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Record W6889927654 · doi:10.34657/5586

Middle- and High-Latitude Mesosphere and Lower Thermosphere Mean Winds and Tides in Response to Strong Polar-Night Jet Oscillations

2019· article· en· W6889927654 on OpenAlexaboutno aff

Bibliographic record

VenueTIB Repositorium · 2019
Typearticle
Languageen
FieldPhysics and Astronomy
TopicIonosphere and magnetosphere dynamics
Canadian institutionsnot available
Fundersnot available
KeywordsThermosphereMesosphereAtmospheric tideGeopotentialPolar nightGeopotential heightMeteor (satellite)Atmosphere (unit)Jet (fluid)Mean flow

Abstract

fetched live from OpenAlex

The dynamical behavior of the mesosphere and lower thermosphere (MLT) region during strongly disturbed wintertime conditions commonly known as polar-night jet oscillations (PJOs) is described in detail and compared to other wintertime conditions. For this purpose, wind measurements provided by two specular meteor radars located at Andenes (69°N, 16°E) and Juliusruh (54°N, 13°E) are used to estimate horizontal mean winds and tides as an observational basis. Winds and tidal main features are analyzed and compared for three different cases: major sudden stratospheric warming (SSW) with (a) strong PJO event, (b) non-PJO event, and (c) no major SSWs. We show that the distinction into strong PJOs, non-PJOs, and winters with no major SSWs is better suited to identify differences in the behavior of the mean winds and tides during the boreal winter. To assess the impact of the stratospheric disturbed conditions on the MLT region, we investigate the 30-year nudged simulation by the Extended Canadian Middle Atmosphere Model. Analysis of geopotential height disturbances suggests that changes in the location of the polar vortex at mesospheric heights are responsible for the jets observed in the MLT mean winds during strong PJOs, which in turn influence the evolution of semidiurnal tides by increasing or decreasing their amplitudes depending on the tidal component. © 2019. The Authors.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.023
Threshold uncertainty score0.988

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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 head0.004
GPT teacher head0.203
Teacher spread0.199 · 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

Citations0
Published2019
Admission routes1
Has abstractyes

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