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Record W2513688039 · doi:10.1175/mwr-d-16-0153.1

The Baroclinic Moisture Flux

2016· article· en· W2513688039 on OpenAlexaff
Ron McTaggart‐Cowan, John R. Gyakum, Richard W. Moore

Bibliographic record

VenueMonthly Weather Review · 2016
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicMeteorological Phenomena and Simulations
Canadian institutionsMcGill UniversityEnvironment and Climate Change Canada
Fundersnot available
KeywordsBaroclinityDiabaticEnvironmental scienceClimatologyRossby waveAtmospheric sciencesPotential vorticityVortex tubeAdiabatic processMeteorologyGeologyVortexVorticityPhysicsThermodynamics

Abstract

fetched live from OpenAlex

Abstract As subsaturated air ascends sloping isentropic surfaces, adiabatic expansion results in cooling and relative moistening. This process is an effective way to precondition the atmosphere for efficient moist processes while bringing parcels to saturation, and thereafter acts to maintain saturation during condensation. The goal of this study is to develop a diagnostic quantity that highlights circulations and regions in which the process of parcel moistening by isentropic ascent is active. Among the many features that rely on this process for the generation of an important fraction of their energy are oceanic cyclones, transitioning tropical cyclones, warm conveyor belts, diabatic Rossby vortices, and predecessor rain events. The baroclinic moisture flux (BMF) is defined as moisture transport by the component of vertical motion associated with isentropic upgliding. In warm conveyor belt and diabatic Rossby vortex case studies, the BMF appears to be successful in identifying the portion of the circulation in which this process is actively bringing parcels to saturation to promote the formation of clouds and precipitation. On a broader scale, the climatological maxima of the BMF highlight regions in which parcel moistening by isentropic ascent is anticipated to have a nonnegligible impact on the atmospheric state either through the action of the mean flow or via the repeated occurrence of isolated large-BMF events. The process-centric foundation of the BMF makes it useful as a filtering or exploratory variable, with the potential for extension into predictive applications.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.030
GPT teacher head0.253
Teacher spread0.224 · 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

Citations17
Published2016
Admission routes1
Has abstractyes

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