Characteristics of Precipitation and Wind Extremes Induced by Extratropical Cyclones in Northeastern North America
Bibliographic record
Abstract
Abstract This study investigates important characteristics of extreme (above the 99th local percentile) near‐surface wind speed and precipitation hourly events associated with extratropical cyclones (ETCs) over North America, using 20‐year ERA5 reanalysis and IMERG satellite‐based precipitation data. For seasonal and geographical occurrence frequency, wind extremes prevail in winter over ocean and in autumn over land, while precipitation extremes show relatively weak seasonal variation over ocean and primarily occur in summer over land. For both variables, over 60% of extreme events are associated with ETCs over northeastern North America (NNA) regardless of the season. When one type of extreme is observed, the probability that it is a compound wind‐precipitation extreme reaches up to 40% along the coasts and ocean, and about 20% in the NNA region. About 90% of compound wind and precipitation extremes in NNA (which occur most frequently in fall) are associated with ETCs. Significant discrepancies exist between the magnitudes of extremes in ERA5 and IMERG; however, both datasets consistently identify ETCs as the primary drivers of extremes in mid‐to‐high latitudes. Extratropical cyclones tend to have longer‐lasting wind extremes (∼6 hr in ERA5) compared to precipitation extremes (∼3 hr in ERA5 and ∼2 hr in IMERG). Rarer and stronger extremes based on a higher threshold are more likely to be associated with ETCs, exhibiting shorter extreme duration timescales.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".