Bibliographic record
Abstract
Abstract. Atmospheric rivers (ARs) are filaments of extensive water vapor transport in the lower troposphere, that play a crucial role in the distribution of water, but can also cause natural and economical damage by facilitating heavy rainfall. Here, we investigate the large-scale spatio-temporal synchronization patterns of heavy rainfall over the western coast and the continental regions of North America (NA), during the period from 1979 to 2018. In particular, we utilize event synchronization and a complex network approach incorporating varying delays to examine the temporal evolution of spatial patterns of heavy rainfall events in the aftermath of land-falling ARs. For that, we employ the SIO-R1 catalog of ARs that land-fall over the western coast of NA, categorized in terms of strength and persistence on an AR-intensity scale which varies from category 1 to 5, along with daily rainfall estimates from the ERA5 reanalysis with 0.25° spatial resolution. Our analysis reveals a cascade of synchronized heavy rainfall events, triggered by ARs of category 3 or higher: in the first 3 days after the first day of an AR strike, rainfall events mostly occur and synchronize along the western coast of NA. In the subsequent days, moisture can be transported to central and eastern Canada and cause synchronized but delayed heavy rainfall there. Furthermore, we assess the robustness of our findings by studying an additional AR detection method. Finally, analyzing the anomalies of integrated water vapor transport, geopotential height, upper-level meridional wind, and rainfall, we find atmospheric circulation patterns that are consistent with the spatio-temporal evolution of the synchronized heavy rainfall events. Understanding and revealing the effects of ARs in the rainfall patterns over NA will lead to better anticipating the evolution of the climate dynamics of the region in the context of a changing climate.
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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 | 0.002 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.021 | 0.018 |
| Insufficient payload (model declined to judge) | 0.179 | 0.131 |
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".