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Record W2537014362 · doi:10.1002/qj.2932

The evolution and sensitivity of katabatic flow dynamics to external influences through the evening transition

2016· article· en· W2537014362 on OpenAlexaff
Derek D. Jensen, Daniel F. Nadeau, Sebastian W. Hoch, Eric R. Pardyjak

Bibliographic record

VenueQuarterly Journal of the Royal Meteorological Society · 2016
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicMeteorological Phenomena and Simulations
Canadian institutionsUniversité Laval
FundersOffice of Naval Research
KeywordsKatabatic windTurbulenceAtmospheric sciencesWind speedMeteorologyGeologyPhysics

Abstract

fetched live from OpenAlex

Data collected over an arid shallow slope (2–4°) during the Mountain Terrain Atmospheric Modeling and Observations (MATERHORN) Program are used to study the katabatic structure and onset of katabatic flow through the evening transition. An unprecedented suite of instrumentation, including a transect of five turbulence towers with 29 sonic anemometers, is used for the investigation. Fifteen transition periods with well‐defined katabatic flow and relatively little synoptic forcing are used in the study. The katabatic onset, jet velocity and jet height all show a large degree of interdiurnal and intersite variance. The slope‐aligned budgets of momentum and potential temperature are used to define time‐scales that describe the evolution of the katabatic flow. Composite wind velocity time series are used to show that ≈30 min elapses from the time when the katabatic flow initializes at 0.5 m to the point of initialization at 20 m. A simple katabatic model utilizing surface energy‐budget modelling is developed and used to model the interdiurnal katabatic variance. Finally, uni‐ and multi‐variate statistical analyses are used to diagnose the influence of specific external variables. Valley wind speed, turbulence structure, soil moisture, and shadow front speed are all found to influence the katabatic dynamics to varying degrees.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.669
Threshold uncertainty score0.415

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 studies0.0010.000
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 head0.013
GPT teacher head0.221
Teacher spread0.209 · 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

Citations23
Published2016
Admission routes1
Has abstractyes

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