Large-Scale Dynamics, Thermodynamics, and Predictability of the 4–25 February 2019 Extreme Precipitation Period in Eastern North America
Bibliographic record
Abstract
Abstract Extreme precipitation is often challenging to predict but can have substantial impacts through flooding and loss of life and property, especially when it is persistent and affects a large region. The 4–25 February 2019 extreme precipitation period in eastern North America (NA) was exceptionally persistent, contributing to extreme winter rainfall and flooding in the Ohio and Tennessee River valleys and unusually heavy snowfall to the north. The period featured anomalous upper-level ridges in eastern NA and the central North Pacific (NP) and an anomalous upper-level trough in western NA, a favorable synoptic configuration for precipitation in eastern NA. The central NP ridge was prominent and extremely persistent, helping to slow and amplify the planetary-scale weather pattern. Within eastern NA, precipitation was lighter, less convective, and more synoptically forced in northern areas, while it was heavier at times and more convective in southern areas. Numerical weather models did not skillfully forecast the weather pattern associated with the onset of the extreme precipitation period beyond a lead time of 7 days, but they were able to more accurately forecast the continuation and persistence of the weather pattern once it started. For this case, simulating the synoptic structure over the NP before the extreme precipitation period accurately is crucial for simulating the later upper-level ridge building in the central NP and resulting downstream weather pattern favorable for persistent precipitation in eastern NA. Significance Statement The 4–25 February 2019 extreme precipitation period was exceptionally persistent, contributing to extreme winter rainfall and flooding in the Ohio and Tennessee River valleys and unusually heavy snowfall to the north, and its onset was not well predicted. The period featured an anomalous, amplified, and persistent large-scale weather pattern favorable for extreme precipitation in eastern North America. Within eastern North America, precipitation was lighter in northern areas, while the precipitation was heavier at times and more convective in southern areas. Weather models’ lack of accurate simulation of the large-scale weather pattern over the North Pacific before the extreme precipitation period was a critical factor in the poor forecasts of the extreme precipitation period’s onset.
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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 | 0.000 | 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".