Study of a mixing‐fog event using <scp>WRF</scp>‐<scp>LES</scp> numerical simulations
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".