Propagations From Extreme Integrated Vapor Transport to Extreme Precipitation Events in North America
Bibliographic record
Abstract
Abstract Extreme integrated vapor transport (IVT) is a crucial driving factor of extreme precipitation events (EPEs). This paper presents a complex network‐based characterization of propagations from extreme IVT to EPEs. Specifically, the propagations are tracked from extreme IVT to EPEs by event synchronization; and then the source zones of extreme IVT contributing to EPEs are identified by two‐layer complex network. A case study is devised for North America based on the daily NCEP/NCAR Reanalysis 1 from 1948 to 2021. Overall, eight communities of EPEs are identified: the west coast of United States (US) tend to receive substantial EPEs from the Pacific Ocean; the Gulf of Alaska tends to receive oceanic EPEs propagating inland; western Canada typically experiences large amount of out tendencies and the EPEs tend to accumulate in the Baffin Island and Labrador Peninsula; the southeastern US and the northern Great Plains tend to experience northward propagations from Mexico. Along the west coast of North America, the propagations from extreme IVT to EPEs typically originate from the eastern North Pacific between 160°W and 110°W, and make landfalls in 4 days. These propagations are influenced by anomalous cyclonic circulations developing over the Gulf of Alaska forced by eastward Rossby waves. The coincidence rate of these propagations with atmospheric rivers is, respectively, 85.31% in autumn, 91.35% in winter, 73.94% in spring, and 64.52% in summer. Overall, the observed propagations from extreme IVT to EPEs yield insights into the mechanism of atmospheric moisture transport and the predictability of precipitation.
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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".