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Record W4406340171 · doi:10.1002/qj.4928

Study of a mixing‐fog event using <scp>WRF</scp>‐<scp>LES</scp> numerical simulations

2025· article· en· W4406340171 on OpenAlexaboutno aff
Reneta Dimitrova, Harindra J. S. Fernando, Z. Silver, Stef L. Bardoel, Clive E. Dorman, Darko Koračin, Evgeni Vladimirov, Paolo Giani

Bibliographic record

VenueQuarterly Journal of the Royal Meteorological Society · 2025
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicMeteorological Phenomena and Simulations
Canadian institutionsnot available
Fundersnot available
KeywordsWeather Research and Forecasting ModelMixing (physics)Event (particle physics)Environmental scienceMeteorologyPhysicsAstrophysics

Abstract

fetched live from OpenAlex

Abstract The prediction of fog using numerical weather forecasting models is a continuing challenge due to the combined influence of processes at different spatial and temporal scales as well as nonlinear interactions between them, especially in coastal regions. The focus of this work is on a specific mixing‐fog event observed during the “Toward Improving Coastal Fog Prediction” field campaign at the Ferryland field site, when a cold‐frontal air mass arriving from the northeast approached the Downs peninsula, Newfoundland, Canada. Simulations with the Weather Research and Forecasting model using a Large Eddy Simulation option (WRF‐LES) were used to study physical processes at different scales contributing to the life cycle of fog. The model was thoroughly evaluated by conducting simulations with a suite of available model options and comparing results with observations, and the best set of options selected were used for simulations of the present field case to reveal the formation, evolution, and dissipation of fog. Our analysis suggests four factors that play a role on the fog formation: (i) synoptic scale – advection of cold moist air over shallow warmer coastal waters creates a shallow marine boundary layer capped by inversion; (ii) mesoscale – low cloud formation strengthens the inversion by releasing latent heat at the top of the cloud, thus generating convective instability and downward mixing in the cloud layer; this further enhances the descent of the lower boundary (base) of the cloud layer; (iii) local scale – near‐surface turbulence induced by the collision of cold denser air mass with an orographic barrier can promote mixing, cooling of the air and fog initiation; and (iv) microscale – mixing of near‐saturated with saturated air and decrease in temperature drive the water vapor condensation. All described processes have significant roles and play together to provide favorable conditions to patchy fog/mist formation for the described case.

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)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.035
Threshold uncertainty score0.069

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.270
Teacher spread0.240 · 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 designSimulation or modeling
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

Citations3
Published2025
Admission routes1
Has abstractyes

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