Application of Dual-Polarization Radar Melting-Layer Detection Algorithm
Bibliographic record
Abstract
Abstract A polarimetric melting-layer detection algorithm developed for an S-band radar has been modified for use by the King City C-band radar in southern Ontario, Canada. The technique ingests radar scan volume data to determine the melting-layer top and bottom and to diagnose temporal and spatial variations of the melting-layer heights. The thickness of the melting layer is also derived from the algorithm. Detailed case studies of two frontal systems over this region are described, comparing the radar-derived melting-layer height with aircraft measurement of the height of the 0°C isotherm. The analysis demonstrated the ability to detect rapidly changing melting-layer heights during frontal passages in the region. A range of melting-layer heights for a 3-yr period was investigated and produced detections from close to the ground up to about 5.0 km. Comparison of algorithm results to output from a numerical weather prediction model over the 3-yr period showed good agreement. The correlation coefficient of the heights of the 0°C wet-bulb temperature with the radar-derived melting-layer tops was 0.96. The time series of the algorithm output was used to detect frontal passages and showed that the algorithm should be useful for approximately 19 frontal passages per year in this region.
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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 | 0.000 |
| 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 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".