A Satellite-Based Fog Detection Scheme Using Screen Air Temperature
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract A warm fog detection (air temperature > −5°C) algorithm using a combination of Geostationary Operational Environmental Satellite-12 (GOES-12) observations and screen temperature data based on an operational numerical model has been developed. This algorithm was tested on a large number of daytime cases during the spring and summer of 2004. Results from the scheme were compared with surface observations from four manned Canadian weather stations in Ontario, including Ottawa, Windsor, Sudbury, and Toronto. Initially, when all cases were included, fog detection (hit rate) by the satellite scheme ranged between 0.26 and 0.32. It is suggested that mid- or high-level clouds within the satellite imagery during the observed foggy periods affected the scheme’s performance in detecting surface-level fog for the majority of the cases. When cases with mid- and high-level clouds were removed using model-based screen temperatures, the hit rate ranged between 0.55 and 1.0. With an average false alarm rate of 0.10, the inclusion of model-based sounding values can be seen to improve results from the satellite-based algorithms by an average of 0.42. Average differences between the screen temperature and the surface-observed air temperature were found to be up to 2°C and this can likely account for some discrepancies in detecting fog. Finally, averaging GOES and model data to scales representing single data-point observations likely resulted in some of the failure of the fog algorithm.
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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) | 0.000 | 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 it