Parameterization of visibility in snow: Application in numerical weather prediction models
Bibliographic record
Abstract
Several parameterizations of extinction coefficient (σ) and visibility (lv) 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 (lce), 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 lv is mainly associated with S. Both aircraft and SBM data indicate that there is a significant dependence of lv on S and a relatively weaker dependence on T. The observed lv is correlated with lce, but the dependence of lv on RH is relatively weak. There is some nonlinear dependence of lv 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 lce further improves the CC from 0.87 to 0.9. It was also found that both lv and lce 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 lv agreed well with observations.
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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) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.000 | 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".