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Record W2751299226 · doi:10.1002/2017gl074759

Experimental Evidence of Arctic Summer Mesospheric Upwelling and Its Connection to Cold Summer Mesopause

2017· article· en· W2751299226 on OpenAlexafffund
Fazlul I. Laskar, Jorge L. Chau, J.‐P. St.‐Maurice, Gunter Stober, Chris Hall, Masaki Tsutsumi, Josef Höffner, Peter Hoffmann

Bibliographic record

VenueGeophysical Research Letters · 2017
Typearticle
Languageen
FieldPhysics and Astronomy
TopicIonosphere and magnetosphere dynamics
Canadian institutionsUniversity of Saskatchewan
FundersJet Propulsion LaboratoryCanada Excellence Research Chairs, Government of CanadaCanada Research Chairs
KeywordsMesopauseMesosphereAtmospheric sciencesGlobal wind patternsWind speedGeologyThermosphereGeostrophic windDivergence (linguistics)Environmental scienceClimatologyStratosphereGeophysicsIonosphereOceanography

Abstract

fetched live from OpenAlex

Abstract Common volume mesospheric meteor detections from two radar stations separated by about 130 km were used to retrieve horizontal wind fields between 82 and 96 km altitudes at high latitudes, near 69°N. The horizontal wind divergence was estimated from the gradients of the wind fields. This determination is the first of its kind for the mesosphere. Twelve years of nearly continuous data sets reveal systematic summer signatures in the horizontal wind divergence field, namely, a minimum just below the mesopause. There are indications that the horizontal divergence near the mesopause minimum is correlated with the mesopause temperature. Also, the altitude corresponding to the mesospheric divergence minimum tends to increase over the years. We derived the vertical velocity from the horizontal wind divergence at the mesosphere, which shows upward winds peaking near the mesopause. These winds indicate that adiabatic cooling was strongest at the region of the deep temperature minimum seen in the summer mesopause. Common volume mesospheric meteor detections from two radar stations separated by about 130 km were used to retrieve horizontal wind fields between 82 and 96 km altitudes at high latitudes, near 69°N. The horizontal wind divergence was estimated from the gradients of the wind fields. This determination is the first of its kind for the mesosphere. Twelve years of nearly continuous data sets reveal systematic summer signatures in the horizontal wind divergence field, namely, a minimum just below the mesopause. There are indications that the horizontal divergence near the mesopause minimum is correlated with the mesopause temperature. Also, the altitude corresponding to the mesospheric divergence minimum tends to increase over the years. We show that the reversal in the horizontal wind divergence at the mesosphere is consistent with upward winds peaking near the mesopause. These winds indicate that adiabatic cooling was strongest at the region of the deep temperature minimum seen in the summer mesopause. Common volume mesospheric meteor detections from two radar stations separated by about 130 km were used to retrieve horizontal wind fields between 82 and 96 km altitudes at high latitudes, near 69°N. The horizontal wind divergence was estimated from the gradients of the wind fields. This determination is the first of its kind for the mesosphere. Twelve years of nearly continuous data sets reveal systematic summer signatures in the horizontal wind divergence field, namely, a minimum just below the mesopause. There are indications that the horizontal divergence near the mesopause minimum is correlated with the mesopause temperature. Also, the altitude corresponding to the mesospheric divergence minimum tends to increase over the years. We show that the reversal in the horizontal wind divergence at the mesosphere is consistent with upward winds peaking near the mesopause. These winds indicate that adiabatic cooling was strongest at the region of the deep temperature minimum seen in the summer mesopause.

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)0.000
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

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)0.0010.000

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 head0.061
GPT teacher head0.357
Teacher spread0.296 · 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

Citations18
Published2017
Admission routes2
Has abstractyes

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