Bibliographic record
Abstract
Abstract Despite considerable progress in mesoscale numerical weather prediction (NWP), the ability to predict summer severe rainfall (SSR) in terms of amount, location, and timing remains very limited because of its association with convective or mesoscale phenomena. In this study, two representative missed SSR events that occurred in the highly populated Great Lakes regions are analyzed within the context of moisture availability, convective instability, and lifting mechanism in order to help identify the possible causes of these events and improve SSR forecasts/nowcasts. Results reveal the following limitations of the Canadian regional NWP model in predicting SSR events: 1) the model-predicted rainfall is phase shifted to an undesired location that is likely caused by the model initial condition errors, and 2) the model is unable to resolve the echo-training process because of the weakness of the parameterized convection and/or coarse resolutions. These limitations are related to the ensuing model-predicted features: 1) vertical motion in the areas of SSR occurrence is unfavorable for triggering parameterized convection and grid-scale condensation; 2) convective available potential energy is lacking for initial model spinup and later for elevating latent heating to higher levels through parameterized convection, giving rise to less precipitation; and 3) the conversion of water vapor into cloud water at the upper and middle levels is underpredicted. Recommendations for future improvements are discussed.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".