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Record W2271839103 · doi:10.1155/2016/6318460

The Impact of Storm Tracks on Warm-Season Precipitation in the Midwest: Contrasting the 1988 Drought and 1993 Flood

2015· article· en· W2271839103 on OpenAlexaboutno aff
Timothy Eichler, Zaitao Pan

Bibliographic record

VenueAdvances in Meteorology · 2015
Typearticle
Languageen
FieldEnvironmental Science
TopicClimate variability and models
Canadian institutionsnot available
FundersNational Oceanic and Atmospheric AdministrationNational Science Foundation
KeywordsFlood mythStormCyclone (programming language)PrecipitationClimatologyPrecipitable waterTropical cycloneMiddle latitudesStorm trackExtratropical cycloneSpring (device)Environmental scienceGeographyGeologyMeteorology

Abstract

fetched live from OpenAlex

To assess the role of cyclone tracks in contributing to floods and droughts, we highlight the role of midlatitude cyclones played in the 1988 drought and the 1993 flood. Our results demonstrate that the 1988 drought featured a poleward-displaced cyclone track with a reduced role for cyclone-induced precipitation, especially in the spring of 1988. The 1993 flood featured a cyclone track from Mexico northeast to Missouri in the spring, while the summer featured two cyclone tracks: one in the southwestern US and the other across Canada linked to the right-entrance and left-exit regions of a strong 200 hPa Jetstream across the upper Midwest. Enhanced 850 hPa inflow from the Caribbean northeast to the Midwest with high precipitable water values occurred in conjunction with the right entrance portion of the 200 hPa Jetstream. Linking storm tracks and the 200 hPa Jetstream to a storm-rain index for the Midwest showed that these extreme events conformed to features of the general circulation normally associated with wet/dry episodes in the warm half of the year. Although El Niño did not play a role in the 1993 flood, the 1988 drought was associated with a poleward displacement of cyclone tracks in response to La Niña.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation 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 score0.259
Threshold uncertainty score0.287

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.021
GPT teacher head0.306
Teacher spread0.285 · 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 teacher head, 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
Published2015
Admission routes1
Has abstractyes

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