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Record W6921383785 · doi:10.7274/25537252

Field and Numerical Investigation of Moist Thermodynamical Structure of Marine Fog

2024· dataset· en· W6921383785 on OpenAlexaboutno aff

Bibliographic record

VenueUniversity of Notre Dame · 2024
Typedataset
Languageen
Field
Topic
Canadian institutionsnot available
Fundersnot available
KeywordsStratification (seeds)AdvectionBuoyancyTurbulenceSubsidenceScalingWind speedMixed layerForcing (mathematics)Surface layer

Abstract

fetched live from OpenAlex

A series of studies on the interplay between atmospheric moist thermodynamics (e.g., related to fog and clouds) and stratified turbulence is described. The first study presents ship-based measurements of fog off St John’s, Newfoundland, on 13 September 2018 during the Coastal Fog (C-FOG) field campaign. The measurements included cloud-particle spectra, cloud-base height and aerosol backscatter, radiation, turbulence, visibility, and sea surface temperature. Fog occurred in two episodes during the passage of an eastward-moving synoptic low-pressure system, characterized by multiple inversions with capping subsidence inversion, and one well-mixed fog layer capped by a subsidence inversion. Low wind speeds and stable stratification maintained weak surface-layer turbulence during fog. Counter-gradient heat fluxes observed are attributed to turbulence, entrainment, and stratification that overwhelmed the air–sea temperature difference influence. While synoptic-scale dynamics preconditioned the area for fog formation, the final step of fog appearance was nuanced by stratification–turbulence interactions, local advective processes, and microphysics. Inspired by recent field campaigns, the interaction between stratification, thermodynamics and turbulence, in particular, the emergence of layered stratification in marine surface layer was investigated. Large Eddy Simulation with newly added moist thermodynamics and phase changes was applied to idealized case with various stratification and forcing magnitudes. Spatial correlation between density interfaces and cloud layers was discovered, with cloud underneath the interfaces. Scaling analysis shows that, at quasi-stationary state, the normalized mean mixed-layer height linearly increases with a length-scale based on the root-mean-square velocity and buoyancy frequency. The proportionality constant differs due to phase change, and the mean vertical mixed-layer thickness decreases with phase change, due mainly to the increase of the local buoyancy frequency with heat release. The mixing efficiency converges to 0.2 for cases with phase change and to 0.25 for cases without. The normalized turbulent eddy diffusivity linearly increases with the square of a Froude number.

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: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Dataset · Consensus signal: Dataset
Teacher disagreement score0.069
Threshold uncertainty score0.903

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.006
GPT teacher head0.195
Teacher spread0.190 · 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 designNot applicable
Domainnot available
GenreDataset

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
Published2024
Admission routes1
Has abstractyes

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Same venueUniversity of Notre DameFrench-language works237,207