Profiling of Shallow Marine Fog using a UAV and Remote Sensing Observations over the Sable Island during FATIMA
Bibliographic record
Abstract
The objective of this study is to investigate the vertical profiles of shallow fog events that occurred during the FATIMA (Fog and turbulence in the marine atmosphere) field campaign. The project took place over Sable Island and the surrounding ocean during July 2022. The profiles of meteorological and physical parameters were collected by instruments on 1) an Aeryon UAV (Unmanned Aerial Vehicle), 2) a TBS (Tethered Balloon System), 3) a MWR (microwave radiometer), 4) meteorological towers, and 5) radiosonde balloons released approximately every 3 hrs. Atmospheric profiles of temperature (T), relative humidity with respect to water (RHw), horizontal wind speed (Uh), as well as particle counts from the OPC-N3 (23 bins from 0.3 μm to 40 μm) when RHw~100%, and the fog vertical microphysics structure using in-situ observations are evaluated. Various parameters such as liquid water content (LWC), droplet number concentration (Nd), mean volume diameter (MVD), and aerosol number concentration (Na) are analyzed to elicit issues related to measurements. Based on the profiles of visibility (Vis), droplet size spectra, and meteorological parameters such as RHw, T, and Uh from the UAV and turbulence towers, we will be able to investigate the vertical variability for several shallow marine fog events.In this presentation, issues related to atmospheric boundary layer profiling and vertical mixing processes will be investigated using in-situ observations from the profiling platforms, as well as a well instrumented UAV. Results will be discussed by emphasizing the future sensor developments and investigating the microphysical parameterizations.This work was funded by the Grant N00014-21-1-2296 (Fatima Multidisciplinary University Research Initiative) of the Office of Naval Research, administered by the Marine Meteorology and Space Program.
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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 | 0.000 | 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) | 0.000 | 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".