Transport of Atmospheric Moisture during Three Extreme Rainfall Events over the Mackenzie River Basin
Bibliographic record
Abstract
Abstract Lagrangian trajectories were computed for three extreme summer rainfall events (with rainfall exceeding 100 mm) over the southern Mackenzie River basin to test the hypothesis that the low-level moisture feeding these rainstorms can be traced back to the Gulf of Mexico. The three-dimensional trajectories were computed using the Hybrid Single-Particle Lagrangian Integrated Trajectory model (HYSPLIT). For all three events, parcel trajectories were identified that originated near the Gulf of Mexico and terminated over the southern Mackenzie River basin. Specifically, the transport of low-level moisture was found to occur along either quasi-continuous or stepwise trajectories. The time required to complete the journey varied between 6 and 10 days. Closer examination of the data suggests that, for the three cases in question, the transport of modified Gulf of Mexico moisture to high latitudes was realized when the northward extension of the Great Plains low-level jet to the Dakotas occurred in synch with rapid cyclogenesis over Alberta, Canada. In this way, modified low-level moisture from the Gulf of Mexico arrived over the northern Great Plains at the same time as a strong southerly flow developed over the Dakotas and Saskatchewan, Canada, in advance of the deepening cutoff low over Alberta. This moist air was then transported northward over Saskatchewan and finally westward over the southern Mackenzie River basin, where strong ascent occurred.
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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 | 0.000 |
| 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) | 0.001 | 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".