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Record W3099951005 · doi:10.1029/2020gl091453

Quasi‐10‐Day Wave and Semidiurnal Tide Nonlinear Interactions During the Southern Hemispheric SSW 2019 Observed in the Northern Hemispheric Mesosphere

2020· article· en· W3099951005 on OpenAlexaff
Maosheng He, Jorge L. Chau, J. M. Forbes, Denise Thorsen, Guozhu Li, Tarique Adnan Siddiqui, Yosuke Yamazaki, W. K. Hocking

Bibliographic record

VenueGeophysical Research Letters · 2020
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicAtmospheric Ozone and Climate
Canadian institutionsWestern University
FundersNational Natural Science Foundation of ChinaNational Science Foundation
KeywordsMesosphereNorthern HemisphereAtmospheric sciencesGeologyStratopauseSouthern HemisphereGravity waveSudden stratospheric warmingPhysicsClimatologyStratosphereGeophysicsAstrophysicsPolar vortexGravitational wave

Abstract

fetched live from OpenAlex

Abstract Mesospheric winds from three longitudinal sectors at 65°N and 54°N latitude are combined to diagnose the zonal wave numbers ( m ) of spectral wave signatures during the Southern Hemisphere sudden stratospheric warming (SSW) 2019. Diagnosed are quasi‐10‐ and 6‐day planetary waves (Q10DW and Q6DW, m = 1), solar semidiurnal tides with m = 1, 2, 3 (SW1, SW2, and SW3), lunar semidiurnal tide, and the upper and lower sidebands (USB and LSB, m = 1 and 3) of Q10DW‐SW2 nonlinear interactions. We further present 7‐year composite analyses to distinguish SSW effects from climatological features. Before (after) the SSW onset, LSB (USB) enhances, accompanied by the enhancing (fading) Q10DW, and a weakening of climatological SW2 maximum. These behaviors are explained in terms of Manley‐Rowe relation, that is, the energy goes first from SW2 to Q10DW and LSB, and then from SW2 and Q10DW to USB. Our results illustrate that the interactions can explain most wind variabilities associated with the SSW.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.187
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.039
GPT teacher head0.261
Teacher spread0.223 · 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; both teacher heads agree on what is shown here.

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

Citations36
Published2020
Admission routes1
Has abstractyes

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