Severe weather intensity index using the 1-km global environmental multiscale limited area model output
Bibliographic record
Abstract
Severe weather (SW) can have a huge impact on someone's life and property. Presently at Environment Canada (EC), there is no useful automated tool to help the forecasters in their SW forecast. The goal of this thesis was to develop a useful automated tool to help the SW forecasters in their SW predictions. A severe weather intensity (SWI) index was created from the 1-km Global Environmental Multiscale Limited Area Model (GEM-LAM) outputs. The GEM-LAM 1-km was run on summer days in 2008 and 2009 over Alberta, Ontario, and Quebec. The dataset of summer 2009 was used to create algorithms that use the model's outputs to detect severe thunderstorm structural features, compute the quantity of the ingredients needed to initiate severe thunderstorms, and estimate the intensity and the type of SW expected. The post-processed fields were subjectively verified with the SW observations and radar images for the summer of 2009 leading to a decision tree for the SWI index for each region. An object-oriented method was used to verify the SWI index forecasts with the SW observations for the summer of 2008. The results showed that the SWI index forecast was very accurate over Ontario, accurate over Quebec, and much less accurate over Alberta. The lack of SW observations and the model's spin up mainly affected the results. Finally, the skill of the SWI index forecast was compared to the forecaster-derived SW forecast to verify if the index could help the SW forecasters to improve their SW forecast. The results indicate that the SWI index could improve the prediction of SW events, but not the positioning.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".