Simulation of Canadian severe weather events using Cloud Model
Bibliographic record
Abstract
We present a high-resolution modeling study of Canadian severe storms using Cloud Model 1 (CM1), with a focus on tornado- and hail-producing convective events. A curated dataset of 20 severe weather cases, 10 tornado and 10 hail events, across Canada, drawn from the Northern Tornadoes and Northern Hail Projects, forms the basis for the convection-resolving simulations. Initial conditions are constructed using thermodynamic profiles from ERA5 global reanalysis data. An ensemble of simulations is performed to evaluate the representativeness of the ERA5 vertical profiles and to identify potential limitations in using reanalysis data as ground truth. The ensemble includes the original ERA5 profiles and modified versions, as well as variations in convective initiation mechanisms and different microphysics schemes. Simulations are conducted on the high-performance computing system of the Shared Hierarchical Academic Research Computing Network (SHARCNET)/Digital Research Alliance of Canada. Key outputs include an integrated analysis of storm structure, dynamics, intensity, and evolution, along with comparisons to observed data. Results are visualized and made available through an interactive dashboard. This work provides new insight into the meteorological conditions of Canadian severe weather events and establishes a reproducible framework for convection-resolving ensemble storm simulations tailored to regional observational data. We plan to extend this work to more cases in the future.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".