Application of Artificial Neural Networks on North Atlantic Tropical Cyclogenesis Potential Index in Climate Change
Bibliographic record
Abstract
Abstract A methodology using artificial neural networks is presented to project twenty-first-century changes in North Atlantic tropical cyclone (TC) genesis potential (GP) in a five-model ensemble of global climate models. Two types of neural networks—the self-organizing maps (SOMs) and the forward-feeding back-propagating neural networks (FBNNs)—were employed. This methodology is demonstrated to be a robust alternative to using GCM output directly for tropical cyclone projections, which generally require high-resolution simulations. By attributing the projected changes to the related environmental variables, Emanuel’s revised genesis potential index is used to measure the GP. Changes are identified in the first (P1) and second (P2) half of the twenty-first century. The early and late summer GP decreases in both the P1 and P2 periods over most of the eastern half of the basin and increases off the East Coast of the United States and the north coast of Venezuela during P1. The peak summer GP over the region of frequent TC genesis is projected to decrease more substantially in P1 than in P2. Vertical wind shear (850–200 hPa), temperature (600 hPa), and potential intensity are the most important controls of TC genesis in the North Atlantic basin (NAB) under the changing climate.
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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 | 0.000 | 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) | 0.000 | 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".