CanCPLD: Convective Parameters and Lightning Data to Support Future Thunderstorm Projections in North America
Bibliographic record
Abstract
Thunderstorms cause natural hazards, including hail, floods, strong winds, and lightning. Simulating thunderstorms in climate models is challenging due to their small scale, the complexity of their physical drivers, and the need to parameterize subgrid processes. Thunderstorm activity can be inferred by identifying relevant historical environmental parameters, e.g. convective available potential energy, humidity, and wind shear, and building statistical models that use these parameters as proxies for thunderstorm occurrence. Climate model projections of the parameters can be used with the statistical models to assess future thunderstorm activity. In this context, a multi-decade dataset with lightning flash totals and 201 convective parameters has been compiled for North America, focusing on areas north of 40°N. Parameters from the European Centre for Medium-Range Weather Forecasts reanalysis version 5 are available at 3-hour intervals on a 0.25° grid. The same variables are calculated for HighResMIP climate model simulations at 6-hour intervals for the historical period coinciding with global warming of 1°C above preindustrial and future periods at 2°C, 3°C, and 4°C warming.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".