1 Quality Control of Canadian Radar Reflectivity Data
Bibliographic record
Abstract
Abstract — Echoes in radar reflectivity data may correspond not just to precipitating particles, but may also be due to biological targets, anomalous propagation (AP) or ground clutter (GC). In earlier work, we described the development of an automated technique to quality control radar reflectivity data from WSR-88D so that the cleaned data may be used by severe weather and precipitation estimation algorithms. However, that work relied heavily on the use of velocity data that was nearly collocated in time with the reflectivity information. This is not the case for Canadian radar data. In this paper, we describe the development of a reflectivity-only classifier that is capable of identifying non-meteorological echoes in Canadian radar products. The classifier incorporates radial preprocessing, entropy checks, a neural network to combine texture and vertical features based on just reflectivity and a post-processing step that classifies regions instead of individual radar gates. There are, however, crucial differences between the automated classifier developed for WSR-88D and the one used for Canadian radar data. This paper describes the adaptations done to the classifier developed for WSR-88D S-band Doppler radars to enable it to work on C-band non-Doppler radars with a different scanning strategy. I.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".