A Case Study: Evaluation of PAFOG One‐D Model With Advection in Simulations of Fog/Stratus From C‐FOG Experiment
Bibliographic record
Abstract
Abstract Fog simulation is a challenge due to the complex microphysical, radiative, and turbulent processes. C‐FOG (Coastal Fog) was a comprehensive experiment which aimed to improve our understanding and forecasting skill of fog in coastal areas of Atlantic Canada. An intermittent fog event, containing two fog periods occurring over the Grand Banks of Newfoundland beginning at 00:00 UTC on September 13, 2018, is investigated in this study. Our approach is to apply the Weather Research and Forecasting (WRF) model, in conjunction with a 1‐D model for the atmospheric boundary layer, PAFOG (Parameterized Fog model). Model results are evaluated against observations collected onboard the research vessel Hugh R. Sharp. We introduced an advection term to PAFOG to investigate the ability of the model to simulate the two‐level fog/stratus system and we developed a set of sensitivity experiments. Results suggest that PAFOG with the advection terms calculated from ERA5 shows a good ability to simulate intermittent fog caused by fog lifting as stratus, and by comparison, outperform results with advection terms derived from WRF simulations. Regarding the latter, in an experiment with high vertical resolution, WRF gives a delayed fog event, whereas with low vertical resolution, WRF gives descending stratus. Our results provide a possible methodology to study the detailed structure of fog/stratus using PAFOG, a sounding profile as initial conditions, and advection profiles derived from ERA5, with low computing cost.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".