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Record W2555259712 · doi:10.1175/jas-d-16-0085.1

The Importance of Critical Layer in Differentiating Developing from Nondeveloping Easterly Waves

2016· article· en· W2555259712 on OpenAlexaff
Ali Asaadi, Gilbert Brunet, M. K. Yau

Bibliographic record

VenueJournal of the Atmospheric Sciences · 2016
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicTropical and Extratropical Cyclones Research
Canadian institutionsEnvironment and Climate Change CanadaMcGill University
Fundersnot available
KeywordsTropical waveGeologyZonal and meridionalAfrican easterly jetJet (fluid)GeophysicsClimatologyPhysicsMechanicsTropical cyclone

Abstract

fetched live from OpenAlex

Abstract Recently Asaadi et al. found that an easterly wave (EW) train over the Atlantic and eastern Pacific is oriented in a southeast–northwest direction because of the observed tilt in the easterly jet. This tilt results in only one out of four (~25%) waves to be located at the cyclonic critical layer south of the jet axis in a comoving frame, and they subsequently developed into named storms. Asaadi et al. suggested a geometrical view for developing disturbances, which is the coexistence of a nonlinear critical layer and a region of weak meridional potential vorticity (PV) gradient over several days. Asaadi et al. focused on the developing waves and did not investigate the nondeveloping ones. To determine whether the nondeveloping EWs are not associated with a critical layer, a simple objective tracking technique is used to identify EWs. Composite views of the large-scale structure and characteristics of nondeveloping EWs show that ~91% of nondeveloping waves are not located on a critical layer, while the remaining ~9% indicate characteristics similar to the developing waves. Examination of the composite Okubo–Weiss parameter indicates that the nondeveloping waves are characterized by larger negative values, implying that they are dominated by deformation, unlike developing waves, which tend to be more immune from the deformation.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.027
Threshold uncertainty score0.253

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.036
GPT teacher head0.281
Teacher spread0.245 · 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 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

Citations15
Published2016
Admission routes1
Has abstractyes

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