Validation of the CloudSat precipitation occurrence algorithm using the Canadian C band radar network
Bibliographic record
Abstract
The ability of CloudSat to detect precipitation in cold season cloud systems is examined using data from the Environment Canada C band weather radar at King City, Ontario. The factors complicating the comparison are the time mismatch, the differences in sensitivity, and the changes to the geometry of cross section with range from the ground radar, W band radar attenuation, and the effect of ground clutter. A total of 40 overpasses with precipitation were observed over the King City radar from September 2006 to April 2007. In about 14% of the precipitation profiles, time mismatches were diagnosed. When these cases were removed, the skill scores of the CloudSat precipitation occurrence product were excellent. The most frequent cause of a false detection was an incorrect precipitation threshold in the algorithm. The most frequent cause of a miss in detection was ground clutter removal of valid echoes by the algorithm. Overall, the CloudSat algorithm handled the effect of attenuation very well. Improvement to the algorithm would arise from a better tuning of the precipitation threshold, a threshold of −10 dBZ instead of −18 dBZ being more appropriate for winter storms in the Great Lakes area, and more effective ground clutter filtering in the lowest four range bins of the CloudSat data. The methodology employed here and the 1456 verified precipitation profiles from CloudSat can serve as a framework for a test bed to evaluate precipitation products from CloudSat.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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".