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Record W2104098757 · doi:10.1175/2010jas3332.1

Interpreting Stationary Wave Nonlinearity in Barotropic Dynamics

2010· article· en· W2104098757 on OpenAlexaff
Lei Wang, Paul J. Kushner

Bibliographic record

VenueJournal of the Atmospheric Sciences · 2010
Typearticle
Languageen
FieldEnvironmental Science
TopicClimate variability and models
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsBarotropic fluidBaroclinityRossby waveNonlinear systemPhysicsEddyStationary stateForcing (mathematics)Standing waveStokes waveWavenumberMechanicsTransient (computer programming)Wave propagationClassical mechanicsBreaking waveTurbulenceAtmospheric sciencesOpticsComputer scienceQuantum mechanics

Abstract

fetched live from OpenAlex

Abstract Stationary wave nonlinearity describes the self-interaction of stationary waves and is important in maintaining the zonally asymmetric atmospheric general circulation. However, the dynamics of stationary wave nonlinearity, which is often calculated explicitly in stationary wave models, is not well understood. Stationary wave nonlinearity is examined here in the simplified setting of the response to localized topographic forcing in quasigeostrophic barotropic dynamics in the presence and absence of transient eddies. It is shown that stationary wave nonlinearity accounts for most of the difference between the linear and full nonlinear response, particularly if the adjustment of the zonal-mean flow to the stationary waves is taken into account. The separate impact of transient eddy forcing is also quantified. Wave activity analysis shows that stationary wave nonlinearity in this setting is associated with Rossby wave critical layer reflection. A nonlinear stationary wave model, similar to those used in baroclinic stationary wave model studies, is also tested and is shown to capture the basic features of the full nonlinear stationary wave solution.

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)0.002
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 score0.007
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.001
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 head0.012
GPT teacher head0.246
Teacher spread0.235 · 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

Citations6
Published2010
Admission routes1
Has abstractyes

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