Real-Time Radar Reflectivity Calibration from Differential Phase Measurements
Bibliographic record
Abstract
Abstract An algorithm based on the self-consistency between the horizontal reflectivity ZH and the specific differential phase KDP has been devised for the calibration of the reflectivity measurements of the McGill S-band dual-polarization radar and implemented in real time in the fall of 2012. By combining pairs of measured and theoretical differential propagation phases (ΦDP) along rain paths from several azimuths, elevation angles, and radar cycles, a robust calibration estimate is obtained even in relatively light precipitation, provided the number of pairs is of the order of at least 103. It confirmed the stability of the radar system as further corroborated by disdrometer and ground echo comparisons. However, the two-parameter ZH–KDP technique proved to be inadequate in convective situations because it overestimates ΦDP differences of paths with heavy precipitation. An ex post facto analysis has revealed that a three-parameter (ZH–KDP–ZDR) relationship provides a much better agreement with the measured ΦDP differences regardless of the intensity of the precipitation along the rain paths. The main usefulness of the technique remains its ability to derive a reliable calibration correction factor even in light precipitation; thus, it is readily applicable in climate regimes and/or at times of the year characterized by the absence of strong convection capable of providing the large ΦDP differences previously thought necessary for such a technique to be successful.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".