Probabilistic Parameterizations of Visibility Using Observations of Rain Precipitation Rate, Relative Humidity, and Visibility
Bibliographic record
Abstract
Abstract This study analyzes the occurrence of the visibility (Vis) versus precipitation rates (PR) for rain and versus relative humidity (RH) from surface observations that were collected during the Fog Remote Sensing and Modeling (FRAM) field project, which was conducted near Toronto, Ontario, Canada, during the winter of 2005/06 and in Lunenburg, Nova Scotia, during the summers of 2006 and 2007. The main observations used in the analysis were PR and Vis for rain episodes from the Vaisala, Inc., FD12P present-weather sensor and RH and temperature from the Campbell Scientific Instruments, Inc., HMP45 sensor. The PR is compared with those from a total precipitation sensor to check the accuracy of the FD12P measurements. Vis parameterizations related to precipitation type have been previously studied by many other researchers and showed large variability in Vis (up to 1 order of magnitude) for a fixed PR. The results from the work presented here suggest that 1) significant differences exist among the various parameterizations of Vis (deterministic approach) and 2) statistical relationships obtained using fits applied to percentiles (probabilistic approach) can be a feasible alternative for model applications. Comparisons of previous parameterizations with the new Vis relationships suggest that simulated Vis values based on probabilistic approaches could be used in extreme-weather applications.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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 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".