Probabilistic Parameterizations of Visibility Using Observations of Rain Precipitation Rate, Relative Humidity, and Visibility
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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 it