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Record W2216129615 · doi:10.1175/mwr-d-15-0238.1

The Mississippi Valley Convection Minimum on Summer Afternoons: Observations and Numerical Simulations

2015· article· en· W2216129615 on OpenAlexafffund
Daniel J. Kirshbaum, Frédéric Fabry, Quitterie Cazenave

Bibliographic record

VenueMonthly Weather Review · 2015
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicMeteorological Phenomena and Simulations
Canadian institutionsMcGill University
FundersCompute CanadaNatural Sciences and Engineering Research Council of CanadaMcGill University
KeywordsOrographyWeather Research and Forecasting ModelConvectionTerrainGeologyMeteorologyClimatologyNumerical weather predictionAtmospheric sciencesGeographyPrecipitationCartography

Abstract

fetched live from OpenAlex

Abstract Analysis of 15 years of composite radar images over the continental United States reveals a distinct minimum of deep-convection occurrence over the interior lower Mississippi Valley on summer afternoons, relative to surrounding areas. To understand the mechanisms behind this convection signature, quasi-idealized numerical simulations with the Weather Research and Forecasting (WRF) Model are performed. The simulations, which broadly reproduce the valley convection minimum, suggest that convective inhibition is maximized, and low-level ascent minimized, over the flat valley terrain. By contrast, weaker inhibition and stronger mechanically forced ascent over the hills flanking the valley combine to initiate convection more readily. Although the orography of the region is unremarkable, it has a stronger influence on the regional convection pattern than do variations in land use.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.096
Threshold uncertainty score0.191

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.000
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 head0.108
GPT teacher head0.283
Teacher spread0.174 · 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 designSimulation or modeling
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

Citations15
Published2015
Admission routes2
Has abstractyes

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