Evaluation of Tropical Cyclones in the Canadian Global Modeling System: Sensitivity to Moist Process Parameterization
Bibliographic record
Abstract
Abstract Deep convection is one of various complex processes driving the evolution of tropical cyclones (TCs). The scales associated with deep convection are too small to be resolved by global NWP models. In the deep convection parameterization used by the Canadian Global Deterministic Prediction System (GDPS), the trigger function depends on various criteria, one of which is the adjustable “trigger velocity” parameter, a vertical velocity threshold used in the parcel stability test of the scheme. In this study, the sensitivity of the GDPS TC activity and precipitation distribution to convective triggering parameters is investigated by varying this threshold. Multiple basins are considered for three TC seasons, and the impacts of trigger velocity variations on TC statistics (forecast hits, bias, false alarms, and track and intensity errors) and on the model’s genesis potential index (GPI) are measured. It is shown that a reduction of the trigger velocity, from 0.05 to 0.01 m s−1, over the tropical oceans leads to increased convective stabilization of atmospheric columns, as well as an increase in convective precipitation amounts but a reduction in total (subgrid plus grid scale) precipitation accumulations. The trigger adjustment also yields a significant reduction of TC false alarm ratios, with no impact on forecast mean errors for true cyclones other than an expected deterioration of the intensity bias, and a systematic reduction of the average GPI over various basins at all lead times. A conceptual model is proposed to explain the relation between trigger adjustments and TC development.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".