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Record W2222812508 · doi:10.1175/jamc-d-15-0239.1

Topographic Impacts on the Spatial Distribution of Deep Convection over Southern Quebec

2016· article· en· W2222812508 on OpenAlexafffundabout
Michael J. Kovacs, Daniel J. Kirshbaum

Bibliographic record

VenueJournal of Applied Meteorology and Climatology · 2016
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicMeteorological Phenomena and Simulations
Canadian institutionsMcGill University
FundersFonds de recherche du Québec – Nature et technologiesCompute CanadaNatural Sciences and Engineering Research Council of CanadaMcGill University
KeywordsMesoscale meteorologyConvectionConvective available potential energyGeologyClimatologyGeostrophic windConvective storm detectionDeep convectionStormConvective inhibitionAtmospheric sciencesMeteorologyGeographyOceanography

Abstract

fetched live from OpenAlex

Abstract Observations and numerical simulations reveal pronounced mesoscale variability in deep-convection occurrence over southern Quebec, Canada. A 22-yr climatology from the McGill radar just west of Montreal shows that deep-convection maxima exist (i) within the St. Lawrence valley surrounding Ottawa; (ii) within the Champlain valley of upstate New York, extending north to just east of Montreal; and (iii) in the lee of the Laurentian Mountains northeast of Trois-Rivières. These features are sensitive to the background low- to midlevel geostrophic wind direction, shifting northward as the southerly wind component increases. A meridional axis of suppressed convection also extends from Lake Ontario and the Adirondacks of New York north through Montreal and into the Laurentians. To physically interpret these features, a suite of quasi-idealized convection-permitting simulations is conducted. Analysis of the simulations, which broadly reproduce the observed extrema in convection occurrence, reveals that the maxima develop within pockets of moisture and mass convergence at the junctions of major river valleys and in the lee of prominent mountain ridges. In these locations, enhanced boundary layer humidity and convective available potential energy (CAPE) coincides with minimal convective inhibition (CIN). The minima occur over and downwind of water bodies, where limited surface heat fluxes reduce CAPE and increase CIN, and over the higher terrain, where reduced low-level moisture limits storm intensity.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.140
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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)0.0010.000

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 head0.011
GPT teacher head0.214
Teacher spread0.203 · 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.

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

Citations10
Published2016
Admission routes3
Has abstractyes

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