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Record W1492152512 · doi:10.1029/2012gl051689

Joint horizontal‐vertical anisotropic scaling, isobaric and isoheight wind statistics from aircraft data

2012· article· en· W1492152512 on OpenAlexaff
J. Pinel, S. Lovejoy, Daniel Schertzer, A. F. Tuck

Bibliographic record

VenueGeophysical Research Letters · 2012
Typearticle
Languageen
FieldEnvironmental Science
TopicWind and Air Flow Studies
Canadian institutionsMcGill University
Fundersnot available
KeywordsScalingPhysicsTransverse planeHorizontal and verticalSpectral lineIsobaric processTurbulenceIsobarStratification (seeds)MeteorologyStatistical physicsGeodesyGeologyGeometryMathematicsThermodynamicsNuclear physicsQuantum mechanics

Abstract

fetched live from OpenAlex

Aircraft measurements of the horizontal wind have consistently found transitions from roughly k −5/3 to k −2.4 spectra at scales Δ x c ranging from about 100–500 km. Since drop sondes find k −2.4 spectra in the vertical, the simplest explanation is that the aircraft follow gently sloping trajectories (such as isobars) so that at large scales, they estimate vertical rather than horizontal spectra. In order to directly test this hypothesis, we used over 14500 flight segments from GPS and TAMDAR sensor equipped commercial aircraft. We directly estimate the joint horizontal‐vertical (Δx, Δz) wind structure function finding ‐ for both longitudinal and transverse components ‐ that the ratio of horizontal to vertical scaling exponents is H z ≈ 0.57 ± 0.02, close to the theoretical prediction of the 23/9D turbulence model which predicts H z = 5/9 = 0.555…. This model also predicts that isobars and isoheight statistics will diverge after Δ x c ; using the observed fractal dimension of the isobars (≈1.79 ± 0.02), we find that the isobaric scaling exponents are almost exactly as predicted theoretically and Δ x c ≈ 160, 125 km, (transverse, longitudinal). These results thus give strong direct support to the 23/9D scaling stratification model.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.787
Threshold uncertainty score0.999

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 studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.002
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.002

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 head0.054
GPT teacher head0.303
Teacher spread0.249 · 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.

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

Citations21
Published2012
Admission routes1
Has abstractyes

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