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Record W2573785291 · doi:10.1175/jas-d-16-0228.1

Scaling Laws for the Longitudinal Structure Function in the Atmospheric Surface Layer

2017· article· en· W2573785291 on OpenAlexaff
Marcelo Chamecki, Nelson Luı́s Dias, Scott T. Salesky, Ying Pan

Bibliographic record

VenueJournal of the Atmospheric Sciences · 2017
Typearticle
Languageen
FieldEnvironmental Science
TopicWind and Air Flow Studies
Canadian institutionsUniversity of British Columbia
FundersConselho Nacional de Desenvolvimento Científico e Tecnológico
KeywordsTurbulenceScalingDissipationPhysicsAtmospheric instabilityAdvectionMechanicsBuoyancyTurbulence kinetic energyPower lawClassical mechanicsStatistical physicsMeteorologyThermodynamicsGeometryMathematicsWind speed

Abstract

fetched live from OpenAlex

Abstract Scaling laws for the longitudinal structure function in the atmospheric surface layer (ASL) are studied using dimensional analysis and matched asymptotics. Theoretical predictions show that the logarithmic scaling for the scales larger than those of the inertial subrange recently proposed for neutral wall-bounded flows also holds for the shear-dominated ASL composed of weakly unstable, neutral, and all stable conditions (as long as continuous turbulence exists). A 2/3 power law is obtained for buoyancy-dominated ASLs. Data from the Advection Horizontal Array Turbulence Study (AHATS) field experiment confirm these scalings, and they also show that the length scale formed by the friction velocity and the turbulent kinetic energy dissipation rate consistently outperforms the distance from the ground z as the relevant scale in all cases regardless of stability. With this new length scale, the production range of the longitudinal structure function collapses for all measurement heights and stability conditions. A new variable to characterize atmospheric stability emerges from the theory: namely, the ratio between the buoyancy flux and the TKE dissipation rate.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.210
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0030.001
Scholarly communication0.0000.000
Open science0.0020.000
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 head0.030
GPT teacher head0.271
Teacher spread0.242 · 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

Citations38
Published2017
Admission routes1
Has abstractyes

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