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Record W7115039358

Analysis of Winter Extreme Precipitation Regimes (EPRs) in eastern North America

2025· dissertation· en· W7115039358 on OpenAlexaboutno aff

Bibliographic record

VenueOpen MIND · 2025
Typedissertation
Languageen
FieldEnvironmental Science
TopicClimate variability and models
Canadian institutionsnot available
Fundersnot available
KeywordsPrecipitationClimate changePeriod (music)Snow cover
DOInot available

Abstract

fetched live from OpenAlex

Extreme precipitation is often challenging to predict but has substantial societal impacts, especially when it is persistent and affects a large region. Despite this, few studies have examined long-duration precipitation periods on a systematic climatological basis. This thesis aims to expand the work on precipitation events to extreme, long-duration, and large-scale precipitation periods during the eastern North American winter. We define extreme precipitation regimes (EPRs) as periods of at least five consecutive days of extreme precipitation, except allowing for one-day breaks, during the eastern North American winters from 1940-41 to 2021-22. The large temporal and spatial scales of EPRs, as well as the climatological study of EPRs, distinguish this study from previous precipitation studies, which are mostly on shorter-duration events. We analyze the synoptic-scale and thermodynamic environments associated with EPRs and elucidate the associated large-scale physical mechanisms contributing to the favorable environments for EPRs. EPRs are associated with a persistent weather pattern extending from the North Pacific to North America favoring sustained southerly transport of moisture from the Gulf of Mexico and/or Caribbean Sea into eastern North America. The moisture is then often deposited in atmospheric rivers in frequently occurring cyclones from the midwestern United States to southeastern Canada as heavy precipitation. The strength of the southerly flow in eastern North America is critical in producing large precipitation amounts, while the persistence of the central North Pacific ridge, as well as the long-wavelength and slow-moving nature of the synoptic structure, are critical to the longevity of EPRs. The persistence of the weather pattern is enhanced by a positive feedback loop through warm advection, anticyclonic vorticity advection, and condensational latent heating induced by upstream cyclones to the west. There also appears to be some influence from longer-duration oscillations such as the Madden-Julian and Pacific Decadal Oscillations.Given the substantial variability in weather patterns, precipitation amounts, and duration among EPRs, we also perform a case study of a very impactful extreme precipitation period composed of a succession of three EPRs in February 2019 to ascertain in more detail how the meteorological features and physical processes contributing to EPRs in general influence the evolution of a single event and the dynamics ultimately contributing to precipitation in eastern North America. This extreme precipitation period contributed to the extreme winter rainfall in parts of the lower midwestern and southeastern United States and unusually heavy snowfall over the upper midwestern United States and eastern Canada. The period featured a similar but more amplified and persistent weather pattern to that of the EPR composite. The persistence of the weather pattern appears to be enhanced by positive feedback loops causing upper-level ridges to persist over the same area, as found for EPRs in general. Within eastern North America, precipitation is lighter, steadier, less convective, and more synoptically-forced in northern areas, while it is heavier, more intermittent, and more convective in southern areas. Numerical weather models did not skillfully forecast the weather pattern associated with the extreme precipitation period beyond a forecast lead time of 10 days, but they were able to more accurately simulate the continuation and persistence of the weather pattern once it started. For this case, simulating the precursor synoptic structure over the North Pacific accurately is crucial for simulating the later downstream weather pattern leading to persistent and extreme precipitation in eastern North America

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.027
Threshold uncertainty score0.053

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.044
GPT teacher head0.305
Teacher spread0.260 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

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