Parameterization of Runway Visual Range as a Function of Visibility: Implications for Numerical Weather Prediction Models
Bibliographic record
Abstract
Abstract A parameterization of runway visual range (RVR) has been developed using relevant meteorological parameters such as visibility (Vk), relative humidity (RH), temperature (T), precipitation intensity (PI), and precipitation type (PT) measured in years between 2009 and 2011 at Toronto Pearson International Airport during the Canadian Airport Nowcasting Project. The FD12P probe measured PI, Vk, and PT. The observed Vk and PT were tested against data reported by hourly surface observations (SAs). The measured Vk has correlated well with the SA with a correlation coefficient (r) of 0.76 for Vk < 5 km, but the FD12P underestimated visibility by about 20% with a mean difference (MD) of about 196 m. For Vk < 2 km, the FD12P overestimated visibility by about 7% with an MD of 60 m. The SA reported slightly more snow events—22% as compared to 17%—but the FD12P reported many more snow grain cases than the SA. Both the SA and the FD12P reported rain at similar frequency—4% and 5%, respectively. Using a theoretical approach, a parameterization that can be used to determine RVR as a function of Vk has been developed. Using the observed T, RH, and dewpoint temperature (Td), a new parameterization for predicting Vk/RVR in fog has been also developed. These parameterizations agreed with observations (r ≈ 0.8). The parameterizations have been tested using the Canadian Environmental Multiscale Regional model. The results show that when PI, RH, and T are reasonably predicted and the fog events are correctly diagnosed, the model can be used to forecast RVR.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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 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".