Alternate Transmission of +45° and −45° Slant Polarization and Simultaneous Reception of Vertical and Horizontal Polarization for Precipitation Measurement
Bibliographic record
Abstract
A polarimetric weather radar with alternate transmission of slant linear +45° and −45° polarization and simultaneous reception of both linear vertical and linear horizontal polarization is considered. The equations of the radar observables for a model medium containing nonspherical hydrometeors are presented. Assuming the hydrometeors to be axially symmetric with a canting angle distribution symmetric about the mean canting angle, a set of equations for separation of propagation and backscattering effects is developed. The mean apparent canting angle, the degree of common orientation of the hydrometeors, and the differential phase shift are obtained. Using empirical relationships, the mean and differential attenuations are estimated by means of the differential phase shift. The intrinsic value of the reflectivity, the differential reflectivity, and the copolar correlation coefficient at zero lag time are then determined. Application of this to a model convective rain cell shows that the use of simultaneous transmission and reception of linear vertical and linear horizontal polarization at S, C, and X bands provides accurate estimates of the intrinsic scattering properties of precipitation. The analysis of the bias on the radar observables due to the assumption of a medium of equioriented hydrometeors shows that all the observables with the exception of reflectivity can be severely affected.
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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) | 0.001 | 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) | 0.001 | 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".