Parameterization of visibility in snow: Application in numerical weather prediction models
Bibliographic record
Abstract
Several parameterizations of extinction coefficient ( σ ) and visibility ( l v ) as a function of temperature ( T ), liquid water equivalent snowfall rate ( S ), have been developed assuming a gamma size distribution for ice particles and using aircraft data collected in extratropical stratiform clouds. Using surface‐based measurements (SBM) of S , T , relative humidity (RH), cloud ceiling ( l ce ), and σ during the winter months in 2005, 2006, and 2007 at the Centre for Atmospheric Research Experiments site in Ontario, Canada, other parameterizations have been developed and compared with that based on the aircraft data. The analysis of the SBM data indicates that low l v is mainly associated with S . Both aircraft and SBM data indicate that there is a significant dependence of l v on S and a relatively weaker dependence on T . The observed l v is correlated with l ce , but the dependence of l v on RH is relatively weak. There is some nonlinear dependence of l v on wind speed. Using SBM, several parameterizations of σ have been developed using a multiple linear regression method by increasing the number of terms starting with S . The addition of T increases the correlation coefficient (CC) r from 0.85 to 0.87. The addition of RH has no significant effect, but the inclusion of l ce further improves the CC from 0.87 to 0.9. It was also found that both l v and l ce can be described well using the inverse Gaussian probability density function. Model predictions using these parameterizations show that, when the model correctly forecasts the precipitation field, the predicted l v agreed well with observations.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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 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".