Microphysics and Optical Attenuation in Fog: Observations from Two Coastal Sites
Bibliographic record
Abstract
Abstract A total of 15 fog events from two field campaigns are investigated: the High Energy Laser in Fog (HELFOG) project (central California) and the Toward Improving Coastal Fog Prediction (C-FOG) project (Ferryland Newfoundland). Nearly identical sensors were used in both projects to sample fog droplet-size spectra, wind, turbulence, and thermodynamic properties near the surface. Concurrent measurements of visibility were made by the present weather detector in both experiments, with the addition of a two-ended transmissometer in the HELFOG campaign. The analyses focused first on contrasting the observed fog microphysics and the associated thermodynamics from fog events in the two locations. The optical attenuation by fog was investigated using three methods: (1) derived from Mie theory using the measured droplet-size distribution, (2) parametrized as a function of fog liquid water content, and (3) parametrized in terms of total fog droplet number concentration. The consistency of these methods was investigated. The HELFOG data result in an empirical relationship between the meteorological range and liquid water content. Validation of such relationship is problematic using the C-FOG data due to the presence of rain and other factors. The parametrization with droplet number concentration only does not provide a robust visibility calculation since it cannot represent the effects of droplet size on visibility. Finally, a preliminary analysis of the mixed fog/rain case is presented to illustrate the nature of the problem to promote future research.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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 teacher head, 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".