An Evaluation of Tropical Cyclone Genesis Forecasts from Global Numerical Models
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Bibliographic record
Abstract
Abstract Tropical cyclone (TC) forecasts rely heavily on output from global numerical models. While considerable research has investigated the skill of various models with respect to track and intensity, few studies have considered how well global models forecast TC genesis in the North Atlantic basin. This paper analyzes TC genesis forecasts from five global models [Environment Canada's Global Environment Multiscale Model (CMC), the European Centre for Medium-Range Weather Forecasts (ECMWF) global model, the Global Forecast System (GFS), the Navy Operational Global Atmospheric Prediction System (NOGAPS), and the Met Office global model (UKMET)] over several seasons in the North Atlantic basin. Identifying TCs in the model is based on a combination of methods used previously in the literature and newly defined objective criteria. All model-indicated TCs are classified as a hit, false alarm, early genesis, or late genesis event. Missed events also are considered. Results show that the models' ability to predict TC genesis varies in time and space. Conditional probabilities when a model predicts genesis and more traditional performance metrics (e.g., critical success index) are calculated. The models are ranked among each other, and results show that the best-performing model varies from year to year. A spatial analysis of each model identifies preferred regions for genesis, and a temporal analysis indicates that model performance expectedly decreases as forecast hour (lead time) increases. Consensus forecasts show that the probability of genesis noticeably increases when multiple models predict the same genesis event. Overall, this study provides a climatology of objectively identified TC genesis forecasts in global models. The resulting verification statistics can be used operationally to help refine deterministic and probabilistic TC genesis forecasts and potentially improve the models examined.
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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 | 0.000 | 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) | 0.002 | 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 it