Tropical Moisture Exports, Extreme Precipitation and Floods in Northeastern US
Bibliographic record
Abstract
A statistically and physically based framework is put forward to investigate the relationship between Tropical Moisture Exports (TMEs), extreme precipitation and floods in the Northeastern United States (NE-US). We found that the NE-US floods in the four seasons are closely related to TMEs and four major moisture sources of TMEs in the tropics account for approximately 85% of all the TMEs that enter the NE-US. The seasonality and interannual variation of the birth processes in the four source regions determine their contribution to the NE-US. Moisture born in Gulf of Mexico (GP) and Gulf stream (GS) are the year-around sources, with some winter contribution from Pineapple Express (PE) region, and West Pacific (WP) region contributes the least. The overall order of their contribution to NE-US is GP>GS>PE>WP. Seasonal association between TMEs birth and ENSO are also found. The seasonal and interannual variations in atmospheric circulation patterns also play an important role in determining the TMEs’ entrance to NE-US. Strong influence of active TMEs periods on the occurrence of extreme rainfall is also identified. We show that the extreme daily precipitation events are dominated by extreme TMEs’ entering the NE-US in every season.
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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 | 0.001 |
| 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".