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Record W4401575818 · doi:10.1175/jas-d-23-0092.1

Cloud-Edge Motion by a Ducted Gravity Wave

2024· article· en· W4401575818 on OpenAlexafffund
Raymond P. Walsh, David J. Muraki

Bibliographic record

VenueJournal of the Atmospheric Sciences · 2024
Typearticle
Languageen
FieldEnvironmental Science
TopicAtmospheric aerosols and clouds
Canadian institutionsSimon Fraser University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsGravity waveMechanicsBoussinesq approximation (buoyancy)BuoyancyPhysicsAdvectionAtmospheric ductCloud physicsAmplitudeMeteorologyCloud computingWave propagationGeologyConvectionOpticsNatural convectionAtmosphere (unit)Computer science

Abstract

fetched live from OpenAlex

Abstract The reflection of a wave at a fluid interface is fundamental to the atmospheric wave duct. As latent heating distinguishes the buoyancy response between cloudy and clear air, cloud edges can serve as a ducting interface for gravity waves. However, advection of the thermodynamic conditions by the ducted wave itself can cause evaporation or condensation, where the motion of cloud edges results from shrinking or enlarging regions of saturated air. For an idealized ducted-wave mode trapped by a cloud layer, a linear Boussinesq analysis shows that its vertical motions produce a sinusoidal corrugation of the cloud edge that travels with the wave. When thermodynamic conditions are continuously varying, the cloud edge propagates as the moving onset of phase change, and not as a material interface. Using this Boussinesq solution to initialize the full-physics CM1 model, the simulation confirms the amplitude and speed of the cloud-edge wave. In a comparison of simulations for domains of decreasing height, a convergence to the Boussinesq ducted wave can be quantitatively established. This demonstration suggests a theory-based convergence benchmark for the motion of a cloud edge by phase change.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.020
Threshold uncertainty score0.039

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.227
Teacher spread0.217 · 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 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
Published2024
Admission routes2
Has abstractyes

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