Fog clearing in the lee of an isolated, flat island: A fog shadow
Bibliographic record
Abstract
Abstract This study investigates the phenomenon of fog clearing in the lee of Sable Island, Nova Scotia, termed a “fog shadow”, using observations from the 2022 Fog And Turbulence Interactions in the Marine Atmosphere field experiment and Coupled Ocean–Atmosphere Mesoscale Prediction System numerical weather prediction model. The fog shadow occurs when a shallow layer of marine fog dissipates downstream of this low‐elevation island due to surface heating and turbulent mixing. Analysis focuses on two cases: July 24, 2022, when a fog shadow was both predicted and observed through multiple platforms, including satellite imagery, and July 26, 2022, when the model erroneously forecast a fog shadow. Though the model successfully predicted some fog shadow events, it consistently overestimated surface heat fluxes over the island, leading to excessive fog dissipation in forecasts. The study reveals that fog shadows can form when sufficient surface heating combines with turbulent mixing to erode shallow fog layers, though the precise mechanisms and conditions required remain to be fully understood. These findings highlight the challenges in accurately modeling air–sea interactions and fog evolution around small islands, suggesting several areas for model improvement, including enhanced resolution, better surface flux parameterizations, and more sophisticated turbulence schemes.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".