing (ICI) poses a threat nowadays for airplane pilots crossing the anvils of tropical mesoscale convective systems (MCSs). The use of fine‐scale operational numerical weather predictions as provided by the French limited‐area model AROME could help to better understand this phenomenon and to help its anticipation. To enable AROME to simulate ICI‐prone conditions, modifications of its single‐moment microphysical scheme Intercity‐Express 3 (ICE3) are tested. Using a temperature‐dependent snow particle distribution deeply impacts the organization and the ice phase of the simulated MCS. Notably, while the size of convective regions decreases, the size of anvil clouds increases and the low stratiform rain increases as well. As a result, by increasing the quantity of snow and decreasing the quantity of graupel, the simulation of ICI‐prone conditions in the anvils of convective systems is enabled. Using this parametrization, further modifications fine‐tune the representation of snow and further increase the size of the anvil cloud. The Marshall–Palmer snow distribution is replaced by a generalized gamma and the terminal fall velocities of snow hydrometeors are parametrized so that they are in closer agreement with observations.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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) | 0.004 | 0.001 |
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".