Statistical Reconstruction of Seasonal Tropical Cyclone Variability in the North Atlantic Basin
Bibliographic record
Abstract
Abstract Seasonal forecasting of tropical cyclones is a topic of considerable interest to the public, government and private sectors. To improve understanding of the dynamics controlling the predictability of tropical cyclone (TC) activity, and improve the accuracy of forecasts, multiple studies have related TC activity to empirically‐defined indices including the El Niño‐Southern Oscillation, the Atlantic Multidecadal Oscillation, and the North Atlantic Oscillation. These indices were not developed to forecast TC activity but rather summarize other aspects of atmosphere‐ocean variability. In this study we use a statistical approach, based on redundancy analysis, to define two indices related to overall activity and steering of TCs. We focus on North Atlantic TCs that reached tropical storm strength (≥34 kt) between August and October 1948–2016. TC occurrences are binned using an equal area grid that covers the North Atlantic. The redundancy indices are linear combinations of mean sea level pressure for the same season. Cross validation is used to guard against over fitting in the definition of the indices. This approach provides two physically interpretable redundancy indices related to North Atlantic TC activity. The leading redundancy index is used to successfully reconstruct the total number of TCs and the accumulated cyclone energy, over the extended period 1878–2014 using seasonal mean sea level pressures from the National Centers for Environmental Prediction 20th‐Century Reanalysis version 2c. Extensions of the approach for seasonal forecasting 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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| 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) | 0.001 | 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".