Vertical and Horizontal Polarization Observations of Slowly Varying Solar Emissions from Operational Swiss Weather Radars
Bibliographic record
Abstract
The electromagnetic power that arrives from the Sun in the C-band has been used to check the quality of the polarimetric, Doppler weather radar network that has recently been installed in Switzerland. The operational monitoring of this network is based on the analysis of Sun signals in the polar volume data produced during the MeteoSwiss scan program. It relies on a method that has been developed to: (1) determine electromagnetic antenna pointing; (2) monitor receiver stability; and (3) assess the differential reflectivity offset. Most of the results from such a method had been derived using data acquired in 2008, which was a period of quiet solar flux activity. Here, it has been applied, in simplified form, to the currently active Sun period. This note describes the results that have been obtained recently thanks to an inter-comparison of three polarimetric operational radars and the Sun’s reference signal observed in Canada in the S-band by the Dominion Radio Astrophysical Observatory (DRAO). The focus is on relative calibration: horizontal and vertical polarization are evaluated versus the DRAO reference and mutually compared. All six radar receivers (three systems, two polarizations) are able to capture and describe the monthly variability of the microwave signal emitted by the Sun. It can be concluded that even this simplified form of the method has the potential to routinely monitor dual-polarization weather radar networks during periods of intense Sun activity.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".