Implementation of Polarization Diversity Pulse Pair Techniqueusing airborne W-band radar
Bibliographic record
Abstract
Abstract. This work describes the implementation of polarization diversity on the National Research Council Canada W-band Doppler radar and presents the first-ever airborne Doppler measurements derived via polarization diversity pulse pair processing. The polarization diversity pulse pair measurements are interleaved with standard pulse pair measurements with staggered pulse repetition frequency; this allows a better understanding of the strengths and drawbacks of polarization diversity, a methodology that has been recently proposed for wind-focussed Doppler radar space missions. Polarization diversity has the clear advantage of making possible Doppler observations of very fast de-correlating media (as expected when deploying Doppler radars on fast moving satellites) and of widening the Nyquist interval, thus enabling the observation of very high Doppler velocities (up to more than 100 m/s in present work). Cross-talk between the two polarizations, mainly caused by depolarization at backscattering deteriorated the quality of the observations by introducing ghost echoes in the power signals and by increasing the noise level in the Doppler measurements. In the different cases analyzed during the field campaigns, the regions affected by cross-talk were generally associated with highly depolarized surface returns and depolarization of backscatter from hydrometeors located at short ranges from the air craft. The variance of the Doppler velocity estimates can be well predicted from theory and were also estimated directly from the observed correlation between the H-polarized and V-polarized successive pulses. The study represents a key milestone towards the implementation of polarization diver sity in Doppler space-borne radars.
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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 | 0.000 | 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".