Fatima-GB: Searching Clarity within Marine Fog
Bibliographic record
Abstract
Abstract Fog constitutes a thick, opaque blanket of air hugging Earth’s surface, laden with small water droplets or ice crystals. Fog disrupts transportation, poses security threats, disorients human perception, and impacts communications and ecosystems. Collusion of atmospheric, terrestrial, and hydrologic processes produces fog droplets that pullulate over hygroscopic aerosols that act as condensation nuclei. Marine fog is particularly complex, since underlying dynamic, thermodynamic, and (bio)physicochemical processes span fifteen decades of spatial scales, from megameter-sized synoptic weather systems to nanometer-scale bioaerosols. This paper overviews the first international field campaign [Fog and Turbulence Interactions in the Marine Atmosphere-Grand Banks campaign (Fatima-GB)] of the project dubbed Fatima conducted during 1–31 July 2022 in the Grand Banks region of the North Atlantic. Therein, weather systems and commingling cold and warm oceanic waters provide entrée for fog genesis. Measurement platforms included an islet southwest of Nova Scotia (Sable Island), a research vessel ( Atlantic Condor ), an offshore oil platform, and autonomous surface vehicles. The instrument array comprised of extant remote and in situ sensors augmented by novel sensing systems prototyped and deployed in marine fog to penetrate the smallest scales of turbulence, examine aerosols, and quantify radiation budget. The comprehensive dataset so gathered, together with satellite and reanalysis products, mesoscale model, and large-eddy simulations, demonstrated that the long-held hypotheses of marine fog formation by warm air advection over colder water and in areas of enhanced (shelf) turbulence need to be revisited. The study also elicited new phenomena, for example, the fog shadow (clearings of fog downstream of islands). Significance Statement Fog research has escalated recently per climate change implications and directed energy (electromagnetic systems) applications. Here, we report selected findings of Fatima-GB, a comprehensive multidisciplinary field campaign conducted in Grand Banks, one of the world’s foggiest areas, for improving the understanding and predictability of marine fog. Our findings indicate historical understanding of marine fog life cycle requires a fundamental rethink to incorporate complexities of scale interactions. Fog covers 15 decades of spatial scales, wherein megameter-scale synoptic systems sway millimeter-scale turbulent eddies, within which micron-scale fog droplets spawn on either tens (bioaerosols) or hundreds (sea salt) of nanometer-sized marine aerosols. We demonstrate the collusion of meteorological, oceanographic, turbulence, thermodynamic, and (bio)physicochemical processes during marine fog evolution, which should help develop future subgrid parameterizations for numerical weather prediction (NWP) models.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".