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Record W4411090591 · doi:10.1016/j.apm.2025.116233

Numerical modelling of internal gravity waves generated by a thermal forcing in an anelastic atmospheric flow with vertical shear

2025· article· en· W4411090591 on OpenAlexafffund
Lucy J. Campbell

Bibliographic record

VenueApplied Mathematical Modelling · 2025
Typearticle
Languageen
FieldPhysics and Astronomy
TopicIonosphere and magnetosphere dynamics
Canadian institutionsCarleton University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsForcing (mathematics)ThermalMechanicsInternal waveShear (geology)Internal heatingGeologyPhysicsMeteorologyAtmospheric sciences

Abstract

fetched live from OpenAlex

A numerical model is presented for internal gravity waves generated by deep or shallow latent heating in the lower atmosphere. The configuration comprises a background fluid flow with vertical shear and vertical stratification in a two-dimensional computational domain. A thermal forcing term is included in the energy conservation equation for the fluid flow. This term is sinusoidal in the horizontal direction and localized in the vertical direction and it generates a perturbation in the form of an upward-propagating internal gravity wave with a horizontal wavelength corresponding to that of the thermal forcing oscillation. If there is no critical level where the background flow speed is equal to the wave phase speed, then the wave propagates to the upper boundary of the domain where a non-reflecting boundary condition or radiation condition is imposed numerically. With a vertically-sheared background flow, where a critical level is present, nonlinear wave-mean-flow interactions occur. Vertical fluxes of momentum and energy lead to the development of higher horizontal wavenumber and zero wavenumber components. There is a transfer of momentum and energy to the background flow which reduces the wave amplitude in the upper levels of the domain and results in changes in the background velocity and temperature and the development of regions of convective instability near the critical level.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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: none
Teacher disagreement score0.436
Threshold uncertainty score0.996

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.009
GPT teacher head0.212
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.

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

Citations2
Published2025
Admission routes2
Has abstractyes

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