Interannual Variability of Northern Hemisphere Storm Tracks in Coarse-Gridded Datasets
Bibliographic record
Abstract
Extratropical cyclones exert a large socioeconomic impact. It is therefore important to assess their interannual variability. We generate cyclone tracks from the National Center for Environmental Prediction’s Reanalysis I and the European Centre for Medium Range Prediction ERA-40 reanalysis datasets. To investigate the interannual variability of cyclone tracks, we compare the effects of El Niño, the North Atlantic Oscillation (NAO), the Indian Ocean Dipole (IOD), and the Pacific North American Pattern (PNA) on cyclone tracks. Composite analysis shows similar results for the impacts of El Niño, NAO, and the PNA on NH storm tracks. Although it is encouraging, we also found regional differences when comparing reanalysis datasets. The results for the IOD suggested a wave-like alteration of cyclone frequency across the northern US/Canada possibly related to Rossby wave propagation. Partial correlation demonstrates that although El Niño affects cyclone frequency in the North Pacific and along the US east coast, its impact on the North Pacific is accomplished via the PNA. Similarly, the PNA’s impact on US east coast storms is modulated via El Niño. In contrast, the impacts of the NAO extend as far west as the North Pacific and are not influenced by either the PNA or El Niño.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| 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) | 0.002 | 0.001 |
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".