Spatial and temporal structure of the fog life cycle over Atlantic Canada and the Grand Banks
Bibliographic record
Abstract
Abstract Marine fog impacts human health, naval strategy, and biological productivity. Despite its importance, the skill of operational and global environmental models in forecasting marine fog and its optical properties remain limited due to our incomplete understanding of the physical processes that drive fog, particularly over its broad range of temporal and spatial scales. In this work, we present findings from a 71‐year climatological analysis covering a broad range of spatial and temporal scales of marine fog over Atlantic Canada and the Grand Banks of Newfoundland, Canada. Using International Comprehensive Ocean and Atmospheric Dataset observations from 1950 to 2020, European Centre for Medium‐range Weather Forecasts Reanalysis v5 products, and satellite imagery, we discuss fog formation in this region. Spatially, the Atlantic Canada continental shelf induces submesoscale ocean features along its rapid variation in bathymetry, which influence fog formation. Sharp sea‐surface temperature (SST) gradients and air–sea temperature differences coincide with the over‐the‐shelf fog maxima in summer (June, July, and August). The air–sea temperature differences show a clear signal that fog occurrence is higher with negative air–sea temperature differences (SST minus air temperature). This higher occurrence of fog is mainly isolated on the continental shelf, where colder SST typically exists. Satellite imagery of a fog event during the 2022 Fog and Turbulence Interactions in the Marine Atmosphere Multidisciplinary University Research Initiative campaign, funded by the Office of Naval Research, highlights the complicated interplay of shelf break dynamics and near‐surface atmospheric conditions. A fog bank is shown to form in the colder water regions over the shelf, outlining the shelf break and pointing to boundary‐layer and smaller‐scale processes that are driving fog formation. These observations are crucial in characterizing the spatial and temporal structure of the fog life cycle and provide a better understanding of fog occurrence in this region.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".