A comparison of visibility parameterizations using surface observations
Bibliographic record
Abstract
Authorities for aviation, marine environments, and surface transportation require more efficient fog forecasting and accurate visibility (Vis) values to reduce financial and human loses. Better fog forecasting can improve both safety and traffic management in critical adverse weather situations. Fog microphysical parameterization schemes in the forecasting models need to be improved for better forecasting skills. A method developed by Gultepe et al (2006) using in-situ observations from the Radiation and Aerosol Cloud Experiment (RACE), representing low-level boundary layer clouds, was suggested as a new parameterization scheme. In their work, visibility is parameterized based on a combined parameter that is obtained using both droplet number concentration (Nd) and liquid water content (LWC). The objective of this work is to test their proposed visibility parameterizations using the observations collected during the Clermont-Ferrand fog project that took place in France during the winters of 2004-2006.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".